Daily Brief

Code has become the fastest medium for producing technical content, but the most important piece isn't a secret agent skill or a magical framework: its the same thing that makes a great developer experience. Speakers: - Matt Palmer (Conductor): Matt Palmer is a DevRel & product leader focused on AI devtools, developer education, and making complex concepts accessible. Away from the keyboard, you can find him lifting, hiking, riding motorcycles, or caring for plants. X/Twitter: https://x.com/mattppal LinkedIn: https://www.linkedin.com/in/matt-palmer/

I pointed my lab at one problem, inference, after 200 users burned $1,000 in credits and the math just wouldn't close. So I built the thing, felt the cost, and went looking for why renting intelligence never pencils out. Turns out everyone in this market sells a gospel shaped like their own invoice. Jensen: build a token factory. Nadella: don't even think about the meter. Fireworks: own your model (on our infra). Three smart people, three different layers, three pitches that all end at "keep paying us." My rule: rent to learn, own to run. Rent the model while you're hunting PMF, own the infere

We ran auto-improvement loops on a paper classification task against a ground-truth dataset. A real problem, narrow enough to measure precisely, and in fact one of the few clear cut target functions out there. We’ll share how to properly set up an agent for auto-improvement, what task specificity and target function quality is actually required for it to work, and why the most efficient path to a continuously improving agentic system is one where domain experts and automation know when to hand off to each other.​​​​​​​​​​​​​​​​ Speakers: - Annabell Schäfer (Langfuse): Annabell is a Growth En

As a developer, AI is fun, exciting, and full of potential – but users don't always feel the same way about it. From a UX perspective, AI comes with a whole new set of considerations around user trust, privacy, and security. From a UI perspective, AI brings new interaction patterns, new icons, new visual cues, and so much more! If we want people to get the most from what we build, we have to teach our users how to use AI. Let's look at ways to introduce new capabilities in our apps and guide our users through new patterns and processes – ideally without making them throw their phone out a wi

Your agent passes offline evals at 90%. You ship. Production immediately finds failure modes your eval never saw. Sound familiar? The culprit is almost always the same: the "customer" in your offline eval is an off-the-shelf LLM that sounds nothing like your real users, and your synthetic test set doesn't capture how messy, angry, or off-topic real conversations get. Your eval was too easy. At Lyft, our customer-care agents resolve roughly a third of all customer issues — millions of conversations a month. To trust them at that scale, we built an adversarial user simulator: a fine-tuned LLM

Performance issues silently pile up in mature codebases. Teams know things could be faster, but can never justify pausing feature work to investigate. You have to put engineers on it just to find out if there's something worth fixing, and the effort is completely unpredictable: it could take an hour or three weeks. In this talk, we'll walk through a real case study of adding runtime intelligence to coding agents to enable continuous performance optimization in production. We'll cover the pain that led us here, the technical approach (agents analyzing real production context to surface high-RO

We built a demo agent to show customers how to connect agents to their tools. A simple chat assistant — Gmail, Calendar, a handful of connectors. It ran on a 15-minute schedule. And every 15 minutes, our production database strained. Latency crept up and alerts fired. Then settled. Then, it fired again. It took us a while to find it. One line - a "last seen" timestamp updating on every tool call. Written for a human who logs in once. Our agent was calling it sixty times a second. We had built infrastructure to show customers how to connect agents to their tools. We hadn't noticed we'd built

This talk examines the engineering challenges of building foundation models for single-cell biology from a non-biologist’s perspective. Speakers: - Akram Baharlouei (Altos Labs): Machine learning engineer at Altos Labs working on foundation models for biology. Previously at Meta AI and Qualcomm. LinkedIn: https://linkedin.com/in/akram-baharlouei-61784421