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Jonathan Whitaker
@johnowhitaker
Data scientist, AI researcher, aspiring gentleman scientist
1.2K Following    10.3K Followers
Beautiful thin film effects in this biofilm on one of my algae samples!
Great sentiment!
Some milestones move a leaderboard. A rare few change how you see the future. MiniMax H3 Max did both. We released MiniMax H3 as an open-weight model with the hope that the community would adapt it, challenge it, and take it further than we could alone. @fal did exactly that. With its own post-training and inference expertise, pushed the frontier on both quality and latency, and created experiences that simply weren't possible before. Faster-than-real-time video is no longer just an idea. It is opening the door to perpetual streams, interactive worlds, real-time storytelling, and experiences we are only beginning to imagine. They made the value of open weights feel real to our team in a way it hadn't before. Advancing technology together has always been a core belief at MiniMax. H3 Max made that belief tangible. It showed that the frontier no longer belongs to a few, it belongs to everyone willing to build, experiment, and share what they discover. We hope H3 Max is the first of many breakthroughs shaped by different teams. To every builder ready to push video forward: our weights are open, our door is open, and we're ready to stand behind you. Let's build, together. ✨
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I recorded a video on my DIY gene gun efforts. Still lots of work to do dialing things in, but it might be some time before I get to that so I figured I should share the process in the meantime :) Video: Examples of transformed cells:
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Johno and Alexis chat about Open Character Training (Maiya2025)
TIL, Anthropic's production alignment procedure uses ~30M training examples :) I'm recording a video (maybe live) with @alexisgallagher today on the Open Character Training paper, if you're interested in this kind of thing :)
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New Fellows Research: Can Claude autonomously align other AIs? We gave Claude 48 hours and 1 GPU to improve the alignment of small models. It researched and proposed methods, then trained and tested the models on its own. It worked surprisingly well.
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PSA be careful trying to sprint like those robots in the humanoid games, I may have thrown my back out a little 😂
Do you think that if I train a cool enough policy in simulation I can convince HF to use some of that new Nvidia money to gift me a mini-duck? Worth a shot I think! Will post fun fails to this thread :)
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I finally got around to doing a little write-up of my neural physarium experiments: Will continue to polish and make a video at some point, but low priority, figured I'd share this in the meantime :)
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This is me I ended up leaving OpenAI, not because there's anything wrong with OAI (I actually think they're underrated as a biz), but because to me, building commercial software doesn't feel fun anymore Because agents write ~all the code: - There’s less human to human collaboration, so it’s a lonelier experience - You’re constantly context switching and not going as deep into the code, which means there’s ~zero flow state - Most of the job becomes code review and trying to reduce complexity, which is very mentally draining Again just my experience, and none of this specific to OAI. There are many people who seem to be enjoying building software in this era! But I've always wanted to try making films, and now seems as good a time as any
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What happens if we keep fungi hungry or overfed? We discover fascinating dynamics: hunger sends an exploratory network racing outward, while overfeeding yields plenty of growth but ever less architecture. The most elaborate network emerges in between! Rhizomorphs are root-like fungal networks that transport water and nutrients and allow fungi to explore new territory. In new work with Sarah Naeher, we find that the architecture of these networks is strongly and non-monotonically controlled by nutrient availability. Moderate nutrient limitation drives the fastest, most coherent exploratory growth. Intermediate enrichment produces the largest organized network, reaching ~975 mm in total strand length. Yet adding still more nutrients reverses the trend: overall fungal coverage remains high, while the organized rhizomorph network becomes less elaborate. This reveals a resource-dependent architectural strategy: exploration under scarcity, extensive network construction at intermediate resources, and increasingly locally consolidating growth when resources are abundant. The amount of fungal growth and the architecture of that growth can therefore become distinct biological variables. ▶️ Why this matters: Fungi are increasingly being explored as living building blocks for sustainable materials. For instance, mycelium-based materials are lightweight, biodegradable and potentially useful in construction and packaging, but their mechanical properties remain comparatively weak and variable. Rhizomorphs are particularly interesting because they are highly organized, differentiated structures capable of long-range transport and mechanically invasive growth. ▶️ How we did it: We tracked their development over time using imaging, SAM 3 segmentation, skeleton-based network analysis and growth modeling. Interestingly, excluding ambient light produced comparatively little detectable change; nutrient availability was the much stronger control variable in these experiments. The engineering implication is exciting - we can learn to program the environmental conditions and let the organism build the architecture for us. Nutrients, moisture, temperature, confinement and mechanical cues could become design variables, potentially combined with imaging and feedback control to steer living systems toward targeted network structures. Preprint, code and dataset see below.
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I've officially left I've had a good rest, with plenty of time for travel & tinkering, and now I'm thinking about what to do next. I've got a few ideas to share soon, but I'm also open to suggestions - feel free to reach out :)
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