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This 277-page PDF unlocks the secrets of Large Language Models. Here's what's inside: 🧵

Google's new algorithm just shrunk 31GB of memory down to 4GB 🤯 TurboVec is a new open-source tool that stores the data your AI app searches through, using 16x less memory. It runs on Google's TurboQuant, which skips the slow setup step every other tool needs. → Faster search than the popular alternative (FAISS) → Works on both Mac and standard servers → Narrow results to exactly what you want → Plugs straight into LangChain and LlamaIndex Your data never leaves your machine. Runs fully offline, works with Python out of the box. 100% Open Source.

As an AI Engineer. Please learn >Harness engineering, not just prompt engineering >Context engineering, not just long prompts >Prompt caching vs. semantic caching tradeoffs >KV cache management, eviction, reuse, and memory pressure at scale >Prefill vs. decode latency and why they optimize differently >Continuous batching, paged attention, and throughput optimization >Speculative decoding vs. quantization vs. distillation tradeoffs >INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality >Structured output failures, schema validation, repair loops, and fallback chains >Function calling reliability, tool contracts, argument validation, and idempotency >Agent guardrails, loop budgets, tool budgets, and termination conditions >Model routing, graceful fallback logic, and degraded-mode UX >RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness >Retrieval evals: recall, precision, grounding, attribution, and citation quality >Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals >LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift >Cost attribution per feature, workflow, tenant, and user journey not just per model >Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries >Multi-tenant isolation, cache safety, and cross-user context contamination prevention >Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool >Latency, quality, cost, and reliability tradeoffs across the full inference stack >Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions

GPT-5.5 + Claude Sonnet 4.6. Free $20 trial. No card required. Zed just hands it to you. Most AI IDEs run on Electron. Zed is built in Rust - rendering goes directly through Metal, DirectX, Vulkan. No Node runtime. No Chromium overhead. The result: responses that feel instant compared to Cursor or VS Code. >AI is built into the core of the editor, not added as an extension. That's the difference between native and bolted-on. Here's the setup: → zed(.)dev - sign in via GitHub (account needs 1+ month of activity) → download the IDE → click Start Trial → $20 in credits lands instantly, no card, no form GPT-5.5 and Claude Sonnet 4.6 available immediately. Zed also connects to Gemini, Ollama locally, and any provider via Open CDP. One more thing: real-time multiplayer collaboration built in natively. Like Google Docs but inside your IDE. No extensions, no plugins. $20 free. Fastest AI IDE available right now. One GitHub login.

$1,000 in free GPT credits. Official OpenAI form. Takes 3 minutes to fill. People are sleeping on this. OpenAI opened a promo form for Codex Enterprise credits. My contacts applied and received $1,000 in API tokens after one week. The form is still live. Here's the exact flow: → go to openai(.)com/form/codex-enterprise-promo → enter the email tied to your OpenAI account → fill out all fields - explain what you're building and why you need the credits → wait one week Write something real. The more specific your use case, the better the chances. Generic answers get ignored. $1,000 in API credits. Official OpenAI program. One week wait. Apply today while the form is open.

I just recorded a deep dive into Pi's architecture. It's a minimalist AI coding agent with a beautiful design. Here's how it actually works 👇

If you're trying to learn about databases, distributed systems, etc, it can be really helpful to discuss papers with other people I've been attending a systems reading group for the last few months, and I've found that discussing a paper with others hammers it into my brain way more than reading it alone If you're into this kind of stuff, I'd definitely recommend finding (or starting) a small reading group (also this article is how I discovered the systems reading group at Microsoft)

INSTEAD OF opening Obsidian just to dump more notes tonight. Spend 30 minutes studying these workflows. 12,900+ GitHub stars in under three months. Free. The ones who connect plugins, workflows, and Claude will turn their vault into working memory.

Be a world-class BACKEND ENGINEER once you complete watching all these courses:

LeetCode but for Linux, Docker, Kubernetes, and Networking problems? 🤔 245 hands-on challenges (over half are free) based on real-world scenarios that come with: - Preconfigured environments - Automated verification - Hints and editorial solutions labs.iximiuz.com/challenges