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Note: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

export const COMPETITORS = [
  {
    name: "OpenAI",
    url: "https://openai.com",
    description: "OpenAI homepage"
  },
  {

- [ ] Get OpenTownie or Gemini or Claude or OpenAI to synthesize the core of these patterns into a prompt we can use to make more ReactRouter apps, such as...
- [ ] Convert this or into the basic react router guest book (and preserve this forum app in another project?)
- [ ] To what extent can these patterns be packaged up into a Val Town Router project? Would be neat to get the version pinning thing all centralized, can this as-a-library be that centralized place?

_2 or_3) to create a fresh table.
### OpenAI
```ts
import { OpenAI } from "https://esm.town/v/std/openai";
const openai = new OpenAI();
const completion = await openai.chat.completions.create({
  messages: [
    { role: "user", content: "Say hello in a creative way" },

import { email } from "https://esm.town/v/std/email";
import { OpenAI } from "https://esm.town/v/std/openai";
export default async function (incomingEmail: Email) {
  try {
    const openai = new OpenAI();
    const emailContent = incomingEmail.text || incomingEmail.html ||
      "No content available";
    const completion = await openai.chat.completions.create({
      messages: [
        {

// @ts-ignore
import { OpenAI } from "https://esm.town/v/std/openai?v=4";
// --- AI BEHAVIORAL GUIDELINES ---
  <footer class="text-center mt-6 text-gray-500 text-sm">
    <p>Pestodon &copy; 2025. Powered by <a href="${sourceUrl}" class="text-blue-400 hover:underline" target="_blank">Val Town</a> & OpenAI.</p>
  </footer>
  if (req.method === "POST" && action === "enhanceNotes") {
    try {
      const openai = new OpenAI();
      const body = await req.json();
      }
      const completion = await openai.chat.completions.create({
        model: "gpt-4o",
        messages: [

2. **Environment Variables** (left sidebar)
   ```bash
   OPENAI_API_KEY=your_openai_api_key_here
   ```
## Customization
Edit the system prompt in `openai.ts` to change how issues are categorized.
## Output

  }))
  const { categorizeIssues: categorizeWithAI } = await import('./openai.ts')
  return categorizeWithAI(processedIssues)
}

import OpenAI from 'npm:openai'
const openai = new OpenAI({
  apiKey: Deno.env.get('OPENAI_API_KEY')
})
export async function categorizeIssues(issues: any[]) {
  const completion = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: `Analyze these GitHub issues and categorize them into a two-level system:
  const aiResponse = completion.choices[0].message.content
  if (!aiResponse) throw new Error('OpenAI returned empty response')
  
  try {
    return JSON.parse(aiResponse)
  } catch (error) {
    console.error('Failed to parse OpenAI response:', aiResponse.substring(0, 1000) + '...')
    
    // Try to repair truncated JSON by finding the last complete array
      return JSON.parse(repairedResponse)
    } catch (repairError) {
      throw new Error(`Invalid JSON response from OpenAI: ${error.message}`)
    }
  }

import { OpenAI } from "npm:openai";
import type {
  AppMentionEvent,
import { slack } from "./slack.ts";
import { readFile } from "https://esm.town/v/std/utils/index.ts";
import { zodTextFormat } from "npm:openai/helpers/zod";
import { z } from "npm:zod@3.23.8";
];
const openai = new OpenAI({ apiKey: Deno.env.get("OPENAI_API_KEY") });
async function getHistory(
  event: AppMentionEvent | GenericMessageEvent,
): Promise<OpenAI.Responses.ResponseInput> {
  const { messages } = await slack.conversations.replies({
    channel: event.channel,
async function shouldReply(
  threadHistory: OpenAI.Responses.ResponseInput,
): Promise<boolean> {
  const ShouldReplyOjbect = z.object({
  });
  const response = await openai.responses.parse({
    model: "gpt-4o-2024-08-06",
    input: [
export async function reply(
  event: AppMentionEvent | GenericMessageEvent,
  messages: OpenAI.Responses.ResponseInput,
) {
  // reply in thread or start one
  });
  const input: OpenAI.Responses.ResponseInput = [
    { role: "developer", content: SYSTEM_PROMPT },
    ...messages,
  for (let step = 0; step < MAX_STEPS; step++) {
    const response = await openai.responses.create({
      model: "gpt-4.1",
      input,