Val Town Code SearchReturn to Val Town

77async function generateFunFacts(previousFacts) {
78  try {
79    // Get API key from environment
80    const apiKey = Deno.env.get("ANTHROPIC_API_KEY");
81    if (!apiKey) {
82      console.error("Anthropic API key is not configured.");
83      return null;
84    }
85
86    // Initialize Anthropic client
87    const anthropic = new Anthropic({ apiKey });
88
89    // Format previous facts for the prompt

20### 2. HTTP Vals
21
22- Create web APIs and endpoints
23- Handle HTTP requests and responses
24- Example structure:
66- Generate code in TypeScript
67- Add appropriate TypeScript types and interfaces for all data structures
68- Prefer official SDKs or libraries than writing API calls directly
69- Ask the user to supply API or library documentation if you are at all unsure about it
70- **Never bake in secrets into the code** - always use environment variables
71- Include comments explaining complex logic (avoid commenting obvious operations)
190- For AI-generated images, use: `https://maxm-imggenurl.web.val.run/the-description-of-your-image`
191- **Storage:** DO NOT use the Deno KV module for storage
192- **Browser APIs:** DO NOT use the `alert()`, `prompt()`, or `confirm()` methods
193- **Weather Data:** Use open-meteo for weather data (doesn't require API keys) unless otherwise specified
194- **View Source:** Add a view source link with `import.meta.url.replace("esm.town", "val.town")` and include `target="_top"` attribute
195- **Error Debugging:** Add `<script src="https://esm.town/v/std/catch"></script>` to HTML to capture client-side errors
196- **Error Handling:** Only use try...catch when there's a clear local resolution; avoid catches that merely log or return 500s - let errors bubble up with full context
197- **Environment Variables:** Use `Deno.env.get('keyname')` and minimize their use - prefer APIs without keys
198- **Imports:** Use `https://esm.sh` for npm and Deno dependencies to ensure compatibility on server and browser
199- **Storage Strategy:** Only use backend storage if explicitly required; prefer simple static client-side sites
230
231### Backend (Hono) Best Practices
232- Hono is the recommended API framework (similar to Express, Flask, or Sinatra)
233- Main entry point should be `backend/index.ts`
234- **Static asset serving:** Use the utility functions to read and serve project files:
251  });
252  ```
253- Create RESTful API routes for CRUD operations
254- Be careful with error handling as Hono tends to swallow errors
255- Always include this snippet at the top-level Hono app to re-throwing errors to see full stack traces:
268- Use React 18.2.0 consistently in all imports and the `@jsxImportSource` pragma
269- Follow the React component pattern from the example project
270- Handle API calls properly with proper error catching
271
272### Database Patterns
299   - For files in the project, use `readFile` helpers
300
3015. **API Design:**
302   - `fetch` handler is the entry point for HTTP vals
303   - Run the Hono app with `export default app.fetch // This is the entry point for HTTP vals`

10import { NotebookView } from "./NotebookView.tsx";
11
12const API_BASE = "/api/memories";
13const MEMORIES_PER_PAGE = 20; // Increased from 7 to 20 memories per page
14
90
91    // Fetch avatar image
92    fetch("/api/images/stevens.jpg")
93      .then((response) => {
94        if (response.ok) return response.blob();
104
105    // Fetch wood background
106    fetch("/api/images/wood.jpg")
107      .then((response) => {
108        if (response.ok) return response.blob();
133    setError(null);
134    try {
135      const response = await fetch(API_BASE);
136      if (!response.ok) {
137        throw new Error(`HTTP error! status: ${response.status}`);
176
177    try {
178      const response = await fetch(API_BASE, {
179        method: "POST",
180        headers: { "Content-Type": "application/json" },
199
200    try {
201      const response = await fetch(`${API_BASE}/${id}`, {
202        method: "DELETE",
203      });
231
232    try {
233      const response = await fetch(`${API_BASE}/${editingMemory.id}`, {
234        method: "PUT",
235        headers: { "Content-Type": "application/json" },
606    <div className="font-pixel text-[#f8f1e0]">
607      <style jsx>{`
608        @import url("https://fonts.googleapis.com/css2?family=Pixelify+Sans&display=swap");
609
610        @tailwind base;

5date: "2025-04-26"
6url: "https://dcm31--45d1947422b711f0be5c569c3dd06744.web.val.run"
7imageUrl: "https://chatgpt.com/backend-api/public_content/enc/eyJpZCI6Im1fNjgwZDBjYzQxYTcwODE5MWFlZTZmNzhjZmJjNGVhNGQ6ZmlsZV8wMDAwMDAwMDU0MTg2MWY2YTRiOWMxOWE3NTA4Yjk5NCIsInRzIjoiNDg0OTEyIiwicCI6InB5aSIsInNpZyI6IjFhODc1NDE1ZjJkNmNlM2EwYjY4YjMzYmZhYTIwMDM2YmIzMWI5NjU2YWYzZTdkYmM2N2IxOWRmMzdlZjliYzIiLCJ2IjoiMCIsImdpem1vX2lkIjpudWxsfQ=="
8tags: ["markdown", "blog", "val-town", "template"]
9---

vtProjectSearchmoi.md2 matches

@dcm31•Updated 1 week ago

2title: "Val Town Project Search"
3description: "A search engine for Val Town projects, vals, and users"
4imageUrl: "https://chatgpt.com/backend-api/public_content/enc/eyJpZCI6Im1fNjgwYzQyM2VmNTIwODE5MTkzOGNjY2Q1NTkxZmU0YjQ6ZmlsZV8wMDAwMDAwMGIwNGM2MjMwOGY5MTUwMjdlYjhiMDM3ZCIsInRzIjoiNDg0ODk4IiwicCI6InB5aSIsInNpZyI6ImQ5MjNjZGY4MTlkY2RmMTkwMzliM2E0NDY4N2RlYWNlZjNjNzQwYmE1ZGFlODg2NjNmMGU0ZWNmNjE0MzYwNzAiLCJ2IjoiMCIsImdpem1vX2lkIjpudWxsfQ=="
5url: "https://charliesusersearch.val.run/"
6author: "dcm31"
7tags: ["search", "val-town", "typescript", "api"]
8---
9

26type TestResponse = { llmOutput: string; error?: string };
27
28// Final result structure for API responses including logs
29type FinalRes<T> = { data?: T; error?: string; dtls?: string; logs: LogEntry[]; reqId: string };
30
342  log("DEBUG", "LLM", `OpenAI Call`, { sysL: systemPrompt.length, usrL: userPrompt.length }, reqId);
343  try {
344    // IMPORTANT: Replace with your actual OpenAI API key mechanism (e.g., environment variable)
345    const oa = new OpenAI(); // Assumes OPENAI_API_KEY env var is set
346    const completion = await oa.chat.completions.create({
347      // Consider using a more powerful model for prompt generation, maybe cheaper for testing
361  } catch (err: any) {
362    const errD = { msg: err.message, name: err.name, status: err.status, type: err.type, code: err.code };
363    log("ERROR", "LLM", `OpenAI API call failed`, { error: errD }, reqId);
364    throw new Error(`LLM API error: ${err.message}${err.code ? ` (Code: ${err.code})` : ''}`);
365  }
366}

117 const finalSys = sysPrompt + `\n\nREMINDER: Info only, not medical advice. Verify & use clinical judgment.`;
118 try {
119   const oa = new OpenAI(); // Assumes API key is set in environment
120   const completion = await oa.chat.completions.create({
121     model: "gpt-4o-mini", // Use appropriate model
130 } catch (err: any) {
131   const errD = { msg: err.message, name: err.name, status: err.status, type: err.type, code: err.code };
132   log("ERROR", "LLM", `OpenAI API task ${tid} failed`, { error: errD }, mid, tid);
133   throw new Error(`OpenAI API error: ${err.message}${err.code ? ` (Code: ${err.code})` : ''}`);
134 }
135}

12  },
13  {
14    prompt: "weather dashboard for nyc using open-meteo API for NYC with icons",
15    title: "Weather App",
16    code:

12  },
13  {
14    prompt: "weather dashboard for nyc using open-meteo API for NYC with icons",
15    title: "Weather App",
16    code:

12  },
13  {
14    prompt: "weather dashboard for nyc using open-meteo API for NYC with icons",
15    title: "Weather App",
16    code: