Thomas Boser

bad cop @hiloopai

[Thomas Boser @thomasboser]

@adelwu_ @deepmatmul

[Thomas Boser @thomasboser]

Dinner or not, I’d love to chat with folks interesting autoresearch at AI4 next week!

[Karan Brar @deepmatmul]

What happens when AI systems can improve themselves?

At Ai4, we’re bringing together founders and AI/ML leaders for a small dinner on systems that learn from real-world use, run their own experiments, and keep getting more accurate, faster, and cheaper.

Join us: https://t.co/kMdO2F32KT

[Thomas Boser @thomasboser]

@Affanyfan @joshnkeezy This is beautiful

[Karan Brar @deepmatmul]

Every AI company should own its data, its models, and the research lab that keeps making them better.

Today, @thomasboser and I are launching @hiloopai to make that possible.

The frontier advantage isn’t access to a model. It’s the research organization continuously improving it.

Your product already generates the raw material for better intelligence: proprietary data, production feedback, evaluations, and domain expertise. Very few teams have the research capacity to turn those assets into better models.

Hiloop builds and operates that capability with you.

Bring us the model your product depends on, the data that makes it different, and an evaluation that defines success. We reproduce your baseline and run an autonomous research campaign against it.

Agents pursue competing hypotheses in parallel, build on previous results, and promote only improvements that survive verification. Our researchers validate the winners and bring them into production.

The work can run hosted or inside your environment. You retain control of your data, and the resulting models, evaluations, and research artifacts are yours.

We’re starting with model training, post-training, and inference optimization, where progress is measurable and the value is immediate.

But this isn’t a one-off model improvement. It’s a persistent research capability that begins each campaign with everything learned from the last.

Over time, the lab accumulates research memory and improves its own tools, evaluations, and agents. The process used to create better intelligence gets better itself.

That’s infrastructure for recursive self-improvement.

If your product depends on a model and an important metric has stopped moving, bring us the model you can’t make better. We're much better at research than making videos!

Reach out to us: [email protected]

[Thomas Boser @thomasboser]

A more detailed overview of what we're doing at hiloop. Something we don't really mention - we're also making this secure for enterprise customers (data exfiltration controls, fully deployable in your cloud, etc. Reach out to learn more!

[hiloop (YC S26) @hiloopai]

Check out our launch YC post for more details on what we're doing!

https://t.co/l21mftjQQ0

[Thomas Boser @thomasboser]

@deepmatmul @tboser @hiloopai 🔥🔥

[Thomas Boser @thomasboser]

We're still working on our cinematography skills, but we're pretty good at autoresearch. Hit us up if that's something you want to get better at too!

[Karan Brar @deepmatmul]

Every AI company should own its data, its models, and the research lab that keeps making them better.

Today, @tboser and I are launching @hiloopai to make that possible.

The frontier advantage isn’t access to a model. It’s the research organization continuously improving it.

Your product already generates the raw material for better intelligence: proprietary data, production feedback, evaluations, and domain expertise. Very few teams have the research capacity to turn those assets into better models.

Hiloop builds and operates that capability with you.

Bring us the model your product depends on, the data that makes it different, and an evaluation that defines success. We reproduce your baseline and run an autonomous research campaign against it.

Agents pursue competing hypotheses in parallel, build on previous results, and promote only improvements that survive verification. Our researchers validate the winners and bring them into production.

The work can run hosted or inside your environment. You retain control of your data, and the resulting models, evaluations, and research artifacts are yours.

We’re starting with model training, post-training, and inference optimization, where progress is measurable and the value is immediate.

But this isn’t a one-off model improvement. It’s a persistent research capability that begins each campaign with everything learned from the last.

Over time, the lab accumulates research memory and improves its own tools, evaluations, and agents. The process used to create better intelligence gets better itself.

That’s infrastructure for recursive self-improvement.

If your product depends on a model and an important metric has stopped moving, bring us the model you can’t make better. We're much better at research than making videos!

Reach out to us: [email protected]

[Thomas Boser @thomasboser]

@hu_yifei I can guess who this was

[Thomas Boser @thomasboser]

@theAlexQuach But not to your friends :(

[Thomas Boser @thomasboser]

@deepmatmul Not my ceo

[Thomas Boser @thomasboser]

@theAlexQuach Give me your top 5 codex pet power user tips

[Thomas Boser @thomasboser]

@joshnkeezy @reductoai Me please

[Thomas Boser @thomasboser]

@AlvinRyanputra @deepmatmul 500k each from OpenAI and anthropic. Who knows how long it’ll last…

[Thomas Boser @thomasboser]

@raunakdoesdev yeah that makes sense. i definitely agree that fable feels a lot worse to talk to than opus. fair enough, we'll see tomorrow when they GA sol.

[Thomas Boser @thomasboser]

@_vatsadev These guys are amazing! Anybody solving hard verifiable problems should definitely reach out.

[Thomas Boser @thomasboser]

@_vatsadev Tell me what stocks to buy please

[Thomas Boser @thomasboser]

@adelwu_ Looks fun 🥲

[Karan Brar @deepmatmul]

A bit of a late update, but @thomasboser and I got into YC S26. We're building @hiloopai, and we got a pretty cool result. Check it out!

[Thomas Boser @thomasboser]

Life update - I’m now working on building infrastructure for scaling autoresearch.
@deepmatmul has been hard at work and has already achieved SOTA results on the original autoresearch task!
If you’re solving hard problems we’d love to work with you!

[hiloop (YC S26) @hiloopai]

https://t.co/4ADgZ9iNIk