David Such

Reefwing Software · Mobile, AI, and Embedded Development · Robotics · Flight Control #drones #iOS #developer #robotics #startup #embeddedai

[David Such @reefwing]

Claude charges you twice for every PDF page, once for the text and once for the image. Converting to Markdown drops half the bill, as long as the document’s value is not in its figures.

https://t.co/QQnIq6LwWi

[David Such @reefwing]

In-Country Is Not Sovereign: Building a Corporate Memory You Actually Own https://t.co/5xkEr4Fhu1 via @LinkedIn

[David Such @reefwing]

Why PrimalBot Needs to Sleep - how does a robot manage knowledge over time? How does it learn something new on Tuesday without forgetting what it learned on Monday? The answer, comes from biology. And it turns out that one of the most important things a brain does is sleep.

If you have ever pulled an all-nighter and then tried to recall what you studied, you already have an intuition for what neuroscience has confirmed: sleep is not idle time. It is an active maintenance cycle that your brain requires in order to consolidate new memories, prune unnecessary connections, and restore its capacity to learn.

https://t.co/ZUDb6Eksdu

[David Such @reefwing]

Who is Liable for Onboard AI? As foundation models move from the cloud into physical robots, a fundamental question emerges: who is accountable when an AI-controlled machine makes a decision that causes harm?

In this episode, we examine the growing collision between embodied AI, functional safety, and emerging regulation. We explore how new frameworks such as the EU AI Act and the Machinery Regulation are reshaping expectations for developers, manufacturers, and deployers of intelligent robots. From humanoid robots and autonomous mobile manipulators to AI-enabled industrial machinery, the challenge is no longer simply making robots smarter. It is making them governable.

We investigate a proposed architectural solution that is gaining traction across industry and academia: the hardware-isolated safety supervisor. By separating non-deterministic AI reasoning from deterministic safety-critical control systems, this approach aims to create clear lines of accountability while preserving the benefits of onboard intelligence.

https://t.co/PeW2xCrURV

[David Such @reefwing]

A Chip That Thinks using 7 μW - A team at the University of Michigan has built a tiny computing device that controls a balancing propeller using about seven millionths of a watt. For comparison, the LED bulb in your kitchen burns through about ten watts. The Michigan device runs the control task on roughly a millionth of that power.

https://t.co/ok4pfJDNwD

[David Such @reefwing]

By 2026, language models have moved off the cloud and onto the device in your pocket. What was a research demonstration two years ago is now a routine engineering capability, and the centre of gravity for artificial intelligence has begun to migrate from distant data centres to local silicon.

The episode traces the four engineering moves that made this possible. Quantization, which shrinks a model by storing its parameters with less precision. Optimized key-value caches, which let a model hold a long conversation without exhausting memory. Neural Processing Units, the dedicated AI accelerators now standard in flagship phones. And specialized frameworks such as LiteRT-LM and llama.cpp, which finally make all three usable from a single application.

The consequences reach further than performance figures. Privacy becomes the default rather than a feature, because data never leaves the device. The cost structure of AI applications changes, because there are no per-query cloud fees. And the link between training capital and deployment capability begins to decouple, opening the door for small teams to ship genuine intelligence on hardware they already control.

https://t.co/Y7bk4Rh37Y

[David Such @reefwing]

In a recent talk, Geoffrey Hinton offered a thought experiment he thinks settles a long-running argument about machine consciousness. The setup is simple. You have a multimodal chatbot with a camera, a robot arm, and language. You place an object in front of it and ask it to point. It points. You then sneak a prism in front of the camera lens. You ask again. It points off to one side, because the prism has bent the light. You tell it about the prism. The chatbot replies: “Oh, I see. The prism bent the light. The object is actually straight in front of me, but I had the subjective experience that it was off to one side.”

I had to think about this, because Hinton is that guy, and the example is doing more work than it first appears. But I also want to say where I think the argument is weaker than it is being sold, and where I think it is stronger.

https://t.co/aQxPMyRkvC

[David Such @reefwing]

How I used Claude to build a 5,000-entry index for a 600 page technology book without going crazy. The hard part of indexing is not inserting tags, but you will want to automate the boring mechanical labour.

I have spent the last couple of years writing a book about Embedded AI for No Starch Press (NSP). It has been three times the amount of work that I was expecting. The editing process feels never ending and by the end you will never want to read your book again. It does make for a better book though, and I am now an advocate for having an external editor.

https://t.co/0ApZ7OOcNV

[David Such @reefwing]

What a strange theory about REM sleep tells us about building AI that doesn’t quietly fall apart. If your visual cortex starts getting taken over within an hour of going dark, what happens every single night when you close your eyes for eight hours?

A neuroscientist named David Eagleman thinks he has the answer, and it is one of the more interesting hypotheses about why we dream that has come along in a while. He calls it the defensive activation theory, and once you understand it, you start seeing the same pattern everywhere, including in some surprisingly broken corners of how we currently build AI.

https://t.co/EA4A6Phaw1

[David Such @reefwing]

A chip that controls a balancing propeller on seven microwatts

[David Such @reefwing]

Spot the Robot Can Reason, but it can’t hold a can of soda. Boston Dynamics and Google DeepMind announced last month that Spot is now running Gemini Robotics-ER 1.6, a high-level embodied reasoning model. The headline is that Spot has been taught to reason. The commercial pitch is industrial inspection: wandering around a facility, reading gauges, spotting spills, deciding what to do when something looks wrong. So far, so good.

Buried in the demo video is a small failure that says more about the architecture than the press release does. Asked to recycle cans in the living room, Spot picks one up sideways. If there is any liquid left in the can, it ends up on the carpet. The problem is using a language model to solve something handled by other more primitive layers in the organic world. #embeddedAI #LevelUpCoding

https://t.co/QVJysMMw9m

[David Such @reefwing]

@JeremyNguyenPhD Hi @JeremyNguyenPhD - yes have a look at the book page on my website - https://t.co/cFJUX5gwKs, in degree of difficulty this is an intermediate book.

[David Such @reefwing]

My first book, Embedded AI, is released later this year from No Starch Press. It explains what happens when you move AI off the cloud and try to cram it into a microcontroller.

The book includes 25 hands-on projects

https://t.co/mCQNrY8GPd

[David Such @reefwing]

@bcg1976 It would only be a twig when you had finished.

[David Such @reefwing]

Coming soon from @nostarchpress.
Embedded AI: 25 hands-on projects deploying machine learning on microcontrollers. Real hardware. Working code.
Subscribe for launch updates and bonus content

[David Such @reefwing]

@bcg1976 Ha! I wish!