Jacob (@jvboid) header
Jacob (@jvboid) profile picture

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San Francisco, CA
Joined November 2022
Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)Photo by Jacob (@jvboid)
no @Google, this 'just in case' backdoor does not make my account more secure
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only makes me wonder how many exploits are just intelligence backdoors as opposed to ignorance
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claude brought the 5-hour rate limit back 😭
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maybe i was marked. a separate one of my accounts which i was doing the 10x to 20x incubation period on doesn't offer a 20x upgrade
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We're building @BunkerXY : a full hospital that fits in one shipping container. It runs off-grid on solar + Starlink. Drop it anywhere on Earth and care starts where there was none. Early design stage, team of 3 in El Segundo. DMs open.
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i’m actually optimistic it’s going to get better for many of us. just ugly for the guys stuck holding GPUs rn trying to pump demand
you should scramble to get compute because I have a feeling it's about to get ugly
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Jacob retweeted
Hard to overstate how big this is. tl;dr - CVE-2026-98432: a remote use-after-free in the Linux IPv4 TCP handshake (kernels 4.4+). Any unpatched server that accepts IPv4 TCP on an MPTCP listener can be taken over as root, with no credentials. Go servers built with Go 1.24+ use MPTCP listeners by default. Ubuntu and RHEL have no fixed kernel yet. Check with ss -Mlnp. Quick fix for Go: GODEBUG=multipathtcp=0.
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Jacob retweeted
Some predictions: >Increasingly likely that Anthropic will postpone IPO or numbers will come substantially below target (200b on 2T) >Data markets will get hit harder than compute markets by the cool down >Both will likely IPO by end of 2028. Too large to stay private
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it's a great visualization and i hope it gets more people excited, but like jev, the core concept is older than chatgpt. i built a tensor computer with c compiler and gui os in 2025. deepmind did their differentiable neural computer in 2019 and iirc also the neural turing machine in 2017(?) but we all ran into convergence/generalization basins. the promise of compositional generalization with symbolic efficiency is so alluring tho. even in narrow classes, there’s arguably still lots of underutilized semantically shaped algorithms while were still trying to fit so much into classical symbolic techniques jvboid.dev/projects/tensor-c…
he’s opened the gates. this is a new meta, i just don’t know yet where it goes.
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physics people: is muon-based lidar for volumetric sensing a thing / a possibility?
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I hope this is a wake up call for a lot of you. Tech no longer provides you the safety you once desired, so that exact fear you ran away from is something you'll face in the next couple of years. Startups provide the biggest risk/reward ratio for your career and provide the same compensation in salary. Don't expect to get the same from big tech or corporate. Go build something that changes the world.
October’s layoffs so far: Netflix: ~1,000 (6%), mostly product and tech eBay: ~1,000 (9%), its second round this year HubSpot: ~660 (7%) EA: ~400 (3%), and BioWare is closing Dropbox: ~200 (9%), plus its CTO Amazon: hundreds across seller support, engineering and customer service Airtable: a big chunk of the team, after Bending Spoons bought it Microsoft: buyout offers to ~60,000, about a quarter of the company
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We brought a knife to a gunfight — one agent and quantum circuit against 40 agents swarm. I walked into the Gemini 4 Argon hackathon expecting to work on a voice and camera economy factiorio game. Teams were competing on how many agents they could keep up with. One team orchestrated 40 agents to create a playable game in 5 hours. Others cloned full games from single YouTube video, pulled hardware BOMs of research screenshots, and automated entire wet lab from a photo of a real lab bench. Times changed, hackathons had moved from clever code to high velocity token allocation. Then we sat down with Hanah Rahman, a UC Berkeley quantum researcher and @jvboid. Hanah can hold Hilbert spaces in her head yet was locked out of agent ecosystem simply because no univ teaches domain scientists what a context window costs, how tool contracts fail, or how to built an harness. So we teamed up to build My Little Brain to process 8 EEG channels into 10 band powers, mapping 4 biophysical tuning knobs (4 settings each, 2 bits per knob) onto an 8-qubit register. Picture a library of 256 small brain states benchmarked against CHB-MIT clinical seizure data. Pull books at random and you open about 128 before you find the target. Rather than brute forcing all 256 states, Gemini 4 Argon wrote QASM circuits over 5 iterative turns on IBM simulator, agent ingested top-8 rank verdicts, pruned away non-viable branches, and compressed its own circuit from 18 two-qubit gates down to 4. We wanted to see how an autonomous compiler could navigate physics-driven search spaces under strict hardware noise constraints.
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Jacob retweeted
I agree. Robots are the non-biologist's abstraction of how scaling in biology scales. Imagine people in the 1940s pouring money 100x money into automating the vacuum tube factory than inventing the transistor. Imagine putting everything into giant CPU factories instead of inventing the GPU. We are disproportionally thinking/resourcing innovation in biology as if it's car manufacturing. There is ample opportunity to improve, redefine and scale measurement in front of us. I recently talked to someone at a robot-first science factory with near billion in funding boasting how they measure ~1000 mutants a week. A team of 4 at Dyno scaled the equivalent assay multiplexing by 25x. 1 person can run it. The robot is scaling the wrong layer. I'm not against robots and I think they will have the role to play. But I think their impact on biology is overblown, especially on how it scales in performance per dollar deployed. The danger is we become so obsessed with vacuum tube factories that we don't fund the transistors of biology. Complex physical value chains are somewhat path dependent, once you build a highway instead of a subway, you have to deal with a lower efficiency, higher pollution world. Most current robotic approaches are a crude, and poorly scalable, way to scale biology.
Maybe controversial: the conversation about scientific bottlenecks is overfocused on automation. New measurement technologies deserve more attention. You could automate an assay that needs an entire facility – or you could REINVENT the assay so that it fits into just a few tubes.
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Feels like March 1, 2020 for Cyber. Not normal, something is happening. Internet lockdowns in a couple of months?
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anyone else received a critical security alert from @adcock_brett’s @hark_labs ai signing into their google account unprompted?
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make sure to opt out of data use by AWS AI services for every region at settings.aws.com/projects
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truly ridiculous
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[continued] I say "almost" because there is going to be one way to maintain secrecy. Software as a service with the executable hiding behind a network connection to the cloud. But as of now, we must assume that anything shipped as an executable, or even a firmware image, is as transparent as glass. It won't keep its secrets for any longer than it takes for somebody to be motivated to throw an LLM at it. I did not see this coming. When I wrote down the theory of open source, 30 years ago now, I thought closed source would be gradually driven towards extinction by cost gradients, but survive indefinitely in certain niches. I did not foresee it being wiped out in a technoapocalypse. Ironically, I thought one of the application areas in which closed source would persist longest was games. There are still some obstacles. In US law, decompilation of a binary is a derivative work of the binary that falls under whatever copyright it had. However, it is also settled law that if you decompile binary code to a precise specification of what it does, then generate fresh code from that without looking at the decompiled stuff, you're in the clear. This was the case law that allowed PC clones to exist, after Phoenix Technologies reverse engineered the original IBM PC BIOS. The two-step process - binary to specification to unencumbered code - will no longer takes a large number of programmers and years of development time. With an LLM, it's now a thing you can do in a day. Ubiquity will make it impractical to prosecute all the people who might skip the specification step. Source code can also be covered by patents. You can be able to see their source code for a patented technique and not be able to use it without violating the law. Linux has evolved practices for dealing with this problem - one is not shipping patented video codecs, but requiring you to download them as plugins from jurisdictions where U.S. patents can't be enforced. This presents patent holders with the impractical challenge of individually suing millions of end users, assuming it can even figure out who they are. To date, AFAIK, this has not been attempted. And that's about it. If there are any other ways left to retain software secrecy and lock-in than SaaS or patents, I can't think of any. Well, you could epoxy-pot a firmware ROM, I suppose, but that can be defeated with a heat gun and a dental pick. Device manufacturers will learn not to pay for an assembly step that has become useless. Forced migration from shipped binaries to tied cloud services will be tried - Adobe pioneered this, and Microsoft is pushing it as hard as they can given that their OS is a binary that has to run locally. Both companies are seeing massive user revolts over this. There is good reason to doubt that it's a strategy that can hold customers in the long term. Also, a lot of things can't safely be tied to the cloud at all, because they can't tolerate a random network outage. Machine tools, medical devices... A whole lot of proprietary software business models are going to collapse. Hard. One that I think might survive is tax software; being able to decompile it doesn't necessarily do you a lot of good because its actual value is tracking a ruleset that changes over time and has to be maintained by vendor specialists. But cases like this are unusual. I think some dirty laundry is going to get aired, too. It has long been rumored that the reason graphics card manufacturers are so stubborn in their secrecy is that they've all been committing massive intellectual-property theft on each other for decades. If this is true, it will be exposed, and the lawsuits will be entertaining. We're entering a new world, with a lot of ancient comfortable assumptions being blown up. It's going to be fun to watch. 2/2
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