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48      "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" ",
49    ),
50    { headers: { "Content-Type": "image/svg+xml" } },
51  );
52}

11|-----|-----|
12| Web page | https://alexwein-fabwbogglelike.web.val.run |
13| Observable Plot for image | https://observablehq.com/plot/ |
14| wordnik from words | https://github.com/wordnik/wordlist |

24  }
25  const svgText = await response.text();
26  return `data:image/svg+xml;base64,${btoa(svgText)}`;
27}
28
53    // Upload blob to Bluesky
54    const { data } = await agent.uploadBlob(convertDataURIToUint8Array(svgDataUri), {
55      encoding: "image/svg+xml",
56    });
57
60      text: "",
61      embed: {
62        $type: "app.bsky.embed.images",
63        images: [{
64          alt: "Generated SVG visualization",
65          image: data.blob,
66          aspectRatio: {
67            width: 600,

1111        <meta property="og:description" content="Turn your ideas into fully functional apps in less than a second – powered by Llama3.3-70b on Cerebras's super-fast wafer chips. Code is 100% open-source, hosted on Val Town."">
1112        <meta property="og:type" content="website">
1113        <meta property="og:image" content="https://stevekrouse-blob_admin.web.val.run/api/public/CerebrasCoderOG.jpg">
1114        
1115            

1111        <meta property="og:description" content="Turn your ideas into fully functional apps in less than a second – powered by Llama3.3-70b on Cerebras's super-fast wafer chips. Code is 100% open-source, hosted on Val Town."">
1112        <meta property="og:type" content="website">
1113        <meta property="og:image" content="https://stevekrouse-blob_admin.web.val.run/api/public/CerebrasCoderOG.jpg">
1114        
1115            

1111        <meta property="og:description" content="Turn your ideas into fully functional apps in less than a second – powered by Llama3.3-70b on Cerebras's super-fast wafer chips. Code is 100% open-source, hosted on Val Town."">
1112        <meta property="og:type" content="website">
1113        <meta property="og:image" content="https://stevekrouse-blob_admin.web.val.run/api/public/CerebrasCoderOG.jpg">
1114        
1115            

1111        <meta property="og:description" content="Turn your ideas into fully functional apps in less than a second – powered by Llama3.3-70b on Cerebras's super-fast wafer chips. Code is 100% open-source, hosted on Val Town."">
1112        <meta property="og:type" content="website">
1113        <meta property="og:image" content="https://stevekrouse-blob_admin.web.val.run/api/public/CerebrasCoderOG.jpg">
1114        
1115            

11- Parse and analyze various types of content:
12  - PDF attachments
13  - Image attachments (using GPT-4 Vision)
14  - Website content from links in your email (if markdown extraction service is available)
15- Get detailed, context-aware responses directly to your inbox
314. Compose your email:
32   - Write your query or request in the email body
33   - Attach any relevant PDFs or images
34   - Include links to websites you want analyzed (if markdown extraction is available)
35   - Send it to the Val email address
47
48- PDFs: Text content will be extracted and analyzed
49- Images: Will be analyzed using GPT-4 Vision API
50- Websites: Content will be extracted and converted to markdown for analysis if the markdown extraction service is available
51- Other file types are not currently supported and will be ignored
81
82  // Step 3: Process different types of content
83  const { pdfTexts, imageAnalysis } = await processAttachments(attachments, openaiKey, transformedPrompt);
84  const websiteMarkdown = await extractWebsiteMarkdown(links, mdApiKey);
85
88    transformedPrompt,
89    pdfTexts,
90    imageAnalysis,
91    websiteMarkdown,
92    receivedEmail,
169}
170
171// Process image attachments with GPT-4V
172async function analyzeImage(imageAttachment, apiKey, transformedPrompt) {
173  try {
174    const response = await fetch("https://api.openai.com/v1/chat/completions", {
184            role: "system",
185            content:
186              `You are an AI assistant tasked with analyzing images in the context of a specific query. Use the following transformed prompt to guide your analysis and provide relevant context:\n\n${transformedPrompt}\n\nFocus your analysis on aspects that are most relevant to this prompt.`,
187          },
188          {
191              {
192                type: "text",
193                text: "Analyze this image and provide relevant context based on the given prompt:${transformedPrompt}",
194              },
195              {
196                type: "image_url",
197                image_url: {
198                  url: `data:${imageAttachment.type};base64,${imageAttachment.content}`,
199                },
200              },
208    return data.choices[0]?.message?.content || "No analysis available";
209  } catch (error) {
210    console.error("Error analyzing image:", error);
211    return "Error analyzing image";
212  }
213}
303}
304
305// Process all attachments (PDFs and Images)
306async function processAttachments(attachments, apiKey, transformedPrompt) {
307  const pdfTexts = [];
308  const imageAnalysis = [];
309
310  for (const attachment of attachments) {
312      const pdfText = await extractPdfText([attachment]);
313      pdfTexts.push(...pdfText);
314    } else if (attachment.type.startsWith("image/")) {
315      const analysis = await analyzeImage(attachment, apiKey, transformedPrompt);
316      imageAnalysis.push({
317        filename: attachment.filename,
318        analysis,
321  }
322
323  return { pdfTexts, imageAnalysis };
324}
325
358
359// Generate the final prompt with all context
360function generateFinalPrompt(transformedPrompt, pdfTexts, imageAnalysis, websiteMarkdown, email) {
361  let contextDump = [];
362
366  }
367
368  // Add image analysis
369  if (imageAnalysis.length > 0) {
370    contextDump.push("Image Analysis:", ...imageAnalysis.map(img => `${img.filename}: ${img.analysis}`));
371  }
372
396        role: "system",
397        content:
398          `You are a helpful AI that replies to emails. Address the sender by their first name if provided, and sign off as 'AI Assistant' in a friendly and professional tone. Be concise and thorough in your analysis. As you're replying via email, use email syntax and not markdown. Analyze all provided content, including PDFs, images, and website content (if available), in the context of the email request. Follow the structured format provided in the transformed prompt to organize your response. Here's the transformed prompt to guide your response:\n\n${transformedPrompt}`,
399      },
400      {

10- Parse and analyze various types of content:
11  - PDF attachments
12  - Image attachments (using GPT-4 Vision)
13  - Website content from links in your email
14- Get detailed, context-aware responses directly to your inbox
284. Compose your email:
29   - Write your query or request in the email body
30   - Attach any relevant PDFs or images
31   - Include links to websites you want analyzed
32   - Send it to the Val email address
44
45- PDFs: Text content will be extracted and analyzed
46- Images: Will be analyzed using GPT-4 Vision API
47- Websites: Content will be extracted and converted to markdown for analysis
48- Other file types are not currently supported and will be ignored

1111        <meta property="og:description" content="Turn your ideas into fully functional apps in less than a second – powered by Llama3.3-70b on Cerebras's super-fast wafer chips. Code is 100% open-source, hosted on Val Town."">
1112        <meta property="og:type" content="website">
1113        <meta property="og:image" content="https://stevekrouse-blob_admin.web.val.run/api/public/CerebrasCoderOG.jpg">
1114        
1115