The essence of intelligence is to acquire information, synthesize it and apply the version that best suits your current situation with the aim of having optimum result. Copy and paste is a very poor use of brain, if any.
THERE IS A FREE MIT LECTURE THAT TEACHES AGENTIC CODING BETTER THAN MOST PAID COURSES.
Missing Semester. Lecture 7. Agentic Coding.
Coding agents are not autocomplete.
They are conversational models with real access, reading and writing files, running shell commands, searching the web.
The lecture covers parallel agents, running multiple copies on the same task simultaneously and keeping their changes from colliding using git worktrees.
It covers MCP, the protocol that lets your coding agent read and write directly to tools like Notion, turning "draft an implementation plan" into something the agent actually executes instead of just describing.
Completely free. From MIT.
Bookmark this. Follow @cyrilXBT
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
1,000,000 PEOPLE BUILD QUANT BOTS THAT CANT BEAT RANDOM
MIT proved why in one theorem.
If your strategy is a fair game, no exit rule wins.
The edge has to come from real signal - not the loop.
Bookmark this before you deploy.
Anthropic engineer:
"At Anthropic, 90% of our engineers use loops and ‘dreaming’ to build self-improving agentic systems.
сlose the agent loop. give an agent a way to verify its own output."
in a 30-minute session, an Anthropic team member explains how to build an agent that improves itself.
Claude + loops + dreaming + CLAUDE.md - that’s the secret.
Watch the talk, then save the playbook below.
A senior Google engineer dropped a 424-page doc on agentic design patterns.
424 pages.
Most engineers bookmarked it and never opened it again.
I read the whole thing.
Here are the 15 patterns that actually matter — explained in plain English, with exactly when to use each one ↓
Met a guy making $1.6 million a year.
Three days ago he was at a Meta conference. Told me he saw the best AI talk of his life.
Boris Cherny was on stage. Showed how the Anthropic team actually uses Claude day to day.
Boris deleted his IDE eight months ago. Now he codes from his phone.
I watched it last night. Had to pause it twice.
Not because it was hard. Because I realized I've been using Claude like a toy.
He sent me the recording. It was never published.
Posting it below.
Anthropic engineers just showed how they build a full app from scratch, using a loop of agents
40 minutes from the team behind Claude Code
they used three agents: one to plan, one to build, one to judge, cycling until the app actually works
the winners won't have the smartest model, they'll have the best loop
watch it, then read the full guide on how to actually use loops below
Google CEO, Sundar Pichai:
"If you don't learn to how to orchestrate agents now, you'll spend 2027 catching up to people who started today"
In 30 minutes he explains why the best engineers stopped writing code and started running agents.
Watch the interview, then save the exact setup below 👇
This 45 minute Stanford lecture will teach you more about building companies than every startup book combined.
Bookmark & give it 45 minutes today, no matter what.