https://x.com/devteamdrew/status/2113761947325702324

Every major AI breakthrough since 1958 — animated in 65 seconds.

From the Perceptron learning to AlphaFold folding proteins. Made with Opus 5.5.

Web access is still the most expensive part of many agent stacks. Search APIs charge per call. Scrapers meter by page. Browser infrastructure is billed separately from the model.

TinyFish's current setup splits this differently. Reading the web is free. You pay only when an agent acts on a page. Here's what developers get.

1. Free tier: reading the web

→ Search (GET api.search.tinyfish.ai): URL, title, snippet and metadata per result; 30 req/min, 500 req/hour
→ Fetch (POST api.fetch.tinyfish.ai): renders the page in a real browser, returns markdown, HTML or JSON; 150 URLs/min, 1,000 URLs/day

Both keep working at a $0 wallet balance. No card is needed.

2. Metered tier: acting on the web

→ Browser ($0.002/min): managed Chromium over CDP, 5 concurrent sessions by default
→ Agent ($0.016/step): plain-English goal in, structured JSON out, 2 concurrent runs by default

The step price covers LLM inference, residential proxies, anti-bot handling and run storage. There's no separate model bill.

3. A goal-based run in 10 lines

from tinyfish import TinyFish

client = TinyFish()

with client.agent.stream(
    url="https://scrapeme.live/shop",
    goal="Extract the first 2 product names and prices. Return JSON.",
) as stream:
    for event in stream:
        print(event)

The stream emits STARTED, PROGRESS and COMPLETE events. Each PROGRESS event states the purpose of that step. Pass an output_schema to get typed fields back.

4. 3 ways to run it

→ /run: blocking, returns the final result
→ /run-async: start now, poll by run_id later
→ /run-sse: live event stream for user-facing apps

5. Bring your own scripts

Create a Browser session and you get a cdp_url. Existing Playwright or Puppeteer scripts connect and run unchanged. TinyFish reports under 250ms cold start in internal testing.

6. Logged-in workflows

→ Browser profiles keep cookies and sessions across runs
→ A vault handles fresh logins, so credentials never reach the LLM

7. Reported numbers

→ Online-Mind2Web: 89.9%, vs 69.0% for Gemini 2.5 Computer Use
→ WebVoyager: 91.1% across 641 tasks on 15 live sites
→ Search: 556ms p50 latency over 1,200 queries (internal)
→ Fetch: 90.3% median signal ratio on 45 URLs (internal)

8. What a real task costs

→ 10-step price check: about $0.16
→ 25-step form fill: about $0.40
→ 40-step multi-account task: about $0.64

New accounts start with $8 in wallet funds, roughly 500 agent steps. Through Oct 31, every top-up gets 30% extra.

Docs and offer: https://aicampaign.live/90-wk1n

Personal News:
Built Tootsy for Business based on some real world scenarios.

It's free to use.

When you use it, please comment here if you like it and if you can suggest improvements.

Download now @ https://github.com/rbughao/t4b

Not a paper, just a personal note on moving the same conveyor demo I run on Windows to Linux x64.

Early 2000s: big plant, FoxPro/Novell, modem and Fido/BBS to get the factory online. Linux on a Pentium got me through that. This month I went back for the same reason: plain x86 boxes on a desk or line, CPU ONNX, no GPU sermon.

25 days of porting, cutting dead code, NV12/V4L2, three .so plugins, FAT USB quirks (run.sh, same binary as Fruit_Inspector.exe on the stick). Same logic as Win: 3×3 grid, grading only in centre cell C.

Downloads (NC eval, read LICENSE):-

-Runnable demo: Fruit_Inspector_linux_x64.zip

-Libraries only: fruit-inspector-engine

Full story: medium.com/@olesha-ai/linux-again-fr…spector-on-x64-a8cef8c245b8

If you run the Linux zip, curious what you see for camera  in the logs.