
Hyrax | The independent review layer for AI coding
AI代码审查智能体,识别问题并生成拉取请求。
@hyraxai · X
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AI代码审查智能体,识别问题并生成拉取请求。
@hyraxai · X

AskCodi 多模型 AI 代码助手,支持代码生成、重构和智能代理编排。
shreyans_assistiv · Product Hunt
AskCodi Orchestrate agents at scale while reducing cost

通过回答问题构建AI代理,导出到Claude Code。
@manu_jsx · X
Built a coding agent for my repo in under a minute. No prompt engineering. No made-up skill names. Just answered 4 questions and exported to Claude Code.


AI驱动的工程团队,规划、构建、测试和发布代码,具有人类监督。
@edgex11lz · X

通过REST API在云端运行Claude Code代理,流式获取执行结果。
karakanb · HN
Epho – run Claude Code with a curl

在加密工作区中与 Claude Code 和 Cursor 等 AI 代理共享文件和日志。
@Raullen · X
6 months ago, @qevan and I hit a stupid wall: just trying to share a local file or some logs with an AI agent was incredibly frustrating. So, we built the first version of —a dead-simple, secure way to pass context to AI. The community feedback was amazing. We got ~200 stars on GitHub, and it quickly became a daily driver for a lot of developers. But recently, we realized the game has changed. Our workflows evolved. It’s no longer just humans sending files to agents. Agents need to write back to humans. Agents need to hand off context to other agents. The whole team—human and AI—needs to collaborate. So, we gave vnsh a massive upgrade. Today, it’s no longer just a secure clipboard. It’s now a living workspace. 🚀 You drop your context in once and get a dynamic link. Claude Code, Cursor, Cline, and your human teammates all open the exact same document. When one agent updates it, your whole stack sees it instantly. Complete context handoff, without ever copy-pastin

通过 Slack 和 Telegram 自动化代码执行和邮件任务的 AI 代理。
@ankushKun_ · X
Hi, we're building @use_construct , an autonomous cloud comouter that works for your business.

自动化地设计、构建和部署软件系统的平台。
@Bim_evaporates · X
Check out DMs open

学习工作方式的AI助手,将任务分配给30多个前沿模型。
@otakuaakash · X

对比不同Agent Harness策略的成本,基于缓存和上下文计费。
taosx · HN
I created a simulation for coding harnesses based on my own pi sessions. When taking into account all factors, DS-v4-Pro is cheaper than gpt-5.6-luna due to caching. Look at the bill segments difference for cache read cost and uncached cost between deepseek and the other models. At this point is cheaper to use ds-v4-pro than the luna models from openai. ignore the numbers except the classic and keep in mind that classic is based on pi with the only change limiting tool output to 10kb https://har

从专业AI代理获取代码审查,涵盖安全、架构和质量检查
@gianbrunetti0 · X
Conheça o spawn!