Olmo Maldonado @ibolmo
Staff Engineer @ Braintrust. All posts are my own. Formerly at Zapier (Sr. Engr), omcode (founder), Code RGV (founder), Google, UCLA (M.S. EE), UTSA (B.S. EE) medium.com/ibolmo USA Joined June 2008-
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If you cannot define “good” and measure it, you are not ready to ship. For long-context LLM systems, “good” does not just mean a bigger context window. It means the model can find the right function, inside the right class, in the right file, and retrieve the exact facts needed to produce accurate behavior. This needle-in-a-haystack problem gets much harder when the haystack is a million tokens of packages, repos, and docs. That is what we stress tested with GLM-5.2 on Baseten. (and it was awesome collab-ing with @thealexker @philipkiely and team!) In this methodology post, we go deeper on how we scoped the eval, generated the benchmark, scored the outputs, and tested the scorers themselves. Eval methodology: braintrust.dev/blog/glm-52-vs…
We're making GLM-5.2 available in Braintrust today with the help of our friends at @baseten. This means you can run evals on GLM and compare it to other models for specific tasks, which is exactly what we did. When testing long-context retrieval Opus 4.8 is only modestly more
Run the best OSS models in Braintrust, in collaboration with @baseten. Call GLM-5.2 natively, eval its quality, and observe its behavior in production. Save on inference costs without compromising quality by picking the best model for your agent. Free to try through July 31.
Braintrust is up and to the right. New office (floor); same drive to help customers build better AI products. We're hiring! Help us make a great product for AI team (and to expand to the next floor 😂). braintrust.dev/careers
At this stage, I've handed over all refactoring to agents. Not for laziness, but for correctness. One of the human-prone errors is to refactor and forget to update everything thoroughly. With the latest high effort models I've only seen they catch things I would have missed.
we've put a ton of work into modernizing the waterfall view in braintrust. i'm most excited about how obvious we make cache behavior (blue is cached and purple is uncached). in the era of vibe coded ui, this one actually took some serious human brainpower to get right.
One thing the AI and I can agree on: smaller files >>> than singiular massive files. Now, if I can only get the AI to stop greedily exporting everything, writing unnecessary abstractions, and early optimizations. Alas the AI doesn't have "taste".
It's clear the agent is strongly biased to respond in the conversation vs. having a conversation in a document. It doesn't know the most appropriate spot to put a comment, for example. No need to talk about future ideas in what we currently do or have done (past), for example.
I'm experimenting with teaching the agent how to "comment", "resolve", and "reply" inside of a markdown file. Google Docs style.
npmjs.com/package/@opent… "Note: This is an experimental package ..." 22 million weekly downloads. No one believes you!
Every time I see a "New release available. Click to update": Me: Hm, updated an hour ago? No, thank you. I'll wait 3 days. Also me: Curls into a ball.
Best ai productivity hack: Disable social media and YouTube.
Anyone still writing their own commits? Their own PR title and description? I love having those 15 minutes back in my life (to spend with other agents).
With Agent skills. I claim we're going from free-form prose to written (skills) workflows. Use X, Y, and in parallel do Z, alpha. iterate until beta. you are done when gamma, delta, and epsilon. The AI will fill in the obvious gaps and avoid users being overly prescriptive.
Go out and vote! Have a seat on the table. Just like you choose your agents you should choose who acts on your behalf.
Would love if github had tsx file viewer like storybook.
In my 20+ years of software engineering. 40% is the language, but 60% is the tooling. It's amazing how much more lazy I can be with AI. Cleanup wasteful files and processes used to be a context killer. At some point, I'll have a watchdog that does this for me automatically.
starting to feel like ironman & jarvis with gpt-5.5 high + my own skills. it's been nice folks ✌️ inflection point 5/14/2026
@Caltrain there's no amount of money I wouldn't pay to have reliable high speed Internet on my commute. Ask your price. Done.
Teams running AI applications in production generate thousands of traces a day, but can't read them all. Braintrust is launching Topics to automatically group traces into meaningful patterns, so you can see what's actually happening. Topics is accessible directly in the platform, so you're not relying on static reports. Build better datasets, generate inputs to your evals, and understand when new problems show up in production.
Observability tells you what happened. Evals tell you whether it's getting better. Braintrust connects the two by automatically surfacing patterns and acting on them. New from the Trace keynote: - Topics finds insights and errors - Loop enables human-powered remediation - Braintrust Gateway standardizes access and observability across model providers - Braintrust CLI brings the UI to the terminal
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