
Misata Studio — Best Synthetic Data Generator for Python & AI Agents (2026)
使用Misata为Python生成精确声明的合成测试数据。
@RasinMuhammedX · X
Declarative Synthetic Data Generation.
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使用Misata为Python生成精确声明的合成测试数据。
@RasinMuhammedX · X
Declarative Synthetic Data Generation.

检查 RAG 块并可视化 AI 代理工作流、内存架构和执行轨迹。
@Higgs0110 · X

在浏览器中持续运行和监控编码代理。
sakuraiben · HN
Reachpad – Run all your coding agents from anywhere from the browser

JavaScript和TypeScript代码审查工具,使用AI无需执行代码即可识别问题。
@adib_011 · X
Built RepoLens at the Codex Meetup, an evidence-first JS/TS code review tool that shows reviewers where to look, without executing repository code. Built with Codex + GPT‑5.6. Try it: Demo: @OpenAIDevs @mitul_shahriyar #CodexMeetup

压缩提示词并检测重复工具调用,降低LLM代币成本
@DeveloperL92487 · X
I built my first app in 60min And now I got $500 MRR in one month Check here if you are interested It’s a tool to reduce agent token consumption, speed up agent response, and clean up memory cache

用并行AI智能体构建全栈应用的开发平台
@Ajathinfotech · X
How do Ajath's parallel agents actually build full-stack apps in mins? Let you work with up to 10 specialized AI agents in parallel to eliminate traditional development bottlenecks. Try Ajath for free: #VibeCoding #NoCode #AIAgents

包含 30+ 免费交互效果的 React/Next.js 库,可即时预览和安装。
@jyotipathak___ · X
We launched Vault today and we’re excited to take you through an experience designed to impress from the first scroll to the last. Vault is a React/Next.js effects library. Preview the effect. Install it. Ship it. Constructive feedbacks appreciated

在统一界面编写、模板化并启动跨多个AI平台的提示词。
@promptboxxx · X
Have all your AIs in one place and never repeat a prompt

提供稳定中继 Claude Fable-5 模型的 API 服务。
@iveyzen · X
鹿友AI上线了 最贵的AI中转站,只因为想创造稳定fable-5访问专线

分析 GitHub 仓库发现付费工作与贡献机会。
@bashops_dev · X
Building BashOps Radar—repository intelligence for developers. Helping developers identify contribution opportunities that can lead to contracts, paid sprints, and long-term work.

为Claude Code和Codex设计的开源遥控器,支持iOS、Android和网页。
@Ex3NDR · X
Few days ago Happy had a birthday. Tried to refresh one of the most neglected part of it: our Happy Website: also i built a new harness called “rig”. i started to use at work recently instead of codex/claude code and i liked how it does work. i want to share this with everyone. it is a codex and cause code reimplemented using Pi, but keeping native prompts and expanded a little for uniformity (like added workflows to codex yay!). when something new released that is worthy of porting - i ask Sol to mim for prompts or read sources directly and i can have a feature within an hour. why this is needed? i was constantly blocked by the needing of integrating a lot of models together, which each has their own harness or many doesn’t have any, but i didn’t want to go too far from native and don’t like the idea of extensions - i want experience to be the same everywhere. i think configuration > extensions. more soon

用英文描述想法,QuikRun自动生成代码并提供实时URL。
nparashar150 · HN
Over the weekends, I've been revamping an old side project of mine: https://quik.run/?ref=yc It started as a simple platform for writing and sharing code snippets. Over time, it evolved into a way to run snippets instantly with support for imports and packages, without worrying about setup, deployment, or infrastructure. Think of it as a lightweight AWS Lambda for quick experiments. One thing I've realized is that even though AI has made a lot of older developer tools and ideas feel less relevan