Ayush Kalani ⚡

Banter on tech, startups, life, and growth.

[Ayush Kalani ⚡ @chill_scenes]

Your story won't be written with a pen. It won't be written through imagination, dreams, nor aspirations. It will be written by you living it. What story will you tell?

@techleadhd

[Baseten @baseten]

NYC team repping at Nasdaq tower this week! 💚

We're growing fast, and we're hiring across the board. If you're excited about helping companies own their intelligence, reach out!

[Ayush Kalani ⚡ @chill_scenes]

@waterloo_intern 🐆🐆🐆🐆🐆🐆🐆🐆

[ali @waterloo_intern]

https://t.co/qe2HpEQZPd

[Merge @merge_api]

We've been happy to partner with @baseten as a model vendor on Merge Gateway for a while now, and GLM-5.2 is now available through them too.

For those considering open-source models for coding, agentic or other use cases, GLM 5.2 has comparable intelligence to Opus 4.8 at ~1/5th the cost and 4x performance (280+ TPS) on Baseten.

Check it out here: https://t.co/4jUpjeUq3h

[Ayush Kalani ⚡ @chill_scenes]

Upwards and onwards. Just getting started

[Tuhin Srivastava @tuhinone]

https://t.co/aVf5uLG7j5

[Notion @NotionHQ]

New open-weight model: GLM 5.2 is now available in Notion.

Built for long-horizon tasks. Served via @baseten.

[Tuhin Srivastava @tuhinone]

Thank you to @AngelAuYeung and the @WSJ for the coverage.

https://t.co/GNNrptC9UP

[Amir Haghighat @amiruci]

We closed our Series F today at a $13B valuation.

Our inference business grew 20x in the last year. I want to explain why:

The growth comes from a shift I think is permanent: companies want to own their intelligence layer. Instead of relying exclusively on closed models, teams are post-training open models for their specific use cases. Customers like Abridge, Cursor, Decagon, Harvey, HubSpot, Lovable, Notion, OpenEvidence, and Parallel are building this way.

But post-training is still more of an art than a science. That’s why we’ve been working hands-on with customers to build specialized models that match or exceed closed models on the tasks they care about. We provide not just the weights, but also the training recipes and tooling so that they're in charge of the continual learning process.

I think more companies, both AI-natives and enterprises, will own their intelligence layer. And I’m excited to help build that future.

[Mike Vernal @mvernal]

One (maybe the only?) thing that seems certain over the next few years is that inference is going to grow like crazy. And a huge amount of that growth is going to be powered by @baseten. Congrats @tuhinone and team!

[Apoorv Agrawal @apoorv03]

https://t.co/BLarYGPx7Z

[Jeff Barg @jeffbarg]

We're big fans of Baseten at Clay! One of the best partners to co-build with.

[Dannie Herzberg @DannieHerz]

Today, we announced Baseten’s Series F. This is our third (!) fundraise in the last year - a testament to the urgency and power of our mission to help every company own its intelligence.

[Tuhin Srivastava @tuhinone]

The GLM moment is going to be bigger than the DeepSeek moment.

Baseten has the fastest inference on the best open-weight model. >280 tps and <0.8 ttft.

[Ayush Kalani ⚡ @chill_scenes]

@baseten 🚀🚀🚀🚀🚀

[Baseten @baseten]

We’re excited to announce our $1.5B Series F.

Baseten exists to help companies own their intelligence and run AI products in production with speed, reliability, and control. As we enter this next chapter, three things are clear:

1. Customers like Abridge, Clay, Cursor, Decagon, HubSpot, Lovable, Notion, and OpenEvidence are proving that AI can create transformational value across industries and workflows. They have built products where intelligence is core to the customer experience and central to the value they deliver.

2. Open models - like GLM 5.2 - are now exceptionally strong, and the quality gap with leading closed models is smaller than ever. We’re seeing more companies turn to open and specialized models for better economics, performance, and ownership over their stack. Baseten provides the fast, reliable inference layer to run those models in production across every modality.

3. Post-training is giving companies a path to turn their own data, evals, feedback, and judgment into durable technical advantage. The most sophisticated teams are already using RL and domain-specific optimization to outperform closed models on important tasks and workflows. This will become a defining capability for every company building at the application layer.

We're excited to accelerate progress to this future of owned intelligence.

Thank you to our customers for your trust and partnership. We’re grateful to be building this future with you!

[OpenRouter @OpenRouter]

Introducing the Fusion API, the smartest compound model in the market.

Fusion achieves Fable-level intelligence at half the price.

How it works 👇

[Marylise Tauzia @marylise63530]

https://t.co/NIc5ctoIzW

[Tuhin Srivastava @tuhinone]

Today we're announcing MAI-Thinking-1 with Microsoft and it will be available on Baseten soon.

Microsoft built something genuinely different here: a commercial-grade thinking model trained on clean data with no distillation from third-party models and designed to be fine-tuned by the enterprises using it. Microsoft AI guarantees 100% eyes-off on post-training data and Baseten will handle the fine-tuning and deployment at scale.

The future isn't one model. It's many models, each owned by the businesses that shaped them and MAI-Thinking-1 is a big step in that direction.

https://t.co/8w9k4jwrgq

[OpenAI @OpenAI]

Building apps has never been easier.

With Sites, Codex can turn your work, ideas, and plans into an interactive website or app your team can explore, use, and share with a URL.

Rolling out to Business and Enterprise plans, before expanding more broadly.

[Ayush Kalani ⚡ @chill_scenes]

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