The podcast by and for AI Engineers! In 2025, over 10 million readers and listeners came to Latent Space to hear about news, papers and interviews in Software 3.0. We cover Foundation Models changing every domain in Code Generation, Multimodality, AI Agents, GPU Infra and more, directly from the founders, builders, and thinkers involved in pushing the cutting edge. Striving to give you both the definitive take on the Current Thing down to the first introduction to the tech you'll be using in the next 3 months! We break news and exclusive interviews from OpenAI, Anthropic, Gemini, Meta (Soumith Chintala), Sierra (Bret Taylor), tiny (George Hotz), Databricks/MosaicML (Jon Frankle), Modular (Chris Lattner), Answer.ai (Jeremy Howard), et al. Full show notes always on https://latent.space Sponsorship and business inquiries: [email protected] www.latent.space
Recursive Language Models (RLMs) and advanced agent harness design represent a fundamental shift in AI development, moving beyond standard autoregressive decoders toward compositional systems that leverage code-based sub-agent calls. Alex Zhang, a researcher and contributor to GPU Mode, emphasizes that current AI progr...
OpenAI’s latest developments in computer use agents and API infrastructure mark a shift toward highly efficient, autonomous software interaction. By providing agents with dedicated Linux virtual environments, these tools now execute complex, multi-step tasks—such as meal planning or YouTube management—faster than human...
Agentic coding has evolved from a niche experiment to a standard engineering workflow, driven by the rapid development of Claude Code and its associated harnesses. Effective interaction with these agents requires building a precise mental model of their capabilities, particularly in handling ambiguity and verifying com...
OpenRouter functions as a critical infrastructure layer for the AI ecosystem, bridging the gap between model labs and developers by providing a neutral, scalable marketplace for LLM inference. By treating models as consumable SKUs, the platform enables developers to navigate the rapidly evolving landscape of frontier m...
Runway co-founder Anastasis Germanidis details the evolution of generative video technology, tracing the company's path from early creative tools to the development of general-purpose world models. The discussion highlights the shift from simple content generation to simulating physics and human dynamics, emphasizing t...
Generative genomics leverages large language models trained on DNA to read and write the fundamental fabric of life, enabling breakthroughs in scientific discovery and human health. Unlike traditional bioinformatics, models like Omni utilize unsupervised pre-training to predict regulatory functions and identify causal ...
AI-driven scientific discovery centers on the AERA framework, which transforms complex scientific problems into "scorable tasks" optimized by AI agents. By leveraging large language models like Gemini, researchers can iteratively generate and refine code to maximize specific scientific objectives, effectively offloadin...
Jev, a newly launched "System One" model, represents a shift toward machine-native AI designed for programmatic consumption rather than human-facing chat. By prioritizing intelligence-per-dollar and reliability, Jev functions as a foundational infrastructure layer for software developers, moving away from the mode-drop...
Risk, rather than technical capability, remains the primary bottleneck for widespread AI adoption in critical sectors like banking and healthcare. AIUC addresses this by building "confidence infrastructure" through a standardized, quarterly-updated certification framework for AI agents. This process combines rigorous t...
The "Eureka Machine" represents a vision for superintelligence designed to automate scientific and technological invention across fields like physics, chemistry, and biology. Rather than fearing a rapid "hard takeoff," the development of such systems faces significant physical and economic constraints, necessitating a ...
Integrating AI with physical world modeling through neural operators enables high-fidelity simulations that outperform traditional numerical methods. Neural operators generalize neural networks by learning mappings between function spaces, allowing for multi-scale resolution and efficient modeling of complex physical p...
Simulation technology serves as a powerful tool for human decision-making by modeling complex social behaviors rather than merely predicting future outcomes. Joon Sung Park, co-founder of Simile, notes that current frontier models often lack the deep, behavioral "dark knowledge" of humanity, which requires specialized ...
AI-driven protein design is transforming drug discovery from a slow, trial-and-error process into a precise engineering discipline. Chai Discovery, represented by co-founders Matt McPartland and Neil Patil, leverages generative models to design therapeutic candidates like antibodies with sub-angstrom structural accurac...
Inference engineering has evolved from simple GPU optimization into a complex discipline integrating training and inference loops. Modern production-ready APIs rely on techniques like cache-aware routing, disaggregated prefill and decode, and speculative decoding to maximize throughput and minimize latency. Quantizatio...
ChatGPT Work functions as a "super app" for productivity, designed to bridge the gap between complex technical capabilities and accessible knowledge work. By integrating the Codex harness with ChatGPT, the platform enables users to generate artifacts like spreadsheets, websites, and research tools without requiring dee...
Poolside founder Eiso Kant details the company's mission to democratize artificial intelligence through open-source foundation models. The discussion centers on the "model factory" approach, which treats model building as an industrialized, engineering-heavy process rather than purely experimental research. By emphasiz...
Xaira Therapeutics is advancing drug discovery by integrating AI-native platforms with high-throughput biological experimentation. The core innovation, the "virtual cell" model X-Cell, predicts cellular responses to genetic and chemical perturbations by leveraging massive, high-quality causal datasets generated through...
Lila Science treats scientific experimentation as an infinite token generator, leveraging scaled compute and automated "AI science factories" to train frontier reasoning models. By integrating physical lab infrastructure with API-driven automation, the platform treats experimental protocols as tool calls, enabling rapi...
Modal serves as a specialized cloud platform engineered for AI applications, including inference, training, and sandbox workloads. Originally conceived as a serverless runtime to simplify complex Kubernetes-based workflows, the platform has evolved to address the unique demands of AI-driven compute, such as elastic aut...
AI-driven drug discovery focuses on overcoming the historical limitations of modeling protein-small molecule interactions. By shifting from generative adversarial networks to diffusion-based primitives, researchers achieve the sub-angstrom resolution necessary to accurately predict binding affinities and molecular stru...
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