Learning Resources
Standalone interactive pages for exploring ideas and concepts.
2026-06-12
TurboQuant: Vector Quantization for LLM Efficiency
Learn how TurboQuant compresses high-dimensional vectors using two-stage quantization and random rotation, with real-world applications in LLM key-value cache compression.
Open →2026-06-11
How GitHub Apps Work — Build a Bot You Can @Mention
Interactive guide to GitHub Apps: app identity, JWTs, installation tokens, user OAuth, webhooks, and the full architecture for a bot that creates and reviews PRs when you @mention it on an issue.
Open →2026-06-09
What Salesforce Learned from 20,000 Agent Deployments
Key lessons from Salesforce's analysis of 20,000 enterprise Agentforce deployments — architecture, failure patterns, and what it takes to succeed at scale.
Open →2026-06-06
MacBook M4 Pro as a Local AI Powerhouse
How the MacBook Pro M4 Pro with 48GB unified memory can replace ChatGPT, Cursor, and Midjourney with fully local, private, offline AI tools.
Open →2026-06-06
Running an AI-Native Engineering Org
An interactive learning guide to running an AI-native engineering organization — how planning, context gathering, code review, team composition, and metrics shift when agentic coding becomes the default.
Open →2026-06-06
How Non-Engineers Learned to Ship with AI
How Sentry's growth team migrated 2,500 CMS pages to Git and unlocked AI-native workflows so non-technical contributors could ship like engineers.
Open →2026-06-03
Skip-level One-on-ones: A Leader's Guide
An interactive learning guide to running effective skip-level one-on-ones — surface blind spots, transfer social risk, and build trust across organizational layers.
Open →2026-06-01
The Private AI Business Blueprint
How to build a $1,000/mo private AI business for law firms using RTX 5090, Ollama, RAG, and Open WebUI — hardware, software, business models, and reality checks.
Open →2026-05-30
How is Linear So Fast? — Interactive Breakdown
An interactive breakdown of how Linear achieves instant UI interactions — exploring sync engines, optimistic updates, and local-first architecture.
Open →2026-05-30
Decoding LLM Model Names — Parameters, MoE, and Active Counts
Demystify AI model naming conventions — what parameter counts mean, how Mixture-of-Experts changes active vs total parameters, and why model size alone doesn't predict performance.
Open →2026-05-30
Building Self-Improving Tax Agents with Codex
How to build tax agents that write, test, and improve their own code using OpenAI Codex — covering agent loops, self-evaluation, and iterative refinement patterns.
Open →