
Agentagon - Find the failures customers already feel
Analyze production agent traces to identify and fix recurring failures.
@guru3s · X
IMF team for when your AI agent ( Ethan Hunt ) is about to fail v0 at
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Analyze production agent traces to identify and fix recurring failures.
@guru3s · X
IMF team for when your AI agent ( Ethan Hunt ) is about to fail v0 at

Detects failures in AI agent applications and opens eval-backed pull requests for you to review and merge.
pranny · HN
Selfship.ai – Surface and fix isues with your agentic applications 24x7

Analyzes AI prompts to identify failures and return corrected versions.
u/pulptaken · Reddit
I built a website to audit AI prompts I'm giving away a limited number of early access invites for anyone who wants to try Marzel. You'll also be able to try the product once directly from the landing page, without joining the early access. This initial phase is focused on validating the core features and collecting feedback. After that, Marzel will also include a browser extension and a desktop app for auditing Claude Code and Codex CLI sessions, which is the project's main goal. If yo

Trace, replay, and verify AI agent decisions with signed audit trails.
u/Funky_Chicken_22 · Reddit
OSS to SaaS positioning problem: when the user persona and the buyer persona are completely disjoint Founder here. Sharing a positioning problem I think a lot of OSS-to-SaaS founders hit and don't talk about publicly. Context: I have been running an OSS project (world-model-mcp) with ~2,500 monthly PyPI installs. Two weeks ago I opened up the hosted companion, Etch, at etch.systems. Launched publicly on Product Hunt at 12:00 PDT yesterday. The positioning problem: OSS user persona: in

Inspect and debug AI agents to identify step-by-step where they fail.
mathurpriya19 · HN
Kalytera – Find out why your AI agent failed, step-by-step

Autonomous agents that test web and mobile apps to discover flows, find bugs, and replay scenarios.
@AbdullahYusufY · X
Here is ours We are developing autonomous QA agents feel free to check it out.

Uses AI agents to audit repository architecture and verify production-readiness.
@profericardo24 · X
Alcatraz Projects is a forensic audit layer that uses OpenClaw agents to interrogate your repo, ensuring your architecture is production-ready, not just 'vibe-coded' debt.

A dashboard for traders to replay trades, analyze behavior patterns, and optimize their trading system.
@Crypto77qi · X
最近一直在@dappOS_com上体验 @xBubble_ai 的 Coding,这两天我给自己做了一个 AI Trading Replay(AI 交易复盘助手)。 👉 做交易时间越久,我越觉得,真正拉开差距的,并不是找到更多交易机会,而是不断减少重复犯同样的错误。 现在币圈已经有很多成熟的交易工具,行情、链上数据、资金流向、技术指标几乎都能找到对应的平台。但这些工具更多是在告诉我们市场发生了什么,却很少帮助我们分析,为什么面对同样的市场,每个人最终的交易结果却完全不同。 有时候并不是没有机会,而是因为犹豫错过了;有时候方向看对了,却因为害怕提前离场;还有时候连续几次亏损之后,下一笔交易开始变得犹豫,最终又错过真正的行情。 这些问题,并不是增加一个指标或者再多看一份数据就能解决。 所以我没有继续做一个新的行情工具,而是直接用 xBubble Coding 做了一个 AI 交易复盘助手。 它不会预测市场,也不会告诉我什么时候买卖,而是把每天的市场变化、自己的交易记录和决策过程整理下来,通过 AI 自动分析哪些判断是正确的,哪些错误一直在重复发生,并生成每天、每周的交易复盘,让整个交易过程形成一套可以不断优化的闭环。 整个开发过程也比我想象中轻松很多。 没有写代码,没有配置服务器,也没有部署环境,只需要把自己的需求描述清楚,一个可以直接访问的网站很快就搭建完成了。从原来的一个想法,到真正变成可以使用的产品,中间几乎没有任何开发门槛。 我觉得,这也是 xBubble Coding 最有价值的地方。 它并不是让大家重复开发一个市面上已经存在的软件,而是让每个人都能把自己的经验、工作习惯和思考方式快速变成真正属于自己的工具。 未来真正有价值的,或许不是拥有越来越多标准化的软件,而是每个人都能根据自己的需求,打造一个最懂自己的 AI 工作伙伴。

One tracker shared by people and coding agents with atomic claims and prerequisite blocking.
u/Scary-Philosopher-77 · Reddit
Linear on crack! I had the issue where when I do dev work in a group, it feels like that I am moving slower when I do work alone. I tried to use linear but I still had issues with task orchestration and having to manually manage tickets created more issues. Over the course of a few month I built SiftQ which is what I wished Linear to be. It solves the pain of: Feel like you'd genuinely move faster if you just worked solo. Get overwhelmed by 100s of tickets and don't know what is

An LLM agent that tracks goals and plans across sessions while showing exactly what it retrieves, verifies, and fails on.
u/OGMYT · Reddit
I built LOLM, a lower-cost LLM agent that shows what it actually did — looking for blunt feedback I’m one of the founders/builders behind LOLM. Most AI products show an answer but hide whether the system retrieved anything useful, verified the result, switched models, hit a limit, or simply stopped. LOLM exposes those parts through controller events and run receipts. It includes: - Live agent - CLI - Coding and small app-building workflows - Memory and self-hosting options - Control decis

Managed hosting platform for AI agents with persistent workspaces, memory, and scheduled backups.
@hijak01 · X
check out the demo

Build production AI agents with semantic, episodic, and procedural memory management.
@AiShivam · X
AgentStacKPro is an OS for AI agents designed to make them production-ready, and it holds incredible potential. You can also check out the project I currently developed