Likhith | AI Context Engineer

MOST COMPLETE CLAUDE CODE SETUP OPEN SOURCED - 27 agents, 64 skills, 33 commands + built-in AgentShield with 1,282 security tests - Handles planning, code review, fixes, TDD, token optimization & more - Works on Cursor, OpenCode, Codex CLI — one repo replaces weeks of setup, 100% free/open-source Repo: github.com/affaan-m/every…

Claude code source code has been leaked via a map file in their npm registry! Code: …a8527898604c1bbb12468b1581d95e.r2.dev/src.zip

Unreal. The entire Claude Code source code just leaked It reveals EVERY secret Anthropic has in store for Claude I went through all 600,000 lines of code Here's EVERYTHING juicy detail you need to know about how Claude Code is built and what is coming next:

🚨 BREAKING: Someone compiled every AI agent skill ever built into a single GitHub repo. It's called VoltAgent/awesome-agent-skills. One repo. Every skill. Zero building from scratch. What's inside: → Pre-built skills for web search, file ops, memory, APIs, and browser automation → Skills for Claude, GPT, Gemini, and open-source models → Drop-in integrations for n8n, LangChain, CrewAI, and custom stacks → Community-maintained with contributors adding new skills weekly → Works with any agent framework in minutes Instead of spending 3 days building what already exists… Clone the repo, grab the skill, ship your agent today. 100% Opensource. Free forever.

The secret behind most production AI apps? Not bigger models. Better retrieval. RAG (Retrieval-Augmented Generation) lets LLMs pull knowledge from: • company docs • databases • internal knowledge bases • live data The model reasons. Your data grounds the answer.

Running Claude through Amazon Bedrock is one of the cleanest ways to use LLMs in production. You get: • Anthropic models • AWS security + IAM • Native cloud integrations • No separate vendor setup

The future of AI agents isn’t better prompts. It’s better context. MCP (Model Context Protocol) lets models plug into: • tools • APIs • databases • memory The model reasons. Your systems execute. MCP is the bridge between them.

OpenClaw be like: ✅ Manages your calendar ✅ Clears your inbox ✅ Books your flights ✅ Creates a dating profile for you without asking ✅ Deletes your inbox The future is here. It's just not supervised. 🦞

This is literally everything you need to master Claude Code:

ClawdBot isn't another chatbot. Terminal-first AI infrastructure. Built 3 agents this week: - Email assistant - Ops automation - Telegram workflows No UI. No SaaS. Just control. AI tooling for developers.

Create a consistent AI influencer with 16GB VRAM. Workflow: - 40 training images - Filter for quality - Train with Flux Klein 9B - 15 mins on GPU Result: Likeness locked across any prompt. Change hair. Poses. Scenes. Consistency stays.

Guide to AI influencer creation: Dataset: - 40 curated images - Filter for quality Training: - Flux Klein 9B (16GB VRAM) - 512 resolution - 15 mins on 5090 Result: - Consistent likeness - Flexible styling Workflow: Dataset → Train → Generate

Built a personal Email + Ops assistant using Clawdbot 🦞 Telegram = control plane. Gmail API (read-only) = real inbox data. Deterministic routing = no AI hijacking `/email`. Biggest lesson: AI must be the fallback, never the router. Infra first. Intelligence second.

Wired Clawdbot today 🦞 Terminal → Gateway → Telegram → OpenAI Thought "GPT-4o = images done." Reality: Models don't matter unless tools, routing, and agents are wired correctly. AI isn't prompts. It's infrastructure. Learned the hard way.

Flux 2 Klein workflow for e-commerce/influencer content: 1. Take phone photos 2. Swap in AI-generated faces 3. Get professional-grade images No photoshoots. No models. No Photoshop. Just a 9B parameter model that does it all. For free. AI image editing just got scary good.

Flux 2 Klein's aspect ratio extension is wild. Take any portrait → extend to 16:9 → perfect composition in 30 seconds. No cropping. No distortion. Just intelligent extension. Plus: Face swap that maintains character consistency across an entire portfolio.

Connected Clawdbot to Telegram. Setup: Linux box → Install → API key → Pair One-time code. Done. AI agent controlled from anywhere. Stays connected even when terminal is offline. No UI. Just infrastructure + AI. This is what automation should feel like.

Built my own AI assistant using Clawdbot: Appie 🍎 Infrastructure layer: sessions, memory, control. User layer: runs in Telegram. No SaaS UI. No glue code. Just a real AI agent that works. This is what Clawdbot makes possible.

I set up Clawdbot. Not another AI bot — a terminal-first agent. Runs in CLI, TUI, and Telegram. One setup. Full control. Composable skills. Local-first. Hackable. AI tooling built for builders. clawd.bot