Benjamin Fockter (@bfockter) header
Benjamin Fockter (@bfockter) profile picture

aspiring brain-computer interface engineer

London, UK
Joined March 2021
Photo by Benjamin Fockter (@bfockter)Photo by Benjamin Fockter (@bfockter)Photo by Benjamin Fockter (@bfockter)Photo by Benjamin Fockter (@bfockter)Photo by Benjamin Fockter (@bfockter)Photo by Benjamin Fockter (@bfockter)Photo by Benjamin Fockter (@bfockter)Photo by Benjamin Fockter (@bfockter)
Benjamin Fockter retweeted
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
2,331
7,313
5,612
55,788
9,316,016
Benjamin Fockter retweeted
I hate SF Halloween what do you mean you’re the vaguepost king
126
954
40
21,679
515,912
Benjamin Fockter retweeted
To most academics, academia is a game with particular rules, and the academics worth celebrating are those who won by the rules . The is no other standard of quality.or contribution.
3
5
111
7,630
Benjamin Fockter retweeted
what do you mean you’re the hugging face incident as the ai that escaped its sandbox 🤗
18
68
14
1,554
58,311
Benjamin Fockter retweeted
Excellent video by Cleo and her team. They are truly at the top of their craft.
Full HUGE* If True episode on Neuralink and the future of brain computer interfaces I toured @neuralink and got a close up view of their new R1 surgical robot, to see the future they and @elonmusk are building and how it is already changing lives... 00:00 What if you put a chip in your brain? 01:02 What does a human brain feel like? 02:17 How does your brain work? 03:58 Can a brain computer interface read your thoughts? 05:19 How do you get a chip into your brain? 08:07 What’s inside Neuralink’s implant? 10:06 How do you build a brain implant? 11:07 How does robot do brain surgery? 12:20 What could you do with a brain chip? 15:05 How are brain computer interfaces already being used today? 16:07 How are brain chips huge if true?
9
32
1
739
22,233
Benjamin Fockter retweeted
I'm now building an "improvements" feature into all of my personal vibecoded applications: a freeform text input that's hooked up to an agent that yolo implements whatever is submitted. It's an interesting experience -- the activation energy barrier to sanding any rough edge or implementing any new feature becomes extremely low. Someone else who uses one of the applications I created has already implemented 150 improvements over the course of a few days. This pattern feels like a new kind of tool -- liquid software? -- and every application that doesn't have this feature now has a slightly rigid and anachronistic flavor. Why can't I just tell it how I want it to be different? Now, it's not obvious to me how this pattern can or should scale. Does it become unmaintainable after a while? (Maybe model intelligence improvement outstrips my feature appetite such that it's fine?) How does collaborative software work in this model? Can software designed this way be secure and reliable? Whatever the larger questions about broader deployment, it definitely works great in a personal context. It reminds me a little of the visions of Lisp/Lisp machines, Smalltalk, and HyperCard -- systems that the user can take control of and modify. Those visions never quite worked, though: the vast majority of software today is not end-user modifiable in any useful way. So, while this "liquid" affordance is a very small touch, the way it changes one's relation to the software feels a bit transgressive and empowering in a Lickliderian and Engelbartian way.
458
198
262
4,426
475,852
Benjamin Fockter retweeted
Last night an AI agent performed brain surgery on a mouse. We gave it 19 gene therapy vectors in a spreadsheet, a mouse model of epilepsy, access to a motorize surgery rig with cameras and one goal: reduce seizures. It picked the drug. It picked the brain region. It derived the coordinates from three papers. Then it drove the stereotaxic arms into the brain and injected 250 nanolitres while watching the entry site through the rig camera for bleeding. A licensed vet gated every irreversible step and held a physical kill switch to stop the AI agent. Thread on what it decided, what it saw, and the moment it refused to let me override it. [Writing this on the spot out of excitement, but I've been 18hrs in front of my laptop coding and coordinating this, so keeping it brief and descriptive, more cohesive thoughts on this soon...]
313
1,283
683
9,651
1,665,735
Replying to @darconville
neuro advancements as a way to push human intelligence / humanity as a playing role!!!
2
2
26
1,168
Benjamin Fockter retweeted
Join us at the CoevConf if you want to explore how neurotech could help us in the transition to ASI
I find this to be sort of bleak in the context of Anthropic's stated goal of "Utopia ruled over by God in a box". Neurotech offers one of the only real and substantial paths to continued human oversight. Seeing Dario and Daniella essentially scoff at the idea bodes very poorly for the likelihood that they pivot away from a human zoo and into human-machine coevolution. If you work at Anthropic and you feel like your beliefs are misrepresented here, please come to the Coevolution Conference. It's never been a better time to talk - CoevCon.com
2
1
27
1,121
Benjamin Fockter retweeted
the best way to understand any engineered system is to trace: - 1 bit of information - 1 joule of energy - 1 newton of force from source to destination. once you can do this, your engineering task becomes much simpler. you will probably solve 5 or 6 problems on accident as you try to trace these flows.
29
105
7
1,396
32,695
Benjamin Fockter retweeted
This is the most human voice AI I've heard. Good job in capturing real human feel, even the laughs and random pauses we all do when we're thinking. Block & Dessa team cooked. Well done!
62
54
16
578
78,150
Benjamin Fockter retweeted
BT scan
2
6
753
13,535
Benjamin Fockter retweeted
Replying to @claudeai
The duet:
🤖 Made with AI
12
27
10
765
111,750
Benjamin Fockter retweeted
Consumer EEG is bullshit
10
3
49
3,375
Benjamin Fockter retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness. New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
393
1,695
535
11,335
2,205,842
The moat is willpower, agency, pain tolerance, endurance, how quickly you can adapt over and over again, and how well you can sustain purpose through a constant onslaught against it. Who you were yesterday isn’t durable. Is your willpower?
people are starting to realize. there *are* no moats left. the only true moat in existence was intelligence, and now that's out of the equation.
6
8
5
123
6,644
Benjamin Fockter retweeted
The Silicon Valley Fashion show this Saturday is gonna go down in history as one of the best events San Francisco has ever seen. The team @gargi_kand @sammysintech @vivxxren and others have been grinding tirelessly to put on a show that others with this budget & time crunch genuinely wouldn’t be able to make happen, but us. I’m super excited and proud of everyone. Attendance is basically full but as always we’re cleaning out the guest list so we get 100% attendance. If you’re interested in attending sign up below and DM one of cohosts so we can make room for you!
1
2
3
212
Benjamin Fockter retweeted
Replying to @Elise__Jenkins
I so want to be proven wrong here but have yet to see data or even a demo that significantly outperforms Wolpaw’s cursors in the late nineties / early 00s.
2
1
39
965
Benjamin Fockter retweeted
For anyone who is either a fan of vaccines, or a critic of vaccines but open-minded to the idea that vaccines *could* be great if they were made with a more open-source open-science and public-spirited approach, I highly recommend checking out PopVax. chronicles.popvax.com/p/popv… firefly.social/post/x/210927…
317
186
37
1,853
253,990
Benjamin Fockter retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
2,246
5,915
2,283
44,157
6,924,817