Rahul Chalamala

🫡

[Alexander Taubman @alextaubman]

Today, Long Lake completed our $6.3B acquisition of Amex GBT.

Long Lake acquires and transforms generational businesses with AI across the American services economy.

Amex GBT is the travel partner for 17,500 businesses in 140 countries. Last year, Amex GBT booked 35 million trips for 10 million travelers.

This marks Long Lake’s 40th acquisition, and our family of companies now employs nearly 30,000 people.

We founded Long Lake three years ago with the thesis that AI is going to change every company, but there’s a large overhang between AI capabilities and how most businesses leverage AI tools.

We partner with strong, profitable, and growing businesses to help them deploy AI and improve their customer service.

Our first cohort of companies has doubled their EBITDA in less than two years through topline and productivity growth, all while increasing headcount. (We’re proud to say that we’ve never conducted a layoff.)

We’re excited to share a bit more about what we’ve been up to.

[Michael Lee @mjlee_2014]

We started Sequence (@seqholdings) with a clear conviction: the holding company of the next century will be a technology company that owns and operates businesses. Today is an important step in that journey.

We’re thrilled to announce that Sequence, along with the Dell Family Office and the Baldwin family and management team, have agreed to take The Baldwin Group private. I’m excited to partner with Trevor Baldwin and the broader team as we help extend Baldwin’s lead as the insurance firm of the future.

Read more here: https://t.co/DWvumylgOQ

[Alex Rubin @alexrubin18]

We built @seqholdings to help scaled companies reimagine themselves for the future by bringing together frontier engineering, operating expertise and patient capital.

Today, we’re excited to announce that Sequence, along with the Dell Family Office and the Baldwin family and management team, have agreed to take The Baldwin Group private and help extend its lead as the insurance firm of the future.

Read more here: https://t.co/VNH6T6h62P

[Neil Chowdhury @ChowdhuryNeil]

I’ve joined @thinkymachines to work on safety & alignment.

The default trajectory is that as AI gets more powerful, control over it will concentrate in the hands of a few. I’d rather build safety systems that let control over AI be shared widely.

Longer thoughts below.

[Cognition @cognition]

Introducing SWE-2, our closest model yet to the frontier.

On leading evals, it scores on par with recent frontier models – at up to 70% lower cost.

We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.

[Varun Shenoy @varunshenoy_]

at long lake, we've bought 40+ services businesses to build AI where work actually happens

we're working on:
- deploying agents to complete knowledge work
- frontier eval generation from real workflows
- post-training open models on data no lab has

come build with us @llmh

[Rahul Chalamala @rchalamala]

🐐

[Scott Wu @ScottWu46]

This one feels special - a lot of folks on this list we've admired for years and now finally get to work with.

Above all I'm grateful for the incredible team that we have. Every day at Cog still feels like getting in a room together and trying to build something great.

Also, grateful that Devin is actually good now. Would have looked really dumb otherwise.

[Shreyan Jain @shreyanj98]

I think Mike really nailed here what it takes to stay competitive in venture today: genuine technical depth and fluency in AI, extreme founder-centricity, and a hyper-flexible investment mandate. So much so that I’ve decided to join @hanabicapital! I'm very excited to help build a modern venture firm shaped to the coming decades of technology progress.

[Noah Shinn @noahrshinn]

I’m Noah, the founder of Instinct.

Instinct is a personal agent that we’ve been building for the past few months. The interface is simple: there are no new interfaces. You can text or call it. It's trained to use a phone and a computer in the same way that humans do. Instinct combines simplicity with extreme capability.

I’m thrilled with everything our early users are doing with Instinct. They’ve told us they’ve planned cross-country road trips, bought weekly groceries and concert tickets, and cancelled hundreds of dollars of subscriptions. Someone’s even planning their wedding with Instinct.

We want to make Instinct the best personal agent for all of you. It’s available in an invite-only beta program while we’re actively bringing up more compute. I’m excited to see what you all do with it.

https://t.co/lVra3kd4TT

[Rahul Chalamala @rchalamala]

Hone has been exceptional to work with @modal :D

[Hone @hone]

Announcing Hone

Intelligence has become abundant. Yet the world looks remarkably similar to how it did five years ago. With every model release, the gap between what frontier AI can do and the economic value derived from it widens.

Closing the gap requires re-organizing work around organizational outcomes, not individual tasks. Hone builds AI that creates, orchestrates, and improves agents and software continuously to own organizational outcomes over weeks and months.

We are ex-founders and early core contributors to Cognition, Mercor, Ramp, and OpenAI. We obsess over real-world value, not theoretical benchmarks.

Our core beliefs on closing the gap in the thread below.

[Jacob Teo @jacobtpl]

agi is here

[Enigma @Enigma_AI]

Drop #3 is here: 2 new types of robots are now LIVE online. Those are real robots.

1. Scientist robot: A robot that does real chemistry experiments.

2. Bomb-Defusing robot: A robot on a Special Ops mission to defuse a bomb.

Try them out for yourself at robots dot online.

[Genius AI @trygenius_ai]

Genius AI is bringing intelligence to the physical economy, where some of the most valuable and human work in our daily lives takes place. Since Day 1, we have been obsessed with helping talented practitioners do what only they can do as technology takes on the repetitive tasks.

We’re excited to share that we raised a Series D at a $1.15B valuation to accelerate our vision. We are also announcing a new brand, Genius AI, as our platform and customer footprint have evolved. Alongside our core flagship GlossGenius product, we’re releasing products that run more of the admin work on their own, so business owners can finally do what they dreamed of.

To the 125,000 businesses who have trusted us to save them 112M hours of admin work and earn billions of dollars in extra revenue already, thank you for your partnership. Our work is just beginning as we make your most repetitive workflows disappear.

Grateful for our investors, including @Lux_Capital that led the round with participation from @BessemerVP @ImaginaryFund @2048vc @L_Catterton and @StepStoneVC .

Here’s a note from our founders @DCohenShohet @Leahcs with more detail below ⬇️

[Leon @iamleonli]

Scaling laws have transformed how we understand pretraining, and recent work has begun to characterize scaling in RL. But these stages are almost always studied in isolation. Can we study pretraining and RL jointly, and derive a unified scaling law for the full pipeline? 🧵

[Arya Tschand @AryaTschand]

You’ve probably heard that power is our biggest constraint, but how does it influence training and inference systems?

I wanted to share what I’ve learned about designing the stack for power efficiency from my time at nvidia architecture research

TLDR and experiments in thread!

[Rahul Chalamala @rchalamala]

🐟

[Federico Cassano @ellev3n11]

king of the kings

[Franklin Wang @f_x_wang]

🫪

[brandon wang @fluorane]

i am once again offering a prize to whomever outdrinks albert, this time 20k korean won

[Yucheng Lu @_yucheng_lu]

MTP makes autoregressive LLMs fast. Can the same trick work for diffusion LMs?
Had a fun collaboration with @modal exploring exactly that: Multi-Token Residual Prediction (MRP) 🚀

The key change: instead of training a small head to predict the next denoising step’s full distribution, we predict the residual between adjacent steps. It’s a much easier target, so a tiny 3-layer module learns it accurately and applies it across several steps.

We applied MRP in two regimes:
• Static regime → (near-)lossless speedup, up to 1.56× in SGLang.
• Dynamic regime → recovers up to +16 accuracy points lost to aggressive low-threshold decoding.

💻 Code, SGLang impl & models in the blog
🔗 https://t.co/q3NjxA0WZi

[Vipul Ved Prakash @vipulved]

We @togethercompute believe intelligence should be abundant, not expensive.

Today we announced our Series C funding of $800m @ $8.3B valuation, to continue to build the world's most efficient platform for generative AI.

Thanks @nikogallogly for telling our story in @nytimes!

https://t.co/LGQ5t6rzya