harryf

Nicholas Carlini - Black-hat LLMs | [un]prompted 2026

Source: This is a summary of a talk by Nicholas Carlini (Anthropic researcher) at the [un]prompted 2026 security conference. Watch the original video

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Overview

Nicholas Carlini presents alarming evidence that modern LLMs can autonomously find and exploit zero-day vulnerabilities in critical software — including the Linux kernel and production web applications — with minimal scaffolding. He argues this represents a phase shift in the attacker/defender balance comparable in magnitude to the invention of the internet, and urges the security community to treat this as an urgent, present-day threat rather than a future concern.

harryf

Karpathy's "autoresearch" Broke the Internet

Source: This is a summary of a YouTube video by Greg (solo commentary format). Watch the original video

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Overview

Andrej Karpathy released an open-source project called autoresearch — an AI agent that autonomously runs iterative experiments overnight (code edits, training runs, metric evaluation) and keeps only the improvements. The video explains what it is, explores 10 business use cases, and covers how to get started without an NVIDIA GPU.

harryf

How to Switch Between Work and Personal Anthropic Accounts with Claude Code and Claude Desktop

A guide for running two Anthropic accounts (e.g., work Team/Enterprise + personal Pro/Max) on the same macOS machine, with one-command switching for both Claude Code (CLI) and Claude Desktop.

Platform: macOS (uses macOS Keychain; Linux would need adaptation) Claude Code Version: 2.x Claude Desktop: Electron-based (1.x+)

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harryf

How I Use Obsidian + Claude Code to Run My Life

Source: This is a summary of a YouTube video featuring Greg Eisenberg and his guest Vin (Internet Vin). Watch the original video

Overview

Greg and Vin walk through how pairing Obsidian (a plain-text note-taking app) with Claude Code (an AI agent) creates something more powerful than either tool alone — a personal thinking system that notices patterns in your life, helps you reflect, and turns your notes into real actions. The conversation moves from setup basics all the way to live demos of AI reading someone's personal vault and generating surprisingly personal, useful ideas.

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harryf

Lecture 7: Agentic Coding

Source: This is a summary of a MIT Missing Semester lecture on agentic coding. Watch the original video

Overview

This lecture covers coding agents — AI models wrapped in an "agent harness" that can autonomously read/write files and execute shell commands to complete programming tasks end-to-end. The lecturer demonstrates Claude Code live, explains how LLMs and agent harnesses work under the hood, walks through key use cases, and covers advanced features like parallel agents, context management, and sub-agents.

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harryf

AI Marketing Masterclass: From Beginner to Expert in 60 Minutes

Source

This is a summary of a YouTube video featuring James Dickerson (The Boring Marketer) demonstrating how to build a complete marketing system using Claude Code, MCPs, and skills. Watch the original video

Overview

This masterclass demonstrates how to use AI tools, specifically Claude Code, to create a complete marketing system in one sitting. James Dickerson walks through building landing pages, lead magnets, ad creatives, and SEO content using skills, MCPs (Model Context Protocol), and voice prompting—all from a single terminal. The session covers everything from market research to traffic generation, showing how to automate marketing workflows that traditionally required agencies and significant budgets.

harryf

50 tips for Claude Code ( from a Meta Staff Engineer )

Value Summary: This comprehensive guide from a Meta Staff Engineer who uses Claude Code 12 hours daily for 6 months shares 50 battle-tested tips to maximize your AI coding productivity. You'll learn foundation setup, keyboard shortcuts, context engineering, advanced workflows including parallel development across multiple instances, and how to compose skills, MCPs, and subagents into powerful automation pipelines. Perfect for anyone wanting to level up from basic AI coding to professional-grade workflows.

Watch the original video

Overview

This video presents 50 practical tips for using Claude Code effectively, from a Meta Staff Engineer who has transitioned from manual coding to AI-assisted development. The guide covers everything from basic setup to advanced parallel development workflows, emphasizing context engineering as the key skill for modern AI-augmented software development.

harryf

Claude Agent SDK [Full Workshop] — Thariq Shihipar, Anthropic

Source: This is a summary of a YouTube workshop video where Thariq Shihipar from Anthropic presents a comprehensive guide to the Claude Agent SDK. Watch the original video

Overview

This 2-hour workshop provides an in-depth exploration of the Claude Agent SDK, explaining how it differs from regular Claude Code and when developers should choose the SDK over standard Claude implementations. The session covers agent design patterns, the power of bash tools, and includes live coding demonstrations building a Pokemon agent.

Key Topics

harryf

Claude Opus 4.6 vs GPT-5.3 Codex

Source

This is a summary of a YouTube video featuring Greg and Morgan Linton comparing the newly released Claude Opus 4.6 and GPT-5.3 Codex models. Watch the original video

Overview

On a major AI release day, both Anthropic and OpenAI dropped competing coding models. This video provides a deep technical comparison between Claude Opus 4.6 and GPT-5.3 Codex through a practical head-to-head test: building a PolyMarket competitor from scratch. Rather than declaring a single winner, the hosts reveal that these models represent fundamentally different coding philosophies and use cases.

harryf

Making Codebases Agent Ready – Eno Reyes, Factory AI

Source

This is a summary of a conference talk by Eno Reyes from Factory AI about preparing codebases for AI agent integration. Watch the original video

Overview

The talk focuses on how organizations can prepare their codebases to maximize the effectiveness of AI coding agents. The key insight is that the limiting factor for AI agent success isn't the agents themselves, but rather the quality and comprehensiveness of automated validation in your codebase. By investing in rigorous validation criteria—tests, linters, documentation, and other automated checks—organizations can unlock 5-7x productivity gains rather than just 1.5-2x improvements.

Key Topics