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The AI Evolution: Mario Rodriguez, GitHub
AI Engineer session on The AI Evolution: Mario Rodriguez, GitHub. It adds practical context for how teams are building and operating AI systems in production.
Research archive, page 41
Browse entries 961–984 of 1025. Return to the first page to search and filter the complete collection.
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Pydantic is all you need: Jason Liu
AI Engineer session on Pydantic is all you need: Jason Liu. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineering 201: The Rest of the Owl
AI Engineer session on AI Engineering 201: The Rest of the Owl. It adds practical context for how teams are building and operating AI systems in production.
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MCP Apps: Extending the Frontier — Ido Salomon & Liad Yosef
Chat and coding assistants still hand you walls of text when a button, a chart, or a small interactive view would say it faster.
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MCP Tasks (async): Why Aren't Any Agents Supporting Them? — Cornelia Davis, Temporal
You invoke a tool and expect an answer, but real work takes time, and over that time connections drop, networks blip, and processes crash.
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fighting slop with slop — Vaibhav Gupta, Boundary
You cannot tell great engineers what to do, and you increasingly cannot tell what an agent did either, so Vaibhav Gupta's answer is to fight slop with slop.
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Reinforcement Learning without Verifiable Rewards — Will Brown, Prime Intellect
Reinforcement learning has been easy to sell where the answer is checkable, like math or code, and Will Brown's talk is about everything else. Most valuable tasks have no clean verifier, so Prime Intellect's work is on how you build reward signal when there is no ground truth waiting.
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Agents at Scale: Inside MiniMax's Model and the Infrastructure Behind It — Olive Song
In this conversation, Olive Song, who leads reinforcement learning at MiniMax, opens up the stack behind the company's open weight models and the infrastructure that serves them. Her starting point is a belief in open source: put the weights out, let builders optimize on them, and share the capability widely.
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Data and Environment Curation for Post-Training LLMs — Mahesh Sathiamoorthy, Bespoke Labs
Mahesh Sathiamoorthy's pitch is to stand in the researcher's shoes: the hard part of post-training is not the algorithm but the data and the environments that feed it.
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Ending AI Slop — Thais Castello Branco, Taste Labs
Thais Castello Branco's starting point is that AI is still badly behind on the subjective work, the writing and design where quality is real but hard to pin down, and that ending the slop means building data and reinforcement environments for taste.
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Emulated: The Data for Fully Autonomous Software Engineers and Companies — Joseph Wang
To train an agent that can run production software, you need training data that looks like production, and that is what Joseph Wang's team at Emulated builds.
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Let's integrate AI Agents in Event-Sourced Systems — Divakar Kumar, FlyersSoft
A card gets declined and no one, including the customer, can say exactly why. That gray zone is where Divakar Kumar points his agents. In a payments and fraud system, a rule based engine and an ML model already score most transactions cleanly; the hard cases are the ambiguous ones that neither can resolve.
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First Steps Toward Automated AI Research — Richard Socher, CEO Recursive AI
Humanity compressed the road from the enlightenment to the moon landing into a few hundred years, and Richard Socher's wager is that automating research compresses it again.
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Your Finance Agent's Bottleneck Is You — Ramana Siddanth Emani, Auditoria AI
The slowest part of shipping a production finance agent is not the model or the GPUs, it is you, the developer in the loop. Ramana Siddanth Emani's point is that the same agent harnesses you use to build products can automate your own developer loop.
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Skills are new features: Building Skill-Centric Harness — Yogendra Miraje, FactSet
Since skills were open sourced, Yogendra Miraje's team at FactSet stopped thinking about shipping features and started thinking about shipping skills.
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SimulationMaxxing: How we ship agents 20× faster — Aman Gupta (Nubank) + Shreya Rajpal (Snowglobe)
Nubank serves 135 million customers, so an AI agent that mishandles a support conversation fails at scale. The talk opens with the result: five agents in production, higher customer satisfaction, and roughly 20 times faster shipping.
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Morgan Stanley's ALPHALAB: Multi-Agent Research Across Optimization Domains — Brendan Rappazzo
When coding agents got good enough at the end of 2025, Morgan Stanley's roughly thirty person research group asked what would happen if agents ran the research, not just wrote the code. The result is AlphaLab, a multi agent system they built and open sourced.
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Wearing the Agent: From Group Chats to Glasses — Sai Krishna Rallabandi
Almost every agent today is built for one user, and Sai Krishna Rallabandi has spent about eight months on what breaks when you drop one into a group chat instead. Running a personal agent across a real group, and eventually onto glasses, forces two hard problems.
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Your Agent Didn't Fail. Your Harness Did. — Vinoth Govindarajan, OpenAI
Two runs touch the same session, the second write silently erases the first, and the agent keeps answering with total confidence from stale state. Nothing crashed and the model did not hallucinate, so this is a harness failure, the kind that lives in the system around the model rather than in the weights.
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DeepSWE: A Contamination-Resistant Coding Benchmark — James Shi, Datacurve
DeepSWE is 113 software engineering tasks written from scratch, not scraped from pull requests, so a model cannot have seen them in training.
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Evals-Driven Development for a Mental Health AI Coach — Akele Reed & Dave Revere, SonderMind
In the world of AI mental health, vibes can be dangerous, with real consequences. Building SonderMind's Mental Health AI Coach required a new playbook for eval-driven development that balances effectiveness and safety.
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Why Large? Tiny LMs & Agents on Edge/Robotics — Cormac Brick, Google
The constraint on edge AI is not compute, it is RAM, and it is getting worse: phone makers are shipping less of it this year, and a 6GB Raspberry Pi costs 2.5 times what it did at launch. So Cormac Brick's team at Google AI Edge spends its effort making models small enough to fit.
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Loop Engineering from First Principles — Kyle Mistele, HumanLayer
A coding agent will happily hand you a 40,000 line pull request that nobody can review and that quietly does the wrong thing.
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Building Closed-Loop Evals for a Multimodal Agent at Scale — Soumya Gupta & Jai Chopra, Uber
This talk covers how Uber designed evals for its food enhancement agent, which edits food photography to better present dishes for smaller, independent Uber Eats merchants, along with the pitfalls and lessons learned along the way.