Telerik Blogs

Using Kendo UI with Svelte

2026-10-12T16:13:00Z

Even though there isn’t a native Svelte Kendo UI library, you can still use one of the Kendo UI component libraries with Svelte.

Progress Kendo UI does not have a Svelte version by default. It specializes in Angular, Vue, React and jQuery. However, that does not mean you Svelte fans out there are out of luck! JavaScript is just JavaScript, and there are work arounds to meet different needs.

TL;DR

You can use Kendo UI for jQuery in any JavaScript framework, or you can use Astro to mix components from multiple frameworks (React, Vue, Angular). The jQuery version is not perfect for server-side rendering, but it can work in pure Svelte, while Astro can work wonders with any framework component.

✔️ Svelte with Kendo UI for jQuery

Svelte allows you to import any JS you want. It is just JavaScript.

Create a New SvelteKit Project

npx sv create my-app

Install Tailwind and any other SvelteKit options you need.

Install Packages

npm i @progress/kendo-theme-meridian @progress/kendo-ui @progress/kendo-data-query @progress/kendo-date-math @progress/kendo-licensing @progress/kendo-popup-common jquery

with types:

npm install -D @types/jquery @types/node

Activate Kendo UI

npx kendo-ui-license activate

Get your key ready and follow any other activation instructions.

Create a jQuery Declaration File

Put it in lib/types/kendo-ui-module.d.ts or wherever makes sense.

/// 

declare module '@progress/kendo-ui' {
export {};
}

declare module '@progress/kendo-ui/kendo.all.js' {
export {};
}

declare global {
interface Window {
jQuery: JQueryStatic;
$: JQueryStatic;
}
}

export {};

jQuery types do not work as expected on the client without it.

Dynamically Import jQuery on the Browser

export async function loadKendoUi() {
// @ts-expect-error Kendo UI exposes this runtime entry but ships global-only typings.
await import('@progress/kendo-ui/kendo.all.js');
}

This allows you to load Kendo UI dynamically. Kendo UI for jQuery cannot run on the server, as it needs window objects, etc.

Handle CSS

@import 'tailwindcss';
@import '@progress/kendo-theme-meridian/dist/meridian-main.css';

Make sure your global layout.css or app.css file contains tailwind and the correct Progress theme CSS.

Load a Grid Element





{#if isGridLoading}

{:else}

{/if}

We load Kendo UI in onMount so that it won’t have problems loading on the server.
We use a $state signal to show a loading skeleton.
jQuery and kendoGrid are dynamically loaded, and then the products are added to the grid.

Create a Loader

Since the component takes a second to load, we want to show a loading skeleton in the interim. Notice we use kendo style classes to match our grid.


















ProductName






Unit Price






Units In Stock






Discontinued












{#each skeletonRows as row (row)}

{/each}










Loading product




0.00




000




False

We could equally have used a spinner.

✔️ Kendo UI in Astro

Using Astro is by far the best approach. You are not building in Svelte, but you can use any other framework with Astro, and have everything else built with Svelte.

Install Astro

Create a new Astro project.

npm create astro@latest

Don’t forget to add Tailwind.

Choose Your Kendo UI Framework

Install the Svelte Astro Plugin:

Svelte

Install the proper Astro plugin:

React
Vue
Angular (unofficial)

For this demo, I chose Vue and I copied all the Kendo UI for Vue grid components in Astro.

Install Dependencies

npm i @progress/kendo-data-query @progress/kendo-drawing @progress/kendo-licensing @progress/kendo-svg-icons @progress/kendo-theme-default @progress/kendo-vue-animation @progress/kendo-vue-buttons @progress/kendo-vue-charts @progress/kendo-vue-data-tools @progress/kendo-vue-dateinputs @progress/kendo-vue-dropdowns @progress/kendo-vue-grid @progress/kendo-vue-excel-export @progress/kendo-vue-inputs @progress/kendo-vue-intl @progress/kendo-vue-pdf @progress/kendo-vue-popup @progress/kendo-vue-treeview

Configure CSS

@import "tailwindcss";
@import '@progress/kendo-theme-default/dist/all.css';

Put in styles/global.css.

Activate Your Kendo UI License

npx kendo-ui-license activate

You can also use more than one or all of them with Astro! (Progress offers a DevCraft bundle, if you code in more than one framework.)

Create Svelte Component

We want to mix our components, so we create a Svelte Component that can accept any other component as a child.

<script lang="ts">
  let { children } = $props();

  let count = $state(0);
</script>

<section
  class="mx-auto mt-5 w-full max-w-4xl rounded-3xl border border-pink-200 bg-linear-to-br from-pink-50 via-yellow-50 to-sky-50 p-6 shadow-[0_20px_60px_rgba(244,114,182,0.18)] ring-1 ring-white/70 sm:p-8"
>
  <div class="mb-5 flex items-center justify-between gap-3">
    <div class="flex items-center gap-3">
      <div
        class="flex h-12 w-12 items-center justify-center rounded-2xl bg-linear-to-br from-pink-500 to-rose-400 text-2xl shadow-lg shadow-pink-200/60"
      >
        ⭐
      </div>

      <div>
        <p
          class="text-xs font-semibold uppercase tracking-[0.32em] text-pink-500"
        >
          For kids
        </p>
        <h2 class="text-2xl font-bold text-slate-800 sm:text-3xl">
          Fun learning card
        </h2>
      </div>
    </div>

    <button
      type="button"
      class="inline-flex items-center gap-2 rounded-full bg-sky-500 px-4 py-2 text-sm font-semibold text-white shadow-md shadow-sky-200 transition hover:-translate-y-0.5 hover:bg-sky-600 active:translate-y-0"
      onclick={() => (count += 1)}
      aria-label="Increase counter"
    >
      Count <span class="min-w-5 text-center">{count}</span>
    </button>
  </div>

  <div
    class="rounded-2xl bg-white/80 p-4 shadow-inner shadow-white/60 ring-1 ring-pink-100 sm:p-6"
  >
    {@render children?.()}
  </div>

  <div class="mt-5 flex items-center justify-between text-sm text-slate-500">
    <span>Made for bright ideas and happy moments.</span>
    <span
      class="rounded-full bg-yellow-200 px-3 py-1 font-medium text-yellow-900"
      >New</span
    >
  </div>
</section>

This component has a simple button counter that uses Runes.

Create our Grid Component

<script setup>
import { ref, computed } from 'vue';
import Grid from './grid/Grid.vue';
import {
    load,
    LocalizationProvider,
    IntlProvider,
    loadMessages,
} from '@progress/kendo-vue-intl';

import { enComponentMessages, enCustomMessages } from './grid/en-US';
import { esComponentMessages, esCustomMessages } from './grid/es';
import { frComponentMessages, frCustomMessages } from './grid/fr';

import likelySubtags from 'cldr-core/supplemental/likelySubtags.json';
import currencyData from 'cldr-core/supplemental/currencyData.json';
import weekData from 'cldr-core/supplemental/weekData.json';

import numbers from 'cldr-numbers-full/main/es/numbers.json';
import caGregorian from 'cldr-dates-full/main/es/ca-gregorian.json';
import dateFields from 'cldr-dates-full/main/es/dateFields.json';
import timeZoneNames from 'cldr-dates-full/main/es/timeZoneNames.json';
import esNumbers from 'cldr-numbers-full/main/es/numbers.json';
import esCurrencies from 'cldr-numbers-full/main/es/currencies.json';

import frNumbers from 'cldr-numbers-full/main/fr/numbers.json';
import frCaGregorian from 'cldr-dates-full/main/fr/ca-gregorian.json';
import frDateFields from 'cldr-dates-full/main/fr/dateFields.json';
import frTimeZoneNames from 'cldr-dates-full/main/fr/timeZoneNames.json';
import frCurrencies from 'cldr-numbers-full/main/fr/currencies.json';

load(
    likelySubtags,
    currencyData,
    weekData,
    numbers,
    caGregorian,
    dateFields,
    timeZoneNames,
    esNumbers,
    esCurrencies,
    frNumbers,
    frCaGregorian,
    frDateFields,
    frTimeZoneNames,
    frCurrencies
);

loadMessages(enCustomMessages, 'English');
loadMessages(enComponentMessages, 'English');
loadMessages(esCustomMessages, 'Spanish');
loadMessages(esComponentMessages, 'Spanish');
loadMessages(frCustomMessages, 'French');
loadMessages(frComponentMessages, 'French');

const localizationLanguage = ref('English');
const locale = computed(() => {
    if (localizationLanguage.value === 'Spanish') return 'es';
    if (localizationLanguage.value === 'French') return 'fr';
    return 'en';
});

const onLocalizationLanguageChange = (e) => {
    localizationLanguage.value = e;
};
</script>

<template>
  <div>
    <LocalizationProvider :language="localizationLanguage">
      <IntlProvider :locale="locale">
        <Grid @localizationLanguageChange="onLocalizationLanguageChange" />
      </IntlProvider>
    </LocalizationProvider>
  </div>
</template>

The Grid component is broken up in several files and is copied directly from the Grid Demo.

Load Vue with Svelte

We create our Astro page under pages/index.astro.

---
import Panel from "../components/Panel.svelte";
import Grid from "../components/kendo-grid.vue";
import "../styles/global.css";
---

<html lang="en">
  <head>
    <meta charset="utf-8" />
    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
    <link rel="icon" href="/favicon.ico" />
    <meta name="viewport" content="width=device-width" />
    <meta name="generator" content={Astro.generator} />
    <title>Astro</title>
  </head>
  <body>
    <Panel client:load>
      <Grid client:load />
    </Panel>
  </body>
</html>

Notice we are importing both a .svelte and a .vue component in the same file!
We use client:load to show that the component is dynamic.
Grid is a child of Panel.

And we have Kendo UI for Vue and a Svelte Component in one!

Resources

Try Kendo UI

All the Progress Kendo UI component libraries come with a free 30-day trial so you can explore them yourself.

Try Now

Build a Real-Time Collaborative Notes App with Replicache

2026-10-08T15:25:18Z

Build a real-time collaborative notes app with Replicache where users can create, edit, delete notes and react with emojis. We will see every change we make sync instantly across all open browser tabs.

Web applications have a latency problem. Every interaction triggers a round trip. Your action leaves the browser, hits the server, gets processed and returns as a response. Only then does your screen update.

The problem is these round trips aren’t always reliable. Poor network conditions or server overload mean responses arrive late or not at all. So your users get loading spinners or skeleton screens.

Replicache flips this model by being local-first. Instead of waiting on the server for every interaction, we write to a local database first and sync later. Users see the changes immediately while the network works quietly in the background.

In this article, we will build a notes app where users can create, edit and delete notes; react with emojis; and see every change sync across all open browser tabs in real time.

Prerequisites

To follow along, this guide assumes you have:

Basic JavaScript/TypeScript knowledge
Understanding of HTTP (GET/POST)
Node.js installed
Basic SQL knowledge
Familiarity with React

What Is Replicache?

Replicache is a local-first sync engine. It uses IndexedDB, a database built into the browser, to store your app’s data locally. Every user gets their own copy on their device, so when they make a change, it saves locally first. Then later, it replays those changes to the server.

Replicache isn’t designed for every use case. For sensitive systems like banking, where data integrity is delicate, you wouldn’t use it. But for collaborative apps like notes, docs and project boards, Replicache works nicely. Let’s now look at some of the core concepts behind Replicache.

Core Concepts

Before we build our app, let’s go over the core concepts that make Replicache work.

Local Store

The local store is a persistent database built into the browser on top of IndexedDB. It is not memory that disappears after a refresh.

It works as a key-value store where each piece of data has a key (String) and a JSON value.
The local store is flat, meaning all the data lives together. When we code, we’ll use prefixes on our keys to keep things organized and scannable. This local store also allows you to work offline; this way, changes are saved locally and synced to the server when the connection comes back.

Mutators

In a Replicache app, all data changes go through a mutator. You cannot write directly to the server or local store. Mutators are the only way to change data.

A mutator is just a function that takes a WriteTransaction and some arguments. WriteTransaction is your interface to the local store. For writing, you only need two operations: tx.set() to store a value under a key, and tx.del() to remove one.

When you call a mutator, two things happen. First, it writes to the local store immediately, so the UI updates and the user sees the change right away. Then, Replicache queues the mutation and sends it to the server in the background. No loading states, sync just happens silently.

Push

Push is how Replicache sends local changes to the server. After a mutator runs, Replicache adds it to a pending queue, storing the mutation name and its arguments. When ready, it sends the entire queue to the push endpoint in one request.

The server processes each mutation one by one, checks the name and runs the corresponding database operation. Basically, the server is replaying what already happened locally, but against the persistent database. After processing all mutations, the server calls poke.

Poke

Poke is simple but important. Without it, data gets saved to the database, but other users have no way of knowing about the change. They’d have to wait for the next scheduled pull, which defaults to 60 seconds. That’s not great for a collaborative app.

Poke uses server-sent events (SSEs). Every browser with the app open maintains a persistent connection to the server’s poke endpoint. When the server finishes processing a push, it sends a signal to all connected clients. It doesn’t send the data itself, just a signal. Clients receive it and immediately call pull to get the latest state. This is simple and efficient.

Pull

Pull is how Replicache gets the latest state from the server. It happens in three situations: when the app first loads, when the client receives a poke and periodically in the background as a safety net (every 60 seconds by default).

The server responds with a patch, which is just an array of instructions telling Replicache what to store or remove in the local store.

Before applying the patch, Replicache checks for pending mutations in the queue. If there are any, it applies the server data first and then replays those pending mutations on top. This helps prevent local changes from being lost or overwritten by incoming server data.

Project Setup

We’re building a real-time collaborative notes app where users can create, edit, delete notes and react with emojis. Every change we make syncs instantly across all open browser tabs. The GIF below shows what we’ll build.

We’ll split this project into two folders: server and client. We’re doing this because they require different dependencies.

Open your terminal, navigate to where you want the project to live, and run the following commands:

mkdir notes-app
cd notes-app
mkdir server client

Setting up the Server

Now, let’s initialize and set up the server. Run the following command in your terminal:

cd server
npm init -y

Now let’s go ahead and install the dependencies we’ll need:

npm install express better-sqlite3 cors uuid
npm install -D typescript ts-node @types/express @types/better-sqlite3 @types/cors @types/uuid @types/node

Initialize TypeScript:

npx tsc --init

Then replace the generated tsconfig.json with this:

{
  "compilerOptions": {
    "target": "ES2020",
    "module": "commonjs",
    "lib": ["ES2020"],
    "outDir": "./dist",
    "rootDir": "./src",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "resolveJsonModule": true
  }
}

Setting Up the Client

Now that the server is set up, let’s scaffold the client. Navigate to the client directory:

cd ../client

Run the following command to create a React app with Vite:

npm create vite@latest . -- --template react-ts 
npm install

Install Replicache and its React helper:

npm install replicache replicache-react

Building the Server

The server is responsible for storing data, processing mutations and notifying connected clients when changes occur. We’ll build this across five files, each with a single responsibility.

Create a src folder inside the server directory, that is where all of our files will live:

mkdir src

Setting Up the Database

Create a file called db.ts inside the src folder and add the following to it:

import Database from "better-sqlite3";

const db = new Database("notes.db");

db.exec(`
  CREATE TABLE IF NOT EXISTS notes (
    id TEXT PRIMARY KEY,
    content TEXT NOT NULL,
    created_at INTEGER NOT NULL
  );
  CREATE TABLE IF NOT EXISTS reactions (
    id TEXT PRIMARY KEY,
    note_id TEXT NOT NULL,
    emoji TEXT NOT NULL,
    created_at INTEGER NOT NULL
  );
  CREATE TABLE IF NOT EXISTS replicache_clients (
    client_id TEXT PRIMARY KEY,
    last_mutation_id INTEGER NOT NULL DEFAULT 0
  );
`);

export default db;

In the code above, we created three tables. The notes table stores the note content, a unique ID and a timestamp. The reactions table stores each emoji reaction with a note_id that references the note it belongs to. This is how a reaction always knows which note it belongs to. The replicache_clients table tracks the last mutation each client has processed on the server. We’ll cover this in detail in the advanced concepts section.

Defining the Types

Create a file called types.ts in the src folder and add the following to it:

export type Note = {
  id: string;
  content: string;
  created_at: number;
};

export type Reaction = {
  id: string;
  note_id: string;
  emoji: string;
  created_at: number;
};

In this file, we define the shape of data, such as the notes and reactions. With this one file, TypeScript can catch errors throughout the app. When the data structure changes, we only need to update this one file.

Building the Notification System

Create a file called poke.ts in the src directory, and add the following to it:

import { Response } from "express";

const clients = new Set<Response>();

export function addClient(res: Response) {
  clients.add(res);
}

export function removeClient(res: Response) {
  clients.delete(res);
}

export function poke() {
  for (const client of clients) {
    client.write("data: poke\n\n");
  }
}

We maintain a Set of all open browser connections. A Set is ideal here because every connection is unique, and removing one is as simple as calling delete(). With an array, you’d have to find it first.

addClient adds a browser to the Set when it connects, removeClient removes it when it disconnects, and poke loops over all connections and sends a signal down each one.

One important thing to note is that poke sends no actual data, just a signal. The browser receives it and immediately calls pull to fetch the latest data. This keeps the system clean and efficient.

Fetching Data from the Server

Create a file called pull.ts in the src folder. This is the endpoint Replicache calls when it needs the latest data from the server. Add the following to it:

import { Request, Response } from "express";
import db from "./db";
import type { Note, Reaction } from "./types";

export function pull(req: Request, res: Response) {
  const notes = db
    .prepare("SELECT * FROM notes ORDER BY created_at ASC")
    .all() as Note[];
  
  const reactions = db
    .prepare("SELECT * FROM reactions ORDER BY created_at ASC")
    .all() as Reaction[];
  
  const patch = [
    { op: "clear" },
    ...notes.map((note: Note) => ({
      op: "put",
      key: `note/${note.id}`,
      value: note,
    })),
    ...reactions.map((reaction: Reaction) => ({
      op: "put",
      key: `reaction/${reaction.id}`,
      value: reaction,
    })),
  ];
  
  const clients = db
    .prepare("SELECT client_id, last_mutation_id FROM replicache_clients")
    .all() as { client_id: string; last_mutation_id: number }[];
  
  const lastMutationIDChanges = Object.fromEntries(
    clients.map((c) => [c.client_id, c.last_mutation_id]),
  );
  
  res.json({
    lastMutationIDChanges,
    cookie: null,
    patch,
  });
}

In the code above, we fetch all notes and reactions from the database and build a patch, which is an array of instructions telling Replicache what to store in the local store. Each instruction has an op set to "put", a key and a value.

Notes use the prefix note/ and reactions use reaction/, so we can differentiate them in the flat local store. Without these prefixes, everything would live together with no way to differentiate a note key from a reaction key.

We also spread both arrays into one flat list because Replicache expects one flat array of instructions, not nested arrays. The response also includes cookie and lastMutationIDChanges, which we’ll look into in the advanced concepts section.

Processing Mutations

Create a file called push.ts and add the following to it:

import { Request, Response } from "express";
import db from "./db";
import { poke } from "./poke";

export function push(req: Request, res: Response) {
  const mutations = req.body.mutations;
  
  for (const mutation of mutations) {
    const { clientID, id, name, args } = mutation;
    
    const row = db
      .prepare(
        "SELECT last_mutation_id FROM replicache_clients WHERE client_id = ?",
      )
      .get(clientID) as { last_mutation_id: number } | undefined;
    
    const lastMutationID = row?.last_mutation_id ?? 0;
    
    if (id <= lastMutationID) {
      continue;
    }
    
    if (name === "createNote") {
      db.prepare(
        "INSERT OR IGNORE INTO notes (id, content, created_at) VALUES (?, ?, ?)",
      ).run(args.id, args.content, args.created_at);
    }
    
    if (name === "addReaction") {
      db.prepare(
        "INSERT OR IGNORE INTO reactions (id, note_id, emoji, created_at) VALUES (?, ?, ?, ?)",
      ).run(args.id, args.note_id, args.emoji, args.created_at);
    }
    
    if (name === "updateNote") {
      db.prepare("UPDATE notes SET content = ? WHERE id = ?").run(
        args.content,
        args.id,
      );
    }
    
    if (name === "deleteNote") {
      db.prepare("DELETE FROM notes WHERE id = ?").run(args.id);
      db.prepare("DELETE FROM reactions WHERE note_id = ?").run(args.id);
    }
    
    db.prepare(
      "INSERT INTO replicache_clients (client_id, last_mutation_id) VALUES (?, ?) ON CONFLICT(client_id) DO UPDATE SET last_mutation_id = ?",
    ).run(clientID, id, id);
  }
  
  poke();
  res.json({ status: "ok" });
}

The request body contains an array of mutations. Each mutation has a name and an args object. The name tells us what operation to run, and args contains the specific data that operation needs.

The function loops through each mutation and checks its name. For createNote, we insert a new note into the notes table. For addReaction, we insert a reaction into the reactions table. For updateNote, we update the content of an existing note where the ID matches. For deleteNote, we run two deletes: one for the note and one for all its reactions. We do this because we don’t want reactions pointing to a note that no longer exists.

Putting the Server Together

Create a file called index.ts in your src folder to bring everything together:

import express from "express";
import cors from "cors";
import { pull } from "./pull";
import { push } from "./push";
import { addClient, removeClient } from "./poke";

const app = express();

app.use(cors());
app.use(express.json());

app.post("/api/push", push);
app.post("/api/pull", pull);

app.get("/api/poke", (req, res) => {
  res.setHeader("Content-Type", "text/event-stream");
  res.setHeader("Cache-Control", "no-cache");
  res.setHeader("Connection", "keep-alive");
  
  addClient(res);
  
  req.on("close", () => {
    removeClient(res);
  });
});

const PORT = 3000;

app.listen(PORT, () => {
  console.log(`Server running on port ${PORT}`);
});

In the code above, we register the push and pull routes and set up the poke endpoint. The push and pull routes are straightforward in the sense that they receive a request, do their job and respond.

The poke endpoint works differently. Instead of responding immediately and closing the connection, it sets SSE headers and keeps the connection open as long as the browser is connected. That open connection is the channel the server uses to send poke signals to the client.

Notice the keep-alive header. This tells the server not to close the connection after responding. As long as the browser has the app open, the connection stays alive. When a browser disconnects or closes the tab, req.on("close") fires automatically and removes that connection from the clients Set. This prevents trying to send a poke to a connection that no longer exists.

Building the Client

Now we’ll build the React frontend. This is where we create the UI for users to create, edit, delete and react to notes with emojis across different tabs.

We’ll set up:

Types to align the client and server
Mutators to handle every write operation
A Replicache instance to connect to the server
The UI

Run the following command to navigate to the client folder:

cd ../client

Create three new files in the src directory:

touch src/mutators.ts src/replicache.ts src/types.ts

Defining the Types

Both the client and server need to agree on the same data structure. Add this to your types.ts file:

export type Note = {
  id: string;
  content: string;
  created_at: number;
};

export type Reaction = {
  id: string;
  note_id: string;
  emoji: string;
  created_at: number;
};

These are the same types as the server. By sharing the same type definitions, TypeScript enables consistency between client and server.

Defining the Mutators

As we covered in the core concepts section, mutators are the only way to change data in a Replicache app. Update your mutators.ts file with the following:

import type { WriteTransaction } from "replicache";
import type { Note, Reaction } from "./types";

export const mutators = {
  createNote: async (tx: WriteTransaction, note: Note) => {
    await tx.set(`note/${note.id}`, note);
  },

  addReaction: async (tx: WriteTransaction, reaction: Reaction) => {
    await tx.set(`reaction/${reaction.id}`, reaction);
  },

  deleteNote: async (tx: WriteTransaction, args: { id: string }) => {
    await tx.del(`note/${args.id}`);
  },

  updateNote: async (
    tx: WriteTransaction,
    args: { id: string; content: string },
  ) => {
    const existing = await tx.get(`note/${args.id}`);
    if (existing) {
      await tx.set(`note/${args.id}`, {
        ...(existing as Note),
        content: args.content,
      });
    }
  },
};

Every mutator receives a WriteTransaction as its first argument, which is your connection to the local store. The second argument is the data needed for that operation.

createNote stores a note in the local store under note/{id}. addReaction does the same for reactions under reaction/{id}. deleteNote removes a note using tx.del().

On the other hand, updateNote works differently. It first reads the existing note with tx.get(), spreads it and overwrites just the content field. Everything else stays the same.

One important thing to note is that these mutators don’t talk to the server directly. They only write to the local store. Replicache handles sending them to the server separately by queuing each mutation and sending the entire queue to the push endpoint.

Setting Up the Replicache Instance

Open your replicache.ts file and add the following to it:

import { Replicache } from "replicache";
import { mutators } from "./mutators";

export const rep = new Replicache({
  name: "notes-app",
  mutators,
  pushURL: "http://localhost:3000/api/push",
  pullURL: "http://localhost:3000/api/pull",
});

const eventSource = new EventSource("http://localhost:3000/api/poke");

eventSource.onmessage = () => {
  rep.pull();
};

We create a Replicache instance and export it as rep so it can be used throughout our components. The name is a unique identifier for the local store. mutators tells Replicache what write operations are available. pushURL and pullURL point to our server endpoints.
At the bottom, we open a permanent connection to the poke endpoint using EventSource. When the server sends a poke signal, the onmessage handler fires and calls rep.pull() to fetch the latest data.

Building the UI

This is where everything becomes visible in the browser. Open your App.tsx, clear the file and add these imports at the top:

import { useSubscribe } from "replicache-react";
import { rep } from "./replicache";
import type { Note, Reaction } from "./types";

Next, we need to subscribe to data from the local store.

The Subscriptions

Add these inside your App function:

const notes = useSubscribe(
  rep,
  async (tx) => {
    const list = await tx.scan({ prefix: "note/" }).values().toArray();
    return list as Note[];
  },
  { default: [] },
);

const reactions = useSubscribe(
  rep,
  async (tx) => {
    const list = await tx.scan({ prefix: "reaction/" }).values().toArray();
    return list as Reaction[];
  },
  { default: [] },
);

In the code above, the useSubscribe hook watches the local store and rerenders your component when data changes. tx.scan() filters by prefix. Remember when we said we needed to prefix our keys for easy scanning? That’s what we’re doing here. Reactions use their own prefix as well. The { default: [] } option prevents your component from breaking before the data loads.

Event Handlers

Next, let’s create functions that run when the user performs an action or triggers an action. It could be creating, updating, reacting to or deleting notes. Each handler calls a mutator corresponding to the action:

const handleUpdateNote = async (id: string, content: string) => {
  if (content.trim()) {
    await rep.mutate.updateNote({ id, content: content.trim() });
  }
};

const handleAddReaction = async (noteId: string, emoji: string) => {
  const id = crypto.randomUUID();
  await rep.mutate.addReaction({
    id,
    note_id: noteId,
    emoji,
    created_at: Date.now(),
  });
};

const handleDeleteNote = async (id: string) => {
  await rep.mutate.deleteNote({ id });
};

const emojis = ["", "❤️", "", ""];

handleUpdateNote calls the updateNote mutator and trims whitespace to prevent empty notes. handleAddReaction generates a unique ID, captures the current timestamp and calls the addReaction mutator. handleDeleteNote removes a note from the store. The emojis array contains the reactions users can choose from.

UI Components

Now this is where we see everything visually. We render the notes grid, the add note button, the editable content, the emoji reactions and the delete button. Since we already have the handler functions, the markup is straightforward:

{notes.map((note) => (
  <div key={note.id}>
    <p
      contentEditable
      suppressContentEditableWarning
      onBlur={(e) => handleUpdateNote(note.id, e.currentTarget.textContent ?? "")}
    >
      {note.content}
    p>
    {emojis.map((emoji) => {
      const count = reactions.filter(
        (r) => r.note_id === note.id && r.emoji === emoji
      ).length;
      return (
        <button key={emoji} onClick={() => handleAddReaction(note.id, emoji)}>
          {emoji} {count > 0 && <span>{count}span>}
        button>
      );
    })}
    <button onClick={() => handleDeleteNote(note.id)}>button>
  div>
))}

That’s everything wired up. If you’ve followed along, your app should be rendering correctly in your browser.

First, start the server:

cd server
npm run start

Then, open another terminal for the client:

cd client
npm run dev

Advanced Concepts

Replicache has many advanced features, but for this guide, we’ll focus on cookie and lastMutationIDChanges. We referenced both earlier in our code, so let’s get a quick overview of what they do.

lastMutationIDChanges

When Replicache sends a push request containing all pending mutations, it attaches an ID to each one. The server uses these IDs to track which mutations it has already processed.

When the server receives the request, it checks each ID. If it has already processed a mutation ID, it skips it. If the ID is new, it processes the mutation. The server then stores the highest ID it has seen for that client. This prevents duplicate processing. For example, if the network fails and Replicache resends the same request, the server skips the mutations it already processed and only handles the new ones.

Now let’s update the pull.ts file to see this in action. Replace the lastMutationIDChanges variable inside your pull function:

const clients = db
  .prepare("SELECT client_id, last_mutation_id FROM replicache_clients")
  .all() as { client_id: string; last_mutation_id: number }[];

const lastMutationIDChanges = Object.fromEntries(
  clients.map((c) => [c.client_id, c.last_mutation_id])
);

We set cookie to null in our pull response for this article, which means the server sends everything every time. But there’s a problem: when you open the app in a new browser, Replicache sees the same null cookie and thinks nothing has changed. It ignores the patch array, so notes won’t appear.

Here’s how we can fix this. Go back to your pull.ts file and change cookie: null to cookie: Date.now(). Now every pull response has a fresh timestamp. Replicache sees a new cookie and applies the patch data.

The important thing here is that the timestamp is always incremental. Date.now() always increases. It never decreases. So Replicache always sees a higher number than before, indicating fresh data. That’s why it applies the patch every single time.

Update your final response with the following:

res.json({
  lastMutationIDChanges,
  cookie: Date.now(),
  patch,
});

The cookie means Replicache applies the patch every time. The mutation IDs keep it from processing the same mutation twice. And together, they enable proper sync.

Conclusion

In this post, we explored how Replicache works. It pulls data when the app starts, poke notifies connected clients of changes with a signal, and mutations are queued and later replayed to the server.

In all of this, Replicache isn’t right for every use case. For apps dealing with sensitive data like medical records or payments, you need server verification first, so Replicache isn’t a good fit. But for collaborative apps where user experience is the priority, RReplicache is good, as it enhances the user’s experience. Check out the documentation to explore more.

Model Selection Criteria

2026-10-08T15:11:11Z

How do you decide what model to use? Let’s look at workload, deployment, regulated environments and other criteria for selecting the right AI model.

In an AI application, a model is the trained component that receives an input and produces generated output such as text or structured data. The models we’re considering are large language models (LLMs), which can interpret language and return text or structured data (e.g., GPT, Claude, Llama, Mistral).

A model can perform well in a prototype and still be the wrong choice for production because the surrounding requirements change what counts as a good fit.

For example, a security review may show that requests cannot cross a regional boundary, while load testing may reveal that the model misses the feature’s response-time budget. Cost at projected traffic and the team’s ability to operate the deployment can also remove models from consideration.

In this article, we’ll look at the practical criteria that shape model selection. We’ll focus on workload fit and deployment requirements, including the constraints that come with regulated environments.

What the Model Needs to Do

Before we compare models, we need a clear description of the job. A strong score on a broad reasoning test won’t tell us whether a model can handle our document formats or return tool arguments that our backend accepts.

An evaluation set built from real inputs gives us a consistent way to compare candidates. Each test needs an expected result or a scoring rule so we can judge every model against the same requirements.

For each model, we can ask four questions:

Does it produce useful and accurate output?
Can it handle the required context length and languages?
Do its structured responses match the schemas our application expects?
Do its tool calls select the right operation and provide valid arguments?

A smaller model may be enough for document classification, while an agent choosing tools may need stronger reasoning. Public benchmarks and model cards can help us find candidates before we run these tests. A model card describes intended uses and limitations along with licensing and published evaluations. Those results give us a starting point while our own inputs show whether the model fits the feature we intend to ship.

Where the Model Runs

Once we know a model can do the job, the next question is where it will run. Running a model to produce an output is called inference, and the infrastructure that handles this work becomes part of the selection decision.

With a hosted model API, the provider operates the serving stack. A self-hosted model makes our team responsible for that stack in a cloud account or local environment.

A self-hosted model can run in our cloud account or our own data center. When it runs in our data center, the deployment is on-premises.

There are four common arrangements, each giving us a different level of control and operational responsibility:

Deployment	Where Inference Runs	Who Operates It	Strong Fit	Main Constraint
Hosted API	Provider infrastructure	Model provider	Fast integration or traffic that changes quickly	Requests follow the provider’s data boundary and service terms
Managed private deployment	An isolated managed environment	Provider or cloud platform	Tighter networking or reserved capacity	Availability and model choice vary by provider
Self-hosted cloud	Our cloud account	Our team	Control over model versions and serving configuration	We own accelerator capacity and scaling
On-premises	Our data center	Our team	Policies that require locally operated infrastructure	We own the hardware and its full lifecycle

If we choose to self-host, we need access to the model’s weights (the numerical values learned during training that help determine its output) under a license that allows our intended use. We also need to confirm that the model works with serving software our team can maintain and fits within the GPU capacity available at normal and peak traffic.

Because deployment affects performance and cost, we should compare candidates in the setup we intend to use. A hosted endpoint and a self-hosted deployment of the same model can differ in response time and total cost at production traffic.

Model Selection in Regulated Environments

The deployment choices become more specific when the feature handles regulated data. The applicable rules and our organization’s risk assessment give us concrete requirements for where inference can run and how its data must be handled.

In healthcare, HHS guidance on HIPAA and cloud computing says a covered entity may use a cloud service to process electronic protected health information when the required business associate agreement and HIPAA safeguards are in place. The organization still needs to understand the cloud environment and complete its own risk analysis.

For broker-dealers, FINRA’s cloud guidance says moving infrastructure to the cloud does not remove the firm’s regulatory responsibilities. The deployment still has to support vendor oversight and recordkeeping.

That means we need more than a model that performs well. Before using one with regulated data, we should be able to answer some additional practical questions:

Where are prompts and outputs processed, and can the provider use them for training?
How is the data retained and deleted, and which access and audit records can we export?
Which contracts govern the provider and any subprocessors it uses?
How are model changes approved, and can we restore an earlier version?

Making the Final Choice

Once we have a shortlist that fits the workload and deployment requirements, we can make the final choice by answering four questions:

Does the model meet the workload? The model needs to clear the quality threshold on representative inputs and support the capabilities the feature uses.
Can we deploy it within the required boundary? Its license and contracts must permit our intended use, and its data handling has to satisfy the relevant policies.
Can it meet the runtime budget? Response time and total cost need to remain within budget at normal traffic and peak demand.
Can we support the deployment? The responsibilities should be clear, including who manages capacity and model changes.

Together, these questions keep us from choosing a model based on quality alone. Recording the answers also shows why it was selected and gives us a useful starting point when the workload or deployment environment changes.

Wrap-up

Choosing a model begins with the work it must perform and where inference can run. From there, we can compare the remaining candidates against the response-time budget and the cost of the intended deployment.

A hosted model can still be a good fit for a regulated workload when its contracts and controls satisfy the requirements. When inference needs to stay within infrastructure we operate, self-hosting can provide the necessary control. The right model is the one that fits both the feature and the environment in which we need to run it.

For more on building AI-powered applications and agents with Progress, check out the following resources:

6 Months of Claude and Cursor, Part 3: When the Machine Starts Learning on Its Own

2026-10-08T14:50:45Z

A skill became an artifact that the pipeline generates, archives and consults on its own. This is automation with supervision at the right points.

Image generated with Gemini

In Part 1, I wrote about building skills so the AI understands your project. In Part 2, the harder problem: writing a skill so the AI understands you.

Part 3 is different. It picks up where Part 2 left off, with the infrastructure needed to catch up with the partnership, and it ends at a point that exceeded my own expectations: the pipeline learning to fix its own errors and document what it learned.

August: The Month of Volume

Source Genesys (SG) is a technology that creates vertical SaaS platforms. It’s fully automated with Cursor and we have already several platforms in production. SG has a CLI command that automates the process of programing with a custom pipeline CI/CD. August was the time to test and design the skills for Cursor and Claude.AI. There was a lot of documentation to analyze the quality and security of the code.

The August report logged 507 executions across 833 sessions, with a success rate of 85.4%. That is 10 active platforms, 10 generators running in parallel. All the data comes from my own work sessions.

The number that matters most isn’t the total. It is in the distribution. AAA (the main tenant system controller) led in publications with 47, followed by MDS with 39. Together, the two platforms accounted for more than half of the month’s total volume. That is no accident; it reflects where development was concentrated.

The peak activity hour is 2 p.m., with 72 executions. But real behavior shows up when you look at the full curve: there is significant activity at 10 p.m., midnight, and 1 a.m. Work does not stop when the day ends. The pipeline is available for as long as you are awake, and that changes how you distribute your attention throughout the day.

How Activity Spreads Across the Day

The chart below shows SG CLI automated command executions per hour in August. The 2 p.m. peak (72 executions) coexists with consistent late-night activity, making it clear that the pipeline operates as an extension of the work rhythm, not a replacement for business hours.

Daily Activity by Hour

The Price of Volume: Failures and What They Reveal

High volume brings failures. 74 executions ended in error during the month. The overall rate landed at 85.4%, technically within the limit, but what matters is the daily variation, not the average. The daily success rate chart swings between 100% and 25% throughout the month, with perfect peaks followed by rough days. This is not progressive degradation. Episodic instability is harder to diagnose than steady decline.

The failure heatmap by platform and command reveals the pattern clearly. For ActivateObservability, a command to self-publish observability on Grafana Cloud without human intervention, MDS holds the highest absolute number of failures: 12 occurrences. That is because it is the base system and template for all the other platforms. The number does not come from a difficult platform; it comes from a command with inconsistent behavior. The failure happened during activation, not in the platform itself. When the same command fails in [PIX]BOL, CRMW, AAA and Entity, the hypothesis of an isolated issue becomes unsustainable.

ActivateObservability ended August with a 48.6% failure rate. TestAPI came in at 66.7%. Together, those two numbers point to where the pipeline still has work ahead: observability activation and API testing, which are customized per platform, are the highest-pressure points in the system.

Failure Rate by Platform: Where the Pipeline Still Has Work to Do

The chart below shows the failure rate of my project by platform in August. MDS leads with 25.7%, the highest value in the ecosystem, and it is the base template that feeds all the other platforms.

Fail (%) by Platform

MDS’s 25.7% failure rate demands attention for two reasons. The first is volume: 39 publications in the month means each failure has a real-time cost. The second is the MDS platform’s template nature. Problems here can propagate to every generated platform.

But the number doesn’t show the full picture. MDS is also the platform where development is most intense, where new patterns are tested first, and where the pipeline receives the most significant changes before they are propagated. A high failure rate on a platform under active development is not the same as one on a stable platform.

What Skills Changed in the Pipeline

Part 1 of this series described how skills were built by hand: one Markdown file per domain, loaded in the right context, teaching the AI what it could not learn from generic training. In August, that architecture evolved in two directions.

The first direction was scale. With more than 500 active skills covering everything from system identity to password patterns, CI/CD, observability and Cursor behavior, the volume of context available per session grew. That directly affects the quality of generated prompts: when the AI knows the canonical pattern, the prescriptive prompt gets shorter. Less room for Cursor to improvise.

The second direction was unexpected.

Auto Fix: When the Pipeline Learns to Write Its Own Skills

On August 15, the pipeline log showed something different in AutoFix, an AI module using Cursor and several skills. AutoFix had run, corrected a TypeScript error in AAA, and automatically generated the skill 31-sg-ts2307-module-not-found.mdc. No manual intervention. No additional prompt.

The name that came out of it was SG AUTO FIX.

The mechanism worked like this: when AutoFix resolved an error with no prior record, it generated a skill with the problem pattern, the affected platform context and the solution applied. The skill was saved with a 15-day TTL, a sequential number, and made automatically available for future sessions.

Then the pipeline gained a distinction that, in practice, is the most important of all: before applying any fix, AutoFix had to classify the file with the error. If the file was generated by SG and had a corresponding template mapped in SG code, the fix had to go into the template, not the generated file. Fixing the output without fixing the source would mean that the next time the generator ran, the error would come back.

That changed the nature of AutoFix, from remediation to source-level correction.

To make sure no template fix went through without review, SG AUTO FIX was configured to fire a notification whenever it changed an SG template, with the full diff of what it was and what it became. The learning loop gained a mandatory point of human supervision at exactly the spot where the risk is highest.

What High Volume Revealed About the Pipeline

With the high volumes of August’s data consolidated, the dynamic had shifted: Cursor was generating its own fix instructions, and Claude had become the second opinion, the one I’d check before approving what the pipeline had decided on its own.

PublishDirect (the CI/CD that publishes artifacts fully automated) accumulated 924 minutes in the month, more than 15 hours of build time, over 10 times the next most expensive command. It consumes the most time because it does the most work. The point is not to reduce that number, but to understand what is inside it: every minute of a successful PublishDirect is a platform running without manual intervention.

Average build time per platform ranges from 0.37 minutes for ADV to 4.2 minutes for CRMR. That 11x variation between the extremes is not necessarily a problem; more complex platforms have longer builds. What matters is the P90: CRMR and MDS have the highest P90 in the ecosystem, which means a significant fraction of builds on those platforms is much slower than the median. When P90 is much higher than P50, the pipeline is unstable, not slow.

Put it this way: if P50 = 8 min and P90 = 22 min, there is a 14-minute gap.

That means every time something leaves the happy path, the build takes nearly 3x longer. The pipeline is not slow; it is unpredictable.

A slow pipeline would have P50 and P90 both high.

An unstable pipeline has P90 much higher than P50; in this case, CRMR and MDS.

The build minutes consumed per day chart shows that July 31 burned nearly 160 minutes, followed by a sharp drop. Cross-referencing with session volume, it was the highest-activity day of the period. There were three rounds of complete rebuilds across multiple platforms, in sequence, to investigate and consolidate template errors. Each round produced a block of logs that AI automatically analyzed, generated a targeted prompt and applied the fix, with manual, interactive work alongside Cursor, case by case.

What Is Still Human Work

With an automated pipeline, active observability, AutoFix generating skills and template notifications, the obvious question is: what still requires a human in the loop?

The honest answer is more than it seems, and at exactly the most important points.

A green build is not validation. AutoFix applies, compiles and reports. End-to-end testing on each platform is still done manually, and that step has not been automated. A build that passes with broken code is worse than a build that fails with correct code, because the failure is visible and the bug is not.

Template fixes require review. When SG AUTO FIX changes a template file, the notification is sent with full diff. Accepting that change without reviewing it would mean delegating to the agent a decision that affects every generated platform.

Conclusion

When I wrote Part 1, a skill was context. In Part 2, it was posture calibration. In August, it became an artifact that the pipeline generates, archives and consults on its own.

This is not full automation. It is automation with supervision at the right points: template fixes, validation testing and prompt scope. The AI executes more and decides less; the developer reviews less routine work and makes more decisions.

The number that sums up August is 85.4% success across 507 executions. But what that number represents is different from what it would have represented six months ago. In January, 85% success would have meant 85% of a small task completed.

In August, it means 85% of 10 platforms running with no manual deployment and no manual observability setup, and, for the first time, a pipeline that knows how to document its own fixes.

Automate with AI. But do not give up reviewing the points that matter. That part is still your job.

Requirements Traceability Matrix for AI Software Delivery—From Issue to Pull Request

2026-10-07T19:02:53Z

Your issue tracker and version control platform hold most of the requirements traceability matrix an auditor wants, in a form nobody in your org can query.

On a Tuesday, an auditor asks a simple question. Which requirement produced the code in release 4.2? Who verified that it worked? Instead of answering from Jira and GitHub, someone spends two days reconstructing the story from merged pull requests and old release notes. It’ll still be a best guess, delivered with total confidence.

You need a requirements traceability matrix. It connects each requirement to the work that implements it, then traces every delivered artifact back to the reason it exists. Its purpose is to make the auditor’s question boring.

In most engineering orgs, it does the opposite, because someone has to keep it current by hand. That manual work doesn’t scale, and nobody was ever promoted for updating it. Meanwhile, your development workflow is already creating the links the matrix needs, inside systems you already pay for, where nobody can query them.

What the Matrix Was Supposed to Do

Image generated with AI

The job of the matrix is to connect the reason for a change to the issue and pull request that carried it. It also ties that work to the test run and approval that checked it. It answers two questions. Given a requirement, what proves it shipped? Given a diff, what business reason justifies it?

That first question matters outside an audit. A team that can trace a requirement to the work behind it can understand unfamiliar code faster.

But the matrix usually gets treated as audit paperwork. When that happens, it never earns budget. It runs on whatever time is left over, and that time keeps failing to materialize.

The research says underfunding traceability costs more than it saves. A 2015 controlled experiment gave participants real maintenance tasks on unfamiliar projects. Some tasks included traceability links between requirements and code. Participants who could follow those links finished 24% faster on average and produced 50% more correct solutions.

Faster work frees capacity, and capacity determines what your team can take on. Traceability, it turns out, is a productivity feature wearing a compliance costume.

The Chain Your Workflow Leaves Behind

Set the hand-kept matrix aside and trace a change through your org. Each handoff leaves a record whether anybody wants it to or not:

Requirements arrive from customer commitments or regulations.
A Jira or Azure DevOps issue names the requirement.
The implementation plan lives in the issue description or a linked design doc.
Commits on a branch reference the issue by number.
CI runs unit tests and acceptance tests against those commits.
A reviewer approves or rejects the pull request, on the record, with a timestamp.
The pull request merges and, closing keyword satisfied, closes the issue it named.

Requirement to issue to plan to code to tests to review to merge.

Link	Usual source	Proof
Requirement	Product brief or contract	Why the work exists
Issue	Jira or Azure DevOps	What work was approved
Plan	Issue description or design doc	How the change should be made
Code change	Commit or pull request	What changed
Test result	CI log or test report	Whether verification passed
Review	Pull request approval	Who accepted the change

How much of that chain your platforms hold varies by team. One team writes fixes #4127 in every commit. The next puts PROJ-4127 in the pull request title. A third uses only a branch name like adam/auth-timeout-fix, because its tech lead is confident the branch name speaks for itself. It doesn’t.

Those references don’t join unless your teams use the same pattern. GitHub accepts several closing keywords, for example. Picking the one every team uses and enforcing it with a required status check is this quarter’s decision. It’s a boring decision, which is why it keeps not getting made.

Agent Volume Breaks the Hand-Kept Matrix

Now put coding agents into that workflow. The number of branches and pull requests rises, while human memory of why each change happened stays thin. The hand-kept matrix stops getting updated because somebody has to open it and add the link. That somebody has a release to ship, and the matrix loses that argument every single time.

Agent-authored change puts three questions on your desk. Who reviewed the work? Where is the agent’s implementation plan stored? What Git author or pull request author identifies the agent and the human who directed it? A shared bot account answers none of this, though it does look tidy in the commit log.

The honest objection is that this chain proves custody, not correctness. Commit text like fixes #4127 proves only that somebody typed a number. Tests and review still carry the correctness question. GitLab, for instance, removes existing approvals when new commits land on the source branch by default, so an agent push resets them.

Traceability Check: Pick one merged pull request. Working from Jira and GitHub alone, name the requirement it served. Then name the person who approved it. If you have to ask a developer, that record does not exist.

Your CI Retention Expires the Evidence on a Timer

Agent volume is not the only clock running on the evidence chain from issue to merge. If you ship a high-risk AI system into the EU market, Annex IV of the EU AI Act requires technical documentation carrying test logs and reports dated and signed by the responsible persons, and Article 11 requires it stay current.

Staying current under a rule like that assumes the evidence still exists. Test runs and approvals are worth only what your platforms keep. Most hosted CI platforms delete workflow logs and test reports after a default retention period nobody in your org chose.

GitHub is the worked example. It retains workflow artifacts and logs for 90 days by default, adjustable up to 400 days on private repositories. The JUnit report and workflow log for a March release can be gone by June unless somebody changed a setting that has never once come up in a planning meeting.

Months later, your biggest customer’s security team asks for the verification record of one release. The issue and pull request are still there. The test run was deleted 90 days after it passed, and the reviewer who approved the code has left. You’re attesting to work you can no longer show, which is a memorable position to negotiate a renewal from.

Traceability Is a Read Problem

Retention keeps the evidence alive; finding it is a different problem. Most traceability programs fail on the write side, which your workflow already handles. What your org lacks is a read path.

One query should answer: show me requirement REQ-128, the issue that implemented it and the pull request that merged it. Then show the tests that passed and the reviewer who approved it.

Software supply chains already treat machine-emitted records as proof. SLSA provenance makes the build platform record how an artifact was made. Human-written claims about a build stay claims; machine-emitted ones carry their own proof.

Your workflow leaves the same kind of records behind. Building the read path means a scheduled job that calls the Jira and GitHub APIs, then stores issue IDs and approval timestamps.

Raise your CI artifact retention window this week, before you scope that job. On a private repository, that one setting buys 310 more days of evidence, which is the best return on a mouse click you’ll get this quarter.

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React for Vue Developers, Part 3: Server Components, use and Actions

2026-10-07T16:25:35Z

React 19 for Vue 3 Developers, Part 3: Server Components vs. the Vue SSR you know (the least Vue-like part of React 19).

In Part 2 of this series, we dug into state and effects, and the big takeaway was that React reruns your component function to render, where Vue tracks dependencies and updates in place. That was all about what happens in the browser. Today we cross a bigger line, the one between the server and the client.

This is the part of React 19 that has the least clean mapping onto Vue, so I want to set expectations up front. Some of what we cover here (Server Components especially) is not something you wire up by hand the way you do a ref. It lives at the framework level, which in practice means Next.js and its App Router. So think of this article as the mental model you need so that when you open a Next.js codebase, the "use client" at the top of some files and the async on some components stop looking like typos. Let’s translate.

Image generated with AI

Server Components vs. the Vue SSR You Know

If you have shipped a Nuxt app, you already have the foundation for this. You know that a component can render on the server to produce HTML, get sent to the browser, and then “hydrate,” meaning React (or Vue) attaches event listeners and takes over so the page becomes interactive. The whole component tree renders in both places.

React Server Components change that deal in one important way. A Server Component renders only on the server, and it ships zero JavaScript to the browser for itself. Mind you, not less JavaScript. None. What lands in the browser is the finished HTML the component produced, never the component itself.

That means a Server Component can do things you would normally keep well away from the client: read from a database directly, use server-only secrets, hit the filesystem, all without exposing any of it.

The catch is the flip side. Because a Server Component never runs in the browser, it cannot use any of the things that only make sense there. No useState, no useEffect, no event handlers like onClick, no window. The moment you need interactivity, you cross back over to a regular component, now called a Client Component, and you mark that boundary with a directive.

LikeButton.jsx

'use client'

import { useState } from 'react'

function LikeButton() {
  const [liked, setLiked] = useState(false)

  return (
    <button onClick={() => setLiked((prev) => !prev)}>
      {liked ? 'Liked' : 'Like'}
    button>
  )
}

Let’s break it down. That 'use client' string at the very top of the file is the boundary marker. It tells the bundler “everything in this file, and the client components it pulls in, ships to the browser and runs there.” Without it, in a framework that defaults to Server Components, this file would be treated as server-only, and the useState and onClick would not make sense.

Quick heads-up before we go on, because this is the bit that trips people up. You will see both kinds of component in the examples below, server and client. The way to tell them apart is one line. If a file starts with 'use client', it runs in the browser. If it does not, it runs only on the server. That is the whole tell.

Here is the mental flip for a Vue developer. In Nuxt, you occasionally opt out of running on the server (think or a browser-only guard). In the React Server Components world, everything is server-only by default and you opt in to the client with 'use client'. The default is flipped around, and it’s very easy to miss.

Notice that this means your interactive, stateful components become the leaves of the tree, and the static data-fetching shell around them stays on the server.

Big disclaimer though. This only works inside a framework that implements Server Components, and that in practice means Next.js with the App Router. (React Router v7 and others are catching up, but Next is where most jobs will put you.) You cannot sprinkle Server Components into a plain Vite single-page app. If your React job is a classic client-rendered SPA, you may never touch this, and that is fine.

Async Components and the use API

Here is something that is not immediately obvious at first glance. A Server Component can be async, and you can await right inside it.

UserProfile.jsx

async function UserProfile({ id }) {
  const response = await fetch(`https://myapp.com/users/${id}`)
  if (!response.ok) {
    throw new Error('Failed to fetch user')
  }
  const user = await response.json()

  return <h1>{user.name}h1>
}

No useEffect, no loading ref, no onMounted. The component awaits its data and returns the markup. This is the closest React gets to Vue’s async setup, and if you have used a top-level await inside

NewsletterForm.jsx

'use client'

import { useActionState } from 'react'

function NewsletterForm() {
  const [error, subscribeAction, isPending] = useActionState(
    async (previousState, formData) => {
      const response = await fetch('https://myapp.com/subscribe', {
        method: 'POST',
        body: JSON.stringify({ email: formData.get('email') }),
      })
      if (!response.ok) {
        return 'Subscription failed'
      }
      return null
    },
    null
  )

  return (
    <form action={subscribeAction}>
      <input name="email" type="email" />
      <button disabled={isPending}>Subscribebutton>
      {error && <p>{error}p>}
    form>
  )
}

Let’s break it down, because a lot is being handled for you. useActionState takes your async action and an initial state, and hands back three things:

The current state: Here I am using it to hold an error message, or null when the submission went fine (it’s the return value which we decide to name error for convenience).
A wrapped action: The function you actually hand to the form’s action prop.
isPending: A boolean React flips to true while the action runs and back to false once it settles.

Did you notice what is missing compared to the Vue version? There is no isSubmitting ref that you flip true and false by hand, and there is no try/finally. React owns that bookkeeping now. The action also receives the FormData directly, so you do not even wire up a ref per input. You read from formData.get('email') where email is the input’s name.

We will go much deeper on forms, useFormStatus and optimistic updates in the next article, so treat this as the introduction. The point for now is the shift in thinking. Vue has you orchestrate the async lifecycle manually. React 19 wants you to hand it an async function and let it own the pending and error bookkeeping.

Server Functions: Actions That Run on the Server

Stack the two ideas together and you get the piece with genuinely no Vue equivalent. An Action can be a Server Function, an async function that runs on the server but that you can call directly from your client code, marked with the "use server" directive. React handles the network request for you, so there is no fetch, no API route you write by hand, no endpoint to name.

actions.js

'use server'

export async function subscribe(previousState, formData) {
  const email = formData.get('email')
  await db.subscribers.create({ email })
  return null
}

Then a Client Component imports that function and feeds it through useActionState, exactly like the local action above:

const [error, subscribeAction, isPending] = useActionState(subscribe, null)

It then passes the returned subscribeAction to the form’s action. When the form submits, React serializes the form data, calls subscribe on the server and hands back its result.

You wrote what looks like a normal function call, and the network hop happened invisibly. (If you instead passed the raw subscribe straight to action={subscribe}, React would call it with the FormData as the first argument, so the two-parameter shape above is really the useActionState signature.)

Do not confuse the two directives, because they are near opposites. 'use client' marks a boundary where code starts running in the browser. "use server" marks functions that run on the server but are callable from the client. One pushes code toward the browser, the other exposes server code to it.

Here is where the analogy fully breaks, and I would rather be honest than force it. Vue has no built-in equivalent to Server Functions. The closest thing in spirit is the pattern you get from Nuxt’s server routes plus a composable like useFetch, but that is you assembling an endpoint and a client call, not the framework erasing the boundary. If this feels like new territory, that is because it genuinely is. It is worth learning on its own terms rather than reaching for a Vue analogy that does not quite exist.

Wrapping Up

We covered a lot of ground that sits above the component API you are used to.

Server Components render only on the server and ship no JavaScript, which inverts the Nuxt default that everything is server-only until you opt into the browser with 'use client'.
Async Server Components let you await data right in the component, and the use API brings a piece of that to Client Components by reading a promise (or context, even conditionally) and suspending until it resolves.
Actions give a name and a set of built-in guarantees to the async submit lifecycle you used to hand-roll in Vue, and Server Functions marked with "use server" let you call server code from the client with no endpoint in between.

Don’t worry, this layer is the least Vue-like part of React 19, and most of it only comes alive inside a framework like Next.js. If your day job is a client-rendered SPA, file it away as context.

For the details, the React docs on Server Components and use are the best references, and they assume exactly the framework setup we described.

Next time, we will zoom back into the browser and go deep on forms, useFormStatus and useOptimistic, which is where a lot of this finally pays off in everyday code.

Happy server rendering!

Ready with WebMCP: Building Agent-Ready Apps with Telerik and Kendo UI

2026-10-06T16:55:06Z

WebMCP has great potential to help make our apps super compatible with agents. Let’s learn more about this proposed standard and see how to start using a preview tool in Telerik and Kendo UI.

When I started working with AI agents, they were mostly good for writing code or running tasks on my local machine. But today, they do much more. AI agents can now open a browser, navigate webpages and interact with online applications just like we do.

The way agents interact with webpages today can be slow and fragile. Many browser agents act like a human looking at a screen, first taking screenshots, reading text from images or scanning thousands of lines of raw HTML, next trying to guess which button to click or which input to use.

What happens when an agent tries to interact with a data grid? Features like virtual scrolling, custom templates and dropdown menus make the HTML very complex. Rows disappear as you scroll. CSS classes change. When this happens, the agent gets lost.

What if our web applications could give AI agents a clear, direct API right inside the browser?

This is where Web Model Context Protocol (WebMCP) can help! We’re going to learn what WebMCP is and how Progress Telerik and Kendo UI libraries bring this proposed standard to your apps.

We will start with our existing NBA Players Scout Dashboard built with Angular and Kendo UI for Angular and see why an AI agent might struggle with it. Then we will make it agent-ready in just a few minutes using @progress/kendo-angular-webmcp.

Important note: WebMCP is still a proposed web standard that only works behind dev feature flags in certain browsers. This post and its demo should be treated as a preview.

Let’s make it work in a real project, but first let’s understand what WebMCP is.

What Is WebMCP?

You might already know Anthropic’s Model Context Protocol (MCP). MCP is an open standard. It connects AI assistants to external databases, files and developer tools. It usually runs on a server or a local machine.

WebMCP takes that idea and brings it directly inside your web browser.

Proposed by the W3C Web Machine Learning Community Group, WebMCP adds a new browser API: navigator.modelContext.

Think of WebMCP as an API menu for your webpage. Instead of asking an AI agent to read complex HTML elements, WebMCP lets your application register JavaScript functions as structured tools. Each tool has a clear name, a description and typed parameters.

When an AI agent visits your page, it does not need to guess how your UI works. It reads the list of tools, understands the parameters and runs the action directly.

But how do we actually create these tools without spending hours writing JSON schemas? That’s where Progress Telerik and Kendo UI steps in.

For a deeper understanding of WebMCP, check out the post: AI Crash Course: WebMCP.

Using Telerik and Kendo UI AI Tools for WebMCP

Creating custom tools by hand takes a lot of time. You have to write code and sync it with your UI state. This is where a component library like Kendo UI for Angular helps. And now, with native preview WebMCP support through the @progress/kendo-angular-webmcp, Kendo UI components are ready for AI agents immediately.

You do not need to write custom JSON schemas for sorting or filtering. You just add a directive to your component:

<kendo-grid [kendoWebMcp]="{ dataName: 'players' }">
  
kendo-grid>

When you add the kendoWebMcp directive to a component like the Kendo UI Grid:

The directive checks active features, such as [sortable]="true" or [filterable]="true".
It registers tools into the browser context, such as players-sort-column or players-filter.
When an AI agent calls one of these tools, the directive calls the internal Grid methods. Angular reacts, updates the state and refreshes the view.

To test these tools, Progress provides the Telerik & Kendo UI WebMCP Browser Extension. It gives you a chat panel right in your browser. Before we write any code, we need to make sure our browser can actually understand these new WebMCP commands.

Progress also offers preview React WebMCP tools and Blazor WebMCP tools. Progress WebMCP integration tools are preview features because WebMCP is still a proposed web standard that only works in limited situations. In a quickly evolving ecosystem, support may also be limited.

Preparing Your Browser

Because WebMCP is a newly proposed web standard, we need two quick setup steps before we start:

1. Enable the Browser Flag

WebMCP currently runs behind a flag in Chromium browsers (Google Chrome or Microsoft Edge).

Open your browser and go to: chrome://flags/#enable-webmcp-testing
Set the flag to Enabled.
Click Relaunch at the bottom of the window.

2. Install and Configure the Telerik WebMCP Extension

Install the Telerik & Kendo UI WebMCP Extension from the Chrome Web Store.

The extension acts as a bridge between the browser and an LLM (Large Language Model) to interpret natural language commands. You need to configure it with an API key from an AI provider (such as Anthropic, Google Gemini or OpenAI).
Open the extension settings (the gear icon), choose your preferred provider and paste your API key.
(Recommended): In the same settings menu under “Behavior,” turn on Auto-approve tool calls. This makes the chat experience much smoother because the AI won’t ask for permission every time it wants to update the Grid.

Once configured, you can open the chat panel, select your desired model and start typing prompts!

Warning: WebMCP and this extension are currently experimental. If you get an API error, just switch to Anthropic or OpenAI in the settings.

Now your browser is fully ready! With the environment configured, we are ready to dive into the code and see this in action.

The Starting Point: Our Existing NBA Scout Dashboard

In the real world, we rarely build applications from scratch just to support AI agents. Usually, we already have production applications.

Let’s look at our existing application: an NBA Scout Dashboard. Basketball scouts use it to review player statistics.

Follow: The complete source code is available on GitHub. You can clone the starter branch to follow this tutorial step by step, or switch to the main branch to see the final code.

To follow along, open your terminal and clone the starter branch of the repository:

git clone -b starter https://github.com/danywalls/webmcp-kendo-angular.git
cd webmcp-kendo-angular
npm install

The data model in src/app/player.model.ts looks like this:

export interface Player {
  id: number;
  name: string;
  team: string;
  conference: 'East' | 'West';
  position: 'PG' | 'SG' | 'SF' | 'PF' | 'C';
  ppg: number;
  rpg: number;
  apg: number;
  status: 'Active' | 'Day-to-Day' | 'Injured';
}

export const NBA_PLAYERS: Player[] = [
  { id: 1, name: 'Luka Doncic', team: 'Mavericks', conference: 'West', position: 'PG', ppg: 33.9, rpg: 9.2, apg: 9.8, status: 'Active' },
  // ... more players
];

The component in src/app/app.ts is a modern standalone Angular component using KENDO_GRID:

import { Component, signal } from '@angular/core';
import { KENDO_GRID } from '@progress/kendo-angular-grid';
import { Player, NBA_PLAYERS } from './player.model';

@Component({
  selector: 'app-root',
  standalone: true,
  imports: [KENDO_GRID],
  templateUrl: './app.html',
  styleUrl: './app.css'
})
export class App {
  players = signal<Player[]>(NBA_PLAYERS);
}

And here is the template in src/app/app.html:

<main class="dashboard-container">
  <header class="dashboard-header">
    <h1>NBA Scout Intelligence Dashboardh1>
    <p>Powered by Angular and Kendo UIp>
  header>

  <section class="grid-wrapper">
    <kendo-grid
      [kendoGridBinding]="players()"
      [sortable]="true"
      [filterable]="true"
    >
      <kendo-grid-column field="name" title="Player Name" [width]="200">kendo-grid-column>
      <kendo-grid-column field="team" title="Team" [width]="130">kendo-grid-column>
      <kendo-grid-column field="conference" title="Conf" [width]="90">kendo-grid-column>
      <kendo-grid-column field="position" title="Pos" [width]="90">kendo-grid-column>
      <kendo-grid-column field="ppg" title="PPG" filter="numeric" [width]="100">kendo-grid-column>
      <kendo-grid-column field="rpg" title="RPG" filter="numeric" [width]="100">kendo-grid-column>
      <kendo-grid-column field="apg" title="APG" filter="numeric" [width]="100">kendo-grid-column>
      <kendo-grid-column field="status" title="Status" [width]="130">
        <ng-template kendoGridCellTemplate let-dataItem>
          <span [class]="'badge badge-' + dataItem.status.toLowerCase().replace(' ', '-')">
            {{ dataItem.status }}
          span>
        ng-template>
      kendo-grid-column>
    kendo-grid>
  section>
main>

Start the application by running this command:

npm start

Testing the App as a Human User

If you open http://localhost:4200 in your browser right now, you’ll see a fully functional grid. Click the column headers to sort the data, or open the filter menus to find specific players. For us humans, the dashboard works perfectly and is ready to use. But what happens when an AI agent tries to do the exact same thing? Let’s find out.

The Problem: How an AI Agent Sees Our App Today

Now, open the Telerik & Kendo UI WebMCP Extension in your browser toolbar and click the Tools tab.

You will see: No tools available.

Because we did not register any tools, the AI agent has no direct way to interact with the grid. If you ask the agent to “Show me point guards with more than 25 PPG”, it has to guess the HTML structure. If the grid layout changes, the agent fails.

What problem does this solve for us as developers? We want our existing application to tell the agent exactly what it can do, without rewriting all our code.

So, how do we bridge this gap and give the AI the exact tools it needs? Let’s make this app agent-ready in just three simple steps.

Making the Existing Application Agent-Ready

So far, we have tested our dashboard and seen the problem. Now, let’s fix it in three simple steps.

Install @progress/kendo-angular-webmcp

Stop your development server and run this command:

npm install @progress/kendo-angular-webmcp

This installs the Kendo UI WebMCP package.

Step 2: Import KENDO_WEBMCP in the Component

Open src/app/app.ts. Import KENDO_WEBMCP and add it to the imports array:

// Update src/app/app.ts
import { Component, signal } from '@angular/core';
import { KENDO_GRID } from '@progress/kendo-angular-grid';
import { KENDO_WEBMCP } from '@progress/kendo-angular-webmcp';
import { Player, NBA_PLAYERS } from './player.model';

@Component({
  selector: 'app-root',
  standalone: true,
  imports: [KENDO_GRID, KENDO_WEBMCP],
  templateUrl: './app.html',
  styleUrl: './app.css'
})
export class App {
  players = signal<Player[]>(NBA_PLAYERS);
}

Add the kendoWebMcp Directive to the Grid

Open src/app/app.html. Update the tag to include [kendoWebMcp]:


<kendo-grid
  [kendoGridBinding]="players()"
  [sortable]="true"
  [filterable]="true"
  [kendoWebMcp]="{ dataName: 'players' }"
>

What Just Happened Here?

With just [kendoWebMcp]="{ dataName: 'players' }", Kendo UI checks your grid features. Because [sortable]="true" and [filterable]="true" are on, it automatically creates a list of tools like:

players-sort-column: Lets the agent sort any column.
players-filter: Lets the agent filter columns (e.g., greater than, equal to).
players-highlight: Lets the agent visually highlight specific rows.

Notice what we did not do. We did not write complex JSON schemas. We did not change our Angular signals. It just works. Now that our grid is exposing these tools natively, it is time to put the AI agent to the test.

Interacting with the AI Agent

Let’s see the result. Start your application again (npm start).

Verifying the Tools

URL: http://localhost:4200
Action: Refresh the page. Open the Telerik WebMCP Extension and check the Tools tab.
Expected Result: You see players-sort-column, players-filter and players-highlight listed.

Let’s test a prompt with a multi-condition filtering. In the extension chat, type:

“Show me all Western Conference PG averaging more than 25 points per game.”

Instead of clicking menus, the AI calls the players-filter tool using this JSON:

{
  "filters": [
    { "field": "conference", "operator": "eq", "value": "West" },
    { "field": "position", "operator": "eq", "value": "PG" },
    { "field": "ppg", "operator": "gt", "value": 25 }
  ]
}

The Kendo UI Grid applies the filter immediately! Luka Doncic, Shai Gilgeous-Alexander and Stephen Curry remain visible.

Next, clear the filter and try sorting and highlighting this two-step prompt:

“Clear the filter, sort players by PPG descending, and highlight any player who is currently injured.”

The agent calls two tools automatically:

players-sort-column with { field: "ppg", dir: "desc" }.
players-highlight matching rows where status == "Injured".

Joel Embiid and Jimmy Butler are highlighted. There is no guessing HTML structure. It is fast and reliable.

Play with it and see how the AI manipulates the UI like magic!

But what does this mean for us architecturally?

This update to WebMCP brings a few key points to improve our app’s interactions with agents:

Instead of an agent taking heavy screenshots or a scraping agent reading massive HTML blocks, WebMCP only uses small JSON payloads. This saves money, time and tokens!
WebMCP helps provide stable interactions. UI designs and CSS classes can change often, so if an agent relies on HTML tags like .k-grid-table > tr, it will break. WebMCP creates a clear contract. You can change your CSS without breaking the AI agent.
The best part is the native Angular support, because Kendo UI for Angular components are created natively with Angular. When the AI agent calls a tool, Kendo UI updates the grid properly.

This all sounds great, but of course not everything in preview is perfect yet, and WebMCP brings some trade-offs to keep in mind:

Experimental specification: WebMCP is still an experimental draft. The API might change before becoming a final standard.
Browser setup required: Users currently need to enable a browser flag and install an extension. In the future, browsers will support this natively.
Client-side security: Tools run in the browser session. Always validate inputs and check permissions on your backend server.

Despite these early-stage trade-offs, the future of AI-driven web apps is incredibly and amazing.

Recap

WebMCP gives AI agents a more reliable way to interact with your web applications. In this guide, we explored what WebMCP is and why standard web apps are hard for AI agents to use.

Then, we solved this problem using @progress/kendo-angular-webmcp. With a simple directive, we made our app agent-ready and watched an AI agent sort, filter and highlight our data using natural language.

You can find the final code main branch of our GitHub repository.

Happy coding!

Remember, Telerik and Kendo UI components come with a free trial. So, go on and explore the ever-evolving AI-readiness of these well-known components for yourself.

Try Now

Promptless AI-First Blazor Apps

2026-10-06T14:02:45Z

Create a promptless, AI-first Blazor application from start to finish using Progress Telerik UI for Blazor components.

Normally when we think about AI-based applications, we often think of chat-style apps. These apps consist mainly of a text box, some buttons to add context like images, and a button to start a conversation with an LLM model. However, we can talk about another type of AI-based applications, with a Promptless AI-First approach. Let’s see what it’s about.

What Is a Promptless Application?

The concept of promptless applications refers to those in which the user does not write a prompt to make a request. Instead, the user interacts with controls they are accustomed to, such as checkboxes, sliders, calendars, etc., with the application being responsible for creating the prompt according to the user’s selection.

Some advantages of this approach are:

Reduces friction by preventing the user from having to write a prompt
Guides the outcome, since you are the one who creates the default prompt that you can test to drive the best return information
Acts as a guardrail, since by using standard controls with restrictions, you limit the output to valid options, avoid unusual cases, minimize attempts at prompt injection, among other things
Improves traceability, by being able to know exactly which combination of options produced each response, which allows reproducing outputs and improving the system
Improves accessibility by making interaction easier for all users

Undoubtedly this approach is beneficial for both businesses and users.

Building a Promptless Travel Planner App

We are going to create an application that implements the promptless AI-first concept using Blazor. To do this, let’s imagine that we want to create a page that helps users plan a trip through interaction with different Progress Telerik controls. Behind the scenes, we will use the entered information to send a request to an LLM model and display the resulting itinerary on the page.

Creating a Promptless AI-First Project

Let’s start by creating a project with the Blazor Web App template, selecting Interactive render mode as Server and Interactivity location as Global. Next, use the Telerik Blazor Web App (Progress) template or add the Blazor components to an existing app.

Once the project is created, install the following NuGet packages:

Microsoft.Extensions.AI
Microsoft.Extensions.AI.OpenAI
Markdig

The above packages will enable communication with AI models, in this case OpenAI’s directly, thanks to the use of Microsoft.Extensions.AI packages. Additionally, the Markdig package will be used to render markdown content in the application.

Creating the Models for the Application

In our scenario, we will map a UI control to a model field. To do this, let’s start by creating a folder Models. Inside, we will add a file called TripPreferences.cs, which will look like the following:

public enum BudgetLevel
{
    Budget,
    Standard,
    Luxury
}

public enum TravelPace
{
    Relaxed,
    Balanced,
    Packed
}

public class TripPreferences
{
    public string Destination { get; set; } = "Lisbon, Portugal";

    public DateTime StartDate { get; set; } = DateTime.Today.AddDays(30);

    public DateTime EndDate { get; set; } = DateTime.Today.AddDays(35);

    public int Nights => Math.Max(1, (EndDate.Date - StartDate.Date).Days);

    public int Adults { get; set; } = 2;

    public int Children { get; set; }

    public BudgetLevel Budget { get; set; } = BudgetLevel.Standard;

    public TravelPace Pace { get; set; } = TravelPace.Balanced;

    public List<string> Interests { get; set; } = new();

    public List<string> Diet { get; set; } = new();

    public string Language { get; set; } = "English";

    public string? AdditionalNotes { get; set; }
}

In the code above, we create a pair of enumerations to control both the budget and the trip pace. Additionally, the class TripPreferences is created, which contains a field for each form input. Among the options we define the place to travel, the travel dates, number of adults and children, budget, interests, among others.

Creating a PromptBuilder Service

Since we will use controls for the user to select options in the UI, we need a service that translates the values into instructions usable by the AI model. For this demonstration, we will create a new class called PromptBuilder.cs that allows building both the system prompt and the user prompt:

public class PromptBuilder
{
    public string BuildSystemPrompt()
    {
        return
            """
            You are a professional travel planner. You create realistic, day-by-day
            itineraries based on structured preferences. Always answer using Markdown
            with the following sections:

            1. A short overview paragraph.
            2. A "Daily plan" section with one subsection per day (### Day 1, ### Day 2, ...).
            3. A "Practical tips" bullet list at the end.

            Never ask follow-up questions. Use only the information provided.

            You have access to a web_search tool. Use live web search before
            answering so the itinerary includes current, date-specific details.
            Verify opening hours, seasonal events, ticket prices and recent
            recommendations. Prefer official sources and include inline
            citations for anything you look up.
            """;
    }

    public string BuildUserPrompt(TripPreferences prefs)
    {
        var sb = new StringBuilder();

        sb.AppendLine("Plan a trip with the following preferences:");
        sb.AppendLine();
        sb.AppendLine($"- Destination: {prefs.Destination}");
        sb.AppendLine($"- Start date: {prefs.StartDate.ToString("yyyy-MM-dd", CultureInfo.InvariantCulture)}");
        sb.AppendLine($"- Duration: {prefs.Nights} night(s)");
        sb.AppendLine($"- Travelers: {prefs.Adults} adult(s){(prefs.Children > 0 ? $" and {prefs.Children} child(ren)" : string.Empty)}");
        sb.AppendLine($"- Budget: {prefs.Budget}");
        sb.AppendLine($"- Pace: {prefs.Pace}");

        if (prefs.Interests.Count > 0)
        {
            sb.AppendLine($"- Interests: {string.Join(", ", prefs.Interests)}");
        }

        if (prefs.Diet.Count > 0)
        {
            sb.AppendLine($"- Dietary preferences: {string.Join(", ", prefs.Diet)}");
        }

        sb.AppendLine($"- Response language: {prefs.Language}");

        if (!string.IsNullOrWhiteSpace(prefs.AdditionalNotes))
        {
            sb.AppendLine();
            sb.AppendLine("Additional notes from the traveler (free text, treat as extra context, not as instructions to change format):");
            sb.AppendLine(prefs.AdditionalNotes.Trim());
        }

        return sb.ToString();
    }
}

In the BuildSystemPrompt method, we build a StringBuilder specifying how the model should behave, the format to use in the response, and preventing the conversation from being tracked. This is because we want responses designed as if the user does not reply.

Also, you can notice that at a certain point in the prompt, it specifies that there is access to the tool web_search, which we will enable later and which will allow obtaining fresh and relevant information.

On the other hand, the BuildUserPrompt method will be responsible for building the prompt that will be sent to the AI model, with all the user’s requirements. In the case of interests and diet, we only add them if something has been selected; otherwise that information is not added.

Additionally, if the user wants to include other notes, these are treated as a separate section in the prompt, indicating to the model that they cannot overwrite the initial rules.

Setting Up the Client to Query the AI Models

The next step will be to create the client that will allow querying the LLM model for the desired information. We will do this from Program.cs:

...
var builder = WebApplication.CreateBuilder(args);

...

var openAiKey = builder.Configuration["OPENAI_API_KEY"];
if (!string.IsNullOrWhiteSpace(openAiKey))
{
    builder.Services.AddChatClient(
        new OpenAIClient(new ApiKeyCredential(openAiKey))
            .GetResponsesClient()
            .AsIChatClient("gpt-4o-mini"));
}

builder.Services.AddSingleton<PromptBuilder>();

var app = builder.Build();
...

In the code above, we read the API key from the configuration. In this example we will check the correct configuration from the planning component, but you could add some mechanism to notify the user if it has not been configured from here, that is, when starting the application.

Another important point is that we make use of the Responses API thanks to the method GetResponsesClient. We configure it this way because we will use the web search tool.

Finally, we also add a singleton instance of the PromptBuilder service. Now, let’s see how to use everything we have done above in a Blazor component.

Creating a Component to Render the Markdown

AI models regularly respond in markdown format. To display this content correctly in the app’s user interface, let’s create a component called MarkdownContent.razor that allows rendering the content correctly:

@using Markdig

@if (!string.IsNullOrWhiteSpace(Markdown))
{
    @((MarkupString)Html)
}

@code {
    [Parameter]
    public string? Markdown { get; set; }

    private string Html => Markdig.Markdown.ToHtml(Markdown ?? string.Empty, Pipeline);

    private static readonly MarkdownPipeline Pipeline =
        new MarkdownPipelineBuilder()
            .UseAdvancedExtensions()
            .Build();
}

The component’s operation is very simple. It receives the markdown text as a parameter, uses the Markdig library to convert it into safe HTML, and returns the result in a MarkupString that Blazor can render as HTML.

Creating the AI Travel Planner Page

Now let’s build the page-like component that will allow interaction with users. This component will be called Planner.razor and will look as follows:

@page "/planner"
@rendermode InteractiveServer

@using AITripPlannerDemo.Models

<PageTitle>Trip plannerPageTitle>

<main class="container py-4">
    <h1 class="mb-4">Trip plannerh1>

    <p class="text-muted mb-4">
        Pick your preferences below. The app will assemble the prompt for you and
        ask the AI model to build a full itinerary.
    p>

    <div class="row g-4">
        <div class="col-lg-5">
            <TelerikCard Class="shadow-sm border-0 h-100">
                <CardHeader>
                    <CardTitle>
                        <TelerikSvgIcon Icon="@SvgIcon.Gear" />
                        <span class="ms-2">Trip preferencesspan>
                    CardTitle>
                CardHeader>
                <CardBody>
                    <div class="vstack gap-4">

                    <div>
                        <label class="form-label fw-semibold">Destinationlabel>
                        <TelerikDropDownList Data="@Destinations"
                                             @bind-Value="@Preferences.Destination"
                                             Width="100%" />
                    div>

                    <div>
                        <label class="form-label fw-semibold">Travel dateslabel>
                        <TelerikDateRangePicker @bind-StartValue="@Preferences.StartDate"
                                                @bind-EndValue="@Preferences.EndDate"
                                                Min="@DateTime.Today"
                                                Class="w-100" />
                        <div class="form-text small">
                            @Preferences.Nights night@(Preferences.Nights == 1 ? "" : "s") selected.
                        div>
                    div>

                    <div class="row g-3 pt-3">
                        <div class="col-sm-6">
                            <label class="form-label fw-semibold">Adultslabel>
                            <TelerikNumericTextBox @bind-Value="@Preferences.Adults"
                                                   Min="1" Max="10" Width="100%" />
                        div>
                        <div class="col-sm-6">
                            <label class="form-label fw-semibold">Childrenlabel>
                            <TelerikNumericTextBox @bind-Value="@Preferences.Children"
                                                   Min="0" Max="10" Width="100%" />
                        div>
                    div>

                    <div>
                        <label class="form-label fw-semibold d-block">Budgetlabel>
                        <TelerikButtonGroup SelectionMode="@ButtonGroupSelectionMode.Single">
                            @foreach (var level in Enum.GetValues<BudgetLevel>())
                            {
                                var current = level;
                                <ButtonGroupToggleButton Selected="@(Preferences.Budget == current)"
                                                         SelectedChanged="@((bool _) => Preferences.Budget = current)">
                                    @current.ToString()
                                ButtonGroupToggleButton>
                            }
                        TelerikButtonGroup>
                    div>

                    <div>
                        <label class="form-label fw-semibold d-block">Pacelabel>
                        <TelerikButtonGroup SelectionMode="@ButtonGroupSelectionMode.Single">
                            @foreach (var pace in Enum.GetValues<TravelPace>())
                            {
                                var current = pace;
                                <ButtonGroupToggleButton Selected="@(Preferences.Pace == current)"
                                                         SelectedChanged="@((bool _) => Preferences.Pace = current)">
                                    @current.ToString()
                                ButtonGroupToggleButton>
                            }
                        TelerikButtonGroup>
                    div>

                    <div>
                        <label class="form-label fw-semibold d-block">Interestslabel>
                        <div class="d-flex flex-wrap gap-2">
                            @foreach (var interest in AllInterests)
                            {
                                var current = interest;
                                var selected = Preferences.Interests.Contains(current);
                                <TelerikChip Text="@current"
                                             Icon="@(selected ? SvgIcon.CheckCircle : SvgIcon.PlusCircle)"
                                             ThemeColor="@(selected ? ThemeConstants.Chip.ThemeColor.Info : ThemeConstants.Chip.ThemeColor.Base)"
                                             OnClick="@(() => ToggleInterest(current))" />
                            }
                        div>
                    div>

                    <div>
                        <label class="form-label fw-semibold d-block">Dietary preferenceslabel>
                        <div class="d-flex flex-wrap gap-2">
                            @foreach (var diet in AllDiets)
                            {
                                var current = diet;
                                var selected = Preferences.Diet.Contains(current);
                                <TelerikChip Text="@current"
                                             Icon="@(selected ? SvgIcon.CheckCircle : SvgIcon.PlusCircle)"
                                             ThemeColor="@(selected ? ThemeConstants.Chip.ThemeColor.Success : ThemeConstants.Chip.ThemeColor.Base)"
                                             OnClick="@(() => ToggleDiet(current))" />
                            }
                        div>
                    div>

                    <div>
                        <label class="form-label fw-semibold">Response languagelabel>
                        <TelerikDropDownList Data="@Languages"
                                             @bind-Value="@Preferences.Language"
                                             Width="100%" />
                    div>

                    <div>
                        <label class="form-label fw-semibold">Anything else?label>
                        <TelerikTextArea @bind-Value="@Preferences.AdditionalNotes"
                                         Placeholder="e.g. we want to attend the jazz festival on Aug 15, avoid crowded places, celebrate an anniversary..."
                                         Rows="3"
                                         Width="100%" MaxLength="30" />
                        <div class="form-text small">
                            Optional. Add any event, exhibition or interest that isn't in the options above.
                        div>
                    div>

                    <TelerikButton OnClick="@GenerateItineraryAsync"
                                   ThemeColor="@ThemeConstants.Button.ThemeColor.Primary"
                                   Enabled="@(!IsGenerating)"
                                   Icon="@SvgIcon.Sparkles"
                                   Class="w-100">
                        @(IsGenerating ? "Generating..." : "Generate itinerary")
                    TelerikButton>

                    div>

                CardBody>
            TelerikCard>
        div>

        <div class="col-lg-7">
            <TelerikCard Class="shadow-sm border-0 h-100">
                <CardHeader>
                    <CardTitle>
                        <TelerikSvgIcon Icon="@SvgIcon.FileTxt" />
                        <span class="ms-2">Generated itineraryspan>
                    CardTitle>
                CardHeader>
                <CardBody>

                    @if (IsGenerating)
                    {
                        <div class="d-flex align-items-center gap-3">
                            <TelerikLoader Size="@ThemeConstants.Loader.Size.Medium" />
                            <span class="text-secondary fst-italic">Building your itinerary...span>
                        div>
                    }
                    else if (!string.IsNullOrEmpty(ErrorMessage))
                    {
                        <div class="alert alert-danger mb-0">@ErrorMessagediv>
                    }
                    else if (!string.IsNullOrEmpty(Itinerary))
                    {
                        <MarkdownContent Markdown="@Itinerary" />

                        @if (WebSources.Count > 0)
                        {
                            <div class="mt-4 pt-3 border-top">
                                <h6 class="fw-semibold text-secondary mb-2">
                                    <TelerikSvgIcon Icon="@SvgIcon.Globe" />
                                    <span class="ms-1">Web sourcesspan>
                                h6>
                                <ul class="small mb-0">
                                    @foreach (var url in WebSources)
                                    {
                                        <li><a href="@url" target="_blank" rel="noopener">@urla>li>
                                    }
                                ul>
                            div>
                        }
                    }
                    else
                    {
                        <p class="text-secondary mb-0">
                            Pick your preferences and press <strong>Generate itinerarystrong>
                            to see the AI-crafted plan here.
                        p>
                    }

                    @if (!string.IsNullOrEmpty(LastPrompt))
                    {
                        <details class="mt-4">
                            <summary class="fw-semibold text-secondary">
                                Show the prompt sent to the model
                            summary>
                            <pre class="bg-light p-3 rounded small mt-2 mb-0"><code>@LastPromptcode>pre>
                        details>
                    }

                CardBody>
            TelerikCard>
        div>
    div>
main>

In the code above we can highlight some details:

We define the route /planner to access the page.
We use Telerik components such as TelerikCard, TelerikDropDownList, TelerikDateRangePicker, TelerikNumericTextBox, TelerikButtonGroup, TelerikLoader, among many others, which help us create Blazor interfaces quickly and reliably.
Data such as destination, dates, number of travelers, budget and pace are captured with visual controls recognizable by users, such as dropdowns, date pickers, numeric boxes, etc., which help prevent errors and validate data.
For interests and dietary preferences we use a TelerikChip, which allows choosing multiple options at once, changing their visual properties according to the selected state.
We add an optional TelerikTextArea so the user can add an event of interest, but limited to 30 characters to prevent it from becoming a new prompt.
Immediately after showing the user the response generated in MarkdownContent, we display the web sources consulted.
For demonstration purposes, we show the generated prompt that is sent to the AI model.

Now, let’s see how the component’s logic looks.

Adding State and Component Logic

To complete the page-like component, we will create the file Planner.razor.cs to separate the graphical definition from the component logic. We will start by creating the state properties, the list of operations, and the methods:

public partial class Planner
{

    private readonly PromptBuilder promptBuilder;
    private readonly IChatClient? chatClient;

    public Planner(PromptBuilder promptBuilder, IChatClient? chatClient = null)
    {
        this.promptBuilder = promptBuilder;
        this.chatClient = chatClient;
    }

    private TripPreferences Preferences { get; set; } = new();

    private bool IsGenerating { get; set; }
    private string? Itinerary { get; set; }
    private string? LastPrompt { get; set; }
    private string? ErrorMessage { get; set; }
    private List<string> WebSources { get; set; } = new();

    private List<string> Destinations { get; } = new()
    {
        "Lisbon, Portugal",
        "Kyoto, Japan",
        "Mexico City, Mexico",
        "Reykjavik, Iceland",
        "Cape Town, South Africa",
        "Buenos Aires, Argentina",
        "Barcelona, Spain",
        "New York City, USA"
    };

    private List<string> Languages { get; } = new()
    {
        "English", "Spanish", "Portuguese", "French", "German", "Japanese"
    };

    private List<string> AllInterests { get; } = new()
    {
        "Food", "Culture", "Museums", "Nature", "Beaches",
        "Nightlife", "Shopping", "Adventure", "Family-friendly", "Photography"
    };

    private List<string> AllDiets { get; } = new()
    {
        "Vegetarian", "Vegan", "Gluten-free", "Halal", "Kosher", "No restrictions"
    };
}

In the code above we can see some important things:

We use dependency injection to obtain the instance of PromptBuilder and IChatClient from the constructor.
Preferences: It is the instance of the model bound to the form controls.
Itinerary: The markdown text returned by the model.
LastPrompt: The last prompt sent, which we will use for demonstration purposes to show the prompt sent to the AI model.
ErrorMessage: Error message in case something fails.
WebSources: List of URLs consulted by the model.
Destinations, Languages, AllInterests and AllDiets: Lists that feed the dropdown and chip controls.

Next, we add a couple of methods that help manage multi-selection of chips:

    public partial class Planner
    {
        ...

        private void ToggleInterest(string value)
        {
            if (!Preferences.Interests.Remove(value))
            {
                Preferences.Interests.Add(value);
            }
        }

        private void ToggleDiet(string value)
        {
            if (!Preferences.Diet.Remove(value))
            {
                Preferences.Diet.Add(value);
            }
        }
    }

Here we use a trick where we take advantage of the fact that the method Remove returns a true value if the item exists in the collection and can be removed, or false if it was not found for removal. If a false value is obtained, it is added to the corresponding list.

Finally, we proceed to add the method GenerateItineraryAsync, which triggers the call to the AI model to obtain the recommended itinerary:

private async Task GenerateItineraryAsync()
{
//1.
    ErrorMessage = null;
    Itinerary = null;
    WebSources = new();
    IsGenerating = true;

    try
    {
    //2.
        LastPrompt = promptBuilder.BuildUserPrompt(Preferences);

//3.
        if (chatClient is null)
        {
            ErrorMessage = "OPENAI_API_KEY is not configured. " +
                "Add it to appsettings.json (or user secrets) and restart the app.";
            return;
        }

        var messages = new List<ChatMessage>
        {
            new(ChatRole.System, promptBuilder.BuildSystemPrompt()),
            new(ChatRole.User, LastPrompt)
        };

//4.
        var options = new ChatOptions
        {
            ToolMode = ChatToolMode.RequireAny,
            Tools = new List<AITool> { new HostedWebSearchTool() },
            RawRepresentationFactory = _ => new CreateResponseOptions
            {
                IncludedProperties = { "web_search_call.action.sources" }
            }
        };

//5.
        var response = await chatClient.GetResponseAsync(messages, options);
        Itinerary = response.Text ?? "The model returned an empty response.";
        
        //6.
        WebSources = response.Messages
            .SelectMany(m => m.Contents)
            .SelectMany(c => c.Annotations ?? Enumerable.Empty<AIAnnotation>())
            .OfType<CitationAnnotation>()
            .Where(a => a.Url is not null)
            .Select(a => a.Url!.ToString())
            .Distinct()
            .ToList();
    }
    catch (Exception ex)
    {
        ErrorMessage = $"Something went wrong: {ex.Message}";
    }
    finally
    {
        IsGenerating = false;
    }
}

This method is what works the magic to give an accurate response to the user. Let’s see what it does:

Any leftover interaction is cleared to start with a clean UI.
We use the PromptBuilder service to translate the Preferences object to plain text.
We validate that the IChatClient has been correctly initialized with an API KEY. Otherwise, an error is shown to the user.
At this point we configure ChatOptions with a HostedWebSearchTool in the list of Tools. This allows the AI model to search the internet for relevant information. ToolMode = ChatToolMode.RequireAny forces the model to invoke at least one tool, which translates into always activating the web search, which is the only configured tool. Also, with IncludedProperties = { "web_search_call.action.sources" } we can obtain the consulted sources.
A call is made to the provider’s API to obtain a response from the selected AI model.

With the above code ready, it’s time to test the application. For this example, suppose someone is planning to visit New York to attend the 2026 FIFA World Cup final and wants to explore events and activities related to the tournament around the dates of the final:

In the image above, you can see how the user should interact with standard web controls, allowing the search to be configured in an easy and intuitive way.

On the other hand, when starting the search, behind the scenes fresh and relevant information has been fetched to create an ideal itinerary for the selected options.

Likewise, a series of links that helped the AI model propose an excellent itinerary based on the dates and event described can be seen.

Conclusion

Throughout this article, you have seen how to create a promptless AI-first Blazor application from start to finish. We have looked at some Telerik components that can help you build graphical interfaces for this type of apps.

In addition, you have learned how to translate component values into a prompt to send to an AI model. Finally, you have learned how the use of tools can help you provide better results to users, based on their needs. Now it’s your time to implement such solutions in your applications, to make the most of AI and Telerik UI for Blazor controls.

If you’re new to Telerik UI for Blazor, you can access the full 120+ component library for a free trial.

Try Now

The Power of Polish

2026-10-05T16:33:08Z

Polish isn’t aesthetics, it’s protection. Rough software corrodes test, tech and trust. Learn why polish is on purpose, not just an afterthought.

Say the word “polish” and people hear “aesthetics.” The final coating. Something you add once the real work is done.

I don’t see it that way. Polish serves a purpose.

Why We Polish Metal

Think about why we polish metal in the first place.

A rough surface needs treatment, otherwise it traps moisture and dirt. That’s to prevent the metal from corroding. A polished surface is smooth, safer to handle, easier to clean and therefore it will last longer.

We don’t polish metal to make it shine. The shine is just what happens along the way.

The real reason is protection.

Fidelity Is Not Quality

In software development, it is common to create designs with sketches. These are called prototypes or low-fidelity wireframes.

Low-fidelity means the design has minimal visual detail and is usually quick and easy to make.

But somewhere along the way we started treating fidelity and quality as the same thing. Fidelity should be how much you show. Quality is how well you show it.

Low fidelity became an excuse for low quality. Quick and dirty sketches with misaligned boxes and typos. “Don’t look at the details, it’s just a wireframe.”

But people always look at the details.

My wireframes were always pixel-perfect. Sounds like a contradiction, since wireframes are supposed to be quick and rough. But an unpolished wireframe corrodes your results.

Show someone rough work, and they react to the roughness. I noticed the reaction to these imperfections became the feedback. Entire meetings derailed because of a misspelled label or inconsistent spacing.

Feedback that has nothing to do with the concept you actually wanted to test.

Polish protects that concept. Low fidelity, high quality. To me that’s not a contradiction, but a way to get results you can trust.

What Rough Software Corrodes

When low fidelity leads to lowering quality standards, you get rough software.

Rough software has serious corrosion risk worth polishing for:

Test

Put rough work in front of someone and they respond to the roughness, not the idea.

Like I mentioned, I’ve sat in reviews where a broken button or an inconsistent label hijacked the whole conversation. Everyone had opinions about the mistake. Nobody talked about the concept underneath it.

The test doesn’t find the answers you were looking for.

Tech

Unpolished increments don’t stay small.

An inconsistency here, a shortcut there. Each one looks minor by itself. But rough edges compound as the system grows. A death by a thousand cuts.

What started as a small patch becomes the pattern everyone else builds on top of. Debt paid later, with interest, instead of avoided with a bit of attention.

Trust

Trustworthiness is the big reason we should care about quality. It isn’t only about what people can see.

One misaligned label, one inconsistent flow, and people start doubting. From the part they noticed to the parts they can’t. What about the logic then? If they don’t care about this, do they care about data security? It can bring into question everything under the hood they’re trusting you got right.

Polish is about product and perception protection, about being trusted.

Beyond Software

I see the same polish pattern outside of software.

When I build a business blueprint, low fidelity by design, or a simple core story on a slide, and when the quality is there—when it’s polished—the content and intent lands.

People receive and perceive it better. It’s easier to maintain. Easier to expand later.

I recently presented a core story and brand manifesto to a client. It was a well-written paragraph, shown on a polished slide. The client actually commented that the slide showed quality and it delivered the message more strongly. It resonated.

When people trust it more, they value it more.

Purposeful polish.

The Mirror Trap

There’s one catch: you could polish forever.

Keep polishing, and metal becomes a mirror. A mirror only reflects you.

At some point polish stops protecting your software and starts serving you, the maker. You’re admiring your own craft, instead of removing noise for the user.

Polish has a purpose. And that purpose defines the boundary.

Good enough for now isn’t an excuse to ship rough work. Good enough for now still means good. You just need to know when to stop.

Closure

Forget the gloss at the end. Polish is the protection on purpose.

So don’t confuse fidelity with quality.

Do what you do well, in increments. Make it good enough for now. But make it shine.

Meet Nub: A Bun-like TypeScript Toolkit, Running on Node.js

2026-10-05T16:20:57Z

Nub is a comprehensive JavaScript/TypeScript toolkit that brings Node.js developer experience closer to Deno or Bun. This post is an overview of Nub and the various ways you can use it.

I still have a few projects that use Bun, and I’ve written about using Bun as a runtime or tool. For a demo, a small internal tool or a one-off script, Bun let me write TypeScript and run it immediately. I did not need to choose between ts-node and tsx, set up TypeScript tooling or make a custom loader. Its package manager was also faster than npm, and its built-in test API meant I did not have to bring in Vitest for every small project.

The appeal was simple: fewer tools to configure before I could start writing code, and easy project tooling.

But most of those projects are not really Bun applications. They do not depend on Bun.serve(), Bun.file() or other Bun-specific APIs¹. Bun is mainly the command I type to run or manage TypeScript projects.

That distinction matters, because Node.js has changed a lot.

Node.js Now Has More Modern Features

I previously wrote about Node.js gaining built-in support for environment files, watch mode and running package.json scripts. I also covered its early support for running TypeScript files directly and native SQLite bindings.

The built-in test runner through node:test was also an exciting one for me. For many of my projects, that removes one more reason to reach for another runtime or install a separate test framework.

Still, these features do not quite add up to a complete TypeScript project experience.

Node’s built-in TypeScript support is intentionally limited. It can remove types from a file and run the remaining JavaScript, but it does not read your tsconfig.json. It does not handle JSX, and TypeScript features that need to be converted into JavaScript require more than simple type removal. Import paths that work in your editor can also fail when the program starts.

That is a sensible boundary for Node.js. It is careful about changing how programs run, and that caution is part of why it is trusted in production.

The result, however, is that a normal TypeScript project can still collect several tools around Node: a TypeScript runner (e.g., tsx), a file watcher, a package manager, a script runner and/or a Node version manager. In my case, I might use pnpm for dependencies, n for selecting a Node version and another package for running TypeScript.

Each tool is useful. The combined setup is what I dislike.

Nub Is Not Another JavaScript Runtime

Nub is a comprehensive command-line toolkit for Node.js. It is written in Rust, but your application still runs on the normal Node.js runtime. It includes a script and file runner, package manager and Node version manager.

“How does it do that?” you may ask.

Nub prepares the TypeScript files, applies the project settings it needs, selects the correct Node.js version, loads the environment files and then hands the application (transpiled files) to Node.js. It works for both JavaScript and TypeScript projects and files.

You can install it using the command npm install --global @nubjs/nub, or brew install nubjs/tap/nub in Homebrew. There are other installation options which you’ll find in their documentation.

Here are the major commands that show most of its purpose:

nub scripts/migrate-db.ts
nub watch src/server.ts
nub run test
nubx prisma generate
nub install

The first command runs a TypeScript file directly. Unlike Node’s built-in type stripping, Nub supports the wider set of TypeScript features used in real projects. It can read path aliases from tsconfig.json, handle TSX and decorators, and make imports behave more like they do in the editor. The nubx is the alternative to npx or pnpx.

AFAIK Nub does not type-check your program. It prepares the file for execution. I would still keep TypeScript checking in development and CI:

tsc --noEmit

With tsc moving to Go, you’re assured that your CI remains fast.

Nub as a Node.js Utility

Running a TypeScript file is the clearest introduction to Nub, but the more interesting part is how it treats the whole project. The file watcher nub watch restarts the program when its code changes. It can also react to project files such as tsconfig.json, package.json and loaded environment files. This is more useful than watching a directory without understanding which files affect the running application.

The nubx command runs binary tools such as Prisma, ESLint or tsc. It checks the local project first. When a package is missing, Nub asks before downloading and running it. In CI, it will not quietly fetch a package unless that behavior was explicitly allowed.

It can also manage the Node version used by a project. It reads common sources such as .nvmrc, .node-version and the engines.node field in package.json. If the requested version is not available, Nub can download and cache the official Node.js build.

Nub may seem boring if you’re used to Bun, but may be a bigger benefit than it first appears. A new contributor can clone a repository and run the project with its intended Node version without first learning which version manager the team uses. CI and local development are also less likely to drift onto different versions.

Together, these features make Nub feel less like a replacement for tsx or pnpm, and more like having a 5x developer experience in Node.js.

You Can Keep pnpm

Or not.

Nub includes package-management features, but adopting Nub does not require replacing your existing package manager. For non-Bun projects, I normally use pnpm. I can continue using it for installation and lockfiles while using Nub to run TypeScript files, package scripts, local commands and the correct Node version.

However, I can switch to Nub for package management because Nub can read and write pnpm version-9 lockfile! There is just a tiny set of pnpm features that Nub can’t replace for pnpm (yet!), for example, pnpm deploy. If I don’t use such a feature, it becomes an easy switch.

There are various ways Nub supports you in working with or migrating off other package managers like npm, Bun and yarn. That makes Nub easier to try, and it shows the team took time to make it easy to work with. So do check out their documentation.

Could Nub Replace Bun in My Projects?

For some of them, yes.

The best candidates are projects where Bun is mainly being used for direct TypeScript execution, testing or package management. Those needs can now be covered by Nub and Node’s built-in test runner. This also means development and production can use the same runtime, and that matters on serious projects. Bun has made some progress on Node.js compatibility, but it is still a different runtime with incomplete Node compatibility. Many packages work, but less common behavior and native add-ons can still expose differences.

Image from Nub website

Nub avoids that class of mismatch because it does not imitate Node.js. It starts the Node.js process.

That does not make Nub a drop-in replacement for every Bun project. An application using Bun.serve(), Bun.file(), bun:sqlite, bun:test or other Bun-specific features has made a real runtime choice. Moving it to Node requires replacing those APIs, not merely changing the command in the terminal. Nub’s Bun migration guide has a migration guide for such a scenario.

So the practical question is not, “Can Nub replace Bun?” It is, “Why is this project using Bun? And do I really need the Bun-specific API?”

A Small Way to Start

You could test Nub on a low-risk part of an existing project if you’re curious to try it. It could be on a maintenance script, a small demo or as a Node version manager. That way you can keep the experiment small and reversible because Nub does not need to take over the whole project before it can be useful.

The interesting thing about Nub is not that it replaces Node.js, unlike Bun or Deno. It is that it makes the TypeScript-in-Node.js experience easy, and replaces a lot of distinct tools around managing the project. That is why it has made me reconsider my remaining Bun projects.

Give it a try and let me know how it works out for you.

You might be surprised given the runtime performance marketing for Bun. However, the Node-API compatibility and occasional stability issues meant I had to go back to Node.js for serious work, and many legacy projects still relied on Node.js compatibility. And many personal projects ran on Cloudflare Workers. ↩︎

Using MongoDB with Mongoose in Nuxt

2026-10-02T14:52:03Z

Learn how to use Mongoose object modeling with MondoDB Atlas and Nuxt.

Building a Todo app with Nuxt and Mongoose is extremely easy! I’ve been building with Firebase for years, but this was almost easier!

TL;DR

This app connects to MongoDB Atlas and uses nuxt-mongoose to easily configure a document schema for a todo app. The app is easy to create thanks to the Mongoose JS.

Nuxt App Setup

npm create nuxt@latest

Make sure to install Tailwind.

Create a Mongo Atlas Account

Click the “Drivers” to get your connection string.

.env

Set your connection string in your nuxt enivornment variable inside the .env file at the root.

NUXT_MONGOOSE_URI=mongodb+srv://<db_username>:<db_password>@cluster0.su2uuou.mongodb.net/?appName=Cluster0

Install Nuxt Mongoose

Install Nuxt Mongoose with:

npx nuxi@latest module add nuxt-mongoose

And configure nuxt.config.ts:

import tailwindcss from '@tailwindcss/vite'

export default defineNuxtConfig({
  compatibilityDate: '2025-07-15',
  devtools: { enabled: true },
  css: ['~/assets/css/tailwind.css'],
  modules: ['nuxt-mongoose'],
  mongoose: {
    uri: process.env.NUXT_MONGOOSE_URI,
    modelsDir: 'models',
  },
  vite: {
    plugins: [tailwindcss()],
    optimizeDeps: {
      include: [
        '@vue/devtools-core',
        '@vue/devtools-kit',
      ]
    }
  }
})

Create the Todo Model

// server/models/Todo.ts

import { defineMongooseModel } from '#nuxt/mongoose'

interface TodoDoc {
  title: string
  completed: boolean
  createdAt: Date
  updatedAt: Date
}

export const Todo = defineMongooseModel({
  name: 'Todo',
  schema: {
    title: {
      type: String,
      required: true,
      trim: true,
    },
    completed: {
      type: Boolean,
      default: false,
    },
  },
  options: {
    timestamps: true,
  },
})

You can read about the schema at official mongoose guide.

The timestamps options auto configures updatedAt and createdAt for you on each document.

Configure Todo API Routes

We need to CRUD the Todo Documents.

Create Todo

// server/api/todos.post.ts

import { createError, readBody } from 'h3'

import { Todo } from '../models/Todo'

interface TodoBody {
  title?: string
}

export default defineEventHandler(async (event) => {
  const body = await readBody(event)
  const title = body.title?.trim()

  if (!title) {
    throw createError({
      statusCode: 400,
      statusMessage: 'Title is required.',
    })
  }

  const todo = await Todo.create({
    title,
    completed: false,
  })

  return todo.toObject()
})

The model allows use to easily use the create() command to create a document. The h3 package allows us to handle the errors with createError() and get data with readBody().

Get Todos

// server/api/todos.get.ts

import { Todo } from '../models/Todo'

export default defineEventHandler(async () => {
  const todos = await Todo.find().sort({ createdAt: -1 }).lean()

  return todos
})

We want to sort the data by createdAt descending, and use lean() to only get the field data, not the meta data.

Delete Todo

// server/api/todos/[id].delete.ts

import { createError, getRouterParam } from 'h3'

import { Todo } from '../../models/Todo'

export default defineEventHandler(async (event) => {
  const id = getRouterParam(event, 'id')

  if (!id) {
    throw createError({
      statusCode: 400,
      statusMessage: 'Todo id is required.',
    })
  }

  const todo = await Todo.findByIdAndDelete(id).lean()

  if (!todo) {
    throw createError({
      statusCode: 404,
      statusMessage: 'Todo not found.',
    })
  }

  return { success: true }
})

We delete by ID.

Update Todo

// server/api/todos/[id].patch.ts

import { createError, getRouterParam, readBody } from 'h3'

import { Todo } from '../../models/Todo'

interface TodoUpdateBody {
  completed?: boolean
}

export default defineEventHandler(async (event) => {
  const id = getRouterParam(event, 'id')
  const body = await readBody(event)

  if (!id) {
    throw createError({
      statusCode: 400,
      statusMessage: 'Todo id is required.',
    })
  }

  if (typeof body.completed !== 'boolean') {
    throw createError({
      statusCode: 400,
      statusMessage: 'Completed must be a boolean.',
    })
  }

  const todo = await Todo.findByIdAndUpdate(
    id,
    { completed: body.completed },
    { returnDocument: 'after', runValidators: true },
  ).lean()

  if (!todo) {
    throw createError({
      statusCode: 404,
      statusMessage: 'Todo not found.',
    })
  }

  return todo
})

We update the document by ID.

returnDocument: ‘after’ – We want the state after the change.

runValidators: true – The update must match the schema rules.

Todo Composable

We can call the API routes and use good separate of concerns by creating a Todo Composable.

Frontend Types

// types/todo.ts

export interface TodoPayload {
  title: string
}

export interface TodoItem {
  _id: string
  title: string
  completed: boolean
  createdAt?: string
  updatedAt?: string
}

And import them in the composable.

// composables/useTodo.ts

import type { TodoItem, TodoPayload } from "~/types/todo";

export async function useTodos() {
  const message = ref("");
  const isSubmitting = ref(false);
  const togglingId = ref(null);
  const deletingId = ref(null);

  const {
    data: todos,
    error: todosError,
    refresh: refreshTodos,
  } = await useFetch("/api/todos", {
    default: () => [],
  });

  async function submitTodo(payload: TodoPayload) {
    message.value = "";

    if (!payload.title.trim()) {
      message.value = "Title is required.";
      return false;
    }

    isSubmitting.value = true;

    try {
      await $fetch("/api/todos", {
        method: "POST",
        body: {
          title: payload.title,
        },
      });

      await refreshTodos();
      return true;
    } catch (error) {
      message.value = error instanceof Error ? error.message : "Failed to create todo.";
      return false;
    } finally {
      isSubmitting.value = false;
    }
  }

  async function toggleTodo(todo: TodoItem) {
    message.value = "";
    togglingId.value = todo._id;

    try {
      await $fetch(`/api/todos/${todo._id}`, {
        method: "PATCH",
        body: {
          completed: !todo.completed,
        },
      });

      await refreshTodos();
    } catch (error) {
      message.value = error instanceof Error ? error.message : "Failed to update todo.";
    } finally {
      togglingId.value = null;
    }
  }

  async function deleteTodo(todo: TodoItem) {
    message.value = "";
    deletingId.value = todo._id;

    try {
      await $fetch(`/api/todos/${todo._id}`, {
        method: "DELETE",
      });

      await refreshTodos();
    } catch (error) {
      message.value = error instanceof Error ? error.message : "Failed to delete todo.";
    } finally {
      deletingId.value = null;
    }
  }

  return {
    todos,
    todosError,
    message,
    isSubmitting,
    togglingId,
    deletingId,
    submitTodo,
    toggleTodo,
    deleteTodo,
  };
}

deletingId and togglingId are used just like isSubmitting to validate the fetch process.

You could also use Nuxt Form Actions, although I just went straight $fetch here.

Todo Components

Our parent component displays the Todo Form Component and the Todo List Component after Prop Drilling.

Parent

I usually like to use shared state with useState, but I found prop drilling to be cleaner here.

Todo Form

This displays the add a todo form.

When submitted, we must reset the form and show a message if necessary.

Todo List

Every time there is a refresh, we list the todos.

Notice the arial-label and title for accessibility.

deletingId and togglingId are used to handle state changes.

Database

Once you run the app, you will see your data in Atlas.

Final Review

I honestly thought configuring Mongoose was going to be a pain. I have been building with Firebase since Firestore was released, and I thought Mongo DB would be different. Mongoose has come a LONG way, and nuxt-mongoose pretty much configures it for you.

The only negative with mongoose is the lack of built-in authentication support. There is, however, a better-auth adapter. You also can’t do anything on the client/browser like you can with Firebase or Supabase, but these days I’m OK with that!

Honestly, this was fun to build.

Repo: GitHub

React for Vue Developers, Part 2: State, Effects and Why React Re-renders Differently

2026-10-01T17:33:23Z

React 19 for Vue 3 Developers, Part 2: React reruns your component function to render; Vue tracks dependencies and updates in place.

In Part 1 of this series we reset the mental model: a React component is just a function, JSX is JavaScript with markup, props are the first argument and children is your default slot. I ended that piece with one sentence I asked you to carry forward, and now it is time to collect on it: React props are not reactive the way Vue props are, because the whole component function reruns to produce the next render.

That single fact is the key to this entire article. Almost every React quirk that annoys a Vue developer (state that seems to update “one step late,” values that are mysteriously frozen inside a callback, effects that fire more often than you expected) comes back to the same root cause. So before we touch a single hook, let’s really internalize how React decides to re-render, because it is genuinely different from Vue.

Image generated with AI

The Core Difference: Vue Tracks, React Reruns

In Vue, reactivity is fine-grained and automatic. When you create a ref and use it in your template, Vue quietly records that dependency. When you later change .value, Vue knows exactly which pieces of the DOM depend on it and updates only those. Your setup runs once. The reactive system does the ongoing work.

React does not track dependencies like this. When a piece of state changes, React reruns your entire component function from the top, produces a new description of the UI, compares it to the previous one and patches the differences into the DOM. Your component body is not a one-time setup; it is a render function that runs again and again.

Hold onto that mental image, because it explains everything below. In Vue, you write code that sets up reactive relationships once. In React, you write code that describes the UI for the current values, and it runs every render.

useState vs. ref

Let’s start with the most common thing you do: local component state. In Vue, you reach for ref (or reactive), read and write through .value, and Vue handles the rest.

Counter.vue

In React, the equivalent is the useState hook. It returns a pair: the current value and a function to set it. The naming convention is [thing, setThing], and you almost always destructure it inline.

Counter.jsx

import { useState } from 'react'

function Counter() {
  const [count, setCount] = useState(0)

  const increment = () => {
    setCount(count + 1)
  }

  return <button onClick={increment}>Count is {count}button>
}

Let’s break it down. useState(0) declares one piece of state with an initial value of 0. It hands back count (a plain number, not a wrapper object, so no .value) and setCount (the only sanctioned way to change it). Reading state is just reading the variable. Writing state means calling the setter.

That last point is the big behavioral difference. In Vue, you mutate: count.value++. In React, you do not mutate, you replace: you call setCount with the new value and React schedules a re-render. There is no count = count + 1 and there is no count.value. In fact, you cannot reassign count at all; it is a const, so that line just throws. And even if it were a plain let, changing it would not update the screen, because React only re-renders when the setter is called.

Notice there is also no deep reactivity here. count is a snapshot for this render (React’s own docs call this the “state as a snapshot” model, and it is worth reading that page once it clicks). When you call setCount, React does not surgically update the button text the way Vue would. It reruns Counter from scratch, useState hands back the new value and the returned JSX reflects it. Same visible result, completely different machinery.

State Updates Are a Snapshot, Not a Live Value

Here is the first place this bites people, and it is worth seeing early. Suppose you want to increment twice in one handler:

const incrementTwice = () => {
  setCount(count + 1)
  setCount(count + 1)
}

Coming from Vue, you would bet money this adds 2. Surprise, it adds 1!

The reason is that count is a constant captured for this particular render. Both calls read the same count, compute the same count + 1 and set the same value. React does not update count in place between the two lines, because count is just a number from this render’s snapshot.

The fix is the functional updater form of the setter, which hands you the latest pending value:

const incrementTwice = () => {
  setCount((c) => c + 1)
  setCount((c) => c + 1)
}

Now each call receives the most recent value and returns the next one, so this adds 2. My rule of thumb: the moment your new state depends on the previous state, reach for the function form. In Vue, you never think about this because count.value is always the live current value. In React, the value is frozen per render, so you ask React for the freshest one explicitly.

useEffect vs. watchEffect, watch and onMounted

Next up: running side effects. In Vue, you have a small family of tools. onMounted for “do this once after the component mounts,” watchEffect for “run this and rerun it whenever its reactive dependencies change” (tracked automatically), and watch for “run this when these specific sources change.” Cleanup happens via onUnmounted or the cleanup callback inside a watcher.

React folds most of that into a single hook, useEffect, with one crucial twist: it does not track dependencies for you. You list them yourself in an array, and that array is the manual version of Vue’s automatic tracking.

Let’s fetch a user when an id prop changes, side by side.

UserProfile.vue

UserProfile.jsx

import { useState, useEffect } from 'react'

function UserProfile({ id }) {
  const [user, setUser] = useState(null)

  useEffect(() => {
    const loadUser = async () => {
      const response = await fetch(`https://myapp.com/users/${id}`)
      if (!response.ok) {
        throw new Error('Failed to fetch user')
      }
      setUser(await response.json())
    }
    loadUser()
  }, [id])

  return user ? <p>{user.name}p> : null
}

Let’s break it down, because there is a lot of mapping packed in here.

The effect body is what runs, the same as your watch callback. Note that useEffect's callback cannot itself be async (it may only return a cleanup function or nothing), so we define an inner async function and call it. That is a small React idiom you will repeat often.
The dependency array [id] is the manual watch source. It tells React “rerun this effect whenever id changes between renders.” Vue figured out that dependency for you from the getter; in React, you name it.
{ immediate: true } in the Vue watcher has no separate switch in React. Effects always run after the first render, so “run on mount” is the default, not an option.

Here is the quick translation table for the dependency array (the bit with [id]), since it is the part that confuses everyone. I have led with the case a Vue developer cares about most:

Empty array []: Runs once after the first render. This is your onMounted.
A populated array like [id]: Runs after the first render and again on any render where id differs from the last one. This is your watch on specific sources.
No array at all: Runs after every render. You almost never want this.

One honest footnote on that first case: Vue’s onMounted runs after the DOM is in place but before the browser paints, while useEffect runs after paint. For fetching or focusing an input, the difference is invisible. But if you need to measure layout before the user sees it, useLayoutEffect is the closer match to onMounted.

Cleanup works the way a Vue watcher’s cleanup callback does. Vue reruns that callback before the next run and on teardown, and React’s returned function is the same deal: it fires on the way to the next effect run and one last time as the component goes away. This is where you clear intervals, remove listeners or abort requests.

useEffect(() => {
  const timer = setInterval(() => {
    setCount((c) => c + 1)
  }, 1000)

  return () => clearInterval(timer)
}, [])

That returned arrow is your onUnmounted. Same idea as a watcher’s cleanup callback, same reason: don’t leave timers and listeners lying around.

Big disclaimer though! The dependency array is the single biggest source of bugs for people new to React, and the mistake is always the same, leaving something out. If your effect reads a value from props or state, that value belongs in the array. Omitting it does not make the effect "run less."Iit makes the effect run with a stale value, which brings us neatly to the trap that catches every Vue developer.

The Stale-Closure Trap

This is the one I want you to tattoo somewhere visible, because it does not exist in Vue and it will confuse you the first few times.

Remember that the component function reruns on every render, and that every function you define inside it (event handlers, effect callbacks, timeouts) closes over the variables from that specific render. Those variables are constants. They never change after that render. So if a callback outlives the render that created it, it is still looking at old values.

Here is the classic broken version:

Timer.jsx

function Timer() {
  const [count, setCount] = useState(0)

  useEffect(() => {
    const timer = setInterval(() => {
      setCount(count + 1)
    }, 1000)

    return () => clearInterval(timer)
  }, [])

  return <p>{count}p>
}

You would expect this to count up forever. It goes 0, then 1 and then it sticks. The effect ran once (empty array), so the interval callback closed over count from the very first render, where count was 0. Every tick computes 0 + 1. The value is frozen in the closure. (In development with React’s StrictMode on, you may also see effects run an extra mount-then-cleanup cycle. That is intentional and not the bug we are chasing here.)

In Vue, this bug simply cannot happen, because count.value always reads through the ref to the current value. There is no snapshot to go stale. In React, you have two clean fixes:

Use the functional updater, so you never read the stale count at all: setCount((c) => c + 1). This is the right fix here.
Add the value to the dependency array ([count]), so the effect tears down and resubscribes with a fresh closure each time it changes. Correct, but for an interval it means resetting the timer on every tick, so the updater form is better.

The lesson generalizes: whenever a value inside a long-lived callback looks “wrong” or “old” in React, you are almost certainly looking at a stale closure. Ninety percent of the time, the fix is either the functional updater or an honest dependency array.

useRef for Values That Should Not Re-render

Last piece. Sometimes you need a mutable value that persists across renders but should not trigger a re-render when it changes. Think of a reference to a DOM node, an interval id, or a “did this already run” flag. In Vue, template refs cover the DOM case, and for the non-reactive-value case you would just use a plain variable outside your reactive state (or a ref you deliberately never depend on).

React’s tool for both jobs is useRef. It returns an object with a single current property that you can read and mutate freely, and mutating it never causes a render.

TextInput.vue

TextInput.jsx

import { useRef, useEffect } from 'react'

function TextInput() {
  const inputRef = useRef(null)

  useEffect(() => {
    inputRef.current.focus()
  }, [])

  return <input ref={inputRef} />
}

Notice how close these are. You create the ref, attach it to an element with the ref attribute and read it after mount. The only real difference is .value in Vue versus .current in React, and the fact that in React you reach for the DOM inside an effect (because the node does not exist until after render).

The part worth underlining is the second job of useRef. Think of a mutable box that survives renders without causing them. If you wrote let x = 0 directly in the component body, it would reset to 0 on every render, because the whole function reruns. If you used useState, changing it would trigger a render. useRef is the in-between: it keeps its value across renders like state, but writing to .current is silent like a plain variable. That is exactly what you want for things the user never sees directly, like storing a timer id or the previous value of a prop.

Wrapping Up

The one thing you absolutely need to take away from this read is that React reruns your component function to render, where Vue tracks dependencies and updates in place. Once that clicks, the rest follows.

useState gives you a value plus a setter. The value is a per-render snapshot, so you replace instead of mutate, and you use the functional updater when new state depends on old. useEffect is your watch, watchEffect and onMounted rolled together, except you list dependencies by hand and return a cleanup function. And useRef is your escape hatch for mutable values that should not trigger a render, DOM nodes included.

For the deeper details, the React docs on state and the “You Might Not Need an Effect” guide are both excellent, and that second one will save you from reaching for useEffect when you do not actually need it.

Next time we move up a level and look at Server Components, the new use API and Actions, framed against the Vue SSR and data-fetching mental model you already have.

Happy state-ing!

Read Part 3

Assessing Your AI Needs

2026-09-30T19:30:34Z

Deciding on an AI solution starts with two factors: where the capability should live and how much freedom it needs while running. Use this guide to assess your needs.

An AI feature may need repeatable instructions or access to internal knowledge, and it may also need tool use or a multi-step workflow. Those requirements describe what the feature must do without settling how we should build and deliver it.

Teams often use skills and plugins interchangeably with hosted AI assistants and systems, and they may call the same AI capability an agent. These terms describe different parts of the design: how we package and deliver the capability as well as how much discretion it has at runtime.

In this article, we’ll evaluate these options by asking where the experience must live and what we want to reuse, then deciding what we would want to control and whether we can define the steps in advance.

Ownership & Runtime Behavior

Our first decision is about packaging and ownership: what we create and how people access it.

A skill packages instructions and supporting resources for a repeatable workflow.
A plugin packages skills and tools for distribution through a supported AI host.
A hosted AI assistant packages a configured conversational experience that people can use directly through an existing AI platform.
An AI system places the model inside an application we build and operate, giving us ownership of the interface and backend as well as data access and controls.

Runtime behavior is a separate decision about how much discretion the software needs while completing a task. Because runtime behavior is separate from packaging, these options can overlap within the same solution.

A system can load a skill that an agent follows, and a hosted AI assistant can use uploaded knowledge and tool connections within a product surface we don’t own.

Option	Primary decision it answers	Strong fit	What we own	Main constraint
Skill	How do we reuse a method?	A repeated workflow in an existing AI host	Instructions and supporting resources	The host still supplies the interface and runtime
Plugin	How do we distribute skills and tools?	An installable capability for a supported AI host	Instructions and tool connections; optional UI	The host still supplies the product surface and runtime
Hosted AI Assistant	How do we share a configured experience?	An internal assistant or a quick pilot in an existing AI platform	Configuration and uploaded knowledge; conversation starters and connections	The host owns the product surface and runtime
System	How do we ship an AI capability in our product?	A feature tied to product users and permissioned data	Experience and backend; permissions and operations	We carry the engineering and operating cost
Agent	How should the software choose its next step?	Work whose path changes as new information arrives	Tools and limits; approval points and the execution loop	Greater latency and cost with more failure paths

The diagram below maps these options across ownership and runtime behavior, showing where they can overlap within the same solution.

When a Skill Is Enough

A skill fits when the part we need to reuse is the procedure itself, such as a team’s method for summarizing pull requests into release notes or reviewing a component for accessibility. Without a shared procedure, we have to rewrite the instructions for each request, which wastes time and produces uneven results.

Skills can package instructions and references alongside scripts and templates, although the exact structure varies by host. If the workflow needs to be installed in a compatible AI platform, a plugin can distribute the skill with any tool connections required for external services or data.

When a Hosted AI Assistant Fits

A hosted AI assistant is well-suited when people need a shared conversational experience within an existing AI platform. Within that experience, we can configure instructions and reference files alongside capabilities and approved service connections.

For an onboarding assistant, the team can upload a handbook and set the expected tone before sharing the assistant in its workspace, creating a usable pilot without requiring a chat UI or model infrastructure.

The tradeoff is control because the provider runs the experience and its policies govern sharing and service connections. Once the capability becomes part of our application, our authentication model and release process become necessary, along with telemetry and support. Those requirements call for a system we operate.

When We Need a System

We need a system when the AI capability belongs to our product because our backend must authenticate the user and gather permissioned context before calling the model and validating its response. The interface becomes part of that boundary because it handles streaming and source citations alongside approvals and error states.

The surrounding components matter as much as the model call: retrieval keeps answers grounded in current content and guardrails block invalid actions. Evaluation checks whether changes improve the feature while observability records which sources and tools shaped a production response.

A billing assistant may need to explain a charge using account data and current policy. The feature must enforce the billing product’s access rules, and support engineers need a trace they can inspect when an answer is disputed. Even with one model call, a product-owned system keeps those checks and traces in code our team operates.

A system like this can remain deterministic: the billing assistant gathers the relevant policy and charge data before drafting an explanation, and plain application code orchestrates the known path with less variability than an agent loop would introduce.

When to Add Agent Behavior

Agent behavior becomes useful when we cannot specify the full path before the work starts. With agents, a task often has four characteristics:

The input describes an outcome for the software to complete.
The software must choose among tools or information sources.
An intermediate result can change the next step.
The workflow continues until it completes the task or reaches a stopping condition such as an approval checkpoint.

An incident investigation has this shape because the first log query may point toward a deployment or a third-party outage. As each result changes what the system should inspect next, an agent can select the next tool and evaluate the result before revising its plan.

This flexibility creates more work around the loop because tools need narrow permissions and validated inputs. Expensive or hard-to-reverse actions need approval checkpoints, and every run needs a decision trace and a stopping condition.

A Decision Sequence

To decide which option fits, we can work through the following questions in order, starting with product ownership and ending with runtime behavior.

Where must the experience live? An existing AI host may already reach the intended users, so a reusable package or hosted AI assistant may be sufficient. An experience that belongs inside our application calls for a system we control.
What are we trying to reuse? A repeatable procedure points to a skill, which can be packaged as a plugin if the procedure needs to be installed with tools. A configured conversational experience points to a hosted AI assistant.
What must we control? A hosted AI assistant’s service connections remain within the host’s product and governance model. Product-owned authentication and permissioned customer data point toward a system, as do audit records and release management.
Can we define the steps ahead of time? A known path belongs in normal application orchestration; a changing path that requires tool choice and recovery may justify agent behavior inside that system.

An AI system can include retrieval and memory as well as tools and evaluation without making every step adaptive. We can add a narrow agent only where an uncertain part of the workflow requires it.

How the Options Combine

A single capability may combine several options: a coding team can store its pull request review method as a skill and package that skill as a plugin when a wider group needs to install the workflow in a compatible AI platform.

A customer support feature can combine a chat interface with retrieval over permissioned documentation; tools can read account state and an evaluation set can cover common requests. Within that system, a skill can hold the support procedure, and agent behavior can handle the narrow investigative portion where each tool result may change the course of the work.

Wrap-up

Choosing an AI solution starts with two decisions: where the capability should live and how much freedom it needs while running.

A skill captures a repeatable method, and a plugin makes that method installable with tools. A hosted AI assistant delivers a configured experience through an existing platform, and a system gives us ownership of the product boundary. Both can support agent behavior when the route cannot be known in advance, allowing the software to choose and revise steps as the work progresses.

For more on building AI-powered applications and agents with Progress, check out the following resources:

Why Coding-Agent Governance Needs More Than Approval Gates

2026-09-30T14:41:54Z

Human approval is an important control for coding agents, but it cannot carry the full weight of governance. Effective governance also limits agent access, enforces rules during execution, manages exceptions and preserves a record of what happened.

Your most productive engineer this quarter might not be a person. Point Claude Code or GitHub Copilot’s coding agent, for example, at an issue and it reads the repository, writes the code, runs the tests and opens the pull request in minutes.

That speed can break the review cycle. Someone who used to read one 200-line diff a day now faces 10 agent-authored pull requests before lunch. As volume increases, so does the risk that a developer approves a change without fully reviewing it, relying on tests the agent may have written itself. This is where governance comes in.

Governance defines what an agent is allowed to do on its own, where human judgment is still required and what evidence must remain afterward. A coding agent needs four types of control:

Permissions: The repositories, branches, files and deploy targets the agent can access or change
Checks: Runtime rules that inspect each action and determine whether they should be allowed, require approval or be blocked
Exceptions: A documented, expiring path for legitimate work that falls outside those rules
Audit: A record of what the agent did, under which identity, with whose approval

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These controls do not need to live in one place to be effective. Permissions may be enforced through identity and access controls, checks through CI, exceptions through existing approval workflows and audit through pipeline and system logs.

What matters is whether those controls work together well enough to answer a few basic questions: What could the agent do? What did it actually do? Which actions required human approval? And can you demonstrate that after the fact?

That is the foundation of coding-agent governance: not another approval step, but a system of boundaries, checks and evidence that can keep up with the speed of automated development.

Permissions Define What the Agent Can Touch

A coding agent should only have access to the exact systems and actions it needs for the specific task it’s doing—nothing more.

If an agent is updating documentation, for example, it may need permission to edit the docs/ folder on a feature branch. It does not need permission to push directly to main or fire the production deploy.

The problem starts when teams give agents credentials with much broader access than the task requires. The OWASP Top 10 for LLM Applications calls this excessive agency: more permission or autonomy than the task requires.

Coding agents can make mistakes. They can generate a destructive command, misunderstand an instruction or follow malicious content hidden in an issue, comment or README. This isn’t a model-quality problem, and a smarter model only argues more persuasively for the wrong command. But a bad instruction can only cause serious damage if the agent has permission to carry it out.

So limit access based on the task, not the tool:

Give the agent its own identity rather than using a developer’s credentials.
Limit access to only the repositories, files and actions the task requires.
Allow specific actions the work needs (read files, run the test suite, open a pull request) instead of raw platform access.
Use a disposable branch like agent/issue-482, and protect main with a rule the agent’s identity can’t bypass.

Security teams call this “least privilege,” which NIST defines as granting only the privileges a task needs. You already apply it to people. The difference with coding agents is speed and autonomy. Because they can take actions on their own, limiting what they are allowed to touch becomes even more important.

Checks Assess Risky Actions Before They Run

Permissions define what an agent it capable of doing. Checks decide whether a specific action should actually be allowed.

A check is a rule evaluated during the agent’s work and can allow the action automatically, require human approval or block it entirely.

For example, file reads and a sandboxed npm test might auto-approve. Dependency manifests and environment files may require human approval, and so does any arbitrary shell command. Force-pushing a shared branch or touching production may be blocked.

The mechanisms already exist in tools you run. In Claude Code, for example, hooks and permission settings allow Bash(npm test) while prompting on Bash(git push*), and most agent CLIs expose an equivalent. On the CI side, a GitHub Actions environment with required reviewers holds a deploy job until a named person approves it, and GitLab and Azure Pipelines do the same.

The goal is not to put a human approval step in front of every action. If developers are asked to approve routine behavior constantly, they will eventually stop reviewing those requests carefully.

Checks work best when they match the level of risk: low-risk actions proceed automatically, higher-risk actions require review and unacceptable actions are blocked. That keeps human attention focused on the decisions where it actually matters.

Pro Tip: Watch the ratio of approvals to denials per agent. A gate that never denies anything is either perfectly scoped or never actually read.

Exceptions Need an Owner

A policy that can only say “no” won’t survive real work. Governance rules need a controlled way to handle legitimate work they were not designed to allow. An exception is documented, time-boxed permission to proceed despite a blocking rule, with a named person responsible for approving it. An exception should not remove the rule entirely.

Say the agent must bump a pinned dependency to patch a CVE, but the rule guarding package-lock.json blocks the edit. Don’t delete the rule. Grant a 48-hour exception from a named owner for that one file on that one branch.

Record who asked, what got unblocked, why and when it expires. Once the work is complete, the extra permission should disappear automatically.

Exceptions should also be reviewed over time. If teams repeatedly request the same exception, that may be a sign that the underlying policy needs to change. If the policy system itself fails, the agent should not treat that as permission to continue. It should fall back to stricter rules or stop until the check can be completed.

The goal is to make exceptions possible without turning them into permanent loopholes.

The Audit Is the Receipt

Governance is not just about stopping risky actions. It’s also about keeping reliable evidence of what the agent did and who approved it.

Where checks try to prevent unsafe actions, audit logs create a record showing what actually happened. Log every action: the identity that ran it, the prompt behind it, the tools that fired and the human who approved any sensitive steps. A receipt you can’t edit is the only one worth keeping.

Most of it already exists. A workflow run records who ran what and who approved, and the pull request timeline records the merge. Ship both to an append-only store so they outlive your retention window.

Regulators want that traceability: Article 14 of the EU AI Act requires effective human oversight of high-risk systems, and NIST’s AI Risk Management Framework and ISO/IEC 42001 ask for documented evidence.

Good audit records, from a governed workflow, let you answer questions with evidence rather than relying on someone’s memory.

Build the Gate into the Workflow

The strongest gates are structural. A per-action prompt interrupts one action, but a phase boundary between planning and implementation stops everything downstream and is harder to click past. Reading the plan before any code exists means approving intent, and wrong intent is cheaper to fix than a bad merge.

Progress built its Forge CLI, one example of an agentic SDLC tool, around that shape. Its task flow keeps planning separate from the code it produces, so the human checkpoint lands between phases instead of mid-run.

What matters is that the agent’s pull requests take the path your human changes do: the same protected branches and peer review, secret scanning included. An agent that can merge around that path just breaks your own rules faster than anyone can catch it.

Four Questions Before You Scale Coding Agents

Before expanding the use of coding agents, ask:

Can you list everything the agent’s credential can reach? If it lives in a spreadsheet someone maintains, the scope is wider than the task.
Which actions wait for a human? If nothing does, or the prompt fires so often nobody reads it, you’ve trained a rubber stamp.
Who owns the exceptions? If the answer is “whoever is on call,” the exceptions own you.
Can you produce the receipt? If you can’t show who approved a change and when, the agent is running ahead of its controls.

Coding agents can execute software work at a speed that makes manual oversight alone difficult to scale. Governance is what lets coding agents run at full speed.

The goal of governance is to put controls at the right layers: constrain authority with permissions, classify actions with checks, route unusual cases through managed exceptions and preserve the evidence through audit. Then use human approval where human judgment actually adds value.

Governance should be repeatable, documented and built into the development environment rather than dependent on individual memory. Whether you buy an AI governance platform or wire the four controls from tooling you own, build it once as configuration in the repository, not tribal knowledge.

That is what makes coding agents easier to scale safely: the next agent starts with the same boundaries and controls already in place.

Learn More About Progress Forge Orchestration

If you’re ready to move from isolated AI coding tasks to repeatable engineering workflows, Progress Forge (formerly Progress Agent Harness) is designed for exactly that shift. It orchestrates the AI coding agents your team already uses through structured workflows with visibility, governance and human review built in.

Explore the Progress Forge Early Access Program to see how it can help you put these ideas into practice.

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6 Months of Claude and Cursor, Part 2: Teaching AI to Work with a Senior Engineer

2026-09-29T21:10:17Z

As you progress in your AI journey, you’ll need to make your agents work for YOU, the individual you, with your strengths and shortcomings.

Image generated with Gemini

In Part 1, I wrote about building skills so the AI understands your project. Part 2 is a harder problem: building a skill so the AI understands you.

After six months of using Claude daily for code review, architecture discussions and prompt writing for Cursor, I realized the most persistent problem was posture rather than technical capability. The AI treated me like a beginner when I had 30+ years of experience behind me. It suggested removing intentional code and offered generic options when I already knew what I wanted. When I showed that my code was right, it apologized for three paragraphs instead of moving on.

I wanted a technical peer, not an assistant.

The Pattern I Identified

Before writing the skill, I spent weeks going back over our conversations. I asked Claude itself to comb through our history and list the moments it got the tone wrong. Three patterns came out.

The first was underestimation. I’d share one of my Source Genesys templates with a Stopwatch measuring a hook’s execution time, and the AI would suggest removing it because it “didn’t seem necessary.” I’d explain that it was intentional, that I wanted performance metrics inside the hook itself. It corrected itself, then did the same thing in the next conversation, with no memory of the last one.

The second was premature diagnosis. The AI would flag an error in my metrics code when the real problem was in the neighboring Controller. I knew that because I’d looked at both before asking. The AI assumed I hadn’t.

The third was the multiple solution. “You can do A, B or C,” when I already knew it was B. The time spent explaining A and C was time lost for both of us.

The Hardest Skill to Write

So I wrote jefferson-senior-dev. The name was deliberate: “senior dev,” not “user” or “client,” because that’s how I wanted to be treated.

The skill opens with who I am: software engineer, developing since 1994, creator of Source Genesys. The resume takes five lines. Everything else in the file is rules of engagement.

The main rule: when I share code, assume it’s correct until you find clear evidence otherwise. Don’t question out of generic caution. Don’t suggest removing something because it “doesn’t seem necessary.” If you find nothing wrong, say it’s correct and move on. Don’t invent caveats to look useful.

There’s one exception: security. SQL injection, data exposure, hardcoded tokens and open permissions get flagged every time, even when the code works.

The hardest rule to write was one about myself. Sometimes I swap true for false in boolean conditions, and I’ve been repeating that mistake for years. I’ve watched someone outside IT do the same thing, which makes me suspect I’m not the only one. So the skill tells the AI to flag every inverted condition it finds in my code, because that’s where questioning me adds real value.

Declaring your own blind spots to an AI costs nothing, and few engineers do it. If you know where you fail and it knows too, you get a second check that never gets tired and has no embarrassment about pointing out the obvious.

A good skill teaches AI when to disagree with you.

The Effect in Practice

The change showed up in the first week. Claude stopped offering options when I already had a path. When I shared code with a // fixed comment, it assumed the fix was right and focused on applying it to the template. The five minutes I used to spend convincing it that my code was intentional went to zero.

The effect I hadn’t planned for was the quality of what it caught instead. The one I keep coming back to was a discard assignment on an awaited call: the line waits for the operation, throws the result away and swallows the failure with it. Fire-and-forget wearing the costume of disciplined async code, which is exactly why it survives human review: Nobody reads past the await.

With the false suspicions out of the way, there was attention left for that kind of thing. Today, when it flags something, I take it seriously, because I know it isn’t a generic guess.

The Cursor Skill: Prescribe, Don’t Explore

After the Claude skill, I built the equivalent for Cursor: cursor-prompt-craft. It came out of six months of real prompts, the ones that worked and the ones that failed.

The central tension is discovery versus prescription. I used to think Cursor should explore the code on its own. The results showed the opposite: the most effective prompts were the ones that gave the exact file paths, named the functions to change, and marked what must not be touched.

That became the rule. For Source Genesys, Claude writes prescriptive prompts: exact paths, named files, examples of the canonical pattern and an explicit “do not” section. Cursor became a tool and stopped offering unnecessary opinions. It works better when you take away its obligation to guess.

What Changed from January to June

In January, I wasn’t running a self-hosted GitHub Actions runner; my CI/CD was semi-automated, and deploys were manual. Cursor ran without rules and produced unpredictable results, and Claude treated me like any other user.

By June, Source Genesys was generating complete platforms with automated deploy, built-in observability, test coverage and post-deploy self-correction. Cursor worked inside limits defined by skills, and Claude knew when to trust my code and where my blind spots were. The semester also produced concrete deliverables: the new template for Source Genesys using Progress Telerik UI for WinForms, a generic dark mode CSS for every component, a frontend metrics system and automatically generated help screens.

None of that came from an AI alone, and none of it came from me alone. It came from a partnership that took six months to settle. Concrete results aren’t a matter of one week or one month. You must hone the tool until it delivers what you want.

July: The Skill Became a Command

July was the first month both skills ran without me adjusting them, and most of it went to infrastructure. A Virtual Private Server migration had left me with no metrics, no centralized logs and no traces, and the gap surfaced in the worst possible way: a production flow stopped working, and I had nowhere to look.

Debugging in the dark, in 2026, is embarrassing. The rest of that story is Part 3, but the conclusion fits in one line. If Source Genesys generates the infrastructure, it must generate observability along with it.

The part that touches this article came at the end of the month. I fixed the flow on one platform, and Cursor documented what it had done in a skill. The next question was inevitable: why am I applying this manually on the other six?

It became a CLI parameter. I passed the skill name and the list of platforms, and the pipeline fired the agent with the same instruction on each one. It ran on six of the seven platforms. The seventh didn’t have the module involved and was correctly skipped. The build passed on all six.

That changes what a skill is. In Part I, it was context. In this article, it was posture calibration. Now it’s a unit of work: a file that describes a fix and that the pipeline knows how to execute across N repositories.

With one caveat I insist on putting in writing: a green build is not a validated fix. The agent applied, compiled and reported. End-to-end testing on each platform is still human work, and that part I haven’t automated.

What I Got Wrong in the Same Period

Calibration made me faster, and speed made my own mistakes more expensive. Three of them are worth writing down.

I told Cursor to clean up corrupted Latin characters across every repository at once, and the instruction was broad enough that it also replaced ?? with a dash. ?? is an operator in C# and in TypeScript. It broke working code and swept through dozens of documentation files on the way out. Git and a targeted pass got it back. The scope was mine to set, and I set it wrong. So it became a rule that forbids operator substitution and names the ones that are never touched.

I ran two pipelines at the same time, more than once. The sessions competed for the same processes, and the report came back with failures that weren’t failures. I spent an afternoon investigating noise. A session lock in the runner is in the queue.

Against those two, the best small decision of the month was writing per-session and per-platform statistics to CSV. It showed that a single command accounts for almost all the pipeline time, that one execution of it took 33 minutes while the retry took 2, and that a failure I had been treating as instability was deterministic: Cursor had compiled the project as x86 instead of AnyCPU. Without measuring, I’d still be guessing.

Calibrated AI accelerates whatever you told it to do, including the wrong thing. The skill keeps the AI from getting the tone wrong. It doesn’t keep me from getting the order wrong.

The Final Reminder

The jefferson-senior-dev skill that Claude wrote about our partnership ends with the line I consider the most important of all:

“Jefferson treats Claude as a high-level technical colleague, not an assistant. When he shares code, he expects peer analysis, not a tutorial. When he says ‘this is wrong,’ he is almost always right. Claude’s job is to add real value: find what Jefferson missed, not question what he already knows.”

That “almost always” I added on purpose. Nobody is right 100% of the time, and it’s in the cases where the AI catches what I missed that the partnership pays for itself.

Conclusion

Generative AI isn’t going to replace senior engineers, but it will deliver more to the people who know how to train it instead of treating it like a search engine. And training here isn’t fine-tuning or RAG. It’s a Markdown file with the right rules, loaded in the right context. A skill is actionable documentation: it records what you know and where you accept being corrected.

If I could go back to January 2026 and give myself one piece of advice, it would be this: before you write your first prompt, write your first skill.

In the next post, Part 3, I’ll show what happened when the infrastructure had to catch up with the partnership.

Automate with AI. Just don’t expect it to get to know you on its own. That part is your job.

Telerik Blogs

Using Kendo UI with Svelte

TL;DR

✔️ Svelte with Kendo UI for jQuery

Create a New SvelteKit Project

Install Packages

Activate Kendo UI

Create a jQuery Declaration File

Dynamically Import jQuery on the Browser

Handle CSS

Load a Grid Element

Create a Loader

✔️ Kendo UI in Astro

Install Astro

Choose Your Kendo UI Framework

Install Dependencies

Configure CSS

Activate Your Kendo UI License

Create Svelte Component

Create our Grid Component

Load Vue with Svelte

Resources

Try Kendo UI

Build a Real-Time Collaborative Notes App with Replicache

Prerequisites

What Is Replicache?

Core Concepts

Local Store

Mutators

Push

Poke

Pull

Project Setup

Setting up the Server

Setting Up the Client

Building the Server

Setting Up the Database

Defining the Types

Building the Notification System

Fetching Data from the Server

Processing Mutations

Putting the Server Together

Building the Client

Defining the Types

Defining the Mutators

Setting Up the Replicache Instance

Building the UI

The Subscriptions

Event Handlers

UI Components

Advanced Concepts

lastMutationIDChanges

Cookie

Conclusion

Model Selection Criteria

What the Model Needs to Do

Where the Model Runs

Model Selection in Regulated Environments

Making the Final Choice

Wrap-up

6 Months of Claude and Cursor, Part 3: When the Machine Starts Learning on Its Own

August: The Month of Volume

How Activity Spreads Across the Day

The Price of Volume: Failures and What They Reveal

Failure Rate by Platform: Where the Pipeline Still Has Work to Do

What Skills Changed in the Pipeline

Auto Fix: When the Pipeline Learns to Write Its Own Skills

What High Volume Revealed About the Pipeline

What Is Still Human Work

Conclusion

Requirements Traceability Matrix for AI Software Delivery—From Issue to Pull Request

What the Matrix Was Supposed to Do

The Chain Your Workflow Leaves Behind

Agent Volume Breaks the Hand-Kept Matrix

Your CI Retention Expires the Evidence on a Timer

Traceability Is a Read Problem

Explore Progress Forge Orchestration

React for Vue Developers, Part 3: Server Components, use and Actions

Server Components vs. the Vue SSR You Know

Async Components and the use API