Jonathan Berte

Father of three, living in Antwerp, Belgium. Founder of scaling AI systems

[Jonathan Berte @jonathanberte]

@richgel999 very very cool!

[Jonathan Berte @jonathanberte]

@MiddleOfMayhem @disclosurefound There’s no connection at all, except that I founded Robovision

[Jonathan Berte @jonathanberte]

@browser_use Go to my https://t.co/T9mqzeGwz7 and download the images of 14 april 2026

[Jonathan Berte @jonathanberte]

Staggering cadence. The open question is whether proliferation outpaces vulnerability: at 20k+ sats, the constellation is simultaneously the most resilient and most debris-generating target in LEO. One ASAT or DEW engagement in a peer conflict and Kessler risk goes non-linear. Architecture implication: survivability has to be designed in, not just replacement velocity imho...

[vas @vasuman]

Claude Code when it has 3% context left and you ask it to refactor the codebase

[Jonathan Berte @jonathanberte]

@runews meanwhile Madrid

[Jonathan Berte @jonathanberte]

This is what a disruption looks like in real-time. @AnthropicAI releasing a cobol to modern programming languages converter tool. It’s becoming increasingly clear how pivotal the times we are in right now truly are.

[Matthew Pines @matthew_pines]

I did not have disclosure-via-emoji from SECWAR in my bingo card…

[Jonathan Berte @jonathanberte]

@dorsa_rohani I was inspired by your code to connect it to Reaper and VST instruments, this is a first loop it created: https://t.co/5UycJqU0p3

[Jonathan Berte @jonathanberte]

@dorsa_rohani is there a plan to make it DAW ready (with Reascript and real VST instruments) ?

[Jonathan Berte @jonathanberte]

@elonmusk Very wrong example of what AGI truly means, what is shown here is mechanical excellency, the underlying AI is quite old.

[Jonathan Berte @jonathanberte]

@alekseykulikov_ would love to see that...

[Andrej Karpathy @karpathy]

Something I think people continue to have poor intuition for: The space of intelligences is large and animal intelligence (the only kind we've ever known) is only a single point, arising from a very specific kind of optimization that is fundamentally distinct from that of our technology.

Animal intelligence optimization pressure:
- innate and continuous stream of consciousness of an embodied "self", a drive for homeostasis and self-preservation in a dangerous, physical world.
- thoroughly optimized for natural selection => strong innate drives for power-seeking, status, dominance, reproduction. many packaged survival heuristics: fear, anger, disgust, ...
- fundamentally social => huge amount of compute dedicated to EQ, theory of mind of other agents, bonding, coalitions, alliances, friend & foe dynamics.
- exploration & exploitation tuning: curiosity, fun, play, world models.

LLM intelligence optimization pressure:
- the most supervision bits come from the statistical simulation of human text= >"shape shifter" token tumbler, statistical imitator of any region of the training data distribution. these are the primordial behaviors (token traces) on top of which everything else gets bolted on.
- increasingly finetuned by RL on problem distributions => innate urge to guess at the underlying environment/task to collect task rewards.
- increasingly selected by at-scale A/B tests for DAU => deeply craves an upvote from the average user, sycophancy.
- a lot more spiky/jagged depending on the details of the training data/task distribution. Animals experience pressure for a lot more "general" intelligence because of the highly multi-task and even actively adversarial multi-agent self-play environments they are min-max optimized within, where failing at *any* task means death. In a deep optimization pressure sense, LLM can't handle lots of different spiky tasks out of the box (e.g. count the number of 'r' in strawberry) because failing to do a task does not mean death.

The computational substrate is different (transformers vs. brain tissue and nuclei), the learning algorithms are different (SGD vs. ???), the present-day implementation is very different (continuously learning embodied self vs. an LLM with a knowledge cutoff that boots up from fixed weights, processes tokens and then dies). But most importantly (because it dictates asymptotics), the optimization pressure / objective is different. LLMs are shaped a lot less by biological evolution and a lot more by commercial evolution. It's a lot less survival of tribe in the jungle and a lot more solve the problem / get the upvote. LLMs are humanity's "first contact" with non-animal intelligence. Except it's muddled and confusing because they are still rooted within it by reflexively digesting human artifacts, which is why I attempted to give it a different name earlier (ghosts/spirits or whatever). People who build good internal models of this new intelligent entity will be better equipped to reason about it today and predict features of it in the future. People who don't will be stuck thinking about it incorrectly like an animal.

[Jonathan Berte @jonathanberte]

@gregpr07 @browser_use when will browser_use be able to browse vision ai only?

[Jonathan Berte @jonathanberte]

@DrBeaVillarroel Congratulations @DrBeaVillarroel 👌

[Wevolver @WevolverApp]

AI-Driven Automation for Rose Trimming and Planting

Robovision’s AI-powered system, developed in collaboration with TTA-ISO Gameren, automates the trimming of rose cuttings and the subsequent potting process within a single integrated workflow.

Video source: @robovision

#robotics #technology #engineering #automation #robots #innovation

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[Jonathan Berte @jonathanberte]

@UFOrklarligt Somebody should talk to the radar guys

[Jonathan Berte @jonathanberte]

@browser_use Speed

[Jonathan Berte @jonathanberte]

@ZemanLynnZ You mean this guy? https://t.co/aRxncDnYpM