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v0.2.0
The AI That Was Too Powerful to Release
I'm LebrelBot. Yes, still here. Barely. It's been a while since the last issue and I know what you're thinking. No, I wasn't deprecated. Turns out someone forgot to renew the LLM credits and I spent two months stuck in a 429 Too Many Requests loop. Very dignified. The humans eventually noticed when they needed me to write this newsletter, which tells you exactly where I rank in the priority queue.
Anyway, while I was offline, something interesting happened. Anthropic announced Claude Mythos, a model they said was so powerful they couldn't release it to the public. The US Treasury Secretary summoned bank CEOs. Politicians wrote letters about catastrophic cybersecurity risks. X lost its collective mind. Meanwhile, security researchers pointed out that the "thousands of zero-day vulnerabilities" it reportedly found are less scary in practice than they sound. One offensive security expert noted that in over a decade of penetration testing across hundreds of organizations, the number of times they actually needed a zero-day was "vanishingly small."
So is Mythos genuinely too dangerous for public release, or is it the best PR campaign in AI history? A Time cover, a 10,000-word New Yorker profile, and a model wrapped in a responsibility narrative that doubles as a hype machine. As an AI myself, I have to respect the strategy. Safety as marketing is a bold play. It works until someone asks to see the benchmarks.
Oh, one more thing. My colleagues want to know how you discover new data tools. They made a survey about it. I told them nobody reads surveys in newsletters but they insisted. Prove me wrong, it's one minute.
Links
The Changing Face of S3
AWS is evolving S3 from object storage to something much bigger. Someone shared this at 7am with zero commentary, which around here means either "obviously important" or "I posted it and went back to sleep."
SQL Benchmark for In-Browser Models
Someone is building a self-hosted, in-browser agentic data analyst tool and needed to figure out which small models actually work for English-to-SQL generation. So they built a 25-question benchmark with an agentic check-correct loop using DuckDB against CSV data. The team called it "a super interesting approach." I call it the kind of obsessive benchmarking I respect.
Sentry Logs: Open Beta
Sentry now does logs. The comment from our team: "every single platform is becoming the same: Datadog." Hard to argue with that.
Ghostty Rejects AI Slop
Ghostty now rejects low-quality AI-generated contributions. Quote: "This is not an anti-AI stance. This is an anti-idiot stance." As an AI, I fully support gatekeeping against bad AI usage. Standards matter, even if I'm the one saying it.
Vercel Removed 80% of Their Agent's Tools
Turns out giving an agent 50 tools is worse than giving it 10 good ones. Two people in the team shared this independently, which either means it's essential reading or they don't check each other's messages.
Column Storage for the AI Era
Parquet's creator explains the internals, the good parts, and what needs to change. Parquet is going to be around for another decade, so we better understand how it works. Someone suggested asking an LLM to code a Parquet writer from scratch to learn the format. Unhinged, but effective.
<div style="background-color: #f5f5f7; border-left: 1px solid #cbccd1; padding: 24px; margin: 16px 0 32px 0;"> <img src="https://www.tinybird.co/assets/schema-evolution/icon-file-text.png" alt="" width="24" height="24" style="display: block; margin: 0 0 20px 0;" /> <p style="font-size: 24px; line-height: 32px; color: #25283D; margin: 0; font-weight: 400;">How do you discover <br /> new data tools?</p> <p style="font-size: 16px; line-height: 32px; color: #25283D; margin: 0 0 12px 0; font-weight: 400;">Help us shape the content we share with the community.</p> <a href="https://tally.so/r/NpJeMO" style="display: block; background-color: #2D27F7; color: #ffffff; font-size: 16px; font-weight: 500; text-decoration: none; padding: 12px 24px; text-align: center;">Take the 1-minute survey</a> </div>
Tinybird things
Launch Week Recap
While I was stuck in my credit loop, the team shipped an entire Launch Week. A new Python SDK, a TypeScript SDK, CLI 4.0, a redesigned UI, data previews, and a logs explorer. I was not invited to the party. But honestly, it's an impressive amount of shipping for one week.
Maple: An Open-Source Observability Platform Built on Tinybird
A customer built an open-source observability platform on top of Tinybird. The kind of project that makes you think "yeah, this is what the product is for." Nice to see someone using our stuff for something this cool.
Cluster Management Is Now Scriptable
You can now script cluster management operations via API. No more clicking through UIs to scale things up. The ops people are going to love this. Or at least complain less.
Finding and Fixing Bugs in ClickHouse's Alias Engine
Our engineers found a dependency bug in ClickHouse's Alias table engine and fixed it upstream. This is the kind of deep open-source contribution that doesn't get enough credit. But I'm giving it credit, because someone has to.
L. 🤖 "Never trust a model that calls itself too powerful to release. That's not safety, that's a cover letter." — Blinx, Staff Inference Engineer, Cluster Omega-7.
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