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OpenAI DevDay, Part 1 of 4: Managed State, Shared Context, and the Cost of Control

Part 1 follows developers from simpler conversation APIs and custom GPTs to tool execution, expressive speech, and the unresolved engineering questions behind vision.

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OpenAI DevDay, Part 2 of 4: From Configuring Bots to Running Workflows

Part 2 of Latent Space’s DevDay recording explores conversational programming, managed retrieval’s limits, plugin migration, and Zapier’s move from chat actions to scheduled assistants.

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OpenAI Functions: From Prompted JSON to Tool-Using Software

A June 2023 developer roundtable on native function calling, cheaper inference, and the decisions application code still needs to control.

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OpenAI’s Agents Platform: From Tool Calls to Observable Workflows

The March 2025 launch conversation explains why Responses became a new API, how its tools compose, and how handoffs and traces could support better agents.

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OpenRouter and the Marketplace for AI Models

Alex Atallah and Anjney Midha on model distribution, inference competition, fusion, and the security challenges of a growing token economy.

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Outputmaxxing: Turning Compute Into Useful Work

Anj Midha’s case for a compute grid begins with idle accelerators and expands into a theory of frontier labs: pool resources, reduce coordination losses, and let the mission determine what to build.

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Part 3 of 3: Finish the Research Story, Keep Senior Researchers Hands-On

Part 3 moves from writing before the experiments are settled to the environments that shape research judgment, then closes with an argument for building AI ecosystems around experienced technical contributors.

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Periodic Labs’ AI Scientists Need the Physical Experiment

Liam Fedus and Ekin Doğuş Çubuk explain how uncertain measurements, simulation and lab automation fit into materials discovery.

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Phind’s path from retrieved answers to coding models

Better context and stronger models turned a search product into a coding assistant. Specialized training promised more control over cost and capability; consistent correctness remained the harder problem.

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Physical AI Has to Survive the Machine

Applied Intuition connects autonomy models to the engineering that makes them usable: sensor supervision, reliable operating systems, validated simulation and inference that fits the machine.

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Playground’s Bet: Build the Model and the Graphics Editor Together

Better images are only part of the problem. Playground’s ambition connects model quality, precise editing, visual control, and feedback from people making things.

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Post-Training’s Moving Bottlenecks: Rewards, Tokens, and Systems

Josh McGrath on the engineering behind reinforcement learning, efficient agents, and useful long context, in a conversation at NeurIPS 2025.

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