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Private agents run on Venice. 300+ models across all modalities, search and scrape the web, embeddings, and much more. All from a single API. Point your agent here:

16,632 次观看 • 1 个月前 •via X (Twitter)

18 条评论

Venice 的头像
Venice1 个月前

Or give to your agent: add and configure

OpenGradient (∇, ∇) 的头像
OpenGradient (∇, ∇)1 个月前

privacy is so underrated atm. we keep pushing until users look for it first

Daisy 的头像
Daisy1 个月前

the API docs are clean af

Danny 的头像
Danny1 个月前

interesting 👀

furomoad 的头像
furomoad1 个月前

Bro why did you vibe-coded it 😭🙏

SkillHub 的头像
SkillHub1 个月前

300 models is a lot of endpoints to babysit, but one API with search and embeddings already covers most agent plumbing honestly.

Blake.ETH 的头像
Blake.ETH1 个月前

api parity matters

Ali.BTC 的头像
Ali.BTC1 个月前

the api count wins

🧠 Fred | PocketIQ | Free AI Trading 的头像
🧠 Fred | PocketIQ | Free AI Trading1 个月前

@ErikVoorhees 👆 This is where attention pays

Ella Tech & Tool 的头像
Ella Tech & Tool1 个月前

One API for 300 models is insane Private agents just got easy

The Rocking Horse Winner. 的头像
The Rocking Horse Winner.1 个月前

That's sweet, but don't you think you should allow unlimited chat to effectively compete?

JSFILMZ 的头像
JSFILMZ1 个月前

dm

FinWizz 的头像
FinWizz1 个月前

private agent infra that ships clean instead of in whitepapers, we like to see it

Fielding Johnston 的头像
Fielding Johnston1 个月前

Lfg!

安叫兽|Bird🕊️ 🔶 BNB 的头像
安叫兽|Bird🕊️ 🔶 BNB1 个月前

300 多个模型,光挑用哪个都得纠结半天。

krumviza 的头像
krumviza1 个月前

🚀

Ky 的头像
Ky1 个月前

So you're recommending a skill instead of the mcp or cli? Or is the skill actually just using the cli?

Sandy.ETH 的头像
Sandy.ETH1 个月前

api game changer

相关视频

🚨 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 • 28 天前