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 👇

@eng_khairallah1 🫶

Save this post before you start your next AI SaaS product. > Find a problem worth solving > Build the MVP > Design and user experience > Launch and get your first customers You just need to get started.

@eng_khairallah1 🫡

@eng_khairallah1 🫶

LOCK TF IN Life after using X to generate $100M+ in revenue: Bookmark this article and read through it

@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)

LOCK TF IN Life after helping my client become a millionaire with Claude Code: Bookmark it and read the whole thing

@eng_khairallah1 👏

@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)

@eng_khairallah1 👀

LOCK TF IN Life after replacing hours of manual work with 300 powerful Claude prompts: Bookmark this article and read through it

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LOCK TF IN Life after understanding the anatomy of the .claude/ folder: Bookmark it and read the whole thing

@eng_khairallah1 🫡

🚨 BREAKING: Someone just open-sourced your own personal intelligence terminal. It's called Crucix. Bookmark it for later. It watches 27 live data feeds across the entire planet and alerts you when something changes. Governments and intelligence firms pay six figures for this kind of setup. This runs on your laptop for free. No cloud. No subscriptions. No telemetry. Just a single Node.js server and a Jarvis-style dashboard. What it watches every 15 minutes: → ACLED for armed conflict events worldwide → Federal Reserve economic indicators via FRED → OFAC + OpenSanctions for sanctions screening → 17 curated Telegram intelligence channels for social sentiment → Maritime AIS for vessel tracking and sanctions evasion detection → GDELT for global event tracking across 100+ languages → WHO for disease outbreak alerts → ReliefWeb for UN humanitarian data → CelesTrak for satellite constellation tracking: ISS, Starlink, OneWeb → VIX and credit spread risk gauges Every sweep computes what changed, what escalated, and what de-escalated since the last cycle. Hook up an LLM (Claude, GPT, Gemini), and it generates trade ideas based on cross-domain signal correlation. Without an LLM, a deterministic rule engine still evaluates everything. 18+ sources work with zero API keys. No setup friction. 100% Open Source. MIT License. (Link in the comments)

@eng_khairallah1 🫶

🚨 BREAKING: Composio just open-sourced the coordination layer that turns AI coding agents from a toy into a production system. It's called Agent Orchestrator. Bookmark it for later. Running one AI agent in your terminal is easy. Running 30 of them across different issues, branches, and PRs at the same time is a coordination nightmare. Without this, you're manually creating branches, babysitting agents, checking if they're stuck, reading CI logs, forwarding review comments, and tracking which PRs are ready to merge. Agent Orchestrator handles all of it. What it actually does: → Spawns parallel Claude Code, Codex, or Aider agents on any issue → Every agent gets its own isolated git worktree, its own branch, its own PR → CI fails? The orchestrator sends the logs back to the agent. → Agent stuck or needs human judgment? Only then it notifies you → Real-time dashboard at localhost:3000 to monitor every session → 8 plugin slots: swap any agent, runtime, tracker, or notification channel → Works with GitHub and Linear out of the box → 3,288 test cases. Production-ready That agent gets worktree isolation, CI feedback routing, review comment handling, and status tracking. All automatic. Here's the wildest part: Agent Orchestrator was built by 30 agents running Agent Orchestrator. The tool orchestrated its own construction. Every commit has a Co-Authored-By trailer showing which AI model wrote it. 100% Open Source. MIT License. Built by Composio. (Link in comments)