教育学习20 hours ago
StatsLab — Interactive Statistics Playground
探索数据集、计算统计数据、可视化分布和进行推断检验。
@DaytonaRaised · X
Check out what I just built with Lovable!
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60 projects
探索数据集、计算统计数据、可视化分布和进行推断检验。
@DaytonaRaised · X
Check out what I just built with Lovable!
查看Kalshi和Polymarket预测市场的ML预测和性能指标。
@precisionalgo · X
支持MCP客户端集成的AI原生网站分析工具，Google Analytics替代品。
@cdd56656 · X
which helps you know your website user traffic inside your agent
将 basketball box scores 转换为专业球探报告的自动化工具。
@statlyanalytics · X
Turn basketball box scores into insights that win games.
在OpenVibeEval中对比不同AI模型生成前端代码和可访问性评分。
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. •
Upload datasets, configure preprocessing, train and compare machine learning models, test predictions, and export artifacts without writing Python.
@diksaalab · X
查看和对比主流AI模型的公众意见和基准评分。
u/TasteMysterious5285 · Reddit
I built AI Census, a live field bulletin for how people are actually talking about AI models I’ve been building AI Census, a public “field bulletin” for how people are talking about current AI models. I kept running into the same problem: benchmark tables tell me how a model performs on a test, but not whether people are actually finding it useful, frustrating, reliable, etc. So I built a rolling view from public technical conversations across Reddit, Hacker News, Bluesky, GitHub, and Huggi
Interactive machine learning concepts — edit the input data, read the formula with your own numbers substituted into it, and step the algorithm one iteration at a time.
@Lovepre23243941 · X
Most ML explainers give you a diagram. I built one where you edit the data and the formula rewrites itself with your numbers. 42 concepts — k-means, backprop, HMMs, spectral clustering. no signup ,
用机器学习热力图分析用户对设计的注意力预测。
u/dimabreezy · Reddit
Hey, I built a tech that predicts human attention (it's Machine Learning + Data project). I've being using it for the past 2 months and it gives amazing results to AI agents I'm a software engineer and I also love good visuals. And I hate when AI build UI but it doesn't understand what should be GRABBING the attention, so I've build a tech that solves that https://attentionproof.com/ Here you can sign in with the ChatGPT account and get free 2 tries (I got limited compute) so please g
查看体育比赛胜率、表现评分和投注价值，用百分比简洁呈现。
@Domapfl · X
Check out what I just built with Lovable!
创建民意调查、问卷、表单和直播活动，获取AI验证和声誉评分。
@Eli_Greenfeld · X
用0-100 AGI分数对标前沿AI模型的基准性能。
baraklaniado · HN
I audited my AI leaderboard scale – every score dropped 6-15 points