ximihoque · HN

Collaborative context-sharing memory platform for agents and teams

@otakuaakash · X

@francis7_a · X

Building - learning tools for devs and student. happy to connect.

@codalioinc · X

The vibe-coding loop: "fix it" × 7, 512K tokens, still not shipped. The Codalio loop: PRD → build → deploy. Need a change? Edit the PRD. +1,900 tokens. Stop paying tokens to discover your own requirements.

u/surmado_rachel · Reddit

We built a cheaper code review tool for small teams We've been working on a code review tool for small teams, solo founders, and vibe coders who want a second set of eyes on every PR without paying Claude-level prices. The idea is simple: Surmado's Scout reviews your PRs against your own standards, not random generic lint rules. You add a STANDARDS.MD file, and on every push Scout gives you: What looks good/What needs work/A short reviewer brief so a human knows where to focus/Feedback

@hyraxai · X

u/Hopeful-Can-1540 · Reddit

Trying to figure out distribution for a real-time collaboration tool LiveShareIt has been around for a bit, and the product has changed quite a lot recently. The basic idea is pretty simple: open a room, share the link, and start working together. No signup needed. People can share code/text, send files, use a collaborative whiteboard, and jump on a video call in the same room. One feature I’ve been playing with lately is the AI whiteboard. Instead of starting from a blank canvas, you

@prabhjeet1693 · X

The real challenge with vibe coding isn’t generating code—it’s staying able to understand the system as it evolves. Making architecture, dependencies, and workflows visible can help fight code slop. That’s what we’re exploring with JigsawML. Free demo:

@vibexp_io · X

Your plan is now code. Claude Code Dynamic Workflows fan out up to 1,000 subagents, 16 at once, each in its own context, verifying until the results converge. Built for big bug hunts, migrations and audits. Source:

@NodeESP · X

- Project for sale

g418572664 · V2EX

做了一个文档解析与记忆工具，专门辅助给传统行业做 AI 落地的老哥 现在 AI+的工作还挺常见的，就像大佬们说的，“所有行业的产品可能都会用 AI 重新做一遍”。最常见的就是各种 agent ，说要用 AI 赋能传统行业啥的，代替人类专家去处理海量的复杂资料、进行深度分析并做出决策。 举个例子，金融行业的“智能审计与尽调 Agent”。 过去，银行或投资机构想要给一家企业贷款或投资，需要人类审计师去读几十份、每份几百页的招股书和财务报表。现在虽然有了 AI ，但把文件一股脑全丢给它是不现实的，且不说烧 token 的问题，这些文档里有无数的跨行、跨列单元格表格，普通工具一拉，表格数据全串行了。如果 AI 把“第一季度利润”和“第二季度支出”的信息碎在一块，那得出的财务分析就完蛋了。 所以，现在要真想开发出一个能干活，还确保正确率的 agent ，就需要一个专业的、AI-native 的解析工具，把复杂的表结构和章节层级完整还原出来。我做的工具 Knowhere 就是干这个的： https://knowhereto.ai/?utm_source=v2ex 它能把复