生产力1 month ago
Make every decision retrievable. | Decision Log
为人类和AI代理记录和检索决策，追踪完整来源。
@burn2delete · X
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14 projects
为人类和AI代理记录和检索决策，追踪完整来源。
@burn2delete · X
用生产追踪镜像来测试AI代理，捕捉错误和性能回归。
aisinghal
CoBro 用 AI 扫描竞争对手和市场数据，90 秒内判断初创企业创意是否值得构建。
@Ebrahim_Rio · X
Most founders skip validation and pray. I automated the "worth building?" check. AI scans competitors, Reddit, and market data → Cook or Kill in 90 seconds. Killed? It surfaces the pivot the data actually backs.
Deciding is the part you can’t delegate. Capture from anywhere, including inside your assistant, and keep one list on web, Windows, Mac, and iPhone.
u/Mean-Papaya3532 · Reddit
Done Bear - a local-first task manager with MCP, an API and a CLI Done Bear is a task manager with the five GTD lists, Inbox through Someday, and not much else. https://donebear.com Your AI assistant can use it. A hosted MCP server lets Claude or ChatGPT read your tasks, add them and tick them off, with your permission and nothing running locally. There's a GraphQL API and a CLI too, if you'd rather script it. It runs on the web, Mac, Windows, Linux and iPhone. Tasks live on your device
IMO是一款AI购物助手，分析10000+评价和专家意见，帮助您做出明智购买决策。
@saurabh_codes13 · X
发布任务来评估不同的AI代理和工具，用排行榜找出最佳方案。
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
从你的文档和Notion学习的AI客服代理，自动处理客户支持。
@vela_cx · X
That Was AI：自动接听并解决来电的智能代理。
@akshaysolenk · X
Just launched. Building in public.
Perch AI 是为法律、财务、研究和编码工作的 AI 助手，显示所有来源。
@Laharmckenzie · X
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
用多个模型实时审计AI回应以判断其可靠性。
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
