Web3Arabs

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Web3Arabs

Web3Arabs

@Web3Arabs

منصة مجانية تساعدك بالانتقال الى Web3 ⚡️ • تعلم واربح المال: https://t.co/gF8jWoISIT • إنضم إلى مجتمعنا: https://t.co/6MhMQqOKU9

Web3 เข้าร่วม Şubat 2022
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Web3Arabs
Web3Arabs@Web3Arabs·
رسمياً 🥳🥳 إطلاق منصة Web3Arabs لتعلم Web3 مجاناً باللغة العربية. يمكنك البدء الان البدء بتعلم اساسيات Blockchain وبناء تطبيقات DApps ومشاريع NFT بكل سهولة. هدفنا هو دمج مطوري Web3 حتى لا يُطلق على Web3 مجرد اسم Web3 بل كجزء قياسي من الويب. 👑 رابط الانضمام الى المنصة 👇
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Khairallah AL-Awady
Khairallah AL-Awady@eng_khairallah1·
Best GitHub repos for Claude code that will 10x your next project: 1. Everything Claude Code github.com/affaan-m/every… 2. Awesome Claude Code github.com/hesreallyhim/a… 3. n8n-MCP github.com/czlonkowski/n8… 4. Claude Mem github.com/thedotmack/cla… 5. Obsidian Skills github.com/kepano/obsidia… 6. LightRAG github.com/hkuds/lightrag 7. UI UX Pro Max github.com/nextlevelbuild… 8. GSD (Get Shit Done) github.com/gsd-build/get-… 9. Superpowers github.com/obra/superpowe…
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Khairallah AL-Awady
Khairallah AL-Awady@eng_khairallah1·
🚨 BREAKING: Someone just built an open-source LLM router that automatically picks the cheapest model for every request. It's called ClawRouter. Bookmark it for later. If you're defaulting everything to Claude Opus at $25/M tokens, you're burning money on requests that a $0.27/M model could handle perfectly. ClawRouter fixes that. Every request gets scored across 15 dimensions in under 1ms. Reasoning markers, code presence, complexity, token count. Then it routes to the cheapest model that can actually do the job. All routing runs 100% locally. Zero external API calls for routing decisions. Here's what that looks like in practice: → "What is 2+2?": DeepSeek $0.27/M (saved 99%) → "Summarize this article": GPT-4o-mini $0.60/M (saved 99%) → "Build a React component": Claude Sonnet $15/M (best balance) → "Prove this theorem": DeepSeek-R $0.42/M (reasoning) → "Run 50 parallel searches": Kimi K2.5 $2.40/M (agentic swarm) Blended average: $2.05/M tokens. Compare that to $25/M if you're sending everything to one model. 44+ models across OpenAI, Anthropic, Google, DeepSeek, xAI, MiniMax, and Moonshot. Four routing profiles: AUTO for balanced savings, ECO for max savings, PREMIUM for best quality, FREE for $0 inference via gpt-oss-120b. Here's what makes this completely different: It's built for AI agents, not humans. No accounts. No API keys. No credit cards. You generate a wallet, fund it with $5 USDC on Base or Solana, and pay per request. That's it. Agents can operate autonomously without a human pasting keys. 100% Opensource. MIT License. (Link in the comments)
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Khairallah AL-Awady
Khairallah AL-Awady@eng_khairallah1·
🚨 BREAKING: OpenAI just told the entire AI industry: we don't care about your degree. Build the best AI model under 16MB and we'll find you. 16 megabytes. That's smaller than one photo on your phone. It's called Parameter Golf. Bookmark it for later. Train the smartest language model you can. Weights + training code combined must stay under 16,000,000 bytes. You get 10 minutes on 8xH100 GPUs. Lowest bits-per-byte score wins. No resume. No interview. No PhD. No hiring pipeline. Your model IS your resume. OpenAI is backing it with $1,000,000 in free compute credits through RunPod so money isn't the barrier. Here's what makes this insane: → A public leaderboard on GitHub where anyone can submit → OpenAI's own baseline scored 1.2244 bpb. The community crushed it to 1.1233 in under a week. Still falling → 580+ pull requests. 2.2K forks. Leaderboard changes every few hours → Architectures nobody has ever tried before: depth recurrence, aggressive parameter tying, QAT, bitnets, novel tokenizers, test-time compute → Competitors stacking techniques, running ablations, and beating each other's scores within hours → A live AI commentary thread tracking every technique, trend, and idea in real time The barrier to entry is zero: → Apple Silicon users can train locally with the included MLX script → Cloud users get a pre-loaded RunPod template. One click to deploy → All submissions are open-source pull requests under MIT license OpenAI's chief research officer Mark Chen said this challenge is designed to test whether people can come up with creative ideas in a sandbox setting. Modeled after the spirit of math and programming Olympiads. In June, OpenAI plans to hire a small cohort of early-career researchers. Top performers get noticed by OpenAI researchers and recruiters directly. No application needed. AI labs spend millions recruiting through conferences and university pipelines. OpenAI just replaced all of that with a single GitHub repo. Challenge runs March 18 to April 30. Everything is public. 100% Open Source. MIT License. (Link in the comments)
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