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    <meta charset="utf-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1" />
    <title>OpenAI Realtime API Voice Agent</title>
    <style>
      :root {

lightweight/stainlessTodoSweeper/README.md

5 matches

│   │   │   ├── blocks.ts   # Block operations
│   │   │   └── search.ts   # Search operations
│   │   ├── aiService.ts    # OpenAI for fuzzy matching
│   │   └── blobService.ts  # Val Town blob storage
│   └── utils/              # Utility functions
### Strategy 4: AI Fuzzy Matching with Date Disambiguation
If strategies 1-3 don't find a match, the system uses OpenAI (via Val Town's `@std/openai`) to match the todo text against project names and client names.
**Initial AI Match**:
- Sends the todo text and list of projects (with client names) to OpenAI (`gpt-4o-mini`)
- AI returns one of:
  - A specific project ID (if confident match to a project name)
4. The relation named in `TODOS_PROPERTIES.projects` also gets AI matching (Strategies 4-5)
**OpenAI Integration**:
- Uses Val Town's built-in OpenAI integration (`@std/openai`)
- Model: `gpt-4o-mini` (fast, cost-effective)
- AI calls per todo (when needed):

lightweight/stainlessTodoSweeper/aiService.ts

18 matches

/**
 * AI service for fuzzy matching using OpenAI
 *
 * Uses OPENAI_API_KEY env var if set (recommended for production).
 * Falls back to Val Town's shared @std/openai (has 10 req/min limit).
 */
import { OpenAI as ValTownOpenAI } from "https://esm.town/v/std/openai";
import OpenAINpm from "npm:openai@4";
import type { ProjectWithDates } from '../utils/projectUtils.ts';
import type { SummaryResult } from '../../shared/types.ts';
// Use own API key if available, otherwise fall back to Val Town's shared client
function getOpenAIClient() {
  const apiKey = Deno.env.get("OPENAI_API_KEY");
  if (apiKey) {
    return new OpenAINpm({ apiKey });
  }
  // Fall back to Val Town's shared OpenAI (has rate limits)
  return new ValTownOpenAI();
}
  }
  const openai = getOpenAIClient();
  const projectList = projects
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 100,
  }
  const openai = getOpenAIClient();
  const projectList = candidates
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 100,
  }
  const openai = getOpenAIClient();
  const candidateList = candidates
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 100,
 */
export async function generateSummary(context: SummaryContext): Promise<SummaryResult> {
  const openai = getOpenAIClient();
  // Build context sections for the prompt
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 200,

lightweight/todoSweeper/README.md

5 matches

│   │   │   ├── blocks.ts   # Block operations
│   │   │   └── search.ts   # Search operations
│   │   ├── aiService.ts    # OpenAI for fuzzy matching
│   │   └── blobService.ts  # Val Town blob storage
│   └── utils/              # Utility functions
### Strategy 4: AI Fuzzy Matching with Date Disambiguation
If strategies 1-3 don't find a match, the system uses OpenAI (via Val Town's `@std/openai`) to match the todo text against project names and client names.
**Initial AI Match**:
- Sends the todo text and list of projects (with client names) to OpenAI (`gpt-4o-mini`)
- AI returns one of:
  - A specific project ID (if confident match to a project name)
4. The relation named in `TODOS_PROPERTIES.projects` also gets AI matching (Strategies 4-5)
**OpenAI Integration**:
- Uses Val Town's built-in OpenAI integration (`@std/openai`)
- Model: `gpt-4o-mini` (fast, cost-effective)
- AI calls per todo (when needed):

lightweight/todoSweeper/aiService.ts

18 matches

/**
 * AI service for fuzzy matching using OpenAI
 *
 * Uses OPENAI_API_KEY env var if set (recommended for production).
 * Falls back to Val Town's shared @std/openai (has 10 req/min limit).
 */
import { OpenAI as ValTownOpenAI } from "https://esm.town/v/std/openai";
import OpenAINpm from "npm:openai@4";
import type { ProjectWithDates } from '../utils/projectUtils.ts';
import type { SummaryResult } from '../../shared/types.ts';
// Use own API key if available, otherwise fall back to Val Town's shared client
function getOpenAIClient() {
  const apiKey = Deno.env.get("OPENAI_API_KEY");
  if (apiKey) {
    return new OpenAINpm({ apiKey });
  }
  // Fall back to Val Town's shared OpenAI (has rate limits)
  return new ValTownOpenAI();
}
  }
  const openai = getOpenAIClient();
  const projectList = projects
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 100,
  }
  const openai = getOpenAIClient();
  const projectList = candidates
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 100,
  }
  const openai = getOpenAIClient();
  const candidateList = candidates
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 100,
 */
export async function generateSummary(context: SummaryContext): Promise<SummaryResult> {
  const openai = getOpenAIClient();
  // Build context sections for the prompt
  try {
    const completion = await openai.chat.completions.create({
      model: "gpt-4o-mini",
      max_tokens: 200,

losdwind/claude-plugins-registry/AGENTS.md

4 matches

_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" },

lightweight/stainlessTodoSweeper/AGENTS.md

4 matches

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" },

aeio_win/imageAfdianPayment/AGENTS.md

4 matches

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" },

aeio_win/imageStripePayment/AGENTS.md

4 matches

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" },

mparker/sqliteExplorerReact/AGENTS.md

4 matches

_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" },