Akshat Bhatia

Developer Platform @Databricks Previously @Airbnb @Linkedin @Twitter @Microsoft

[Akshat Bhatia @akshatb]

A perfect Last minute Christmas gift - Convert your kids drawings to art. Inspire, surprise and motivate them. A perfect gift for this holiday season - https://t.co/c4xQQkBQ9x

[Akshat Bhatia @akshatb]

Tell me you’re in the Bay Area without telling me you’re in the Bay Area 😂

[Akshat Bhatia @akshatb]

@tibo_maker @thsottiaux Profile pics with similar backgrounds dont help either ;)

[Akshat Bhatia @akshatb]

Full post: https://t.co/wmTXqxx1Gd

[Akshat Bhatia @akshatb]

How do you manage AI coding costs as usage scales? We share what we’ve learned at Databricks, including model routing, caching, and cost controls. Highly recommended reading.

[Patrick Wendell @pwendell]

Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from:

1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more.

2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%.

3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%.

4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.

[Ali Ghodsi @alighodsi]

Check out Omnigent, an open source harness that lets you use all the existing code harnesses (Claude Code, Codex, OpenCode, pi), collaborate and share sessions in many modalities (e.g. Slack/Teams, cli, webui), while having a fine grained security model that really tightens the control on what agents can do/not do.
https://t.co/FRLsbUIxZn

[Matei Zaharia @matei_zaharia]

Really excited to open source a new project: Omnigent, a meta-harness for AI agents.

It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies.

[Ali Ghodsi @alighodsi]

I now constantly get questions about the SAAS meltdown, role of AI, system of records etc. I don't have an answer to all these.

But I do know that we saw an acceleration in our business in Q2, Q3, and now finished the year with accelerating Q4.

The question is, why?

Short answer: AI. But the underlying reason is subtle. We are growing fast because we are finally removing the biggest bottleneck in data: the technical barrier to entry.

For years, if you didn’t know SQL, Python, you were locked out of the value chain. That has changed fundamentally with the 𝐆𝐞𝐧𝐢𝐞 𝐟𝐚𝐦𝐢𝐥𝐲, and it is the "secret sauce" behind our recent momentum:
• 𝐆𝐞𝐧𝐢𝐞: Analysts can query data without any SQL. I use this every day myself.
• 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐆𝐞𝐧𝐢𝐞: Builds end-to-end AI models for you, similar to Cursor for ML on your data.
• 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐆𝐞𝐧𝐢𝐞: Write Spark pipelines, does plumbing, troubleshooting.

We've been talking about DATA + AI democratization, but generative AI finally enabled it in a way that wasn't possible before. That's why we're seeing a market response.

Take 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬. We launched this serverless engine for agents and apps recently. At 8 months into its journey, its revenue is already 2x what our Data Warehouse product was at the same stage.

All this taken together, we ended up with the following stats for Q4:
🚀 $5.4B Revenue Run-Rate, growing >65% YoY
🚀 $1.4B AI Revenue Run-Rate
🚀 FCF Positive for the year
🚀 NRR >>140%

https://t.co/yq3riYyr8r

[Ali Ghodsi @alighodsi]

I am super excited to announce that we have agreed to acquire Neon, a developer-centric serverless Postgres company. The Neon team engineered a new database architecture that offers speed, elastic scaling, and branching and forking. The capabilities that make Neon great for developers are also great for AI agents.

Together, we'll deliver an open, serverless database foundation for developers and AI agents.

https://t.co/CZziwCBsfq

[Hasnain Lakhani @mhlakhani]

(almost) new year, new blog post!

I've been using AI a lot more for personal productivity at [dayjob] and for side projects, and have been asked about my workflow a few times. So I thought I'd write up and explain what I do.

1/N

[Akshat Bhatia @akshatb]

You DO NOT want to miss out on these Black Friday savings - deals deals deals

[Akshat Bhatia @akshatb]

@ShaanVP @sama 😂

[Akshat Bhatia @akshatb]

I have always had utmost respect for @satyanadella's turnaround of Microsoft post the Ballmer era. Its like taking a u-turn on an 18 wheeler, while driving at 60 mph.

Today's hiring of @sama is another fascinating move. Satya's leadership of Microsoft is the stuff of legends.

[Satya Nadella @satyanadella]

I’m super excited to have you join as CEO of this new group, Sam, setting a new pace for innovation. We’ve learned a lot over the years about how to give founders and innovators space to build independent identities and cultures within Microsoft, including GitHub, Mojang Studios, and LinkedIn, and I’m looking forward to having you do the same.

[Akshat Bhatia @akshatb]

@ShaanVP https://t.co/8mZ6ktLpTL

[Akshat Bhatia @akshatb]

@YehudaZahler https://t.co/8mZ6ktLpTL

[Akshat Bhatia @akshatb]

@andruyeung @thesamparr @ShaanVP And both of them are hugely entertaining! None of the professors would come even close.

[Akshat Bhatia @akshatb]

@indiranegi @MrNick_Buzz Why is it hard to ask for money? For alpha customers its ok to give a discount, but I do think paying customers is the only real validation of a product. The hardest hurdle I tend to face is generally distribution. Without having a good audience finding initial customers is hard.

[Akshat Bhatia @akshatb]

@svpino How are you measuring success on this? How do you know the quality of code reviews is high? How does this compare with Human reviewers? Any analysis on what kind of review issues is it finding (nits, code styling, functional, missing tests or architectural?

[Akshat Bhatia @akshatb]

@JanuszKudelka Insanity. Also, sooo SF

[Akshat Bhatia @akshatb]

@indiranegi Yup for side projects.