教育学习1 week ago
Agentic AI Interview Prep and Python Practice | AgenticPrep
智能体AI面试的LeetCode式Python编程练习，涵盖ReAct循环、RAG和工具调用。
@notunknownkyun · X
- leetcode style interview prep for agentic AI interviews and unskilling folks.
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智能体AI面试的LeetCode式Python编程练习，涵盖ReAct循环、RAG和工具调用。
@notunknownkyun · X
- leetcode style interview prep for agentic AI interviews and unskilling folks.
跨14个维度分析Python代码，检测违规并提供详细报告。
@KSFirasa · X
Hello! I built a tool that profiles code (python only atm) across 14 dimensions detecting violations and capabilities outputting a full report. A bit more nuanced than "AI-powered insights". Free while in beta. Thank you!
在智能代理笔记本中构建、运行和评估机器学习工作流。
eldar_hsnv · HN
Show HN: AI Notebook for Data Science – Kind of Like Cursor but for Jupyter
与 AI 教练练习编程面试、系统设计和代码审查。
@preproomai · X
🟢Get ready for #vibecoding and AI #codereview. It is cutting-age & you can't miss: Time to ditch old-school @LeetCode ! #SoftwareEngineering #TechInterviews #CodingSkills #TechCareers #PrepRoom #TechCareers #SystemDesign #ITJobs #CareerGrowth
用 AI 驱动的模拟考试和闪卡学习 NEET 生物学。
@devjedi404 · X
I'm currently working on PrepPilot an AI-powered platform for NEET preparation. Added around 8k questions in total. The entire process was tiring but learnt a lot. For now you can access PrepPilot here :
比较 git、Jujutsu 和 GitButler 在 Claude Code 和 Codex 代理上的性能。
videlov · HN
I was interested in answering this question so I built a benchmark comparing git, jj and gitbutler in agentic context https://vcbench.dev/ Disclaimer - I am a co-founder of GitButler
检查 RAG 块并可视化 AI 代理工作流、内存架构和执行轨迹。
@Higgs0110 · X
免费在线生成器和决策工具，包含 Yes/No 生成器、随机选择器和转盘游戏。
@qt_nest · X
ChatGPT 克隆，支持语音、Markdown 和 Gemini API。
@SayamPadwe · X
Just shipped my ChatGPT Clone 🚀 Built with React, JavaScript, Gemini API, Speech Recognition, Markdown, and Vite. Live Demo 👇 GitHub 👇 #reactjs #javascript #webdev #buildinpublic
用Pyor原生应用审查GitHub的PR，无需打开github.com。
othmanosx · HN
I still don't like the fact that AI is adding more stuff for us to read, it's accelerating the code production but slowing down the code review. I built my own code reviewer as well ( https://pyor.review/ ), surfacing the important stuff first is the right track, but adding more stuff to read is daunting, but asking AI to just point you to what you need to focus on and skim the noise is what I'm leaning more towards.
