Proving Prim and Kruskal with the Cut Property | Stanford CS161 Lecture 15
The heart of MST algorithms is an invariant: the selected edges remain contained in some MST. The cut property proves that every step of Prim and Kruskal is safe.
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Ford–Fulkerson, Residual Networks, and Max-Flow Min-Cut | Stanford CS161 Lecture 16
Ford–Fulkerson augments through a residual network. When no path remains, residual reachability yields a cut equal to the flow, certifying max flow, min cut, and their equality.
Gale–Shapley and Revocable Greedy Choices | Stanford CS161 Lecture 17
Deferred Acceptance permits tentative choices to be revoked. Monotone proposals prove O(n²) termination and stability, with an outcome favoring the proposing side.
From the Algorithmic Toolbox to LP, Coding, and ML | Stanford CS161 Lecture 18
The finale recaps the CS161 toolbox and points toward LP duality, Reed–Solomon coding, and ML-assisted algorithms. Officially, this lecture has slides but no notes.
Reading Guide: Pick What Most People Use — the Other Five Criteria Are Tie-Breakers
The primary criterion has not changed: it is still adoption — and AI makes it matter more, not less, because more users means more training data means higher agent accuracy. The five criteria this series collects (machine-readable docs, types, whether the source is in your repo, data shape, machine-callability) are for breaking ties when adoption is comparable, or for costing out what picking the less popular option will charge you.
AI SDK Message Parts: The Data Skeleton of a Conversation UI
The AI SDK splits an AI message into a parts array — text, reasoning, source-url, tool-* — each an independent typed fragment (introduced in v5, unchanged since). That data structure dictates how modern AI conversation UIs are written: render by switching on part.type, handing each fragment to its component. This post unpacks the design logic of the parts model, useChat's streaming behavior, and how it became the foundation for component libraries like AI Elements.
Drizzle ORM: A SQL-First Database Access Layer for TypeScript
Drizzle ORM is a SQL-first TypeScript ORM — its query builder reads like SQL, so queries written by agents are auditable in diffs. Zero dependencies, ~7.4 KB gzipped, native support for edge databases like Cloudflare D1, Neon, and Turso. Still at version 0.45.2 with no 1.0, yet weekly downloads have reached 16.9 million — surpassing Prisma's 13.8 million.
llms.txt: The Copy of Your Docs Written for Machines
llms.txt is a convention proposed by Jeremy Howard on 2024-09-03 (the spec is now at v2): a Markdown index at your site root written for LLMs. Hand-tested across six frontend docs sites: TanStack, shadcn, Zustand, AI SDK, and Next.js all ship it; React Router is the lone 404. The companion llms-full.txt (full-text version) is live at Anthropic, Cloudflare, and others. This post covers the spec, who uses it, and why it has started to influence library selection.
shadcn Registries and MCP: The Third Way to Distribute Components
Component distribution used to offer two roads: npm packages (black-box dependencies) or manual copy-paste. The shadcn registry standardizes a third — components described as JSON with embedded source and dependencies, installed by CLI straight into your repo as your own code. Anyone can host a registry (AI Elements is one), and the official MCP server lets AI agents browse and install components directly.
Supabase: A Platform Built Entirely on PostgreSQL
Supabase isn't just an open-source Firebase alternative — its core design builds Auth, Storage, and Realtime entirely on PostgreSQL schemas and WAL. The result: everything is queryable with SQL, pgvector works out of the box, and AI agents can operate the entire platform by writing SQL. 108k GitHub stars, Apache 2.0, free tier with 500 MB database.
Tailscale: Your Agent Lives at Home, You're Not Dialing Home
Self-hosting an agent that runs 24/7 means opening something on your own network that must be reachable from outside and must never sit on the public internet. This post takes apart what each Tailscale mechanism actually solves: the tailnet for reachability, subnet routers for private resources, tags plus ACLs for the permission boundary, and seconds-fast policy propagation plus Tailnet Lock for revocation. Pricing checked 2026-08: Personal is free, up to 6 users, unlimited user devices, 50 tagged resources included.
TanStack Router: Making Routes Compile-Time Verifiable
TanStack Router (1.0 in December 2023, ~20M weekly downloads) makes paths, params, and search params compile-time inferred: navigating to a nonexistent route is a type error, not a runtime 404. This post unpacks its three core designs — type safety, first-class search params, and Query-integrated loaders — and why AI agents writing code amplifies their value.
Temporal: Write the Process as Code, and It Finishes Even After a Crash
Temporal is a durable execution platform (Server 1.31.2, Python SDK temporalio 1.31.0, MIT, verified 2026-08). What separates it from BullMQ / Celery isn't scale but the guarantee: a queue guarantees a message gets consumed, Temporal guarantees a multi-call process runs to completion. The price is that Workflow code must be deterministic — and LLM calls are inherently non-deterministic. This post covers how to resolve that tension and when the constraint isn't worth it.
Trigger.dev: Durable Tasks via Process Snapshots, No Determinism Required
Trigger.dev is an Apache 2.0 durable task platform (v4.5.12, checked 2026-08) that uses CRIU to snapshot entire Node.js processes for pause and resume. Unlike Temporal's replay model, it never re-executes your orchestration code and imposes no determinism constraint — LLM calls go directly in the task. The tradeoff: snapshots can't preserve TCP connections (you reconnect manually), and checkpointing is cloud-only — self-hosted deployments don't get it.
WebMCP: Letting a Web Page Hand Its Own Functions to an Agent
WebMCP lets a page register its own functions as agent-callable tools via document.modelContext.registerTool(), replacing the agent's guess-the-button DOM scraping. Chrome opened an origin trial in 149 and estimates stable in 157; Edge followed in 150. But WebKit has formally opposed it ('an agent acting on a user's behalf is, in effect, assistive technology... the site should not single it out for different treatment') and Mozilla filed neutral. This post covers both APIs, where the security gates sit, and whether to invest now with one and a half engines behind it.
Testing Five zh-TW Terminology Linters: One Is Usable, One's --fix Turns 只是 Into 隻是
On 109 posts that genuinely contain Mainland vocabulary, zhtw-mcp scored 29.4% precision at 85.2% recall; twlint scored 21.2% / 82.5% — but 195 of twlint's error-level findings are legitimate Traditional characters misread as Simplified (干→幹 60 times, 只→隻 15), so --fix corrupts the text. The most useful result was not a winner: zhtw-mcp found five Mainland terms my own list had missed, and correctly declined to flag one it wrongly included (審計). These tools calibrate your wordlist; they do not replace it.
Zod: From Form Validation to TypeScript's Universal Contract
Zod's 224M weekly downloads (checked August 2026) put it far beyond 'form validation library': API boundaries, environment variables, route search params, LLM tool schemas and structured output all run on the same schemas. The core mechanism is one definition, two payoffs — runtime validation and static types derived from a single source. Zod 4 (on npm July 2025) is faster, slimmer, and easier on tsc.
Behavioral & Ethics Interview Guide: AI Ethics, Teamwork, and Impact Narratives
Behavioral interviews aren't about improvisation — they're about a pre-prepared story library. AI Engineer behavioral interviews have unique focus areas: AI ethics (bias, fairness, privacy), technical decision impact narratives (why you chose this model/architecture), and experience driving ML projects across teams. Strategy: build 8-10 STAR stories, practice each until you can deliver it in under 2 minutes.
Coding Interview Guide: Strategies for ML-Flavored Programming Problems
AI Engineer coding interviews aren't identical to SWE — beyond LeetCode medium, you'll face ML-flavored problems (implementing a tokenizer, writing a batch inference pipeline, handling sparse matrices). Strategy: practice LeetCode medium to 70% pass rate, then spend remaining time on numpy/pandas operations, data processing pipelines, and ML-related programming problems.
Deep Learning Interview Guide: Core Intuitions from CNN to Transformer
Deep learning interviews don't ask you to derive backpropagation — they test whether you can explain the design intuition behind architectures. High-frequency topics: CNN's locality and translation invariance, why the evolution from RNN to Transformer was necessary, self-attention computation and complexity, BatchNorm vs LayerNorm use cases, and common training tricks (learning rate scheduling, gradient clipping, mixed precision).