Posts

Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs

Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs

A practical guide to the engineering principles and trade-offs involved in training a small language model from scratch on a local machine, based on a workshop by Angelos Perivolaropoulos from ElevenLabs.

Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next

Anthropic's Boris Cherny: Why Coding Is Solved, and What Comes Next

Boris Cherny, creator of Claude Code, discusses the future of software development at AI Ascent 2026. He argues that coding is effectively a solved problem, detailing his personal workflow of shipping dozens of PRs daily from his phone. Cherny explores the shift from typeahead to autonomous agents, the rise of cross-disciplinary generalist teams, and uses the printing press as an analogy for the coming democratization of software creation for all.

Skill Issue: How We Used AI to Make Agents Actually Good at Supabase — Pedro Rodrigues, Supabase

Skill Issue: How We Used AI to Make Agents Actually Good at Supabase — Pedro Rodrigues, Supabase

A deep dive into building, testing, and iterating on Agent Skills to improve AI agent performance. This workshop covers the core concepts of progressive disclosure, eval-driven development, and practical application using a real-world Supabase and PostgreSQL security scenario.

Digital Freedom, AI Regulation, and the Fight for the Western Internet | The a16z Show

Digital Freedom, AI Regulation, and the Fight for the Western Internet | The a16z Show

Under Secretary for Public Diplomacy Sarah Rogers discusses the critical intersection of AI, free speech, and national security. She outlines the shift from promoting internet freedom to combating censorship, the threat of foreign regulations to American tech, and the strategic importance of developing "AI with a Western soul" grounded in democratic values and the rule of law.

CLI vs MCP: How AI Agents Choose the Right Tool for the Job

CLI vs MCP: How AI Agents Choose the Right Tool for the Job

AI agents can interact with the world through either the Command Line Interface (CLI) or the Model Context Protocol (MCP). This summary explores the trade-offs between the two, highlighting CLI's efficiency for tasks the model is trained on, versus MCP's power of abstraction and governance for more complex, high-level operations.

TLMs: Tiny LLMs and Agents on Edge Devices with LiteRT-LM — Cormac Brick, Google

TLMs: Tiny LLMs and Agents on Edge Devices with LiteRT-LM — Cormac Brick, Google

Cormac Brick from Google's AI Edge team details the dual trends of on-device AI: large, system-level models like Gemma 4 enabling complex agent skills, and fine-tuned tiny LLMs for high-performance, in-app tasks. The summary covers the architecture of on-device function calling, the engineering trade-offs for edge deployment, and the practical workflow for fine-tuning and deploying models under 1B parameters on platforms like Android and iOS.