Vladimir Baranov

We just led the $4.5M seed round of Potato. Because we at Draper Associates believe that accelerating science, and unlocking runaway knowledge production, is a bigger unlock than the internet itself. Potato is building autonomous AI scientists: agents that can read research, form hypotheses, design experiments, and run them across code and chemistry. This is how we 10x the pace of life sciences research and unlock discoveries that would’ve taken decades - at a fraction of the cost. This is end-to-end, closed-loop science. Faster, cheaper, and infinitely scalable. → Literature reviews, automated → Protocols, generated → Experiments, executed → Results, reproducible Potato is already powering research at MIT, Stanford, Harvard, Caltech, Berkeley, and beyond. Congrats to Nick Edwards, PhD and Ryan Kosai. Your vision has the potential to redefine how humanity discovers. Read more via GeekWire: https://lnkd.in/gjttp_up

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I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins.  A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa

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With RL techniques like GRPO, it’s become easy to fine-tune small models to outperform foundation models on vertical tasks. We’re open sourcing Osmosis-Apply-1.7B: a small model that merges code (similar to Cursor’s instant apply) better than foundation models. Links in comments! Using foundation models for high specificity, low complexity tasks like code merging can be overkill - instead, it’s better to use a specialized model instead for higher performance and latency. We fine-tuned the model on 100K real-world code commits where the model needs to merge a code edit snippet into an original code snippet. On the test dataset (subset of CommitPackFT), foundation model performance ranged between 0.77-0.93 reward score. In comparison, Osmosis-Apply-1.7B achieved a 0.98 reward score - while also being 3X-5X more affordable than the next cheaper model AND ~10X faster! Why this matters: the best AI agents use multiple models to ensure task specialization and reliability at scale - on top of cost and latency benefits as well. Links to the model and full write-up (with reward function, hyperparameters, etc.) are in the comments - reach out if you’re interested in using reinforcement learning!

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C10 Labs Expanding into the New England AI and innovation ecosystem! C10 Labs and RIHub have partnered to accelerate the growth of applied AI startups across Massachusetts and Rhode Island. The collaboration provides early-stage founders with shared resources, accelerator support, investor access, and mentorship in sectors like applied at the intersection of core sectors like health, energy, climate, biotech, and robotics. #AI #Startups #Innovation #NewEngland #VentureCapital. David Berlin Patricia Geli Beth Porter Marissa Flanagan

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Generative AI’s greatest potential lies in the physical world, where the ability to simulate, design, and iterate faster can reshape trillion-dollar sectors. That’s why we’re thrilled to announce that N47 is leading Luminary Cloud’s $72M Series B. The Luminary team is pioneering the Physics AI Model Factory, underpinned by a GPU-native physics simulation platform. This technology is designed from the ground up to unleash the performance of modern GPUs and generate the high-value datasets that power next-generation AI. Now, engineers can run hundreds of design iterations overnight instead of waiting days. AI models trained on physics-grounded data can suggest new aerodynamic designs or optimize energy systems in ways previously impossible, and companies across industries can accelerate their innovation cycles while reducing cost. With co-founders Juan Alonso and Pete Schlampp, Luminary has the technical depth and operational experience to define the new era of Physics AI. We’re proud to partner with them as they create the future of simulation and redefine what’s possible in the built world. Learn more about Luminary Cloud’s vision here: https://lnkd.in/gbjeWrvy 

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The AI Breakfast this Tuesday 6th May was on the theme of "Vibe Coding" and impact of LLM powered co-pilots and coding agents on software development. We had a great panel, with Jack Jackson founder of Recurse ML, Sean Ward co-founder of iGent AI and Jose Palazon the co-founder of Sagittal.ai and some illuminating but strong views were shared ( Yes a panelist did say that Vibe Coding is as useful as "Cancer" whilst on the other side of the argument another panellist did joke that in 5 years time most software developers may be flipping burgers) I think my main takeaways were: A: There is a big chasm difference between Software Developers/ Engineers using the likes of Cursor or Windsurf to autocomplete repetitive tasks and non technical people using prompting of LLM's and powerful distillation methods to try to reproduce other companies code bases or replicate others digital products - the consensus was beyond clever demo's and simple web sites the product of "vibe coding" fails to work when deployed in the real world and in a real IDE. How long will this be the case is up for hot debate B: Evaluation, Evaluation, Evaluation - How to understand, test, and gain trust and reassurance before and post deployment of a code base is increasingly the role of a SWE. LLM's can help you design and understand the software stack specification you want to build, some LLM's even have Taste ( an intuitive understanding of what could work and what will not that the best SWE have) but final judgement is still the human especially in regards to agile deployment C: "Pareto 80-20 rule" for using code-generators to develop deployable software only applies if you understand where the 80-20 line is - 80% of the journey to where? where and when do you exactly need the human SWE to take over? D: Those that use no-code natural language sites that expertly prompt LLM's to generate a simple code base and/or simple digital products like a website or mobile app because they cannot code will be locked into these platforms. Because coding will remain a black box to these users they will become dependent on using the function improving plug-ins offered by their no code platforms (like Lovable and Rock) and they in turn will probably become market places for no-code LLM powered applications. E: Agentic Dev Opps tools are about to take off. Lots of discussion on what's good and what is coming down the pipeline but there was consensus that LLM powered Dev Ops tools used by humans are going to be very different from those that feed into and are used by LLM powered Agents and this could become another big digital divide. Anyway it was an informative discussion followed by a very good breakfast. The next AI Breakfast on the 3rd June will be on ML Ops - lots and lots of innovation to explore there for AI First startups.

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