Derek Matzen

CEO // High Tide
building Luna - personal AI

[Anduril Industries @anduriltech]

https://t.co/1jJ7BPG4dY

[shift @joinshift]

Today, we're launching shift. We're starting by cleaning your apartment in New York City, for free.

Here's how it works. Book a shift cleaning. A vetted shift operator comes to your home wearing one of our devices. They clean. They leave. You pay nothing.

In exchange, we record the cleaning. Robotics is being built on data about how people do daily tasks, and the value of that recording is what funds the service. Anything personal in it is anonymized before the recording is processed.

By now, you have heard about the shift to AI more times than you can count. About the shift toward you, the part where you actually feel it, you have heard almost nothing. Shift is what starts to make it concrete, in specific cities, with specific services.

Today, cleaning in New York. Soon, handymen, repairs, and errands across the globe. And this is just one side of shift, with more on the way.

Comment “shift” and we’ll send you an early access link.

[Atoms Not Bits @AtomsNotBits]

America's Moon Base

Daily Hard Tech Headlines:

- @Figure_robot secured a commercial agreement with Catalyst Brands LLC to deploy humanoid robots across its logistics network, starting at its Reno distribution center. Catalyst operates JCPenney, Aéropostale, Brooks Brothers, Lucky Brand, and Nautica.

- South Korea is planning to develop nuclear-powered submarines. The country currently operates no nuclear-powered submarines, with a target operational date in the mid-2030s.

- TMV has launched a $200M fund focused on maritime and logistics technology. Anchor LPs include Prologis Ventures and the American Bureau of Shipping. The fund will deploy capital from pre-seed to Series A in areas like autonomy, robotics, operational AI, maritime dual-use technology, and energy.

- The Quad, a strategic partnership between the United States, India, Japan, and Australia, is launching a “Ports of the Future” pilot in Fiji, bringing next-generation port technology to the South Pacific.

- @ArloIndustries (YC P26) has launched. The startup develops distributed passive sensor networks for next-generation air defense, including tracking and detecting missiles and drones.

- @LockheedMartin is partnering with the U.S. Coast Guard to develop high-power microwave technology for maritime security operations.

-@itsware_ has launched. The startup builds factory intelligence software for critical industries like defense, space, data centers, and aerospace, turning activity across complex facilities into trusted operational records for tools, parts, work in progress, equipment, custody, calibration, and exceptions.

- Space data center startup Starcloud will integrate SpaceX Starlink laser terminals across 25+ satellites, enabling high-speed data transfer in orbit without relying on ground stations.

- Voyager Technologies secured a $16.5M DARPA contract to develop new propellant-embedded control technology for adjustable thrust in solid rocket motors.

- Ares Armaments, the Australian C-UAS startup, has announced its expansion to the United States. The startup is opening manufacturing operations in Michigan.

- Edge Case secured a $1.2M+ SBIR award to develop autonomous robotic inspection technology for confined and hazardous environments, leveraging technology from the DARPA Subterranean Challenge.

- Tierra Adentro Growth Capital and DESRI began construction on jointly backed solar and battery storage projects in New Mexico.

- Skyfront, a long-endurance drone company, and AeroIntel Systems, a geospatial intelligence and UAS operations firm, trained U.S. Army soldiers on Skyfront’s P8 drone during a three-week military exercise in Colorado.

- NASA provided new updates on its Moon Base initiative, including a $219M lunar rover contract for @Astrolab_Space, a $220M rover contract for @LunarOutpostInc, and a $188M lunar cargo delivery contract for @blueorigin with a $280M+ option period, ahead of sustained Artemis operations near the lunar South Pole.

[Lenny Rachitsky @lennysan]

My biggest takeaways from ex-OpenAI, Apple, Meta roboticist @kalinowski007:

1. The AI frontier is shifting from digital to physical because labs see the ceiling of keyboard-bound AI. “What you can do behind a keyboard with AI is going to saturate.” Which is why labs, big tech, and startups are increasingly investing in hardware, and why enrollment at universities is rising while CS enrollment is trending down.

2. More change is coming to warfare than to consumer electronics in the next two years. Drones, robotics, and the hardware supply chain all converge on the battlefield, and Caitlin argues we have to be able to control adversarial threats to our hardware layer, not just our chatbots.

3. The hardware industry faces a looming memory crisis that could derail the robotics revolution. Memory prices are spiking—potentially doubling or more—driven by AI data center demand. Companies building consumer robotics can’t compete on price with data centers. Caitlin is advising startups to pre-buy memory and stockpile components if they can afford it, because “we are in trouble as an industry.”

4. VR didn’t become a mainstream product, but it created the tech necessary for robotics (and war). SLAM (simultaneous localization and mapping), depth sensors, spatial computing, and understanding how humans perceive visual data in space is now powering robotics, autonomous vehicles, and drones. The technology needed to understand how a robot moves through space is essentially the same technology developed for VR headsets.

5. Humanoid robots are overhyped. While humanoids are interesting for certain long-tail tasks, most manufacturing and real-world applications need dedicated robots designed for specific jobs. A robot screwing keyboards into laptop cases doesn’t need to be humanoid—it needs to be optimized for that exact task. The future will have robots for construction, electrical work, logistics, and low-volume assembly, and most won’t look humanoid.

6. Supply chain independence is a national security imperative. Over the past 25 years, essentially every layer of the hardware supply chain—from raw magnets to actuators to final assembly—has been outsourced to China, Japan, and Korea. The same actuator technology that makes a drone rotor spin also makes a robot arm move. Without an independent supply chain, the U.S. is vulnerable. As Caitlin warns, “We need to re-industrialize this country significantly in order to be safe in a military sense.”

7. The hardest part of building safe robots is the decisions you don’t think about. If a robot arm is heavy and hard, the impact force when it hits you is dangerous. But there’s also the social aspect—robots need to show intent before moving (looking before turning), acknowledge when humans enter a room, and transmit non-threatening body language. As Caitlin learned from researcher Leila Takayama, “If a robot just suddenly turns and does all this stuff, it scares you. But if a robot looks before it turns and then goes, it’s much less alarming.”

8. Software builders don't understand how fundamentally different building hardware is. Software can compile code hourly, but in hardware you may get only a handful of chances to “compile” before mass production, with each major build taking three to five months. Once you ship, you’re done—there are no over-the-air updates for physical components. Software intuition doesn’t transfer to hardware.

9. In hardware, you never have enough time—so do everything you know you need to do right now. Caitlin learned from Apple executives like Shelly Goldberg and Kate Bergeron that you can’t wait around. Even if you technically have more time, use it, because “in two days there’s going to be a surprise coming around the corner that you need that time to fix.” This ruthless efficiency of clearing known tasks immediately creates a buffer for inevitable surprises.

10. AI hasn’t yet transformed hardware engineering. AI can’t do real CAD (computer-aided design) yet, and AI models don’t understand friction, weight, contact pressure, or surface texture. It can do surfaces and point clouds, but not the dense, equation-based solid entities that hardware engineers need. But when it arrives, it will be transformative.

11. CAD files are some of the most valuable IP any company has. Samsung, Apple, and other manufacturers will never give their 3D CAD to AI model makers. This creates a data scarcity problem for training hardware AI. The solution might start with hobbyists who don’t care about IP protection and just want to build things faster, then eventually move to on-premise AI systems that companies can train on their own data without sharing it externally.

12. The best hardware teams combine three types of people. You need generalists who can apply lessons from other fields to new problems. You need some specialists who have built similar things before and others who have scaled products to high volume. And critically, you need 20-year-olds who are truly AI-native—they approach problem-solving completely differently because they use AI from the ground up for everything. As Caitlin notes, “It’s very hard to find someone who’s in their 30s who can be truly fully AI-native.”

[Zane Hengsperger @zanehengsperger]

We are hosting an industrial hackathon at @noxmetals!

We are inviting software and hardware builders to apply to compete for a $5,000 prize.

Write code and build hardware that gives insight to what the heck the machine is doing. And, how it can be optimized.

Build a $250 sensor system for our Spartan wet saw cutting some sweet sweet 6061.

Kickoff Monday 12pm ET. Demo day June 15 in Detroit.

Link below.

@SchramIAm @640oxford @dugsong

[Rohan Paul @rohanpaul_ai]

Next gen drones infrastructure by Zipline (drone delivery company)

like a robotic hive

[Saman Farid @samanfarid]

You wanted reindustrialization?

[Demis Hassabis @demishassabis]

I’ve always believed the No.1 application of AI should be to improve human health.

That work started with AlphaFold, and now at @IsomorphicLabs with the mission to reimagine drug discovery and one day solve all disease!

We are turbocharging that goal with $2.1B in new funding.

[Justin Skycak @justinskycak]

Your goal is NOT to prove you're smart. It's to make problems go away.

[Anthropic @AnthropicAI]

New Anthropic research: Natural Language Autoencoders.

Models like Claude talk in words but think in numbers. The numbers—called activations—encode Claude’s thoughts, but not in a language we can read.

Here, we train Claude to translate its activations into human-readable text.

[Unity for Games @unitygames]

Unity AI is now in Open Beta 🎉💫

We believe AI has the most impact when it helps creators move faster while staying in control of the creative process.

Use our built-in agent tuned for Unity workflows or connect the AI tools you prefer via AI Gateway and MCP Server.

[Sam Altman @sama]

we want to build tools to augment and elevate people, not entities to replace them.

[Derek Matzen @derekmatzen]

@Swizec @eugeneyan Evals, traces, and guardrails

[Andrej Karpathy @karpathy]

This is the the quote I've been citing a lot recently.

[David Sacks @DavidSacks]

It’s time to demystify Mythos.

Mythos is not magic. It’s not a doomsday device. It’s the first of many models that can automate cyber tasks (just like coding).

OpenAI’s GPT-5.5-cyber can now do the same. And all the frontier models (including those from China) will be there within approximately 6 months.

It’s important to recognize that these models do not create vulnerabilities; they discover them. The bugs are already in the code. Using AI to discover and patch them will actually harden these systems.

The leap from pre-AI cyber to post-AI cyber means that there will be a big upgrade cycle. After that, however, the market is likely to reach a new equilibrium between AI-powered cyber-offense and AI-powered cyber-defense.

Obviously it’s important that cyber defenders get access before cyber attackers. That process is already underway but needs to happen quickly (see point above about Chinese models).

Unlike Mythos, GPT-5.5-cyber appears not to be token constrained so it may be the first cyber model that defenders actually get to use.

[Derek Matzen @derekmatzen]

Announcing:

Monitoring the Draft

https://t.co/gcZNkZ6hyC

Keep track of your favorite players and teams throughout the draft.

[Kyle Anthony Miller @kyleanthony]

We’re starting to layer in motion design

[Michelle Lam @michelle123lam]

Most of what I actually need help with, I never think to tell a model. But why is it on me to remember?

Our new paper asks: what if AI could proactively specialize to individuals and the tasks they’re carrying out at this very moment? 🧵

[Derek Matzen @derekmatzen]

@DanielMiessler @MatthewBerman This is the premise of what I’m building - a system that grows with you and is able to recognize your taste. Easier said than done though