Know Your SMD Footprints
Clean footprint gallery, but IPC standards, KiCad libs, and Altium already serve this.

151 interconnected failure patterns with AI-era distortions already included.
Software engineers, engineering managers, teams conducting retrospectives
Refactoring Guru · Software Engineering Body of Knowledge
Link: https://thehardparts.dev
Currently I've created 4 main section:
- Failure Modes: ways project go wrong - Red Flags: early signals that are worth taking seriously - Tech Decisions: common and not so common trade-offs for hard choices - Playbooks - guided approach for situations that repeat
I've also focused on creating links between them to show how connected many things are: a red flag usually precedes a failure mode, which might connect to a forced decision, etc.
Some entry points to give you an idea:
- The Invisible Deadline: a date that exists socially but not explicitly enough to manage honestly - Eveyone Asks The Same Person: when one person becomes the default source of truth - Build a Practical Rollback Strategy: how to build a reliable rollback strategy
It has 151 entries across the 4 sections.
Curious what you think about the content, format, grouping.
Clean footprint gallery, but IPC standards, KiCad libs, and Altium already serve this.
Receipt-backed promotion decisions with SHA-256 hashes and commit linkage is a practical, low-ceremony way to make spotlight selections auditable. The zero-dependency CLI, freeze modes and drift reports show this was designed for governance-first catalogs rather than casual lists — useful and sensible, but narrowly aimed.
First structured CVE-style database for AI agent failures—nobody else is doing this.
Yet another coding education platform with no clear differentiation from LeetCode.
They ran a variable-isolation study across five prompt layers with 20 runs per condition and shipped experiment.py and results so you can reproduce which layer actually supplies the missing implicit fact. It’s a focused, practical read for anyone designing layered system prompts, but it feels niche and would be more persuasive with cross-model baselines and clearer statistical reporting.
Catches LLMs cheating on evals with a 9-pattern catalog nobody else documents.