Jeremy Howard

The antidote to AI fatigue — Answer.ai Solveit

Oct 2, 2025 · 55:26

Jeremy Howard, Eric Ries, and Johno Whitaker join host Alessio to present Answer.ai's Solveit platform, arguing that human-in-the-loop, step-by-step AI collaboration outperforms fully autonomous agents for complex coding and writing tasks. The 12-person team built their own integrated development and deployment system, eschewing cloud vendors. Solveit provides each user a persistent Linux Docker container with a Jupyter-like interface where the AI can see all steps, enabling immediate error correction and iterative improvement. Johno demonstrates building a perfume scent search engine in under an hour; Eric shows how he uses Solveit to manage writing his book 'Incorruptible', incorporating test reader comments and fact-checking sections. Jeremy reveals they turned Andrej Karpathy's GPT tokenizer video into a polished blog post. The Solveit course launches October 20th at solve.it.com, teaching this philosophy beyond just the tool.

Answer.ai & AI Magic with Jeremy Howard

Aug 17, 2024 · 1:11:00

Jeremy Howard of Answer.AI argues that continuous pre-training should be treated as a continuum, not separate phases, and demonstrates how FSDP+QLoRA enables training a 70B model on just two NVIDIA 4090s. He reveals Answer’s non-hierarchical, manager-free R&D lab model that recruited unusual talent like Benjamin Warner and Ben Claviez, who independently launched BERT 24 to revive encoder-only architectures. Howard introduces FastHTML, a pure-Python web framework built on HTMX and Starlette, for creating modern SPAs without JavaScript. He previews “AI Magic,” a dialogue engineering system that moves beyond teletype-style chat interfaces and code editors, aiming to make AI-assisted development more interactive. The episode also covers Answer’s Public Benefit Corporation structure designed to resist hostile takeovers, and critiques decoder-only hype while advocating for encoder-decoder and state-space models.

The End of Finetuning — with Jeremy Howard of Fast.ai

Oct 20, 2023 · 1:24:48

Jeremy Howard of Fast.ai argues that fine-tuning language models is essentially continued pre-training, not a separate process, and that the common practice of fine-tuning on a single task causes catastrophic forgetting. He recounts the discovery of single-shot memorization in LLMs, where models memorize entire datasets after one epoch, a phenomenon many practitioners ignore. Howard criticizes the current focus on zero-shot and few-shot learning, advocating for transfer learning and small models. He shares his journey from philosophy to founding Fast.ai, the creation of ULMFit (which inspired GPT), and his ongoing work on making AI accessible. He also discusses his involvement with Modular's Mojo language and the importance of democratizing AI technology.