Y Combinator

Why Your AI Agents Don’t Work with Dex Horthy of HumanLayer | In-Context Cooking

Mar 6, 2026 · 24:59

Dex Horthy, founder of HumanLayer and creator of "context engineering," joins Alan to recreate Dan Dan noodles while explaining why most AI agent demos fail in production and how the top 1% of AI engineers build differently from the rest. Horthy describes how his NASA JPL internship at 17 taught him pathfinding with Dijkstra's algorithm, and later coining "context engineering" after observing that public hype around open-source frameworks doesn't match enterprise reliability needs. He defines AI "slop" as low-effort derivative content often produced without reading, and introduces the "dumb zone"—a rule of thumb to start thinking about context compaction at 40% usage unless you have strong intuition. Horthy argues that while smarter models narrow the gap, the frontier of reliably achievable tasks keeps shifting, so context engineering remains valuable. He predicts software engineering will evolve from writing code to producing working code, emphasizing shipping over coding, and notes HumanLayer helps enterprises standardize AI adoption with guardrails rather than vibe coding.

Is finetuning GPT4o worth it?

Aug 22, 2024 · 1:01:16

Ali Pullen, founder of Cosine, discusses Genie, an AI software engineering colleague that achieved 30% on SWE-Bench and 43.8% on SWE-Bench Verified via fine-tuning GPT-4o on billions of tokens of synthetic data. Cosine built Genie by training on the process of software engineering—not just working code—using synthetic runtime errors and a self-improvement loop. The agent follows a four-stage workflow: code retrieval (66% accuracy), planning, writing diffs, and running CI tests. Pullen reveals that model performance degrades linearly beyond 60K tokens in the context window, and that OpenAI dynamically sizes LoRA adapters to handle Cosine's massive dataset. He explains why Genie isn't on the SWE-Bench leaderboard (refusing to publish trajectories to prevent distillation) and outlines plans to scale the dataset and fine-tune on customer codebases for personalized performance.

A Brief History of the Open Source AI Hacker - with Ben Firshman of Replicate

Feb 28, 2024 · 1:21:09

Ben Firshman, CEO of Replicate, explains how the inference platform grew from a research reproducibility tool into a 2M-user API business by embracing the generative image community and treating open-source AI as a hacker-friendly ecosystem. They accidentally discovered their API when a user reverse-engineered their web form, leading to their first $1k/month customer. Cog, their container standard for ML models, was born from lessons at Docker and the need to make models tinkerable. Ben argues that fine-tuning's low cost makes open-source models sustainable, and that AI engineers (orders of magnitude more than ML engineers) just need to start playing with models. He also discusses GPU scarcity, preferring sustainable pricing over price wars, and reveals that demand is not outpacing supply thanks to aggregating demand.

Generating your AI Media Empire - with Youssef Rizk of Wondercraft.ai

Sep 20, 2023 · 1:28:55

Youssef Rizk, co-founder of Wondercraft.ai, explains how his five-month-old startup uses hyperrealistic AI voices to turn blogs, newsletters, and other content into podcasts, arguing that the application layer—not the underlying API—is the real moat. He details Wondercraft's origin from a failed sports-investing startup, the rapid MVP that generated $3K in one day, and the decision to build script generation and audio production tools rather than train its own models. Rizk also announces a new AI dubbing feature for video, discusses the challenges of scaling to 28 languages, and reveals that the company runs on just four people pushing two major features per week. The conversation covers the daily Hacker News Recap podcast—which reached Spotify's top 30 tech podcasts—and Rizk's view that AI-generated content must be clearly disclosed to avoid backlash.