Michal Malohlava (@mmalohlava) header
Michal Malohlava (@mmalohlava) profile picture

Maker at @h2oai, proud father of twins, maintainer of Sparkling Water, contributing into open source AI projects, and still believing that coding is beautiful!

Joined March 2008
Photo by Michal Malohlava (@mmalohlava)Photo by Michal Malohlava (@mmalohlava)Photo by Michal Malohlava (@mmalohlava)Photo by Michal Malohlava (@mmalohlava)Photo by Michal Malohlava (@mmalohlava)Photo by Michal Malohlava (@mmalohlava)Photo by Michal Malohlava (@mmalohlava)Photo by Michal Malohlava (@mmalohlava)
The Wildfire Challenge is opened for submissions! Do not hesitate, register at h2o.ai/wildfire/ and submit your AI App to win prizes 🏆 And if you have any questions visit H2O.ai community slack h2oai-community.slack.com or forum discuss.challenge.h2o.ai.
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Michal Malohlava retweeted
TimeH2O Outperforms Mistral, Google, and Amazon in Time-Series Forecasting! We're proud to introduce TimeH2O-v1, the #1 open-weight time-series foundation model, ranking first among 32 models on the fev-bench leaderboard across 100 forecasting tasks. #TimeSeries #Forecasting #Fevbench
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Michal Malohlava retweeted
A 4-billion-parameter open-weight model just beat Microsoft-Decision-1 and OpenAI's Decisions API on JevBench. Faster and more capable than both. That model? H2O-Lightning-4B under Apache 2.0 ⚡ It's also faster than Jev and nearly as capable while also supporting images. #OpenSource #EnterpriseAI #Jevbenchmark
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Michal Malohlava retweeted
A new paper from Apple engineers ends with a line we couldn’t have written better ourselves: "if idle macOS devices across organizations [...] can be securely federated, they could form a massive, shared GPU resource pool that benefits the broader ecosystem." That’s what we’re building with Darkbloom. The paper's Minions is a system that pool idle Apple Silicon Macs into a distributed GPU cloud for on-prem inference. Queue times went from 10+ hours to minutes. Zero new GPU purchases. Zero infrastructure cost. It runs inside Apple, on coworkers’ Macs, using the company’s own storage and access controls. When a team needs more capacity, they Slack colleagues and ask them to turn on the executor. Even in that setting, the top adoption concern was data confidentiality. People were wary of their jobs running on a colleague’s Mac. Apple’s fix was to keep each team’s work on its own team’s machines. Which brings it back to that one word in the conclusion: “securely.” Inside a company, you can shrink the network until you trust everyone in it. On an open network, the machine belongs to a stranger and there’s no one to Slack. Keeping prompts private and outputs honest on hardware you don’t control is the hard problem that we solved at Darkbloom. Apple built the on-prem version. Darkbloom is the open one.
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Michal Malohlava retweeted
Another sovereign AI partnership landed this week. H2O.ai and CAN.B Group teamed up to bring governed, sovereign AI to Australian government agencies, critical infrastructure, and regulated industries. On-prem, sovereign cloud, air-gapped. Full model governance and auditability. When a company that has raised $256M and serves more than half the Fortune 500 makes sovereignty its headline, the category isn't a niche anymore. It's where enterprise AI is heading. We've been building toward the same thing from the other end of the market. Regulated institutions were told to adopt AI and forbidden from sending their data out to do it. The big platforms are answering with flexible hosting, model portability, and governance layers. All real, all necessary. But there's a layer underneath that most sovereignty stories still skip. You can put the model in a VPC. You can host it on-prem or air-gapped. The harder question is the retrieval substrate, the full index of everything your company knows, plus every query and trace it generates. That's where the sensitive surface actually lives, and it's the part that most often still sits somewhere the customer doesn't fully control. Model isolation is not knowledge isolation. That's the gap we've spent the last four months building @enclavelabsai to close. Sovereign retrieval that runs inside your own cloud, so your index and your data never leave your walls. Sovereignty you can verify in your own logs, not read in a contract. The demo is ready, and we're now looking for early pilot partners. If you're inside a regulated institution wrestling with exactly this, and you want to try sovereign retrieval in your own environment, let's talk. Cal: cal.com/shashank-bhardwaj-fw… hashtag#DataSovereignty hashtag#EnterpriseAI hashtag#AIInfrastructure hashtag#RegTech
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Michal Malohlava retweeted
H2O.ai is proud to join @nvidia and other industry leaders in the Open Secure AI Alliance to help advance open technologies and tools for securing AI software and agents. We’re also supporting the principles behind the Open Weights and American AI Leadership letter.
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Michal Malohlava retweeted
Today we're launching H2O-3 Secure: a production-grade, commercially supported tier of one of the world's most widely deployed open-source ML platforms.
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Michal Malohlava retweeted
Introducing the Science One Framework, an experimental autonomous research prototype that builds verifiable evidence chains. We demonstrate how natively maintaining these chains eliminates hallucinated citations and enables reproducible AI science. Read the blog to learn more: goo.gle/4wzlGWU
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I feel like living in the future every day. This is another example to try: Tau Robotics (@taurobots): a humanoid robot that cleans your home, one hour for $30. Now, i am waiting in waitlist 🦾, you can too tau-robotics.com/waitlist?re…
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Michal Malohlava retweeted
Introducing autoresearch for arXiv papers Change 'arxiv' to 'autoarxiv' in any paper URL An agent deploys to resolve setup issues on the codebase, run a minimal reproduction, and estimate full replication cost. Read more below
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🤯💥🏃‍♂️
These were Sebastian Sawe's splits as he ran a 1:59:30 marathon in London 🤯 5km - 14:14 10km - 28:35 15km - 43:10 20km - 57:21 Half - 60:29 25km - 71:41 30km - 1:26:03 35km - 1:39:57 40km - 1:53:39 Finish - 1:59:30 Yomif Kejelcha also ran sub-two, clocking 1:59:41 on his debut marathon ‼️
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Simply wow!
WHAT HAVE WE JUST WITNESSED? 🤯 Sabastian Sawe has just become the first person in history to run a sub two-hour marathon in race conditions. Yomif Kejelcha was also under two hours for second!
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Michal Malohlava retweeted
I wanted to share a bunch of my favorite hidden and under-utilized features in Claude Code. I'll focus on the ones I use the most. Here goes.
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Michal Malohlava retweeted
Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI
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Michal Malohlava retweeted
GUI for fine-tuning LLMs without code github.com/h2oai/h2o-llmstud…
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Michal Malohlava retweeted
Big momentum at #NVIDIAGTC 🎉 During his keynote, @nvidia CEO Jensen Huang highlighted the rise of AI-native companies — and H2O.ai was featured on the slide. Proud moment to be recognized alongside leading innovators in the ecosystem 🦾 #AINative #H2OSuperAgent #EnterpriseAI
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Michal Malohlava retweeted
Every single one of the 103 companies Jensen called AI Native today.
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Michal Malohlava retweeted
Just added the 12th chapter to my Agentic Engineering Patterns guide, but it's the first one in the sequence: I figured it was time to try and answer the obvious question, "What is agentic engineering?" simonwillison.net/guides/age…
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Michal Malohlava retweeted
My biggest takeaways from @qasar: 1. The real AI revolution over the next 5 to 10 years will happen in the physical world, not in software. While everyone obsesses over ChatGPT, Claude and coding agents, the real impact will come from autonomous vehicles, mining robots, and farming equipment. They’ll save lives (over 30,000 die annually in U.S. car accidents), enable mobility for disabled people, solve labor shortages in dangerous industries where nobody wants to work, and much more. 2. AI isn’t replacing jobs in industries like trucking and farming—it’s arriving just in time to fill a labor gap that already exists. The average age of a farmer in the U.S. is in the late 50s. Long-haul trucking jobs go unfilled not because people can’t do them but because the tradeoff isn’t worth it anymore; a family can choose DoorDash or Uber so the parent can pick up their kid. Qasar’s view is that physical AI will fill gaps created by demographic shifts and changing preferences, not displace workers who want those roles. He’s careful to say this doesn’t mean there are no downsides, but that the framing of “AI is coming for your job” misses the more immediate reality. 3. Comparing Chinese AI companies to American AI companies is a category error. Qasar uses Huawei as his example: the company’s name means “China’s ambition,” roughly a quarter of its employees are Communist Party members, and its goal is not to grow profits but to extend the state. So when people say Chinese EVs are outcompeting Detroit, they’re comparing a government-backed entity with no profit constraint to companies like Rivian that get hammered by public investors for losing money. Qasar says that if American companies were freed from profit expectations the same way, they’d field comparable products. The point isn’t that China is incompetent or not a serious competitor; it’s that the comparison framework most people use is wrong. 4. The Industrial Revolution is the best mental model for AI. Just like the late 1800s brought child labor and monopolies but also unprecedented access to healthcare, heating, cooling, and material goods, AI will have downsides we must address while delivering massive benefits. The key: don’t pump the brakes on technology to protect jobs—that hurts the people you’re trying to help most. Find solutions that account for workers while enabling progress. 5. Building under the radar can be your competitive advantage. Qasar built Applied Intuition for nearly a decade without a social media presence. One of the company’s early core values was “Our best work is done alone and quietly.” His reasoning: every minute spent on a podcast, a post, or content for public consumption is a minute not spent on customers and the product. Qasar adds an important caveat—he could afford to stay quiet because he was already known in the ecosystem. Founders without an existing network may need the visibility that public presence creates. 6. Qasar thinks most Silicon Valley CEOs lack taste—both in the artistic sense and in the sense of making good operational decisions—because their life experience is too narrow. A founder who grew up in Cupertino, went to Berkeley, and immediately started a company has never experienced what it’s like to be at the bottom of a 100,000-person organization. Qasar spent over a decade at GM and Bosch and says that experience—the bureaucracy, the bad tools, the disconnected leadership—directly informs how he leads Applied Intuition today. His broader point is that taste comes from exposure to a wide range of human experience: backpacking, reading old books, working in different cultures and industries. 7. Successful companies almost always show traction early. If you’re two years in and the market isn’t giving you increasingly specific signals about what to build, consider resetting. The foundation might be wrong—co-founders, market, or life phase. Your first startup is practice; treat it as building the muscle of being a founder, not as your magnum opus. 8. Emotions are a filter that distorts decision-making, and the goal should be to remove that filter so the “raw image” of the decision comes through. Qasar doesn’t mean leaders shouldn’t have empathy; he means that attachment to your own idea, the desire to be right, and the tribal instinct to follow the loudest voice are all emotional distortions. His practical heuristic: the same decision, presented to multiple people independently in the company, should produce the same result. If it doesn’t, some emotional filter is warping the signal. This connects to his broader philosophy of creating a culture where the best idea wins regardless of who proposed it or how senior they are. 9. Qasar’s advice on company values: don’t invent them philosophically. Instead, write down the 5 to 10 things that explain why your company is already successful, and those become your values. Applied Intuition’s values include “Move fast, move safe,” “Never disappoint the customer,” “Technical mastery,” “High output matters,” “Laugh a lot,” and “Half of the work is follow-up.” 10. Treat your first startup as a zero—a practice round, not destiny. Qasar tells founders leaving Applied Intuition to start companies that their first three years will likely produce nothing, and that’s fine. Founding is a craft, like woodworking. If your first table is wobbly, you don’t quit—you build another one. He thinks a lot of founders, especially first-timers, put so much pressure on themselves to succeed immediately that they miss the real value of the experience: learning and building the muscle. His own third company is the most successful by far, and he sees this pattern repeatedly. There are entire funds focused exclusively on multi-time founders for exactly this reason.
Marc Andreessen calls him "the best AI CEO nobody knows about." Elad Gil calls his company "the most successful, most quiet company in AI." Qasar Younis (@qasar) is the co-founder and CEO of Applied Intuition—which brings AI to vehicles, like tractors, planes, submarines, mining rigs, cars, and more. The company is valued at over $15B, making ~$1B in ARR, with 18 of the top 20 global automakers (and the U.S. Department of Defense) as customers. And @Qasar's story is wild: Born on a farm in Pakistan. Emigrated to the U.S. at age 5. Grew up in Detroit managing engine lines at GM. Harvard MBA. Became COO of @Y Combinator (during the era that funded OpenAI, Cruise, DoorDash, and Coinbase). Then left to start Applied Intuition in 2017. As Qasar shared, "not many people run a $15B+ physical AI company with revenue and free cash flow. And by not many, I think literally zero other people." In a rare and in-depth interview, we discuss: 🔸 The counterintuitive reason he's stayed quiet and built in private 🔸 Why reading old books and cleaning your own office makes you a better founder 🔸 How to build a culture where the best idea wins, not the loudest voice 🔸 Why the best companies show traction early—and what to do if yours doesn't 🔸 How physical AI will transform farming, mining, and construction before it ever reaches your home Listen now 👇 youtu.be/_rcniEb9bLw
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