Karpathy's LLM Wiki gist proposes that LLMs should maintain a persistent wiki layer between you and your raw sources. It's an elegant pattern. But after implementing it, I found a lighter approach that keeps the best part — knowledge compounding — without the maintenance burden.
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Source: Perplexity Deep Research analysis — March 2026
Methodology note: This analysis was generated using Perplexity AI's deep research mode and then fact-checked against primary sources (Apple specs, Intel datasheets, benchmarks, vendor documentation). Two figures were corrected post-review — M4 memory bandwidth (120 GB/s, not 100) and M4 TDP (24W, not 15W). NPU TOPS updated to reflect Intel Lunar Lake (48 TOPS) and Panther Lake (50 TOPS). A discrete GPU pillar was added after feedback correctly identified a blind spot. Five low-pertinence pillars (entry price, battery, weight, package manager, resale value) were removed to focus on what matters for an AI development platform. Final scoring: 11–1–4.
A 16-pillar comparison evaluating MacBook Air M4 (macOS) vs Intel+Windows laptops as the primary development platform for Augur, a local-first AI personal OS. Only pillars with pertinence ≥ 4 for AI development are includ
Source: Perplexity Deep Research analysis — March 2026
Methodology note: This analysis was generated using Perplexity AI's deep research mode and then fact-checked against primary sources (Apple specs, Intel datasheets, benchmarks, vendor documentation). Two figures were corrected post-review — M4 memory bandwidth (120 GB/s, not 100) and M4 TDP (24W, not 15W). Individual data points may still carry AI-sourced inaccuracies, but the overall scoring is a knockout (16–0–4) — even if a few numbers shift by 10–20%, the directional conclusion holds.
A 20-pillar comparison evaluating MacBook Air M4 (macOS) vs Intel+Windows laptops as the primary development platform for Augur, a local-first AI personal OS.
Status: Implemented Date: 2026-02-19 Deciders: Augur Team Related: ADR-109 (Filesystem-Driven Dashboard), ADR-105 (Hub-Driven Plugin Architecture), ADR-112 (Plugin Completeness), ADR-121 (Hub Ownership Validation), ADR-083 (Colocate Plugin Data), ADR-087 (Eliminate data/ Directory)
Augur's core value proposition is that users can add, remove, and compose skills with zero friction — drop a folder, get a plugin. Today, several gaps undermine this:
Paste this entire prompt into Claude Code. It generates crew profiles, swarm presets, and tier routing — everything needed to run structured Agent Teams.
You are generating an orchestration layer for Claude Code Agent Teams. This consists of:
You are setting up a context management layer for a Claude Code project. Generate the following 4 files exactly as specified, then explain how to use them.
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description: Audit current context usage across active agents and subagents
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