Tristan Handy

Building a modern analytics workflow. Founder & CEO @ dbt Labs, creators and maintainers of dbt.

[Tristan Handy @jthandy]

15x YoY. incredible. 'overnight' success that took 4(?) years.

congrats to erik and team.

[Modal @modal]

Today we're announcing that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation, led by @Accel.

Since our Series C, usage has tripled and we’ve grown more than 15x year-over-year.

We are deeply grateful to the developers and companies building on Modal. There's still so much more to build, and we can't think of a better time to be building software.

[Modal @modal]

Runtime speaker lineup is live!

We're bringing together experts covering AI infrastructure, applications of AI in science and robotics, the future of software engineering, and more.

[Tristan Handy @jthandy]

Incredible what the Modal team has achieved over the past year.

[Modal @modal]

We're expanding across Europe.

This summer we moved into a bigger Stockholm office, and today we're adding London.

We're hiring across GTM and engineering!
Read more: https://t.co/WEWFrWae6R

[Sam Bhagwat @calcsam]

Software engineering is becoming a hierarchy of loops. So today we're launching Mastra Factory: a system for agents to take software from issue into production.

Get started with 𝚗𝚙𝚖 𝚌𝚛𝚎𝚊𝚝𝚎 𝚏𝚊𝚌𝚝𝚘𝚛𝚢:

[Sam Bhagwat @calcsam]

We’re excited to announce today that @mastra has raised a $22M Series A led by Spark Capital.

This brings our total capital raised to $35M:

[Jun Ernesto Okumura @pacocat]

#dbt Lab社のTristan CEOにご来社いただき、とても良いディスカッションができました。dbtはずっとお世話になっているけれども、今後の拡張も欲しかった機能ばかりで楽しみです。引き続きAI-oriented Data Platform頑張っていきます💪

[ethan ding 📊 @TheEthanDing]

Your AI Agents can only analyze 1GB of data
Enterprises have 1,000,000x as much data
We fixed that...

It took 3 years and building an entirely new execution environment to pull this off.

TextQL lets AI agents analyze your ENTIRE enterprise data - not just the 0.0001% they can handle today.

Here's what makes it so different:

1. ALL your data, not samples. AI today: 1GB demos. Your reality: 350TB across 47 systems. TextQL: Everything. At once.

2. Real enterprise questions.ChatGPT writes poems. We answer why renewal rates dropped 3% after ERP upgrades.

3. No more sandbox limitations.Everyone else: Jupyter notebook with 10GB RAM. Us: A data center.
------------------------------
In 2021, I watched Fortune 500s spend millions on AI that could only analyze data that fits on a USB stick.

Today:
- We process 10+ petabytes daily
- Our biggest customer analyzes 100TB in real-time
- Average query spans 50+ data sources

And we just proved what everyone said was impossible - AI that can actually build a digital twin of your enterprise
------------------------------
To celebrate, we're giving away free data architecture assessments.

We'll show you exactly how much of your data is invisible to AI right now (spoiler: it's probably 99.9%).

Comment "TextQL" and we'll send you the assessment link.

[Jason Ganz @jasnonaz]

Introducing the dbt MCP Server!

dbt is the standard for creating high trust datasets. AI workflows need access to high trust data, including structured data.

The dbt MCP server is a meaningful step towards connecting AI systems to your data.

[martin_casado @martin_casado]

I think AI is going to usher in a gold age of infra, not obviate it.

It's just so clear that good CS fundamentals result in better AI built systems. Vibe coding works better with type safety, languages where syntax maps closely to semantics, referential transparency, tight scoping etc.

These approaches have never been widely adopted in CS because they are hard for humans, and in particular novices. But they're not hard for AI. And they map so much better from natural language descriptions.

[Sundar Pichai @sundarpichai]

To MCP or not to MCP, that's the question. Lmk in comments

[Voyage AI by MongoDB @VoyageAI]

Thrilled to share that we've closed $28M in funding, led by @CRV, with continued support from @wing_vc and @saranormous. Also excited to onboard strategic partners @SnowflakeDB and @databricks!

https://t.co/TNsK3zSkoM

Building the world’s best models for RAG and search 🧵🧵🧵:

[Patronus AI @PatronusAI]

We are excited to announce our $17M Series A led by @glennsolomon at @notablecap with participation from @whoisnnamdi and @lsvp, @datadoghq, @matthartman, @jefrankle, @jasoncwarner, @jthandy, @BM_DataDowntime, @aparnabsinha, @NadimHossain and a number of tech industry executives 🚀🚀🚀

[Samuel Colvin @samuelcolvin]

We're live!

@pydantic Logfire is in open beta.

Since I started writing Python in 2010, I've wanted a better way to do logging.

Off the back of Pydantic's unbelievable growth, last year I started a company backed by @sequoia, and lucky enough to hire the brightest people I know.

Now we've gone and built the logging thing I always wanted.

https://t.co/jWcr2Smlc5

[Matt Miller @MattEvantic]

“Community isn’t just what got us here it’s what we need to drive the ecosystem for next decade plus” - amazing to have @jthandy join me at Slush to share his company building journey at @dbt_labs, honored to be your partner.

[Barry McCardel @barrald]

Next Thursday, we're taking the wraps off of Hex 3.0!

We went pretty hard for this, including a live stream video you won't want to miss 📺 Add it to your cal! https://t.co/S0Rel5rS3x

(we're also having a fun little party in SF, DM me if you want to join)

[Sharon Zhou @realSharonZhou]

Super excited to announce Finetuning LLMs, a short-course made with my friend @AndrewYNg !! 🎉

By importing open-source @LaminiAI core & @HuggingFace & @PyTorch & @wandb, you can:

✅ Gain an expert's intuition behind finetuning*
✅ Understand how finetuning fits in vs. prompt-engineering vs. RAG vs. pretraining. Or are they all layers of a cake? 🎂 Yes, always cake.
✅ Finetune your own LLM, continually teaching it new knowledge
��� Pick up practical frameworks for compute & memory requirements needed to finetune vs. run LLMs
✅ Touch on advanced topics, such as parameter-efficient finetuning

*Honestly, "finetuning" could totally be named better, especially if you know the history of finetuning vision models! It involved freezing the model weights and changing the (classifier) head. For LLMs, we often keep the weights unfrozen and don't swap layers of the model, just the data.

Built with favorite AI libraries:
@HuggingFace - stay awesome, keep building
@PyTorch - thank you for saving me in grad school
@wandb - wish I had you in grad school, sorry Tensorboard
@LaminiAI - building this now, so grad school would've been just 1 line of code

[Julia Schottenstein @j_schottenstein]

We’re back with the @dbt_labs Analytics Engineering Podcast! @jthandy and I kick off the season with the LEGEND @Bob_Muglia. 🐐🚨

One of my favorite recordings yet.

🎧 👇
https://t.co/wa0sA3IMbb

[Tristan Handy @jthandy]

Had a ton of fun hanging out with Joe Reis on his podcast! Episode just dropped here:

https://t.co/FTnq32JbdP

[dbt @getdbt]

dbt Labs has been recognized as @SnowflakeDB’s Data Integration Partner of the Year! 🎉

It’s a testament to the incredible results that customers like McDonald’s Nordics, Sunrun & JetBlue see from using dbt Cloud with Snowflake.

At #SnowflakeSummit? Stop by our booth, #2720!

[Tristan Handy @jthandy]

@mullinsms @pdrmnvd @matsonj @ChristianNolan I think this is actually an important distinction; centralized BI didn't actually provide (or aim to provide!) *most* of the data that decision-makers at an org used.

[Tristan Handy @jthandy]

@mullinsms @pdrmnvd @matsonj @ChristianNolan Appreciate you both! I think we're talking past each other. I called out "BI teams" that sit inside of IT: I think that's actually what you're both describing. They didn't report to a VPA or CDO and didn't have organizational power. Very dissimilar to the modern "data team".