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Lecture Notes

Detailed notes from lectures, talks, courses, and books — structured as longer-form summaries with key concepts, technical deep-dives, and personal takeaways.

Unlike the essays, these are more comprehensive reference notes suitable for revisiting and studying from.

  • 8 min read

Lecture Notes: Git by Example — Interactive Guide

Author: Anton Zhiyanov Source: Git by Example — Interactive Reference Category: Programming

Overview

Git by Example is an interactive reference guide that covers Git operations from the absolute basics to advanced workflows. Designed as a practical reference to eliminate repeated Git command searches, it provides clear, concrete examples for every major Git operation.

Anton Zhiyanov is a software developer and writer known for clear technical tutorials. His Git guide is structured as a progressive reference — you can dip in at any level and find exactly the command you need with a working example.

  • 5 min read

Lecture Notes: Harnesses in AI — A Deep Dive

Speaker: Tejas Kumar (IBM) Source: YouTube — Harnesses in AI: A Deep Dive Date: 2026-06-05

Core Thesis: It's Not a Prompt Problem — It's a Harness Problem

For years, the AI community has obsessed over "the prompt problem" — crafting the perfect instruction for a language model. Prompt engineering, chain-of-thought, few-shot examples, system prompts. All valuable. But Tejas Kumar argues we've been neglecting the far more consequential challenge: the harness problem.

The harness is the infrastructure layer that sits between the LLM and the outside world. It determines how the model accesses tools, retains context, manages state, handles errors, and coordinates multiple steps. And it matters more than the model itself.

  • 9 min read

Lecture Notes: World Cup 2026 Complete Player Guide

Source: The Guardian — World Cup 2026 Complete Player Guide Date: 2026-06-05

Introduction: The Expanded Tournament

The 2026 FIFA World Cup is historic before a ball is even kicked. For the first time, the tournament features 48 teams (expanded from 32), divided into 16 groups of 3 teams each. The top two from each group advance to a Round of 32 knockout stage, followed by the traditional Round of 16, quarter-finals, semi-finals, and final.

The tournament is hosted across three nations: the United States, Canada, and Mexico — the first time three countries have co-hosted the World Cup. Matches will be played in 16 cities across North America.

The expanded format means more debutants, more diversity of playing styles, and — inevitably — more lopsided group-stage matches alongside genuine Cinderella stories. This guide covers every team, organized by group.

  • 6 min read

Lecture Note: Inside YC's AI Playbook

Lecture by: Pete Koomen, General Partner at Y Combinator (ex-Optimizely) Source: Y Combinator Lightcone Podcast Date: May 2026

Executive Summary

Pete Koomen presents Y Combinator's internal playbook for building "superintelligence inside a company" — a framework for turning AI from a productivity tool into the operating system of the entire organisation. The core thesis: make AI the shared organisational brain, not just another tool on the desktop.

The Origin Story

Every good lecture begins with a concrete problem. YC's finance team was the bottleneck — swamped with ad-hoc data requests from partners, portfolio companies, and internal teams. Each query required a human to write a SQL query, run it against the production database, and format the results.

The solution: give AI agents read-only SQL access to the production database.

The result was a Jevons Paradox moment. When the cost of a query dropped to zero, the quantity and complexity of queries exploded. Partners started asking questions they'd never have bothered with before — questions that revealed patterns, anomalies, and opportunities buried in the data.

The Core Thesis

"Building superintelligence inside a company means making AI the operating system — a shared organizational brain."

YC's framework rests on three pillars:

  1. Agents are the new apps — each specialised agent handles a domain of organisational knowledge
  2. Tools are the new APIs — agents interact through a shared registry of capabilities
  3. The organisational brain is emergent — the sum of agents and tools becomes smarter than any individual

Infrastructure Primitives

YC has built four core infrastructure components:

1. Shared Tool Registry

A single catalogue of over 350 tools spanning every team — finance, legal, HR, data science, product, portfolio support. Any agent can request access to any tool. New tools are added continuously as teams identify repetitive tasks that can be automated.

Key insight: Tools must be discoverable, versioned, and documented. A tool registry without good metadata is just a junk drawer.

2. Skills & Meta-Loop

Two key concepts:

  • Skillify — a process that converts successful human-agent interactions into reusable skills. When a partner solves a problem with an agent's help, that interaction can be "skillified" into a new tool available to everyone.

  • DRY & MECE Resolver — a system that continuously scans the skill registry for duplication (DRY = Don't Repeat Yourself) and ensures skills are mutually exclusive and collectively exhaustive (MECE). Overlapping skills create confusion; gaps create failure modes.

3. Self-Improving Dream Cycle

This is where the "superintelligence" label starts to make sense.

Every night, a general agent reviews all conversations that happened during the day. It looks for: - Incomplete or failed interactions - Prompts that led to suboptimal results - Opportunities to combine skills in novel ways

Then it automatically improves prompts and skills based on this analysis. The system gets better at its job without human intervention.

Example given by Koomen: A partner creates a "Two-Sentence Pitch" skill. Other partners generate transcripts from office hours. The overnight agent reads the transcripts, identifies patterns in how successful pitches are described, and improves the original skill. The partner wakes up, tests the improved skill, and says: "This thing is now better than I am... at writing those."

4. Data Denormalisation for Agents

Traditional databases are normalised for storage efficiency — they minimise redundancy. But normalised schemas are terrible for AI agents, which need to retrieve information quickly and with minimal joins.

YC's approach: optimise the data layer for agent retrieval, not human querying. This means:

  • Denormalised views that pre-join related data
  • Vector embeddings for semantic search
  • Cached intermediate representations
  • What Koomen calls the "Karpathy Wiki" or "GBrain" concept — a single, unified knowledge store that both humans and agents read from

The Super-Intelligent Organisation in Practice

Koomen illustrates the framework with a concrete example:

  1. A partner creates a "Two-Sentence Pitch" skill — a prompt that helps startups refine their pitch to two sentences
  2. During office hours, other partners use the skill and generate transcripts
  3. The overnight Dream Cycle agent reads the transcripts, identifies patterns in how the best pitches are structured, and improves the underlying prompt
  4. The next day, the skill is measurably better
  5. Over weeks, the skill improves beyond what any single human could have written

The outcome: organisational intelligence that exceeds the sum of its parts. The company as a whole becomes better at a task than any individual within it.

Organisational Requirements

Koomen emphasises that this isn't just a technology problem. It requires specific organisational conditions:

Trust-Default Culture

Conversations are globally viewable. There are no private agent interactions. The assumption is that anything you do with an agent benefits from being visible to everyone. This requires psychological safety and a culture that rewards sharing over hoarding knowledge.

Budget Realism

YC spends between $10,000 and $100,000 per year on tokens. That's real money. Koomen's prediction: token costs will drop 10× in the next two years, making this accessible to every startup. But for now, leaders need to budget seriously for agent compute.

Raising the Floor

The biggest impact Koomen observes is not on star performers — they were already productive. The biggest lift comes from raising the floor for new employees. New hires, interns, and junior team members can use the shared brain to become productive in weeks instead of months.

Key Takeaways

Concept Insight
Jevons Paradox Lower query costs → more queries → more value
Skillify Turn one-off wins into permanent capabilities
Dream Cycle Let agents improve agents overnight
Data Denormalisation Optimise for agent retrieval, not storage
Trust-default Organisational transparency is a prerequisite
Raising the floor Biggest ROI is on the least experienced team members

Discussion Questions

  1. How would a trust-default culture work in your organisation? What would break?
  2. What are the failure modes of a self-improving Dream Cycle? Could it reinforce bad patterns?
  3. Is there a limit to how much organisational intelligence can be offloaded to agents? Where's the human-in-the-loop boundary?
  4. The 350-tool registry at YC — how does a team decide what not to build as a tool?
  5. If token costs drop 10× in two years, what changes about how we build organisational intelligence?

References

  • Source: Inside YC's AI Playbook — Y Combinator Lightcone Podcast
  • Related: Jevons Paradox — William Stanley Jevons (1865), The Coal Question
  • Related: DRY Principle — Andy Hunt & Dave Thomas, The Pragmatic Programmer
  • Related: MECE Principle — Barbara Minto, The Pyramid Principle
  • Related: Karpathy's "GBrain" concept — Andrej Karpathy, various talks on AI and software