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.
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1264 postsTrain-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.
pplx-embed-v2-late: An Open Late-Interaction Model That Searches PDF Pages Without OCR
On October 7, Perplexity open-sourced two ColBERT-style multimodal embedding models (0.6B and 9B, MIT license) that keep one 128-dim vector per token, score with MaxSim, and search PDF page images directly. The 9B scores 92.4% on MADQA (self-reported, with a Gemini 3.5 Flash agent); the cost is an index that grows with document length.
AWS Machine Learning Engineer Associate (MLA-C02): 45 of Its 107 Skills Are New
MLA-C02 replaced the English MLA-C01 on September 29, 2026. The four domains weigh 28 / 24 / 24 / 24, and each domain name now carries AI or FM: the official comparison lists 45 added skills covering vector databases, embeddings, RAG, selecting and fine-tuning foundation models, and deploying and monitoring agents, with 7 removed. It is currently an English-only beta (exam code ME1-C02, 170 minutes, 85 questions, $75). The standard version per the exam guide is 65 questions, pass at 720, valid 3 years, with Japanese, Korean, and Simplified Chinese arriving at general availability; there is no Traditional Chinese version.
GitHub Copilot Certification (GH-300): Beyond Autocomplete to the CLI, Agent Mode, and Organization Policy
GH-300 is the official GitHub Copilot certification. After the August 7, 2026 revision the outline has six areas, the heaviest being 'Use GitHub Copilot features' at 25–30%, which covers the Copilot CLI, agent mode, MCP, sub-agent delegation, and organization-level policy, audit logs, and the REST API. Official specs: $99 in the US, $50 in Taiwan, 100 minutes, 60 scored questions, pass at 700, valid 2 years, five languages with no Chinese, and an official practice assessment. The official weight list has one extra line; prepare from the six real sections.
GitHub Agentic AI Developer (GH-600): It Tests How You Run Coding Agents, Not How You Write Them
GH-600 is GitHub's agent certification (listed as Intermediate), covering how to operate, supervise, and govern AI agents inside a software development workflow. The six areas weigh 15–20 / 20–25 / 10–15 / 15–20 / 15–20 / 10–15, the heaviest being tools and environment (MCP, permissions, CI invocation, error handling). The most common verb in the objectives is configure (15 of 65), followed by identify and implement. Official specs: $165 in the US, $83 in Taiwan, 120 minutes, pass at 700, valid 2 years, English only, no official practice assessment, and under 6 hours of official self-paced material.
Microsoft AI-200 (Azure AI Cloud Developer): AI Is in the Name, but It Tests the Backend That Keeps AI Apps Running
AI-200 (Azure AI Cloud Developer Associate) succeeds AZ-204, retired July 31, 2026. Its four areas weigh 20–25 / 25–30 / 20–25 / 20–25: containers, data services, messaging and serverless, security and monitoring. Nothing in the objectives covers model deployment, prompts, or agents; the closest it gets to AI is vector search on Cosmos DB, PostgreSQL pgvector, and Managed Redis. Official specs: $165 in the US, $83 in Taiwan, 120 minutes, pass at 700, 13 languages including Traditional Chinese, one-year validity, and no practice assessment yet.
Microsoft AI-300 (Machine Learning Operations Engineer): One Exam, Two Operations Disciplines, Split Evenly Between MLOps and GenAIOps
AI-300 (Machine Learning Operations Engineer Associate) succeeds DP-100, retired June 1, 2026. The five areas weigh 15–20 / 25–30 / 20–25 / 10–15 / 10–15: the first two are classic MLOps on Azure Machine Learning at 40–50% combined, and the last three are GenAIOps on Microsoft Foundry at 40–55%. The objectives name MLflow, Bicep, Azure CLI, and GitHub Actions. Official specs: $165 in the US, $83 in Taiwan, 120 minutes, pass at 700, one-year validity, English only.
Microsoft AI-901 (Azure AI Fundamentals): An Entry-Level Exam Where Over Half the Weight Is Building With Foundry
AI-901 replaces AI-900, retired June 30, 2026. The certification is still called Azure AI Fundamentals, but the objectives are now two areas: concepts at 40–45% and implementing with Microsoft Foundry at 55–60%. Python is an explicit requirement, and the objectives include building a lightweight chat client with the Foundry SDK and creating a single agent. Official specs: $99 in the US, $50 in Taiwan, 45 minutes, pass at 700, 13 languages including Traditional Chinese, and fundamentals certifications never expire.
Eleven Coding Agents, Source Code Read: Nobody Uses LangChain, Nobody Uses Vector Retrieval
An 83-page paper from Wavestone AI Lab dissects 11 coding harnesses, including Claude Code, Codex CLI, Gemini CLI and OpenClaw. Across roughly 4 million lines of code, none imports a general-purpose agent framework and none retrieves code with vector embeddings. If you are building or choosing an agent, you can follow the paper's 18 recommendations and start from one bash tool and one while loop.
Poteto's Trust Ladder: Let the Agent Verify Itself Before You Let Go
Cursor engineer Lauren Tan (poteto) says she merges roughly 2,000–2,500 PRs a month, a self-reported figure no one has audited. Her method is three moves: build a verification skill first, turn repeated mistakes into lint and type constraints, and sample-review after merge. The method transfers if your domain can be verified by code.
Uber's MCP Gateway: How to Run 800 MCP Servers
Uber runs one MCP Gateway for 800+ MCP servers and 5,000+ tools: a control plane crawls internal APIs into disabled-by-default tools, a data plane handles protocol translation, authorization and redaction, and Omni MCP, Response Projection and Code Mode keep context small at scale.
Pi Durable: Where a Long-Running Agent Picks Up After a Crash
Pi 1.0 and the experimental Pi Durable package shipped together in early October 2026. Pi Durable turns every model request, tool call and compaction into a task that writes a checkpoint, so a killed process resumes from its last checkpoint; Cloudflare followed with PiHarness on Durable Objects. The key design decision is classifying tools as safe or unsafe to replay.
AI Agent Interview Prep: From Tool Calling and Memory to MCP and Prompt Caching
Agent interviews keep returning to eight topics: the agent loop, structured output, function calling, large tool sets, memory, design trade-offs, MCP versus A2A, and prompt caching. This post strings them into one line (concept, mechanism, how to answer) and corrects when the 2026-07 MCP spec changes actually landed.