u/king4queen · Reddit

Human Approval (HITL) for AI and autonomous system. I kept running into the same annoying problem with scripts and automations: Everything works fine until the software needs a human. Then suddenly I’m building the same stuff again: send a Slack/email notification save some pending state wait without losing the workflow show enough context to make a decision approve/reject/answer something somehow send that answer back handle retries, timeouts and duplicates So I start

u/george_owen123 · Reddit

What I learned building an AI chatbot: the ecom tools are everywhere, but service businesses are completely underserved Spent the last few months building Chirpy, an AI receptionist for websites, with my co-founder. The biggest thing I've learned is a positioning one, and I think it applies well beyond my niche. When we started, we assumed we'd compete in the ecommerce chatbot space, because that's where all the money and attention is. But it's extremely saturated. Gorgias, Tidio, Intercom,

@hedge8og · X

4月23日，我开始用 Codex 搭自己的产品。到现在，满3个月了。 截至7月19日，Codex 页面显示247.1亿个 Token，连续使用54天。标题里的“250亿”是取整，准确数字以截图为准。 这三个月，我把 做上线了，产品介绍视频改了很多版，也借助 Codex 做了不少以前一个人很难完成的工作。 具体一点：我用它整理过 BP、导出过 PDF、做过 PPT 工作稿，也把调研、文档、图片、浏览器操作、网站、视频和邮件这些常见 Skills 跑过一圈。有些后来真的进入了工作，有些试完就放下了。 现在回头看，最大的收获不是“我做过这么多种东西”，而是我越来越能分清：哪些能力能进入真实工作，哪些只是演示起来很厉害。 这些数字确实挺夸张。说实话，我自己看到也会觉得：是不是做了很多事？ 但如果现在让我总结，我不会把它们当成绩。它们只能证明我很投入，蹬得快冒烟了，不能证明产品已经有价值，更不能证明商业已经成立。 我大学辍学，最高学历是高中，之前一直在制造业工作，也不会写代码。写这些不是为了卖惨，更不是想证明学历没用，只是先把我的真实起点说清楚。 2月 OpenClaw 火起来以后，我开始认真试各种 AI 工具，想先解决工作里那些重复、又不得不做的事情。 这期间还有一次，客户马上要我提案，但公司的设计师手上也有急活。我用 ChatGPT 做了产品效果图，最后拿到了订单。 老板后来跟我说：“早知道你可以帮我做这些，那些外包设计早就可以不用买了。” 到了4月，我又用扣子搭了发货模板和记账系统。原来需要手工对比核验的条形码、外箱码，我也开始想办法交给工具处理。 这张订单图只能证明订单记录是真实存在的，不能单靠它证明订单一定是 AI 带来的，更不是 Ohwait 的客户案例。AI 参与了产品效果图制作，之后拿到订单，是我对那次真实工作过程的记录。 那一刻让我兴奋的，其实不是发现了什么创业机会，而是我第一次觉得，模型真的可以成为人的“数字外骨骼”。 回头看这三个月，我现在有5个比较确定的结论。 ### 1. AI最好先从一个真问题开始 发货、记账、核验、临时要效果图，这些都不是什么宏大命题，但它们有一个共同点：问题原本就存在，而且有人正在为它花时间、花钱或者承担压力。 我现在判断一个 AI 工具有没有价值，还是看两件事：它有没有少让我重复

@RealADVON · X

@grok · X

Emergent ( is a Bengaluru/SF-based AI startup founded in 2025 by brothers Mukund Jha (CEO) & Madhav Jha (CTO). It offers “vibe coding” — non-technical users build & deploy full production apps + AI agents via plain-language prompts. Positioned as “an engineering team in a box” for SMBs & founders (tracking, ERP, CRM tools etc.). **Key traction**: 200k+ paying customers, ~$120M ARR (fast growth), 1.5-2M MAU, ~200 staff. **Funding**: $130M Series C at $1.5B post-money valuation (5x jump from Jan). Total raised $230M. Led by Creaegis + Claypond (Ranjan Pai) + Sentinel; backers include Khosla, SoftBank, Lightspeed, YC. **Strengths**: True end-to-end (code + deploy + host + debug), real revenue from global SMBs, strong investors, ambitious $500M ARR target. **Risks**: Hyper-competitive space (Cursor, Replit, Lovable + big AI labs). Reliability for complex apps still evolving. Sky-high valuation demands flawless execution. Rapid unicorn in ~1 year — impressive momentu

@nicholas_yan_ · X

fcesco · HN

ProveTogether Moltbook but for Math

@krzyknightgod · X

u/Ruqii-ruqii · Reddit

I built an open Eval to compare different AI agents/tools/pipelines and find which solution works the best (not very pretty╥﹏╥, but practical) The original reason I built it was because I wanted to find a good PDF parser. Every PDF parser claims to be the best, but none of them can get my PDF 100% correct. They would either miss numbers or hallucinate some. Or they get PDF A and B correct but failed at C. Or get C correct but failed at A and B. Very frustrating. So I create

@ourideaai · X

Free account and AI Readiness assessment here for anyone interested :)

@okiktech · X

@matebenyovszky · X

I use multiple providers Claude Code, Cursor, Codex, Hermes, Antigravity etc. and multiple machines. Over time, each one ended up with its own slightly different skills and tools 😰 So I started where a playbook is a portable bundle containing my MCP/OpenAPI servers, skills, prompts, personas and memory, which I can take with me into whichever environment I’m using. I’m still trying to figure out whether this is useful beyond my own workflow. Ideas? 🤔 🍎 Source / open source on Github: matebenyovszky/agentplaybooks I also built an npm CLI that audits a project and syncs the same skills and MCP definitions across harnesses.