AI Agents3 weeks ago
Tavyn AI | The Enterprise Intelligence Layer
Deploy AI agents that automate enterprise workflows by understanding your business logic.
@kr812345 · X
enterprise ai orchestration platforms to automate using tools. @
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Deploy AI agents that automate enterprise workflows by understanding your business logic.
@kr812345 · X
enterprise ai orchestration platforms to automate using tools. @
Chat with multiple AI models, build agents, generate images, and analyze PDFs.
@ai_kral · X
Read AI-summarized daily news on global AI developments.
@ChenglongW98225 · X
做了一个AI新闻日报，感兴趣的可以点下方链接看一下 有什么需要改进的也可以直接评论我，我都会认真回复
Deploy serverless AI models with a single line of code at competitive rates.
Synexa AI — 一行代码部署 AI 模型 (Replicate 50% 低价平替)，Synexa 是运行无服务器 AI API 最具性价比的解决方案，提供业界最具竞争力的 A100 GPU 价格，相比其他服务商可节省高达 62% 的 AI 算力成本 - [更多介绍](https://github.com/1c7/chinese-independent-developer/issues/47
Monitor AI model calls, agent steps, and retrieval with token tracking, cost analysis, and latency metrics.
ephraimduncan · HN
Observability for Coding Agents and LLM Applications
AI scans competitors and market data to validate if a startup idea is worth building in 90 seconds.
@Ebrahim_Rio · X
Most founders skip validation and pray. I automated the "worth building?" check. AI scans competitors, Reddit, and market data → Cook or Kill in 90 seconds. Killed? It surfaces the pivot the data actually backs.
AI operating system for managing revenue, leads, and business automation.
@AevumAIHQ · X
Your new business center
Route AI coding tasks to the cheapest capable model based on estimated complexity.
@viiforwinn · X
Hey, let's connect! 🚀 I got tired of the manual grind (picking Claude models, checking diffs for secret leaks, and guessing agent parallel counts), so I automated all three. Built kodemux—it's completely free and open source:
Generate AI images from text or images, then compare outputs across multiple AI models.
Seedream 5.0 Pro — 多模型 AI 图片工作台，支持文字生图、图片生图、复杂布局与多语言文字编辑
Create accurate AI assistants for your technical documentation in days.
@emilsnotes · X
Our customers using Claude Code move faster than we can ship features. So we stopped fighting it and leaned in. Here's why it's paying off: 👇️ Some context: powers AI agents that answer millions of technical questions per month. Those conversations are a goldmine - they tell you exactly what users struggle with and where your docs fall short. We've built a lot of analytics on top of this (Coverage Gaps, Top Questions, and more), and teams love them. But here's the honest truth: no dashboard covers every edge case for every company. Meanwhile, our sharpest customers were already exporting kapa data and throwing their coding agents at it in ways we never designed for. So instead of chasing every workflow with a UI, we took the best practices we've seen actually work and packaged them as Kapa Skills - installable in Claude Code, Cursor, or Codex: 1/ Analyze Coverage Gaps: your agent works through the topics kapa couldn't answer and writes the missing content straig
AI-powered crop decision support system for optimizing farm field visits.
@Amrutraj4 · X
I completely vibe coded website during Open AI build week hackathon. Planned using ChatGPT. Executed using Codex. Front end deployed using Vercel Backend deployed using Render Tell me your experience of website.
Benchmark AI models by having them animate a 3D banana plant's full lifecycle.
fran-mora · HN
I gave 5 AI coding agents one prompt: grow a banana plant through its whole life in three.js: sprout, leaves, flower, fruit, rot, then pups that restart the loop. It's deceptively simple and yet very hard to get right from procedural code: you have to write working three.js and understand how the plant is actually built; how it hangs, ages and decays. Get the biology wrong and the code renders something weird. These are agents, not bare models (Claude Code and Codex for now). They can use tools, including playwright to check their work and improve it.