https://www.opensourceprojects.dev/post/1945170206852087918
GitHub Projects Community@GithubProjects
open on x ↗https://www.opensourceprojects.dev/post/1945170206852087918
@GithubProjects
▶@yescynfria
@ChShershLow-key websites I quietly rely on
1) http://roadmap.sh
Gives you a brutally clear learning path for roles like frontend, backend, DevOps, etc
No fluff, just “learn this → then this → then this”.
2) http://playcode.io
An online playground to quickly test HTML, CSS, JS without setting up anything locally
Perfect for quick experiments and debugging ideas
3) http://usehooks.com
A collection of reusable React hooks with real use cases
Saves time and helps you avoid rewriting the same logic again and again
4) http://devhints.io
Concise cheat sheets for languages, frameworks, and tools. Ideal when you forget syntax and don’t want to read a 20-minute blog
5) http://jsoncrack.com
Turns messy JSON into a clean visual tree
Makes understanding large APIs and configs way easier than staring at raw text
6) http://realtimecolors.com
Lets you generate and preview color palettes instantly
Useful when you want decent UI colors without guessing or copying blindly
7) http://regex101.com
Build, test, and debug regex step by step with explanations Honestly, the fastest way to stop hating regex
8) http://bundlephobia.com
Shows how big an npm package really is before you install it
Helps you avoid bloating your app with “tiny” libraries
9) http://caniuse.com
Tells you which CSS/JS features actually work across browsers Essential before using shiny new features in production
10) http://toolbox.googleapps.com
Google’s own diagnostics tools for DNS, email, headers, and network issues
Surprisingly useful for debugging real-world problems
👉 Which one of these do you already use and which one did you not know existed?@shekhu04
▶@coreyhainesco
@rohanpaul_aiI took the @karpathy autoresearch loop and pointed it at markets.
25 AI agents debate macro, rates, commodities, sectors, and single stocks daily. Every recommendation scored against real outcomes. Worst agent by rolling Sharpe gets its prompt rewritten by the system. Keep or revert. Same loop, prompts are the weights, Sharpe is the loss function.
Trained the agents on 18 months of market data. 378 iterations. 54 prompt modifications, 16 survived.
The system learned which agents to trust using Darwinian weights — geopolitical, commodities, and the @BillAckman quality compounder rose to the top.
The agents even figured out their own portfolio manager was the weakest link before we did!
Deployed the trained agents. +22% in 173 days. Best pick: AVGO at $152, held for +128%.
The final prompts are evolutionary products — shaped by market feedback, not human intuition. Now running live with my own capital.
https://github.com/chrisworsey55/atlas-gic
Part hedge fund, part research experiment :)@Chris_Worsey
@bertwitt≡ 12+13
@GithubProjects
@GithubProjects
@GithubProjects
@tom_doerr
▶@junkiyoshi
@geerlingguy
@tom_doerr
@GithubProjects
@GithubProjects
@backpinelabs
@ImSh4yyI've tried all ( 74 😵💫 ) AI Coding Agents & IDEs
[Rork, CodeRabbit, Anima, Zed, Factory, Cursor, Windsurf, Copilot, Lovable, Bolt, v0, Replit, MarsX, Canva, Devin, Github Spark, Vercel, Lindy, Warp, Figma, Cline, Vibe Coder & more]
The most complete list ever made (with demos & notes):@johnrushx
@tahayvr
@dr_cintas
@emilkowalski