Other1 week ago
CanvasKeep: Draw it once. Find it forever.
A free diagramming tool with workspaces, folders, and version history. Sketch an idea now, come back to it next month.
@_aniruddha_d · X
The full gallery
Tech stack
60 projects
A free diagramming tool with workspaces, folders, and version history. Sketch an idea now, come back to it next month.
@_aniruddha_d · X
Compare how different AI models generate frontend code and view accessibility scores.
u/12qwww · Reddit
I built a live benchmark to see which AI actually writes the best frontend code Hey everyone! I built OpenVibeEval because I was tired of "vibe-checking" AI-generated frontend code. I wanted to know which model actually produces the most accessible and clean React/Tailwind output. What I built: •A leaderboard of 24 models (Claude, GPT, DeepSeek, etc.) ranked by axe-core accessibility scores. •A Harness Comparator to show how different system prompts change the same model's output. •
Interactive WebGL visualization of scientific citation networks and papers.
@lushptr · X
I feel really guilty for vibe coding this but it looks cool even tho it is really inefficient and broken you can use it too now I'll desloppify it after exams I swear
Analyze your hair health with AI to get a personalized care routine and product recommendations.
@CdricHans · X
Check out what I just built with Lovable!
AI-powered retrospectives for IT teams that connect to your tools and identify improvement patterns.
@akadhanu · X
an ai sprint retrospectives for it teams (
Open-source engineering onboarding built around a living graph of how your org actually works - services, contributors, and decisions inferred from commit history, surfaced as an o
@Syncyeep · X
Interactive archive of mathematical frameworks developed through AI-human collaboration with calculus verification.
@ErnestPresident · X
I know how to code Sun Yi so I vibe coded a multi layers math pipeline + multi layer verifier pipeline that do advanced calculus, I must do something wrong then.
Visualize AI coding-agent runs as interactive graphs from your transcript files.
u/Express-Phase1532 · Reddit
rungraph - see what your coding agent actually did, as an interactive graph (open source) My coding agent kept doing 20 minutes of work I couldn't inspect afterward. The transcript exists (Claude Code and Codex CLI write everything to disk), but nobody reads 4,000 lines of JSON. So I built rungraph, open source and free (MIT). npx rungraph turns every session on your machine into a clickable graph: your prompts run down the spine, subagents get their own lanes, and it flags stuff like ret
Inspect AI reasoning traces and evaluate model groundedness in your AI systems.
malik_dixon1 · Product Hunt
TraceLogicAI: AI Architecture Evaluation Compare AI architectures with evidence, not guesswork
Inspect RAG chunks and visualize AI agent workflows, memory architecture, and execution traces.
@Higgs0110 · X
AI agent that generates comparable product concepts from ideas, sketches, images, and constraints.
@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
Upload datasets to automatically find statistically significant correlations and causal relationships.
@matthew_meadows · X
Correlation Studio - Discovery Mining • Causation Analysis A powerful new SaaS statistics application that brings the insights of correlation data science to everyone. Data science without the code.