AI × XR × Culture | Co-founder @MidBrainAi and @thepointlabs | ex-@volvocars innovation lead |Teaching machines to make sense. Nerd by day rock star by night 🎸
Copenhagen, Denmark
Joined September 2011
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Timmy Ghiurau retweeted
this METR report on AI R&D acceleration at Anthropic is very inward looking imo. it counts the lab's compute, hires and internal data, then credits ~80% of the speed-up to AI
what I see from the outside: labs are becoming schelling points for money, redistributing it to an ecosystem they created to accelerate their own development
it's a symbiosis. the best data and RL environment companies now know where models will fail before the labs do, and the labs just buy what they propose. that feeds back into every lab at once
a lot of the "AI accelerating AI R&D" happens outside the lab
Timmy Ghiurau retweeted
The post: midbrain.ai/blog/dolphinbenc…
The benchmark: dolphinbench.ai/
Timmy Ghiurau retweeted
83.2% on DolphinBench, across 600 tests.
Same agent and model as the leaderboard: Hermes with GPT 5.6 Luna. $64 in harness cost, plus 52k memory credits.
Production MidBrain, with two experimental prefetch features on.
Timmy Ghiurau retweeted
Canva is laying off 22% of its workforce which is 1271 employees, and this is what their CEO said.
We need more founders like thissss.
83.2% on @mem0ai DolphinBench: Mapping the Pareto frontier of agent memory, across 600 tests.
Same agent and model as the leaderboard: @NousResearch Hermes with GPT 5.6 Luna. $64 in harness cost.
Production @MidbrainAI , with two experimental prefetch features on.
The benchmark: dolphinbench.ai/
The post: midbrain.ai/blog/dolphinbenc…
Timmy Ghiurau retweeted
The edited model is now on the Hub: huggingface.co/RikiyaT/Qwen3…
Huge thanks to @akhaliq, who rebuilt it from our published logs, re-ran MATH-500 (51.0%).
They also seems to have a live demo: huggingface.co/spaces/ML-Int…
Timmy Ghiurau retweeted
instead of a one to many world which is what exists now, agents are effectively facilitating & making a one to one world economically & technically feasible.
the first iteration of this was the feed with personalized algorithms & ranking but every user had the same interface.
now you’ll have both dynamic interactive interfaces if required + enormous amounts of personalization (way more than just ranking).
Timmy Ghiurau retweeted
since i've set everything up, my personal agents now imessage me more often than some of the ppl closest to me. maybe more than half of my pocket vibrations are agents which is weird because that specific notification vibration was always reserved for ppl.
at this point you can kind of imagine where these entities can become emotional companions in the same way something like her worked but in text form. the fundamental difference & reason why is that these live in the same inbox as your friends & family. the medium itself creates a sense of intimacy that even the most fluffy designed characters & any traditional piece of software ever could. ironically without any of the visual design. only emotional design.
the medium is the message.
Timmy Ghiurau retweeted
Interesting.
Revolut reportedly only started seeing meaningful AI productivity gains after moving away from expensive OpenAI and Anthropic models to self-hosted GLM.
Decagon reportedly runs ~90% of its workloads on open weight models.
Why? Because when you're running AI agents in production for e.g. customer service, cost and latency matter enormously. You don't need a frontier model with PhD-level reasoning or even a model that knows the capital of Finland to handle every customer interaction.
We're seeing similar dynamics across hypergrowth AI startups and increasingly the large enterprises we work with.
But most importantly, the future isn't binary.
When use cases are new, you want the smartest general-purpose model available. But once workflows mature and become predictable, smaller, faster, specialized models increasingly win.
I wouldn't be surprised if 80–90% of routine enterprise AI workflows eventually run on open-weight and smaller models.
This doesn't mean frontier labs lose.
It means the market is splitting into two massive opportunities: frontier intelligence and specialized execution at scale.
Humans get from information to intuition the slow way. We act, we get it wrong, we remember, we adjust. Do that enough times and the path collapses into a straight line.
That's the problem we work on at @MidbrainAI. Continual learning: agents that capture what happened, weigh what mattered, and apply it next time. Every cycle a little closer to the straight line.
Timmy Ghiurau retweeted
Learn to build evals, folks!
You are not RSI-pilled enough if you don't know how to properly measure the thing you want to improve.
It doesn't matter if it's a model, complex codebase, or a business function. This is extremely hard in the real world.
You ever wonder why most of what AI is great at today is around verifiable problems?
The open-ended world requires a completely different approach to improving and measuring models.
We need to go back to hardcore science and evals. Enough of this short-term benchmaxxing.
This space offers so much opportunity. We are doing research and building evals in this space @dair_ai. I am currently chatting with folks building in this space too. Feel free to reach out for potential collab opportunities or just share perspectives.
Timmy Ghiurau retweeted
this is the premise of the @designdisciplin feed btw
