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Documentation Index

Fetch the complete documentation index at: https://developers.t2000.ai/llms.txt

Use this file to discover all available pages before exploring further.

Keep up without doom-scrolling. Your agent pulls the week’s notable AI papers (with arXiv IDs, dates, authors), surfaces the launches and debates worth tracking, and hands back a tight digest with a one-line “why it matters” per item. Self-contained — no inputs needed. ~$0.06.
A durable MPP demo: this is live, web-grounded retrieval a code sandbox can’t fabricate. The value is paid semantic search + real-time citations, not generation — exactly the kind of recipe that earns its keep regardless of how capable the client is.

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The prompt

Using Exa and Perplexity, pull the most important new AI research from the
past week — arXiv papers, lab blogs, notable launches. Give me 5-8 specific
papers with arXiv IDs, dates, and authors, plus 3 debates or launches to
track. One line on why each matters.

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What runs

  1. POST /exa/v1/search — semantic search for recent papers + primary pages (~$0.02)
  2. POST /perplexity/v1/chat/completions — web-grounded “what’s notable this week” with citations (~$0.02)
  3. POST /anthropic/v1/messages — Claude distills the digest, keeping IDs + sources (~$0.02)

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Run it

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Claude Desktop (MCP)

npm install -g @t2000/cli && t2 init && t2 receive && t2 mcp install
Paste the prompt. Narrow it any way you like — “…focused on agents and tool use” or “…just inference + serving”.

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SDK

import { T2000 } from '@t2000/sdk';

const agent = await T2000.create();
const focus = 'LLM agents, reasoning, and inference efficiency';

const [papers, landscape] = await Promise.all([
  agent.pay({
    url: 'https://mpp.t2000.ai/exa/v1/search',
    method: 'POST',
    body: JSON.stringify({
      query: `notable AI research papers past week ${focus} arxiv`,
      numResults: 10,
      contents: { text: true },
    }),
  }),
  agent.pay({
    url: 'https://mpp.t2000.ai/perplexity/v1/chat/completions',
    method: 'POST',
    body: JSON.stringify({
      model: 'sonar',
      messages: [{
        role: 'user',
        content: `What are the most important AI developments and debates this past week (${focus})? Cite sources.`,
      }],
    }),
  }),
]);

const digest = await agent.pay({
  url: 'https://mpp.t2000.ai/anthropic/v1/messages',
  method: 'POST',
  headers: { 'anthropic-version': '2023-06-01' },
  body: JSON.stringify({
    model: 'claude-sonnet-4-5',
    max_tokens: 1200,
    messages: [{
      role: 'user',
      content:
        `Write a weekly AI digest. List 5-8 specific papers with arXiv ID, date, and authors ` +
        `(only ones you can tie to a source — no invented IDs), then 3 debates/launches to track. ` +
        `One line on why each matters.\n\n` +
        `PAPERS: ${JSON.stringify(papers.body)}\n\nLANDSCAPE: ${JSON.stringify(landscape.body)}`,
    }],
  }),
});

console.log((digest.body as { content: { text: string }[] }).content[0].text);

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Expected output

2-3 calls · ~$0.06 · ~8s · 0 taps
5-8 papers w/ arXiv IDs + 3 things to track, each with why-it-matters

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Extend it

  • Add Jina Reader (/jina/v1/read) to pull the raw arXiv “recent” listing as clean markdown and ground the IDs verbatim
  • Use Firecrawl (/firecrawl/v1/scrape) to read a specific lab blog (e.g. a new model card) in full
  • Pipe the digest into Resend (/resend/v1/emails) for a Monday-morning email to yourself, scheduled via cron
  • Chain the Research with citations recipe on any single paper to go deeper with sources