Stanford CS146S's Fall 2026 syllabus compresses prompting from a full week into a single bullet, drops the terminal and UI-generation weeks, and adds Agent Skills, Agent-Ready Codebases, Background Agents, and AI-Native Team. Grading moved too: the final project fell from 80% to 50%, with 30% now on open source contributions. This series reads all ten weeks.
The final session is 'self-running, self-improving software systems.' The parts all appeared in the previous nine weeks: deterministic validation loops, skills that can be written back, background agents, centralized governance. One easily missed proportion from the slides — coding is 30% of engineering time, and running it in production is the other 70%.
Researchers initially assumed neural networks are easy to fool because they're nonlinear. That was wrong — Goodfellow's 2014 paper argues the primary cause is their linear nature, and high dimensionality lets every tiny perturbation compound. The second half covers generative models: GANs' three pathologies, and why diffusion sidesteps two of them by adding noise and learning to remove it.
A BCG experiment found a jagged frontier: inside it, AI substantially improved consultants' work; outside it, AI made results worse — and people fell asleep at the wheel. The lecture also takes a strong position: avoid fine-tuning wherever possible, because by the time you're done tuning, the next model already beats your fine-tuned version.
Andrew Ng demonstrates error analysis on a deep researcher: columns are the pipeline stages, rows are 10 to 100 queries, you only look at the ones that went badly, and you mark each cell where something broke. The percentages don't have to sum to 100%. He says it takes three or four hours and saves weeks of going the wrong direction — and the fraction of people who actually do it is far below 100%.
The third reason Go can't be learned with supervision is the interesting one: the ground truth itself is ill-defined — the strongest human doesn't play their best moves every day, and even their best move isn't optimal. The last 15 minutes or so map RLHF fully back onto RL: the agent is the model being fine-tuned, the action is the next token, an episode is one full generation, and the reward is extremely sparse.
Andrew Ng walks a face-recognition door system through the entire project lifecycle, and the whole lecture has one thesis: speed. He gives teams a two-day deadline, on the reasoning that 'time spent preparing data should be commensurate with the time it takes to train the model once.' It closes on a line: my job is to build something that actually works, and that is not the same as building something that works on the test set.
CS230's second lecture derives embeddings through three case studies: day/night classification teaches you to use humans as a proxy for choosing resolution, trigger-word detection teaches you to manufacture a million training examples in three hours, and face verification walks you through designing your first loss function. The final step — from supervised triplets to self-supervised pairs — is why modern models can consume billions of unlabeled images.
Ask a model what a goose looks like to it and it draws a whole flock — because the labeled data tagged a flock as 'goose,' so it thinks the flock is the label. This lecture gives seven ways to open a CNN up, then says honestly: applied to transformers, even the frontier of this research only explains two layers.
CS230's first lecture is a course overview, but Andrew Ng spends most of it on three things: why scaling works, when prompting stops being enough, and why he thinks 'don't learn to code' is one of the worst pieces of career advice ever given.
I tested 10 open-source PDF parsing tools on four scanned NTU graduate entrance exams. VLM-based tools—Firecrawl, MinerU 3.4, and Marker v2—overwhelmingly beat conventional OCR on formulas and code, but installation was the real barrier: MinerU's old package name creates dependency hell, Marker's first model download takes 10 minutes, and PaddleOCR needs a separate engine. In practice, use RapidOCR for screening and MinerU or Firecrawl for close inspection.
The 2026-08-13 release added 363,246 lines and published the For You ranking weights for the first time: favorite 0.5, reply 5.0, report −234.0. But the weights are constants — the P(action) that actually decides order comes from a 2560-dim, 8-layer transformer.
Chroma tested 18 frontier models and all of them degrade as input grows — as a cliff, not a slope. Memory failures are usually retrieval failures in disguise. And the real cost of KV cache is bandwidth, not storage: every generated token reads the whole cache.
In November 2025 three frontier labs jointly broke all 12 previously proposed prompt-injection defenses. EchoLeak's payload passed Microsoft's own dedicated classifier. So the goal is not blocking every attack — it is surviving the ones that land, and that is harness work.
The line between workflow and agent is who decides the steps — the developer at design time, or the model at run time. By that definition most LLM systems in production today are workflows. Plus a usable test for choosing between RAG and an agent.
Salesforce's number from 20,000 deployments: 90% of the work on an agent happens after launch, the reverse of traditional software. Stripe merges 1,300 PRs a week with no human-written code, and credits the environment rather than the model.
MCP governs agent-to-tool, A2A governs agent-to-agent, Skills govern reusable knowledge. The test is whether the data changes: if it changes between calls you need MCP; if it's stable enough to write down, a skill file is simpler and has no runtime that can fail on its own.
Microsoft, OpenAI, Salesforce, Stripe and three others independently say the same thing: reliability comes from the engineering around the model. And 'give the deterministic parts back to code' has been shipped as a product four separate times — Agent Script, Procedures, runtime, blueprints.
Standard RAG gives a wrong answer when it retrieves the wrong chunk, and nothing in the system will notice. Agentic RAG adds a self-check, at the cost of the evaluator paradox: the ceiling on self-correction is whatever the evaluating LLM can judge about relevance.
Most prep guides start at chapter one. This one works backwards: derive the exam date from each vendor's retake rules (AWS makes you wait 14 calendar days, Google escalates 14 → 60 → 365 days, Microsoft allows a retake after 24 hours, and Taiwan's iPAS has no retake at all — you wait six months for the next session), then order your study by domain weight rather than domain number, and take a free official practice set before studying anything. Includes weightings for seven certifications and a five-week AIF-C01 example.