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Language Agents: From Reasoning to Reliable Action Reasoning helps an agent choose its next move. Reliable action also depends on usable tools, meaningful feedback, memory, and control over the workflow. Writeup · Date unavailable	Writeup	—	—
Latent Space’s 2024 Year in Review — Part 1 of 3: From Scaling Models to Shipping Systems Part 1 follows AI engineering’s growing community into inference economics, enterprise agents, synthetic data and the businesses succeeding close to either the machines or the customer. Writeup · Date unavailable	Writeup	—	—
Latent Space’s 2024 Year in Review — Part 2 of 3: From Model Demos to Daily Work Part 2 follows the move from impressive models to usable products: simpler image editing, branching agent execution, portable memory, harder evaluations and falling inference costs. Writeup · Date unavailable	Writeup	—	—
Learning from Human Behavior: Amplitude’s Feedback Loop for AI Products What users keep, edit, share, or discard can help diagnose AI output. Amplitude’s early question-to-chart work connects those signals to product experiments and a longer-term ambition to model behavior itself. Writeup · Date unavailable	Writeup	—	—
Leash Bio’s Lab in the Loop at Compute Speed How DNA-encoded screens, careful controls and an agent sandbox turn measured protein–molecule interactions into experiments for models. Writeup · Date unavailable	Writeup	—	—
Lila’s Lab as a Learning System — Part 1 of 3 Experiments have runtimes. Part 1 follows Lila’s effort to turn physical laboratories into training feedback for scientific AI, from callable instruments to catalysts, materials, and cell therapies. Writeup · Date unavailable	Writeup	—	—
Lindy at Work: Putting Agents on Rails Lindy’s redesign makes required actions explicit while leaving models room to interpret information—a division of labor that shapes its workflows, memory, evaluations, and product strategy. Writeup · Date unavailable	Writeup	—	—
Llama 2: Commercial Access, Preference Data, and the Work Beyond Benchmarks A July 2023 launch-day conversation about commercially usable models, the engineering behind human feedback, and the applications still waiting to be tested. Writeup · Date unavailable	Writeup	—	—
LLMs everywhere: the compiler work behind local inference From XGBoost to browser-based language models, MLC tackles the engineering cost of making models useful across hardware. Writeup · Date unavailable	Writeup	—	—
LLMs in 2024: Cheaper Capability, Harder Boundaries From laptop models to camera conversations, 2024 expanded what people could do with LLMs—and exposed the reliability, interface and trust problems that builders still needed to solve. Writeup · Date unavailable	Writeup	—	—
Long Context, Working Agents, and Learning in Public — Part 2 of 3 Part 2 of the January–February 2024 recap: Gemini’s million-token context, prompt transparency, anniversary demos of Lindy, RWKV, Pixee, and Julius, and listeners on evaluation and technical community. Writeup · Date unavailable	Writeup	—	—
Making Research Accessible, Making AI Interfaces Useful — Part 3 of 3 Part 3 of 3 of Latent Space’s January–February 2024 recap closes with listener questions about research coverage, AI user experience, and organizing a community around working demos. Writeup · Date unavailable	Writeup	—	—