Blog
Most AI is built on a quiet assumption: language is the primary medium of intelligence. That ceiling is becoming visible. Here's what multimodal AI changes — and what it doesn't.
Before Google sorted search results in milliseconds, humans decided where knowledge lived. The story of Dewey, Otlet, and the original information architects.
In 1945, Vannevar Bush published 'As We May Think' — a vision of a machine that would extend human memory through associative trails. He described the hyperlink 45 years early.
In 2012 Britannica printed its last volume. Wikipedia had won. Now AI is siphoning Wikipedia's traffic. What comes after the encyclopedia as a knowledge format?
The original universities were not built to teach — they were built to produce knowledge. Understanding that distinction explains why modern education feels hollow.
The earliest technical manuals weren't written to share knowledge — they were written to hide it. The history of documentation from medieval guilds to the Curse of Knowledge.
Wikipedia was built by human will. AI is now absorbing it. The question isn't whether Wikipedia will survive — it's what kind of knowledge infrastructure should come next.
Yann LeCun argues large language models cannot reach genuine intelligence. He proposes JEPA and world models as the architecture that actually could. Here's what he means.
Richard Feynman threw out the textbooks at MIT and rebuilt mathematics from scratch in his own notebooks. What his method reveals about how real understanding is built.
AI makes onboarding to large codebases feel fast but shallow. Here’s what actually works in 2026—and how to keep hard-won onboarding knowledge from disappearing.
An honest comparison of knowledge graphs and vector databases in 2026 — where each wins, where each fails, and a third paradigm practitioners should know about.
A definitive breakdown of every RAG architecture: Naive, Advanced, Modular, GraphRAG, and Agentic — with trade-offs and when to use each.
Vector search made RAG usable. Structured, human-maintained knowledge makes it truly capable of reasoning—and SILKLEARN has operated this way from the start.
AI memory in 2026 is less about bigger models and more about structured knowledge paths that beat raw vector search on precision and coherence.
Static docs die because they’re not designed for change. SILKLEARN builds living knowledge paths that evolve like great codebases—version by version, contributor by contributor.
A practitioner's guide to chunking, RAG, summarization chains, and why SILKLEARN takes a different approach.
Junior dev job postings are down. AI took the surface work. Here’s what actually comes next — and how deep understanding becomes your career moat.
AI accelerates coding, but review hasn’t kept up. Learn why cognitive load is rising, how invisible technical debt forms, and why deep system knowledge is now your edge.
AI accelerates coding, but it quietly erodes the understanding that makes you a resilient engineer. Here’s why that matters and what to do about it.
AI can write your CRUD, but it can't replace domain depth or shared practitioner knowledge. Here's the framework for building a real career moat.
When your sources disagree, synthesis gets hard. Here is a structured method for reconciling conflicting study results without cherry-picking or ignoring the tension.
Most codebase docs tell you what exists, not what order to read it in. Here is a method for finding the dependency structure yourself — and following it.
The introduction is rarely the right starting point. Here is how to find the actual prerequisite structure in a body of material — and follow it in the right order.
Reading research papers slowly is rarely the bottleneck. The problem is managing what each paper assumes, contradicts, or builds on. Here is how to do it systematically.
We're opening early access for teams that manage complex internal knowledge and want structured, reviewable learning paths.
Teams don't lack docs — they lack visible order through them. The real bottleneck is dependency structure locked in experts' heads.
Source material goes in, a dependency-ordered learning path comes out. Here's what the structure actually looks like.
RAG finds documents fast but misses their dependencies. Why reviewable structure matters more than search for internal knowledge.
When onboarding lives in senior engineers' heads, leaders can't audit, measure, or improve it. Tribal knowledge doesn't scale.
Your onboarding docs are complete but new engineers are still lost. The problem isn’t content; it’s the missing dependency-ordered structure that makes it navigable.
Most teams have a knowledge base but still struggle with onboarding. The missing piece isn’t more docs—it’s a validated knowledge path with explicit prerequisites.
Day 3 of THE RESET: NP-hard learning paths, LeCun’s world models, human-in-the-loop L&D, and building an MVP while AI reshapes work.
Sharpening the product vision: combining Obsidian's knowledge graph with roadmap.sh's dependency paths to give engineering leaders real-time team skill visibility.
Day 1.5 of THE RESET: mapping skills, selecting a market, and validating a skill graph platform idea using Hormozi's framework.
Early access
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