Daniel_Van_Zant
Joined 2 years ago
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Member since
Apr 13
Who architects the models that model architectures?
Co-founder @ Lumen Labs: lumenresearch.co
Personal Blog: www.danielvanzant.com
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Comment · 4 days ago
Super cool research! I think this is still a formally verifiable domain but different than the kind of formal verification I was thinking. Maybe if LLMs continue to be great at this human science turns into making “fuzzy domains” much more formally verifiable (although I don’t think this is possible in some areas).
Comment · 4 days ago
To add to this I think there are “reductionist sciences” and “systems sciences”. Reductionist sciences are ones, like physics where there are lots of copies of a thing with near identical properties at different scales (the periodic table would be considered an incredibly short list and that seems to be one of the longest list of “different things” in physics).
Systems sciences are ones where each scale has lots of things with unique properties. If you think of “biological things between 2-4m” the amount of unique objects with unique properties and unique interactions is absolutely mind boggling. These sciences generally do not lend themselves to clean highly parsimonious models. The best you can do is coarse-grain a thing as a system (some very roughly defined set of interactions between different things) with certain behavior while accepting that you are losing enormous amounts of predictive power.
I suspect AI will remain worse at systems sciences for a long time because the “possible space” of models for a particular system is much larger than in the reductionist sciences and navigating it takes a human taste and instinct that LLMs don’t seem to have (yet).
Comment · 5 days ago
Lots of progress recently in formally verifiable domains. Really curious if this is a product of the machine learning folks behind most large language models being much more likely to have a background in math, physics, etc. Or if it is that large language models have some fundamental limitations in the more "scruffy" sciences like biology.
My guess is that in formally verifiable domains you can use RL-esque processes to get lots of good high-quality training data with reward signals (does it verify?), but the same does not apply to "scruffier" work. I would also imagine that the data volume of high-quality scientific work in "scruffy" domains is too low for the large language models to have a good manifold for "high-quality biology research". Would love to be proven wrong if anyone knows of anyone doing great scientific work with large language models in non-verifiable domains though.
Comment · 1 month ago
Lumen Labs | Robotics / Hardware Engineer | San Francisco, CA | ONSITE | Full-time | $130k–200k + equity | https://lumenresearch.co
I'm Daniel, co-founder. Lumen Labs is building the cognitive layer for physical AI. Nearly every major robotics effort today teaches machines by showing them thousands of hours of human teleoperation. We think that paradigm has a ceiling, and we're working on nature-inspired architectures for unstructured environments (construction sites, mining, pipelines, battlefield). Pre-seed, backed by top VC funds and operators from robotics/AI companies.
We're a team of two and hiring teammate #3 to own the hardware and infrastructure that keeps our research platforms running: the ROS2 (Humble) stack across onboard and offboard compute, sensor integration and calibration (LiDAR, depth, IMU, encoders), the safety pipeline during autonomous runs, and getting trained policies onto real hardware. Today that's wheeled navigation; we're dipping our toes into drones and eyeing weirder platforms like pipeline inspection. Where we're at: zero-shot sim2real transfer with a policy that runs at 100Hz on an ESP32 and took under a day to train.
You: hands-on with real mobile robots (UART, encoders, PWM, IMU, LiDAR), strong Python + embedded, comfortable reading code you didn't write, and can debug hardware and firmware at the same time. If "full-stack" to you means firmware, a multimeter, and a ROS node in the same afternoon, you'll fit right in. Visa/relocation support for the right person.
Full posting: https://desert-bearskin-26d.notion.site/Robotics-Engineer-Ro...
Email [email protected], mention HN, and tell us about a project you've built. If you can link to a time you got a trained policy running on real hardware, you go to the top of the pile.
Comment · 3 months ago
Lumen Labs | Simulation/RL Integration Engineer | REMOTE (US/Canada), then ONSITE SF from fall 2026 | $130k–200k + early equity | Visa OK
Lumen (https://lumenresearch.ai) is building the cognitive layer for physical AI. Most robotics efforts today learn from thousands of hours of human teleoperation; we think that has a ceiling. White paper: http://lumenresearch.co/paper
Team of two ex-founders — you'd be #3. Pre-seed, runway well into 2027, raising a seed later this year.
We test many model architectures against many sims, and hooking them together is our biggest pain point. We use Gymnasium and Shimmy as the plumbing and upstream useful pieces as open-source contributions. You'd build per-sim adapters to a shared interface contract, bridge single-env vs. GPU-vectorized and sync vs. async paradigms, and own our eval harness.
Looking for: strong Python, Gymnasium (especially the vector API), hands-on experience with Isaac, MuJoCo, or Gazebo, and a track record of getting models running in sims where they didn't before.
Email [email protected] about you and your projects — link one where you got a model working in a sim it wasn't built for and you'll jump the pile.
Full posting: https://desert-bearskin-26d.notion.site/Simulation-Engineer-...
Comment · 1 year ago
Honestly having an AI agent that interviews a interviewees agent sounds like a great "first filter" for certain tech jobs if you do it right. As in "here are the api specs, build an agent that can receive questions and reply with information based on your Resume." Would be vibe-codeable by anyone with skills in an hour. I remember seeing a company a while back that switched to only accepting Resumes through a weirdly formatted API and they said it cut down on irrelevant spam for software jobs immensely.
Comment · 1 year ago
As someone that enjoys carpentry, I don't think I would be entirely upset about this way of taking the question haha.
Comment · 1 year ago
I have dealt with this issue as well before. If folks need something more in depth I will use a LLM + some massaging of my own to create a supporting document. Here is an example of a very disorganized conversation and the supporting document I made with it: https://www.danielvanzant.com/p/what-does-the-structure-of-l... It has clear definitions of the key terms. Timestamps for the important moments, and links to external resources to learn more about any of the topics.
Comment · 1 year ago
I agree with the author, but I would also say there is something above goals and constraints. Values. A set of things that, when comparing multiple options, make the choice clear. An example of some values I frequently use is "What will give me the most enjoyment the furthest into the future? "What will result in the world being a better place?" "What will make me become someone who resembles Jesus more?" They are different from constraints as they don't knock out any options by default. Instead, they make triaging when there are many different things I could be doing much easier, and circumvent my messy intuition which is based on hormones, hunger, weather, etc.
I think values, goals, and constraints are all valuable, but it's a hierarchy. We should create constraints that help us become more aligned with our values. We should create shorter-term goals that make it easy to stay within our constraints.
To support both my point and the authors, here is Benjamin Franklin's "Thirteen Virtues," which seem to be a mix of constraints and values (zero goals): https://fs.blog/the-thirteen-virtues/
Comment · 1 year ago
I fight to record any presentations I do as often as possible. When I am asked for the slides I send the full recording instead as the way to manage this exact issue.
Comment · 1 year ago
I'm glad someone else had the same thought. I have been wondering what their "secret sauce" is for a while given how their model doesn't degrade for long-context nearly as much as other LLMs that are otherwise competitive. It could also just be that they used longer-context training data than anyone else though.
Comment · 1 year ago
Is there any effort to organize scientific literature like this? I know journals often generate tags for papers but those can often be quite poor and restricted to the field The journal is in. I would happily join a volunteer effort to create tags and do some tag-wrangling for scientific literature in my research area.
Comment · 1 year ago
Have been searching for a deep research tool that I can hook up to both my personal notes (in Obsidian) and the web and this looks like this has those capabilities. Now the only piece left is to figure out a way to export the deep research outputs back into my Obsidian somehow.
Comment · 1 year ago
I see Cohere, is there any support for in-line citations like you can get with their first party API?