AI Code Research — Read Any Codebase. Compare Tools. Plan Migrations.
Cornerstone explainers, code-level tool comparisons, deep dives on hot AI projects (Claude Code, MCP, Cursor, OpenClaw, ComfyUI, AutoGPT), and migration playbooks — generated by reading the actual source.
AI Code Research
Read any codebase. Compare AI tools at the code level. Decode hot projects (Claude Code, MCP, OpenClaw). Plan migrations grounded in real source.
Monolith to Microservices: 4 Migration Plans After Reading the Original Codebases
Monolith-to-microservices migrations fail in well-known ways: wrong service boundaries, distributed transactions where there should be none, the 'distributed monolith' antipattern. We read four real monoliths mid-migration and extracted what separates the plans that ship from the plans that produce a worse system.
We Read 5 JavaScript→TypeScript Migrations. Here's What Actually Slipped.
JS-to-TS migrations look straightforward in tutorials. In real codebases, the slippage shows up in five predictable places. We read five real migrations (totaling ~400K LOC) and identified the patterns that ship vs the ones that drag.
Legacy Code Modernization: What 3 Real Codebases Taught Us When We Read Them
Legacy modernization projects fail in predictable ways: scope creep, missing tests, undocumented business logic, the original team has left. We read three real legacy codebases mid-migration and extracted the patterns that actually ship — and the patterns that don't.
Best AI Coding Tools 2026: We Read the Repos. Here's the Real Ranking.
Most 'best AI coding tools 2026' lists are paraphrased marketing pages. We read the actual source (where it's open) and the actual public surface (where it's closed) of every tool we recommend, and ranked them by what they're genuinely best at — not by SEO incentives.
LangChain vs LlamaIndex: 7 Decisions That Differ at the Code Level
LangChain and LlamaIndex are the two dominant Python frameworks for LLM applications. Both are open source. The architectural decisions diverge sharply once you read the source — composability vs. data ergonomics, breadth vs. depth, ecosystem vs. polish. Here's the honest comparison from reading both repos.
Vercel vs Netlify: Reading Both Stacks Before You Pick
Vercel and Netlify look similar from the marketing pages — both deploy frontends, both ship serverless functions, both integrate with Git. The architectural decisions diverge once you read past the homepages: Vercel is React+Next-native; Netlify is framework-agnostic. Here's the honest take on which platform wins for which job.
Supabase vs Firebase: A Code-Level Comparison, Not Marketing-Page
Supabase and Firebase are the two dominant managed-backend platforms of 2026. They make opposite architectural choices: Supabase is open-source Postgres-native; Firebase is closed proprietary realtime + NoSQL. We read both stacks at the code level and explain which one wins for which job.
Cursor vs Claude Code: We Read Both Repos. Here's the Real Architectural Difference.
Cursor and Claude Code are the two dominant AI coding tools of 2026 — and they make almost opposite architectural choices. Claude Code is open source (119K stars, terminal-native, agentic). Cursor is closed (IDE fork of VS Code, custom autocomplete model). We read what we can of both and lay out the trade-off, with a buyer's guide based on your actual job.
How AutoGPT Actually Works (We Read the Open Code)
AutoGPT was the original autonomous AI agent — released March 2023, now 184K stars and 46K forks. It defined the agentic loop pattern that nearly every AI agent product since has copied. We read the source and produced a code-level analysis of why AutoGPT mattered and where the architecture stands in 2026.
How ComfyUI Works: The Custom-Node Architecture
ComfyUI is the dominant graph-based diffusion model UI of 2026 — 110K stars, GPL-3.0, Python-primary. The architectural commitment that made it dominant: a node-graph workflow engine with a thriving custom-node ecosystem. We read the source and explain why this shape won.
How Lovable Works at the Code Level (Researched From the Public Surface)
Lovable is one of the dominant 'vibe coding' app generators of 2026 — describe an app in plain English, get a deployed full-stack web product. The platform is closed-source, but the architecture is deducible from the marketing surface, generated-app inspection, and integration documentation.
How v0.dev Works: Decoding Vercel's UI Generator
v0 is Vercel's AI UI generator — describe a component or full UI in plain English, get a working React + Tailwind output. The product is closed, but the public surface (Vercel's docs, AI SDK, public examples) reveals enough about the architecture to write a code-level analysis.
How Manus Actually Works (Researched From the Public Surface)
Manus is a closed-source AI productivity agent (recently acquired by Meta) that creates slides, builds websites, develops desktop apps, and operates browsers autonomously. We researched the public surface — homepage, docs, demos, third-party reviews — to extract the architectural commitments behind the product.
How Cursor Actually Works (Researched From the Public Surface)
Cursor is the dominant in-IDE AI coding assistant of 2026. The product is closed-source — but the architecture is deducible from its docs, the Cursor Forum, the released SDK and CLI, and engineering writeups. Here's an honest code-level analysis based on the public surface, not training-data summaries.
How MCP Works: Reading the Spec and Reference Servers
Model Context Protocol (MCP) is the standard for how AI clients talk to tools and data sources. We read the actual specification (TypeScript schema, JSON-RPC over stdio/HTTP-SSE) and several reference servers to produce a code-level walkthrough — and an honest take on why MCP has 8K stars and is being adopted by Claude Code, Cursor, and others.
How Claude Code Actually Works (We Read the Source)
Claude Code is Anthropic's agentic coding tool — it lives in your terminal, executes coding tasks autonomously, and is fully open source. We read the actual source (119K stars, Shell-Python-TypeScript stack) and produced a code-level architectural walkthrough — including the plugin system, install paths, and what makes the agentic terminal shape distinct from in-IDE tools like Cursor.
How Today's AI Coding Tools Actually Work — Read at the Code Level
The phrase 'AI coding tool' covers four radically different architectures: agentic terminals, in-IDE assistants, protocol layers, and personal AI agents. We read the source of each (where it's open) and identified the architectural decisions that actually distinguish them — with verified GitHub data and links to deep dives on each.
How AI Code Research Actually Works (60 Seconds, Plain English)
The 3-step mechanism behind AI Code Research, three real worked examples at different depths (60-second chat, code-level comparison, 8,500-word Deep Dive Report), and an honest accounting of what the agent reads, what it doesn't, and where the limits are.
DeepWiki vs Greptile vs Reading It Yourself: An Honest Take (From Someone Who Built a Competitor)
An honest, biased comparison of the four real options for understanding a GitHub repo: reading it yourself, DeepWiki, Greptile, or AI Code Research. Pricing, real strengths, real weaknesses, real maintainer quotes — and a framework for picking the right tool for your job.
Why You Can't Read Other People's Code (And You're Not Stupid)
Reading code is genuinely, measurably harder than writing it. Cognitive load theory explains why, even open-source maintainers can't read their own work, and what actually helps when 'just read the code' isn't enough.
What Is AI Code Research? An AI Engineer for Your GitHub Repos
AI Code Research opens any public GitHub repo, reads the actual source, and gives you an engineer's answer — in plain English, in roughly 60 seconds. Here's what it is, how it differs from ChatGPT and DeepWiki, and what you can do with it.