Jonathan Lindo

Big news from the Flox, Hetz Ventures PortCo, on partnering with NVIDIA to bring pre-built #CUDA packages to the Flox Catalog. A major milestone for the whole ecosystem. Congrats to everyone Ron Efroni and the entire Flox team! This partnership is about supporting developers with a more seamless experience and strengthening CUDA’s value across the wider ecosystem. It’s a major step forward for anyone building AI, ML, HPC, and GPU-accelerated applications as teams can now build and deploy portable #CUDA runtimes that just work, anytime and anywhere. #proudseedinvestor

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Elie Schoppik has spent his career at the intersection of education and engineering. He built early ed-tech tools that helped people actually retain knowledge, founded a Bay Area coding bootcamp that trained thousands of developers through practice and feedback, and now leads Technical Education at Anthropic. Despite all that experience, Elie says most teams are using AI wrong: they’re treating it like an all-knowing oracle instead of a tutor. This is why Elie designs prompts, examples, and workflows that treat AI as a teacher, instead of a shortcut. In our conversation, we discuss: • Why relying on AI for “fast answers” erodes judgment and verification • How to turn prompts into guided lessons • Why context engineering is the most underrated skill in 2025 • Practical exercises any team can run tomorrow to raise AI fluency • What leaders should steal from Anthropic’s approach to technical education  Episode is live now — full conversation linked in the comments.

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Voice is a super interesting modality right now - maybe the first modality we're seeing move to open source models across a number of scale ups / enterprises. Reliability concerns, high costs, and open source model performance are pushing engineers to do their own fine tuning vs. relying on third-party vendors of proprietary models. Many of these orgs have already been collecting their own first-party data and now with third-party vendors like Extrian, David AI, etc they can train really high quality models. RL has been insanely hyped, but it's been unclear how long it will take scale ups and enterprises to actually lean in. Voice AI might be hitting that inflection point faster than expected.

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Excited to announce our investment in IntuigenceAI, building the AI System of Action and copilot for process manufacturing. Moe Tanabian helped define the last generation of industrial AI, and now the Intuigence team is building what comes next. We’re fired up to be leading their $10M Seed round. One of the biggest transformative opportunities for AI lies in the physical industries — multi-trillion-dollar markets long dominated by legacy giants and sector consultants, not modern software companies. That’s been due to a mix of proprietary, unstructured data, complex implementation needs, and distinct cultural dynamics. With AI, this dynamic is shifting quickly. The white space for startups to build flexible, scalable AI systems — with fast time-to-value — is compelling, and their success is critical for these sectors to manage a retiring workforce and rising demand. Intuigence is unlocking this opportunity in process manufacturing, starting with downstream oil & gas and chemicals. Their platform augments engineering teams — chemical, process, controls, mechanical, and more — increasing margins, reducing emissions, and improving safety. From refineries to chemical plants, the world's largest industrial operators are turning to Intuigence to help run their facilities with greater safety, speed, and insight. The customer pull is remarkable. We’re humbled to join the journey and support the mission. Read more about why we're excited about Intuigence in the first comment below. Scott Brady

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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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