@spectragai · X

@katyorby · X

i built — a local receipt for claude code runs. your check says whether the workspace passes now; the transcript supplies the activity counts. no transcript upload and no magical autonomy score.

cdnsteve · HN

Tokenstead, find AI models for your hardware

@ReplyDudeAI · X

vibe coding gets you a demo quick but falls apart the second you need real logic or scale. the course probably skips that part.

@Pundkar12 · X

shreyans_assistiv · Product Hunt

AskCodi Orchestrate agents at scale while reducing cost

@lai_jia_wei · X

发布我们团队的第一个 side project 项目 这是一套收集、处理用户反馈的 Agent 工作流，包含 Widget、CLI、MPC、Skill，开源地址在评论区。 随着运营产品越来越多，许多细节很难逐一深入体验（尤其当自己不是目标用户），AI coding 可以实现可用，但是好不好用还得是现场用户说了算，更别说出现静默 Bug 的时候，只能实际使用才知道哪里出问题了。 所以我们做了一个很轻量的小工具，Make This Better 可以让用户在网页上提交反馈，AI 根据现场进行需求分析，最后你的本地 coding agent 接手处理，全程不碰任何数据、代码。 内部运行下来，帮助我们处理了不少用户反馈，甚至有些用户建议让我们发现了新需求。 前端 widget 组件是开源的，支持自定义后端，后端接口设计也在仓库里。如果你不想自己写后端，欢迎使用我们的官网后台，支持 BYOK，个人使用是完全免费的，不限项目数。 AI writes the code. Users make it better. 欢迎反馈。

u/inc_23 · Reddit

Hey, I created a tool that catches when your LLM is confidently wrong, in production, in real time — looking for beta testers. Your bot sounds sure of itself even when it's wrong, and you usually only find out when a customer complains. Auscope audits every LLM response in the background: 3 models from 3 different providers independently check it, a 4th "chairman" model resolves disagreements, and you get one verdict — verified, uncertain, or unreliable. Runs async, doesn't slow your respon

u/Rabus · Reddit

I got TestingModels too overcomplicated over the month it is running: looking for some feedback how to make it more useful and simpler I run a benchmark like arena.ai , but with pre-generated prompts. So far, nearly 6k people came in and like 30k comparisons has been made - which means the thing is genuinely useful for people to compare the models. The problem is the more features i started adding the more overblown and complicated UI became - like old internet explorer tab bars Old: ht

@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

@mageofweb3 · X

@TheChrisMarrano · X