I was going through the insights on Claude's Leak - and one has been crossing my mind since the first chatGPT: "LLMs are trained on human data". I'm a biomedical engineer, and the latest tech findings are more than just interesting. Won't deviate from the the category of this post but I want to state -cloning programs- and everything in between are around the corner (yes, since YEARS ago), but now in a freaky way. We are able to clone cells, replicate human patterns, alter DNA/RNA [overall genetic modification] - we are a blink away of replicating thoughts. Anyway, if you haven't already - go through latest article from google. Google's Article LinkedIn - Repeat Repeat Repeat I took a REPO [see reference below] that granulized latest Claude's architecture and built a small brain logic for production code. My share -> Download & Use with Obsidian -> Let me know your thoughts. If you think is trash, well better said than not. Thanks! I used Claude to build me a brain for Claude. ##Prompt: 'How would you define the vault' ##Response [Claude Sonnet 4.6 Extended]: 'The codingWizard vault is a structured knowledge governance system built for an AI agent operating simultaneously as a distinguished software engineer, cybersecurity engineer, and chief product strategist. It lives as an Obsidian vault — meaning every file is a plain markdown note stored locally, but the vault is wired as a neural graph where 779 bidirectional wikilinks, 418 block anchors, and 11 Dataview queries make knowledge traversable rather than siloed. At the center sit two brain files: Claude.MD, which gets injected into context on every single query turn and governs the Wizard's identity, decision authority, and hard stops, and MEMORY.MD, a lightweight pointer index that tracks where everything lives without storing the data itself. Radiating outward are nine domain clusters — core identity, source architecture, code review, security, legal compliance, GMP standards, executive intelligence, research logs, and decision records — each with its own Map of Content that auto-populates via Dataview as new files are added. The vault gives the Wizard five layered capabilities when handed an organization's source code: reconnaissance and topology mapping, systematic three-pass bug hunting, a full ethical penetration testing engagement following PTES and OWASP, cybersecurity hardening and remediation, and an executive brief that translates technical findings into market risk, legal exposure, and competitive positioning — with every decision traceable back to a dated research log and an Architecture Decision Record.'

With the AMAZING support of you all in the last 24 hours upgrading to premium and VIP I decided to double down and drop some SERIOUS value for all of you for free as a giant thankyou and Proof I will work hard for you all. Yesterday Anthropic accidentally shipped the full source code for Claude Code inside an npm package. 512,000 lines of TypeScript. The entire CLI tool: every tool, every command, every system prompt, every unreleased feature flag. It was mirrored across GitHub within hours and Anthropic is now filing DMCA takedowns to pull it back. I spent the day going through it. Attached is a resource guide with every major repo, the best independent analysis posts, a table of the specific files worth reading, and a security warning you need to read before you touch any of it. Here is what matters for this community. ✨What it is: The source code for the Claude Code command line tool. The orchestration layer. How it manages conversations, picks tools, handles permissions, compresses context when the window fills up, and coordinates multiple agents working in parallel. This is production AI tooling at scale. ☄️What it is not: The Claude model. No weights, no training code, no API backend, no safety infrastructure. This is the client that talks to Claude, not Claude itself. If Claude Code is a car, we got the dashboard and transmission. Not the engine. 💯Why it matters for builders: 90% of this codebase is traditional software engineering. TypeScript, React, Zod validation, file I/O, error handling. The AI is maybe 10% by volume but most of the user-facing value. That ratio should sound familiar. It is the 60/30/10 in practice. The hard problems are not prompt engineering. They are context management, permission architecture, tool orchestration, and figuring out when to compress, when to truncate, and when to let the human decide. 🤫 Why I think it might be a marketing stunt: Every major feature that leaked (an always-on background agent called KAIROS, a tamagotchi pet system, 30-minute autonomous planning sessions, multi-agent coordinator mode) is now getting free press coverage across every tech outlet. These features are fully built and sitting behind compile flags. The "accident" required a specific change to the build config. And Anthropic was actively sending legal threats to protect this codebase ten days before it shipped to npm. Could be incompetence. Could be convenient. I will let you decide.

What are your thoughts on cross prompting? I use free GPT for cleaning my initial prompt. I inject the output to Copilot, and this is my Claude prompt.

Tell us three things: 1. What you do (job, industry, student, career-changer, whatever) 2. What brought you to Clief Notes 3. One thing you're trying to figure out right now related to computing or AI I'll respond to every single one. And read each other's intros too because the person who's stuck on the same problem as you might already be in this thread. I'll go first I am Jake, I have been working in tech for 15 Years, building with Generative AI for 3 Years straight now! Excited to teach and learn! That's it. Simple, scannable, gives you data on who's joining and what they need, and keeps the feed clear for content that retains people past week one.