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Leash Bio's Ian Quigley on DEL data, model cheating, and an agentic chemistry reasoning sandbox

In a Latent Space podcast interview, Leash Bio co-founder Ian Quigley discusses how the company tamed DNA-encoded libraries (DEL) and extracted clean, reproducible protein-small molecule interaction data from them. The interview mentions that in its Kaggle competition, none of the roughly 2,000 teams surpassed random performance on an unseen chemical library, and that a model can guess a chemist's identity from molecular structure, while Hermes, using only its own data, matched Boltz on public benchmarks. Ian also previewed that the team is turning years of accumulated data into tens of thousands of lab-in-the-loop simulations for agents to perform verifiable scientific reasoning; that work later developed into Minos, with a preprint, blog post, and open dataset released.

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Airbnb CTO Ahmad Al-Dahle on How to Transform Airbnb into an AI-Native Company

Ahmad Al-Dahle, former head of generative AI at Meta and now CTO of Airbnb, told Latent Space about Airbnb's AI-native transformation: currently 60% of code is written by AI, feature delivery volume has increased nearly 80% year over year, and per-engineer PR throughput has risen about 1.6x. Customer support was the first user-facing deployment scenario, with about half of tickets resolved by AI, which the company says is consistent with the nearly 45% figure in its Q2 earnings report. The internal organizational context graph Everest enabled two services—grocery delivery and airport pickup—to launch quickly, with the latter taking about six weeks to develop. Airbnb adopts a multi-model strategy, deploying at least 10 custom models and selecting models for different scenarios along the Pareto frontier of cost, performance, and latency: the strongest frontier models for coding, and smaller, faster post-trained models for search. The internal agent AirChat is already connected to MCP organizational context, and the team is advancing event-triggered asynchronous agents to automatically handle on-call.

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GLM-5.2 Is a Turning Point for Open Source Agents

The author of Interconnects believes GLM-5.2 is the first open-weight model that is truly usable in coding agent frameworks, and community benchmarks show it is the only open source model able to compete head-to-head with the latest models from OpenAI and Anthropic. The author tested calling the model in Claude Code via the Fireworks API; overall capability immediately felt usable, but they encountered an issue where the framework sending images to the model caused the API session to break, requiring manual clearing of the context. The author calculates that 204 days separate the release of Claude Opus 4.5 from the release of GLM-5.2, falling within the commonly cited 6-to-9-month performance gap between open source and closed source, and believes this will intensify pricing pressure on heavy token consumers, benefiting the open source model inference and fine-tuning ecosystem.

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Claude Code and What Happens Next

Ethan Mollick had Claude Code autonomously develop a startup project earning $1,000 per month; after the AI asked three multiple-choice questions, it worked independently for 1 hour and 14 minutes, generated hundreds of code files, and delivered a runnable, deployable website. He believes the leap in this kind of coding tool comes from two things: the latest models can autonomously complete more work in programming tasks and self-correct, and they are given an agentic harness of tools and methods. Using Opus 4.5-powered Claude Code as an example, the article explains how it breaks through LLM limitations through mechanisms such as context compaction, Skills, and subagents, and notes that OpenAI Codex with GPT-5.2 and Google Antigravity with Gemini 3 show similar performance.

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