Video Generation: Modeling the Time Axis | CMU 10-423 Lecture 25
Video Generation: Modeling the Time Axis | CMU 10-423 Lecture 25
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World Models and Alignment Theory: Action Interventions and Corrigibility Assumptions | CMU 10-423 Lecture 26
Separate three world representations and evidence of action control, then examine consensus costs and corrigibility theorem conditions.
LoRA: What Changes When Most Weights Stay Frozen | CMU 10-423 Lecture 10
A complete worked example and answered variations for LoRA: What Changes When Most Weights Stay Frozen.
Practice Exam: Diagnose Conceptual Gaps | CMU 10-423 Topic Guide
Use the public practice exam to diagnose assumptions, data flow, and evaluation errors through a worked probability example.
Open-Ended Evolution: A Reading Guide | Stanford CS329A Lecture 7
Open-ended evolution changes agent code and workflows; archives preserve branches, while held-out tests assess generalization.
Software Engineering Agents: A Reading Guide | Stanford CS329A Lecture 13
Program candidates need requirement-based counterexamples; speed matters only after correctness checks pass.
Agent Memory: A Reading Guide | Stanford CS329A Lecture 14
Retrieval, trained KV representations, and cache reuse address different bottlenecks; speed does not guarantee freshness.
Spotify Technology: Internal Developer Tools, Now Packaged as a Brand for Other Companies
On 2026-10-07 Spotify grouped Confidence, Portal, Xirp and its Backstage plugins under one brand, Spotify Technology. Its bet: once AI makes code cheap, judgment becomes the bottleneck and shared organizational context becomes the asset. Revenue is undisclosed; Confidence has public pricing and a free plan; Xirp is a macOS-only beta.
Test-Time Compute: Finding the Right Answer Among More Attempts | Stanford CS329A Lecture 2
More candidates increase the chance of a correct solution; credible selection turns that chance into delivered success.
Robust Verification: Checking Steps Before Trusting an Answer | Stanford CS329A Lecture 3
A correct final answer does not validate its derivation; step feedback locates errors, but verifiers still need checking.
Tool and Code Feedback: Changing the Next Action | Stanford CS329A Lecture 4
Observations, tests, and principle-based critique supply different signals; identify the signal before choosing revision or training.
Multi-Step Planning: Decompose the Step That Fails | Stanford CS329A Lecture 5
Distinguish search, parallel execution, and training; subtask artifacts must fulfill the original task criterion.
Train-Time Improvement: Turning Feedback into Learning | Stanford CS329A Lecture 6
Useful training feedback must distinguish candidates; filtering, relative advantages, and sampling determine the learning signal.
Search and Deep Research: Selecting a Deliverable Answer | Stanford CS329A Lecture 8
Candidate search selects solutions; retrieval fills knowledge gaps. Both require specifications and checkable evidence.
Long-Horizon Evaluation: Completion, Correctness, and Deliverability | Stanford CS329A Lecture 17
Human task duration, artifact quality, and citation support measure different things; evaluate both execution and deliverables.
Future Research: Turn Improvement Bottlenecks into Testable Hypotheses | Stanford CS329A Lecture 20
Diversity, verification, task selection, and efficiency are distinct bottlenecks; experiments need holdouts and falsifiable hypotheses.
Reinforcement Learning Systems: Connecting Inference and Training | CMU 11-768 Lecture 12
An RL system samples with an inference engine, assigns rewards through verification, recomputes logprobs and gradients in a trainer, then synchronizes new weights back to inference. Walk through these five stages in an invented example before examining caching, preserved histories, and asynchronous updates.
Agent Safety — Four Threat Models and Three Limits: Sandboxes, Credential Brokering, Monitoring | CMU 11-768 Lecture 13
Lecture 13 is about limiting what agents can access and do. The slides open with five real incidents, sort them into four threat models (malicious user, prompt injection, agent error, improper means), then give four layers of defense: red teaming and safety training, sandboxes and credential brokering, monitoring, and safety evaluation. The easiest point to miss: hiding the API key does not stop an agent from misusing the access behind it.
OpenHands — A Coding-Agent Framework That Went from Research Prototype to Thousands of Automated Runs | CMU 11-768 Lecture 14
Graham Neubig uses OpenHands, which he leads, to explain framework design: how coding agents evolved from HumanEval, SWE-bench and Devin to 2026's long-running, interruptible, parallel work; the parts of the Software Agent SDK (LLM, tools, conversation, condenser, workspace, confirmation policy); the single- vs. multi-agent trade-off; local vs. cloud and four interfaces (IDE, GUI, CLI, integrations); automations wired to issues and PRs; and revising skills from past trajectories.
After AI lowers the skill barrier: understanding agency and practicing how to talk about taking action
Agency means believing you can change things and taking action. Read the guide, practice three continuous clips with original audio and source-player English captions, and describe a change you could make at work.