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A Claude Code Agent Skill built from Dex Horthy's (HumanLayer) playbook on David Ondrej's podcast.
"Once the model has written thousands of lines of code, it is harder to change. The sessions that generate design docs are context-light โ you get the most model intelligence when you do the hard thinking early."
By default, agents build horizontally: all the backend, then all the frontend, then a 2,000-line diff lands in your lap and reviewing it is your problem. This skill flips that. Every decision that matters gets made before the code exists โ where changing your mind costs a sentence, not a rewrite.
๐ Demo Page
I pieced this together from various articles, forum posts, and published CSS resets.
The goal is to have a full reset that works for most, modern, evergreen browsers.
A few rules could be considered "normalization" - but I'm trying for as much of a clean slate as possible, with the only exceptions being things like <strong> being bold, <em> being italics, and <a> tags having an underline; for accessibility reasons, these are not things we should reset by default.
Based on aggregated reports from 7 sources (1Point3Acres, Blind, Glassdoor, Reddit, GeeksforGeeks, 1p3a OJ catalog, direct submissions) covering 4,577 Amazon interview reports across SDE1, SDE2, SDE3 (Senior), and Principal levels. Source: LeakCode Amazon page.
| Level | Phone Screens | Onsite Rounds | OA Required | Bar Raiser |
|---|---|---|---|---|
| SDE1 (L4) | 1 | 4-5 | Yes (2 problems + Work Simulation) | Yes |
| SDE2 (L5) | 1-2 | 5-6 | Usually | Yes |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
TL;DR โ AgentRouter is a non-profit, OpenAI-compatible API gateway that aggregates Claude, GPT, Gemini, DeepSeek, and 30+ other models behind a single endpoint. New users get $200 in free credits via referral โ no credit card required. Ideal for developers who want model flexibility without juggling five subscriptions.
๐ Claim Your $200 Free Credits โ agentrouter.org/register?aff=DWBb
