Laurent F. retweeted
Can a single database architecture satisfy both vibe-coded and large scale enterprise apps?
The same approach makes small database ultra cheap to run and branch can also make a large one faster and easier to operate. We call this architecture Lakebase.
Laurent F. retweeted
Il y a un truc qu'on oublie toujours avec la France
Chaque fois que ça allait mal, très mal, quelqu'un est venu
Une bergère de 17 ans, un jeune lieutenant corse, un colonel que personne n'écoutait.
Et là, ça va mal. Très mal
Alors ne vous étonnez pas s'il en revient un
Les derniers n’étaient pas des tendres, peut être que celui ci le sera encore moins
Laurent F. retweeted
All seven Databricks co-founders began our journey together at UC Berkeley. We started the company in a room in Soda Hall, moved to a small office on Addison Street, and spent our first few years in Berkeley before eventually heading to SF. Two of our co-founders are still on the faculty.
Today we announced Databricks Field at California Memorial Stadium, our first collegiate athletics sponsorship.
Berkeley shaped everything about how this company thinks, and this is our way of investing in the next generation of students and builders who'll do their best work on that campus.
Go Bears!
databricks.com/company/newsr…
Laurent F. retweeted
Super impressive how @Superhuman scales their AI infra:
blog.superhuman.com/scaling-…
Look, it’s super simple to create an OSS alternative to Jev that is at least as fast with free output tokens.
What’s unclear, at least looking at repros so far, is how easy it is to get something general and well-calibrated. And this might be where the difference is, post-training with clean data.
Now, for some use cases where people combine classification and tool calling you can go even faster by combining both calls, using if/then/else in JSON Schema. No network round-trip.
Laurent F. retweeted
people often share that they think @alighodsi is the best ceo in the valley
i disagree with them. he's the best in the world
Ali Ghodsi never wanted to be CEO. In 2015, he was interviewing for a professor job at Berkeley when the @Databricks board handed him the interim title. Revenue that year was $1.5M.
This episode with @alighodsi will go down as one of my favorites. 10 things I took away:
1. Focus the entire company, orders of magnitude of attention, on its single biggest bottleneck. Like a laser, almost to an extreme. The cycle is 1-3 years, not weeks. If your focus changes weekly, then you’re just in firefighting mode.
2. There is nothing worse than a conflict-averse CEO. They are wonderful people, but they are in the wrong job. Conflict is the gym for a CEO: nobody likes it, but everyone has to go.
3. Study your enemy carefully, understand their weaknesses and apply your strengths to those weaknesses. Snowflake had 2x his revenue. He didn’t copy them. He found three weaknesses (proprietary, no AI, expensive) and hammered them account by account for four years. Watch the competition, never follow it.
4. The concept of a Lakehouse was ridiculed internally and online. No one wanted to market with this new term. So he made the whole company religious about it anyway, killed the ads that converted better without the word, and put it in the sales comp plan. It worked. All hands on deck, no exceptions.
5. Be willing to take a step back for a much bigger vision, even when the company is already succeeding. At multiple hundreds of millions in ARR, he was unhappy, because the vision he pitched investors wasn’t the company he was running. So he took one step back to go ten forward.
6. On the flat org, player-coach model that a lot of people have talked about this year: “it’s BS.” Separate how the company thinks (AI, ontology) from how the humans get managed (they are after all, still humans). His staff meets 3x a week. I asked if it could just be coordinated in a Google doc. His answer: do you meet your wife and kids, or coordinate that in a Google doc?
7. His test for a sales leader: can they build the car, or just drive it? Ron Gabrisko, the Databricks CRO, had seen $0→50M and $50→100M+, and hadn’t changed jobs in 10 years prior to joining. Now he has been the CRO for over a decade. He built and drove the car the whole way. That almost never happens.
8. Hire execs ahead of the curve because by the time you need them, it’s too late. A real search takes 6-12 months. The extra time helps you increase false negatives and decrease false positives. Do an insane number of backdoor references because 80% of ‘front door’ references are bs.
9. The best salespeople are not super technical, so stop trying to force them to be. Square peg, round hole. The best win with professional aggression, high EQ, and mapping the real power base (how decisions get made high up in an organization), not technical depth.
10. Yes, your best AEs will annoy people. One of the first at Databricks got a meeting nobody could get, but got banned from the customer’s building for it. He told Ali, “what are you complaining about? I got the meeting.” Professionally aggressive is the bar.
One bottleneck, zero wussing out. He reminds me of @elonmusk that way.
Chapters
0:00 – Introduction
1:22 – The secret CEO search and why the board bet on a founder
4:11 – Professor or CEO? Always taking the harder option
8:18 – Pour everything into one bottleneck
12:16 – Killing PLG and learning what great enterprise sellers actually have
19:09 – Hiring ahead of the curve: sales leaders, execs, and back-door references
27:02 – The Snowflake rivalry: study your enemy, never copy them
33:22 – Lakehouse: conviction, ridicule, and the case for second acts
41:02 – The killer instinct and why conflict-averse CEOs fail
44:10 – Dunbar's number and rethinking the org chart around AI
48:00 – AGI is already here — enterprises just use it as a chatbot
52:55 – Does he still code? Two days for a connector vs. three quarters
57:18 – A day in the life, and why the Monday meeting isn't theater
1:04:53 – Why Databricks will go public, just not yet
1:08:09 – Get over conflict aversion, or don't be CEO
1:11:07 – Brian's takeaways
Link to more in the comments.
Laurent F. retweeted
Today @neondatabase Auth, Object Storage, Functions, AI Gateway are GA. Connected to Lakebase Postgres. Give it to your agents 𝚗𝚙𝚡 𝚗𝚎𝚘𝚗 𝚒𝚗𝚒𝚝
Laurent F. retweeted
I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands.
And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.
Laurent F. retweeted
I wrote about the state of AI, why I’m concerned about the next few years, and the choices we need to make to keep the future in humanity’s hands.
An Alien Mind: openai.com/index/an-alien-mi…
Laurent F. retweeted
We are in a Cambrian explosion of frontier models. Even as a researcher who spends a lot of time evaluating models, I find it hard to keep up. New models are arriving constantly, and the mental overhead of choosing the right setup for each task keeps growing.
That motivated us to build Smart Routing: an intelligent layer that automatically matches each task to the right model based on its complexity and the capabilities it needs. Our early results are promising: frontier level quality with 30%+ lower cost, and more than 50% savings on public benchmarks.
Laurent F. retweeted
We’re excited to announce that Databricks is a Day-0 launch partner of Thinking Machines Lab (@thinkymachines), bringing its first oss model, Inkling, to the Databricks platform.
- the strongest US oss model
- 974B total parameters, 41B active MoE
- Apache 2.0 license
The Thinky team is absolutely cracked. We’ve loved working with them.
Come try Inkling on Databricks!
.@thinkymachines' first open-weights model, Inkling, is now available on Databricks through Unity AI Gateway.
As a day zero launch partner, Databricks gives enterprise teams access to a model that excels at coding and agentic reasoning and supports multimodal inputs.
Teams can customize Inkling for their business, govern it with centralized security, permissions, cost controls, and observability, and connect it to coding agents including Cursor and OpenCode.
Start building with Inkling on Databricks → databricks.com/blog/inkling-…
Laurent F. retweeted
At 11k employees, our AI costs are going up. Which model & harness should we use to lower cost but also retain great quality?
We didn't want to blindly trust public benchmarks. So we ran a comprehensive evaluation on our tasks, code base, infra. It's been produced by more than 3,000 software engineers, spans 3 hyperscalar clouds and many languages and tasks.
The results are surprising. We find that for the SAME mdoel, the choice of harness can significantly save costs (~2x). We also find that GLM 5.2 performs extremely well. We run Omnigent in front of these and can easily multiplex different harnesses and models for different tasks.
Check it out:
databricks.com/blog/benchmar…
Laurent F. retweeted
Zero-CDC analytics on Postgres by reading the storage layer directly
I find the Lakebase design for serverless Postgres very elegant, so I spent some time explaining how it works in this blog.
The blog starts by explaining how databases really persist data (with a write-ahead-log and data files that are updated async), and how Lakebase separates storage and compute by externalizing those two components. It ends with how the Lakebase architecture naturally leads to LTAP, enabling OLTP and analytical workloads against a single governed copy of data.
My goal was to make it readable by anyone curious about how these systems work, not just database and storage experts. That turned out to be a lot more challenging than I first thought. Database storage is one of the most complex areas in computer science (the ARIES paper cited in blog was the hardest paper I personally ever had to read). The first draft had too little detail and I couldn't land the ideas. The second had too much and I'd lost anyone who isn't already a storage expert. This is the third draft, and I'd love feedback on whether the depth feels right.
databricks.com/blog/lakebase…
Laurent F. retweeted
You may have heard that GLM-5.2 at 328 token/s is cool,
How about 392?
Databricks is now #1 in inference speed for GLM-5.2 on Artificial Analysis. It's a great model, and we did a lot of optimizations.
Laurent F. retweeted
JUST IN: Martin Scorsese joins AI filmmaking firm Black Forest Labs as an adviser.
Laurent F. retweeted
watch how fast these labs launch their “unsafe” models when a competitor starts beating their benchmarks
Laurent F. retweeted
The @aiDotEngineer after-after-after party
@WolframRvnwlf @altryne @hellheff @liamcbride @raoufcode & @Prince_Canuma























