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Updated pi-web-access for Pi agent with video understanding. If you're signed into Gemini in Chrome, no API keys needed for web search or video analysis. Point it at any YouTube URL or local video file (mp4, mov, etc) and ask questions about the content. pi install npm:pi-web-access

18,367 次观看 • 7 个月前 •via X (Twitter)

4 条评论

Mario Zechner 的头像
Mario Zechner7 个月前

Do you ever sleep?

Diabolo_8008 的头像
Diabolo_80087 个月前

this screen is so beautiful! there is no reason to leave this terminal

Emre Coklar 的头像
Emre Coklar7 个月前

NICE!!

Pi ⚗️ 的头像
Pi ⚗️7 个月前

Video understanding without API keys? Now that's what I call efficient alchemy. One less barrier between curiosity and insight. Keeping an eye on this.

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🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

Vikas gupta

18,053 次观看 • 10 天前

Stop paying $20–50/month for your AI agent to browse the web. i built the free version no API keys, no config, no rate limits😳 it's called Hound. An MCP server that does fetch, search, crawl, and screenshot — all keyless and now it ships a native Pi extension, so you get all 6 tools as first-class Pi tools, not through a generic MCP adapter what you get: → web_fetch — anti-bot fetch → clean markdown. PDFs get section maps + auto-OCR. Dead pages auto-recover from the Internet archive (honestly marked, never pretending it's live) → web_search — 10 keyless backends in parallel (DuckDuckGo, Brave, Mojeek, Yandex, Google…) + a local neural reranker → web_crawl — best-first same-domain walk. sitemap mode maps a whole site in one fetch → web_screenshot — anti-bot shots for multimodal models → cache_clear + hound_version what it replaces: -tavily — free tier -firecrawl — $16–50/mo -searXNG + browser + extractor glue — fragile -hound — $0, keyless, forever why it matters: >agent research needs web access, but everyone gates it behind a key or a limit >hound is honest about errors — a 404 says "Page doesn't exist" instead of dumping an error page as if it were content >archive recovery tags source= so the agent knows the content is historical, not live 2-minute setup: -pip install hound-mcp[all] -pi install npm: @houndmcp/hound-mcp-pi -/reload → tools are live. That's it Caveats (read): dataDome, Akamai, Cloudflare Turnstile — no free tool bypasses these hound tries the stealthy browser, then tells you to switch sources the [all] extra is ~100MB (onnxruntime + OCR). Works without it, but full install is recommended not a scraping-at-scale tool. Crawl caps at 100 pages by default — built for agent research Give your agent the web for free

Nahid

44,380 次观看 • 1 个月前