生产力2 weeks ago
File Link AI — Never lose a project file in your inbox again, privacy focused AI
File Link AI：用AI整理Gmail中的项目文件和链接。
@Foton689267 · X
Privacy focused AI for gmail
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File Link AI：用AI整理Gmail中的项目文件和链接。
@Foton689267 · X
Privacy focused AI for gmail
Novix 从代码问题自动生成并测试拉取请求，集成您的构建工具。
@jakemoshel · X
Turn issues into sandbox tested PR’s from your current stack
Hermy helps creators find brand contacts, manage outreach, write personalized pitches, automate follow-ups, and track brand relationships.
@brythebull · X
Lovable Generated Project
@DavidMaesela · X
Check out what I just built with Lovable!
上传文档到私人库，提问文件并设置提醒。
u/Abject_Nature8678 · Reddit
Spent 6 months building an app that reads your contracts — discovered I'd been paying for 3 subscriptions I'd completely forgotten about The original problem was simple: I'd missed a lease auto-renewal and got stuck for another year. I wanted something that reads your documents and tells you what to watch for. Six months later I had Goffer (goffer.ai). But the real test was dogfooding it — loading all my own documents to see what the app found. It flagged two subscription auto-renewals I'
根据AI代币获取应用开发成本估计和可视化构建计划。
u/Ejboustany · Reddit
Knowing your build cost from a tokens formula The bigger the feature you are building, the more tokens you spend and how you can calculate the total cost of your build. You will also spend even more tokens making that feature proper and production ready. Say you want users to sign up, log in, verify their email and reset a forgotten password. Built properly it runs around 400,000 tokens. The formula I thought of is: tokens x $1,500 / 1,000,000 = price So those 400,000 tokens come ou
interQ：发现和分享真实面试题目的社区平台
@Paramjit_saikia · X
🚀 Just launched interQ! A community-driven platform to discover and share real interview questions from top companies. ✨ Real interview experiences 📚 Bookmark questions 🔍 Search by company & role Try it👇 Feedback is welcome! 🚀 #buildinpublic #web
向多个前沿大模型提问，获得经过同行评审的综合答案。
u/Puzzleheaded-Log-27 · Reddit
Building a multi-model AI deliberation tool taught me something about trust LLM Counsel isn't another wrapper around one model - it sends your question to a panel of frontier LLMs, has them peer-review each other anonymously, and an impartial "chairman" model returns one synthesized answer. Free to start, pay-as-you-go after, credits don't expire. What I've learned so far: people trust a synthesized answer a lot more once they can see that the models actually disagreed and how that disagree
提交你的项目以获得创始人和测试者的结构化反馈。
@martin_sllz · X
New founder just shipped a project on Small number, but every one is a real person who chose to get honest feedback instead of "looks great". Building this one roast at a time. #buildinpublic
Ravel：为开发者提供AI驱动的项目管理，支持GitHub跟踪和冲刺规划。
@Gallagh819 · X
用英文描述想法，QuikRun自动生成代码并提供实时URL。
nparashar150 · HN
Over the weekends, I've been revamping an old side project of mine: https://quik.run/?ref=yc It started as a simple platform for writing and sharing code snippets. Over time, it evolved into a way to run snippets instantly with support for imports and packages, without worrying about setup, deployment, or infrastructure. Think of it as a lightweight AWS Lambda for quick experiments. One thing I've realized is that even though AI has made a lot of older developer tools and ideas feel less relevan
