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LLM + GNN: Letting Language Models Read Graphs and Graph Models Read Text | Stanford CS224W Lecture 16

A Fall 2025 slide-grounded reconstruction of Lecture 16, covering Complementary gaps in LLMs and GNNs, Text-attributed graphs, The LLM as predictor or encoder while documenting the public-material boundary.

Agents + Graphs: Retrieval, Planning, and Action in Structured Worlds | Stanford CS224W Lecture 17

A Fall 2025 slide-grounded reconstruction of Lecture 17, covering From graph QA to agents, Multimodal retrieval in STaRK, Tool use and traversal while documenting the public-material boundary.

Deep Generative Models for Graphs: GraphRNN and Goal-Directed Molecular Generation | Stanford CS224W Lecture 18

A Fall 2025 slide-grounded reconstruction of Lecture 18, covering The graph-generation problem and representation, Evaluating generation quality, GraphRNN's autoregressive factorization while documenting the public-material boundary.

Ranking 315K GNN Designs with Anchor Models | Stanford CS224W Lecture 19

The Fall 2025 conclusion studies roughly 315K GNN designs across 32 tasks: run a small set of anchor models, derive task similarity from rankings, and transfer the best designs from similar tasks.

aideep-dive

Steel Browser: An Open-Source Browser API and the Boundary of Self-Hosting

Steel packages Chromium sessions, CDP, proxies, stealth, and debugging behind an Apache-2.0 browser API. Its public repository has about 7,400 stars and it entered the Stripe Projects developer preview in 2026. Self-hosting fits development and data-control needs; Cloud addresses concurrency, managed proxies, CAPTCHA, recordings, and SLAs.

aideep-dive

Together AI: From Serverless Inference to Dedicated Endpoints and Fine-Tuning

Together AI puts serverless APIs for open-weight models, dedicated GPU endpoints, batch inference, and fine-tuning on one platform, letting teams validate per token before moving to reserved deployment when traffic or customization justifies it.

aidebug

How to Write a Claude Code Skill That Doesn't Eat Context: Entry Point, Thresholds, Cost, Sources

I turned Anthropic's session-cost advice into a global skill, and the first version made the very mistakes it was meant to prevent: a description stuffed with trigger keywords, hard thresholds based on file counts and minutes, and 'protect the main context' conflated with 'spend fewer tokens overall'. Three rounds later the entry point is one page, details live in references, numeric thresholds became four judgment dimensions, and every claim from a draft post was checked against official docs.

aideep-dive

Vapi: Managed Voice-Agent Orchestration and the Safety Boundaries Before Going Live

Vapi connects phone and web audio, STT, LLMs, TTS, tool calls, and call observability in a managed voice runtime. Providers are swappable, but Vapi's realtime orchestration is not portable. In May 2026, the company reported one million developers and announced a $50 million Series B.

Vercel Sandbox Deep Dive: Putting the Agent Execution Layer Inside the Vercel Ecosystem

Vercel Sandbox isolates untrusted code in Firecracker microVMs and integrates with Fluid compute, Active CPU pricing, and Vercel OIDC. It fits agents already running on Vercel, but network defaults, memory billing, and persistence still require deliberate design.

aideep-dive

Vertex AI Explained: From Model APIs to Gemini Enterprise Agent Platform

Vertex AI is more than the Gemini API: it puts access to 200+ models, training, evaluation, deployment, and governance under one Google Cloud control plane. Since April 2026, its products and roadmap have moved into Gemini Enterprise Agent Platform, while the Vertex AI API, documentation paths, and many resource names remain in active use.

Web Extraction Quality Benchmark: Crawl4AI, Firecrawl, Jina Reader, and Readability

Extraction tools cannot be compared by HTTP 200s. The same 20 URLs must be scored for body text, headings, tables, code, links, metadata, noise, latency, and cost. This article publishes the corpus, adapter contract, and gates, but no winner without a same-version raw run across all four paths.

aideep-dive

Zep Complete Guide: Temporal Knowledge Graphs for Agent Memory

Zep does not merely vectorize chat history. It turns episodes into entities and facts with validity time, allowing new information to invalidate an old relationship without erasing history. Graphiti is the open-source framework; Zep adds managed scale and governance.

careerguide

Beyond Upwork: Seven Remote Work Platforms Worth Bookmarking

From the free We Work Remotely to the elite 3%-acceptance Toptal, seven platforms each serve a different niche — job boards (FlexJobs, WWR, Remote OK), startup talent (Wellfound), community (Remotive), market research (Working Nomads), and premium freelancing (Toptal). All friendlier than competing on price at Upwork.

Inference When Ghosts Are Invisible | Berkeley CS188 Topic Guide

Lectures 13–18 and Project 4 move from factor operations and variable elimination to exact inference and particle filtering, letting Pacman track invisible ghosts through noisy distance sensors.

Completing CS188: Turn 28 Lectures and Projects P0–P5 into a Portfolio | Berkeley CS188 Course Overview

Lectures 26–28 close with nuclear monitoring, AI safety, and reflection. Independent completion should preserve assumptions, test evidence, and failure analysis for Projects 1–5 instead of reporting only autograder scores.

Choosing Minimax, Alpha-Beta, and Expectimax | Berkeley CS188 Topic Guide

Lectures 5–8 use CSPs to practice variables, constraints, and search order before Project 2 implements minimax, alpha-beta, and expectimax. Their key difference is the assumption made about other agents.

From VPI and Naive Bayes to Attention | Berkeley CS188 Topic Guide

Lectures 19–25 connect rational decisions and VPI to machine learning, while Project 5 uses PyTorch for regression, classification, CNNs, attention, and an optional character-GPT.

From Value Iteration to Q-Learning | Berkeley CS188 Topic Guide

Lectures 9–12 and Project 3 use the same Gridworld to contrast value iteration with a known model, Q-learning from unknown dynamics, and approximate Q-learning that generalizes through features.

Pacman from DFS and BFS to A* | Berkeley CS188 Topic Guide

Lectures 1–4 and Project 1 connect DFS, BFS, UCS, A*, state representation, and heuristic design. The goal is not memorizing algorithms but separating what the frontier, cost, and state each control.

Learn AI Through Projects P0–P5 | Berkeley CS188 Course Overview

CS188 Spring 2026 publishes 28 recordings, 27 lecture slide sets, 11 discussions, and Projects P0–P5. P0 is a Python/autograder tutorial, P1–P4 use Pacman settings, and P5 contains general machine-learning tasks.