This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlst Britain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol. The bet underneath it is economic. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer. The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail. --- --- REFERENCES: organization: [00:01:15] Cosine https://cosine.sh [00:04:14] Mistral AI https://mistral.ai [00:05:50] Anthropic https://www.anthropic.com [00:07:42] Cohere https://cohere.com [00:08:36] DeepSeek https://www.deepseek.com tool: [00:02:52] Isambard-AI https://isambard.ac.uk [00:05:56] Colossus (xAI) https://en.wikipedia.org/wiki/Colossus_(supercomputer) [00:07:52] GLM (Z.ai) https://z.ai [00:11:52] NVIDIA B300 https://www.nvidia.com/en-us/data-center/dgx-b300/ [00:15:37] gpt-oss-120b https://huggingface.co/openai/gpt-oss-120b [00:15:52] Devstral 2 https://mistral.ai/news/devstral [00:16:01] Llama 70b https://www.llama.com [00:17:05] Claude Code https://www.anthropic.com/claude-code [00:26:23] ARC-AGI (Francois Chollet) https://arcprize.org [00:40:38] Swarm (Cosine) https://cosine.sh [00:40:50] OpenAI Codex https://github.com/openai/codex [00:41:16] Lumen Outpost (Cosine) https://cosine.sh [00:41:18] Kimi K2 (Moonshot) https://huggingface.co/moonshotai/Kimi-K2-Instruct [00:49:55] SWE-bench https://www.swebench.com [00:52:40] SystemVerilog https://en.wikipedia.org/wiki/SystemVerilog person: [00:23:40] Andrej Karpathy https://karpathy.ai paper: [00:27:10] GRPO (DeepSeekMath) https://arxiv.org/abs/2402.03300 [00:27:13] GSPO https://arxiv.org/abs/2507.18071 Incompressible Knowledge Probes, Bojie Li https://arxiv.org/pdf/2604.24827 Estimating the Size of Claude Opus 4.5/4.6 https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus --- ReScript: https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f