by armin-ronacher
Coding agents dominate reinforcement learning data, which means the smartest way to build any agent is to lean into the coding agent paradigm — files, code execution, Unix workflows — rather than fight it with custom tools.
by no-boilerplate
The core argument: note-taking systems fail because they stop at capture. The real value comes from a daily processing habit that turns raw captures into atomic notes — using spaced repetition not for memorization, but for backlog triage.
by alexander-opalic
A curated learning path for developers who want to master AI-assisted coding, from basic autocomplete to autonomous agents.
by kit-langton
Effect Institute offers structured courses for mastering Effect, the TypeScript library that brings type-safe error handling, concurrency, and functional patterns to production applications.
by andrej-karpathy
A comprehensive introduction to how large language models work, covering the entire pipeline from internet data collection through tokenization, neural network training, and inference.
by joe-rogan
Naval shares his principles for building wealth without getting lucky, finding happiness as a skill, and why the future of work means everyone becomes an entrepreneur.
by melkey
YouTube tech content has shifted from tutorials and crash courses to entertainment and commentary, with creators who once had millions of views on educational content now making opinion pieces instead.
by demis-hassabis
Documentary tracing DeepMind's journey from a London startup to defeating world champions at Go and StarCraft, revealing the team's pursuit of artificial general intelligence.
by lee-robinson
Lee Robinson reflects on how coding agents have surpassed his coding ability and shares four recommendations for software engineers adapting to 2026.
by andrej-karpathy, dwarkesh-patel
Karpathy argues we're building 'ghosts' that imitate internet documents rather than evolved animals, and that practical AI agents will take a decade—not a year—to fully mature.
A curated map connecting key resources for understanding AI agents and context engineering
by sascha-fast
An analysis of how Building a Second Brain (BASB) and the Zettelkasten Method address different parts of the knowledge value chain, and how combining both systems maximizes knowledge productivity.
by sonke-ahrens
A guide to the Zettelkasten (slip-box) method for capturing, connecting, and synthesizing ideas into quality writing and deeper understanding.
by andrew-huberman
Andrew Huberman explains the neuroscience behind goal setting and achievement, revealing that one neural circuit governs all goal pursuit and providing four science-based protocols for effective goal execution.
by richard-sutton
General methods leveraging computation beat hand-crafted human knowledge in the long run—AI progress comes from scale and learning, not clever engineering.