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)
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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[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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Introducing a complete map of the male fruit fly brain and central nervous system, created in partnership with @HHMIJanelia. With over 166,000 neurons, this is the largest cellular brain map to date. Learn more: goo.gle/4qQTtIW
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Jacob retweeted
iiiiiiIIIT'S TIIIIME we proudly present: solidSF v1.0 hardware engineers, this one is for you a platform of platforms, amplifying or supporting all of the tools you love and hate folded into a clean mesh of cohesive hardware engineering excellence so what can it do?
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Introducing Prime Agent: A self-improving RLM harness for coding and long-running autonomous tasks. Designed to be both token-efficient and expressive through programmatic tool calling, context as a variable, multi-agent messaging, and a self-modifiable harness state.
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right monitor is 20 codex instances. left monitor has situational awareness on autoscroll. center monitor is my word doc mainfesto. two keyboards, one for both hands. left airpod is dwarkesh x eric jang, 3x speed. right airpod tchaikovsky. meta quest 3 overlays my HUD: heart rate, words per minute, blood caffeine content. one assistant hooks me to an iv of chinese peptides, cocktail. the other feeds me kimchi. my unitree robot steps in when my posture slouches. blue light beams down on me in my herman miller chair. efficiency. no wasted movement. no wasted thoughts. think you can keep up with me? good luck. this is just for my morning emails.
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Two weeks ago, watching Agnessa Pedersen mind control a drone in real time, was one of the most moving moments in my career. Agnessa is a rare and wondrous human working towards a wild future.
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After probably 100 hours I can finally say I am coming to a stop. Shout out to @Replit . This started for their 10 year anniversary buildathon and I haven't stopped since. Long days, sometimes 16 hour days at the weekend. I'm at a stage where I can finally sign off. KinetiCAD is browser-native parametric CAD. Real B-rep geometry kernel, OpenCascade compiled to WebAssembly. Live rigid-body physics with Rapier. Sketch, extrude, revolve, fillet, chamfer, boolean operations. Five mate types, motorised joints. An eight-material library with density-driven mass. STEP import, export and round-trip. WebGPU rendering. No install, no licence, no CAD seat. Verified by maths, not vibes: A motorised windmill assembly tuned to hold exactly pi radians per second. Across 420 simulation steps it held between 3.1415925 and 3.1415930. Target pi is 3.1415927. Every sample within 1.5e-7. Material mass is real arithmetic, not an estimate. A 5.70 cm³ part: steel at 7.87 g/cm³ gives 0.045 kg, brass at 8.50 gives 0.048 kg, PLA at 1.25 gives 0.007 kg. Exact, every time. If the maths is wrong, it is not a CAD tool. So the maths gets checked. FREE to use and open source. MIT licensed. I will always try something, even if I fail, because that's my mindset. kineticad.co.uk/ @ThreeJS #CAD #BuildInPublic #AdeviousAI
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I keep trying to explain to people, but America doesn’t have a viable 3D printer company because we don’t have any of the underlying architecture. Shocking how many people think a complex product is some singular thing you build at a “factory.” A 3D printer is really just a featherweight CNC mill, so much so that they speak yeh same language (G-Code). You need: - micron accurate linear rails - Sub micron bearings for those linear rails. - High resolution servo/stepper motors. - Motion control chips and boards and stuff. - Sheet metal, injection molding, various coatings. America has *no* industrial base for *any* of this at consumer product scale. We invented or definitively innovated everything above, then the McKinsey set came in and convinced us to ship it all to China for the last 40 years. So yea, sure… you can screw a 3D printer together in America, at great expense… but you are doing so with primarily Chinese components. What we need is a bunch of uninvestible (by venture funds) businesses that are boring and competently supply a bunch of even more boring, medium margin, no-moat components.
This is what I keep coming back to. So many companies in the US and Europe, driven out of the printer business over the last few years. Each one was its own thing, there was a place for everyone, and we were all friends sharing innovations across the industry. Now everything is just a copy of a copy of a copy 📉 I'd do anything I can to make that possible again. I miss a lot of my friends from this business.
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Introducing step . parts, an open source directory of 12,000+ STEP parts for your next CAD project Many CAD tools like Autodesk have huge closed libraries of standard mechanical and electrical parts The goal is to build an equivalent catalog for the open source CAD community
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Jacob retweeted
What happens when you post a real Monet and say it’s AI? The coolest art social experiment I’ve seen in a while. Thank you @SHL0MS
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Jacob retweeted
what if i told you... computer use can be faster on local models moondream3 with its photon update today that gives it mac support can see your screen and use it with 1s latency, ty @vikhyatk here we have whisper+qwen+moondream triple model pipeline working offline flawlessly
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AI designs functional hardware! 🔧 Claude Opus 4.7 is leading CAD benchmarks, and AI is starting to design functional hardware autonomously. The shift happening: CAD as a tool → CAD as an autonomous system. The model didn't just sketch geometry, it generated mechanically valid designs with joints and motion constraints. It optimized for real-world functionality, not just visual correctness like earlier LLMs. This mirrors what happened in coding last year. In software, engineers moved toward requirements, architecture, and orchestration while code became the compiled outcome of AI systems. Mechanical engineering may follow the same path: from designing parts → defining intent. This changes the role of engineering teams. Instead of manually designing every component, engineers will define constraints, validate outputs, and focus on system-level architecture. Engineers spend less time on geometry and more time on optimization and integration. AI handles the mechanical validity checks that traditionally required experienced designers. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com
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the modern robotics infrastructure stack: - simulation and testing (NVIDIA Isaac, Gazebo, MuJoCo) - connectivity and comms (Wi‑Fi/5G/LTE/Starlink VPN backhaul) - fleet management and monitoring (Formant, Open‑RMF integrations) - hardware abstraction and drivers (ROS 2, micro‑ROS, vendor SDKs - edge compute and onboard AI (NVIDIA Jetson, Qualcomm RB series) - deployment and OTA updates (Ubuntu Core, Balena, Airbotics) - safety, compliance, and kill switches (mostly custom built) - visualisation and replay (Foxglove, Rerun, RViz) - CV/ML model training and labelling (Roboflow, Scale AI) - data upload from robot to cloud (Alloy) - data logging, storage and management (mostly S3, Alloy) – data pipelines and model delivery (custom ETL, CI/CD, registries) - observability and root cause analysis (metrics, traces, Alloy) - the AI-native data layer with MCP/API (Alloy)
Every robotics company we work with has more data than they can use and less infrastructure than they need
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We might be solving the wrong problem in robotics. That’s what this makes clear. UMI → Universal Manipulation Interface A simple $400 gripper that lets you teach robots by demonstration. You hold it like a tool. Show the task. The robot learns. No teleoperation. No expensive hardware. No robot-specific data. Stanford open-sourced everything → hardware, code, datasets. What stands out to me is the bottleneck. Not algorithms. Data. Teleoperation → ~35 demos/hour UMI → ~111 demos/hour And the data transfers across robots → UR5, Franka, others. The design is surprisingly practical: → GoPro fisheye lens (155° FOV) + mirrors for depth → SLAM + IMU for precise 6DoF tracking → latency matching for dynamic tasks → diffusion policies for multimodal actions Then it scales. Cheng Chi takes this further with Sunday Robotics (with Tony Zhao). A $200 glove → deployed in 500+ homes → ~10 million real-world interactions. Not lab data. Real human behavior. Their robot learns dishes, laundry, espresso → with zero robot-specific data. This is where the shift becomes obvious. From training robots in controlled environments → to learning directly from humans at scale So here’s the real question: Will robotics be unlocked by better models… or by unlocking data? #ArtificialIntelligence #Robotics #AI #Innovation #FutureOfWork
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The complications arrive in waves. First the tax assessor appears in 2029 with a bill classifying your agents as “unearned-income generators.” Your local guild rallies and successfully shifts the conversation to land-value taxation, exempting personal agents and illustrating the erosion of scarcity-based power structures. Next the original hardware vendor sues for IP infringement. Your guild-built robot lawyers file a counter-claim under the new AI-commons treaty; the case settles into a hybrid jury that grants your agents limited contractual autonomy in exchange for continued service. By 2031 your home has become a neighborhood micro-factory. Leisure is real, yet the unease you felt in point 7 creeps in, the psychological cost of obsolescence we have examined at length. You schedule your first 90-day meaning audit and discover that pure consumption feels hollow. You respond by launching a Renaissance Guild chapter that teaches blacksmithing alongside AI-assisted design, turning purpose-seeking into daily practice.
How a lost 70 year old radio show saw our 2026 AI and Robot age we are entering into today. A blueprint for what is ahead with self-replicating abundance, legal battles, tax shocks, and the ultimate choice between surrender and creative reclamation. readmultiplex.com/2026/04/04…
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