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July 2, 2026 by David Such Leave a Comment

When Fable 5 Broke, Anthropic Did Not Change the Constitution

Fable 5 is back baby! On June 12, three days after launch, the US government applied export controls to Claude’s Fable 5. Amazon researchers had found a way to prompt the model past its safeguards: it identified a set of previously known software vulnerabilities and, in one case, produced code demonstrating how one of them could be exploited. Anthropic had no reliable way to verify user nationality in real time, so it yanked the model for everyone. Access was restored on July 1, and the fix is interesting. It was not a retrained model, and it was not a longer values document. It was an improved external classifier, a separate circuit that blocks the reported technique in more than 99% of cases and routes flagged requests to the less capable Opus 4.8.

This tacked on fix sits oddly beside the document that is supposed to govern Claude’s behaviour. In January 2026, Anthropic replaced its previous constitution, a roughly 2,700-word list of principles borrowed in part from the UN Declaration of Human Rights and Apple’s terms of service, with an 84-page essay explaining the kind of agent it wants Claude to be. The new version is released under a CC0 public domain licence, so anyone can read it.

Rules Versus Judgement

The constitution names two ways to guide a model: clear rules and decision procedures, or cultivated judgment and values applied in context. It then lists the advantages of rules. Rules give you up-front predictability, they make violations easier to identify, they do not depend on trusting the judgment of the thing following them, and they are harder to manipulate. The document concedes that rules make the most sense “when the costs of errors are severe enough that predictability and evaluability become critical.”

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Filed Under: AI, Embedded, Robotics Tagged With: embedded AI

June 28, 2026 by David Such Leave a Comment

Your Brain Does Not Sleep. It Runs Maintenance

The evidence suggests that sleep is the brain running scheduled maintenance, with different subsystems serviced in different stages. What this means is that the sleeping brain is not doing one thing. It runs several distinct processes in sequence, each scheduled into the stage where it is safe to run. REM handles the visual cortex and emotional memory. Slow-wave sleep consolidates declarative memory, replaying the day’s experience from hippocampus to cortex, and it is also when the physical housekeeping happens: the glymphatic system opens up to flush metabolic waste, growth hormone peaks, and immune activity is at its strongest, which is why lost sleep weakens the response to a vaccine. Underneath all of it, synaptic downscaling runs across the night to keep the system from saturating.

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Filed Under: AI, Embedded, Robotics Tagged With: embedded AI

June 23, 2026 by David Such Leave a Comment

Who Is Accountable When the Robot Decides?

On the night of 18 March 2018, an Uber test car in Tempe, Arizona, struck and killed Elaine Herzberg as she wheeled a bicycle across an unlit road. The car had detected her 5.6 seconds before impact. Its software could not work out what she was. It cycled through “unknown object,” then “vehicle,” then “bicycle,” and never settled in time to brake. About 1.3 seconds before the collision the system concluded it needed to stop. It did not stop, because Uber had switched off the car’s automatic emergency braking during testing and was relying on the human in the seat to intervene.

The human in the seat was a safety operator named Rafaela Vasquez. She was watching a television show on her phone. She did not touch the brake until after the car had hit.

When the courts went looking for someone to hold responsible, they found her. Vasquez was charged with negligent homicide, later pleaded guilty to endangerment, and was sentenced to three years of probation. Uber faced no criminal charges. The federal investigators were blunt about the design: the company had removed the car’s ability to brake for itself and left a distracted person as the last line of defence. The system was built so that the only party who could stop the car was the one least equipped to.

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Filed Under: AI, Embedded, IoT, Robotics, Title Post Tagged With: embedded AI

June 7, 2026 by David Such Leave a Comment

Cutting Claude’s Token Bill by Converting PDFs to Markdown

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.

A 50-page PDF can cost you 75,000 to 150,000 tokens before Claude has read a word of it. On a 200,000-token context window, that is most of your working space gone on one document. The reason is not the text, it is due to the way that Claude ingests a PDF.

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Filed Under: AI Tagged With: embedded AI

June 7, 2026 by David Such Leave a Comment

Why PrimalBot Needs to Sleep

In our original article on the Primal Layers framework, we described a multi-layered AI architecture inspired by the evolutionary structure of the human brain. Layer 1 (the brainstem) handles homeostasis and reflexes. Layer 2 (the limbic system) handles motivation, memory, and emotional valence. Layer 3 (the cerebellum) handles motor learning and predictive control. And the Cognitive Layer (the neocortex) handles abstract reasoning and planning.

What we didn’t address is how these layers manage knowledge over time. How does PrimalBot learn something new on Tuesday without forgetting what it learned on Monday? The answer, like so many in this project, comes from biology. And it turns out that one of the most important things a brain does is sleep.

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Filed Under: AI, Embedded, Robotics Tagged With: development, embedded AI

June 7, 2026 by David Such Leave a Comment

Squeezing AI into your Pocket

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.

Listen to the Podcast…

Filed Under: AI, Embedded, Robotics Tagged With: embedded AI, podcast

June 1, 2026 by David Such Leave a Comment

A Chip That Thinks using 7 μW

Most AI research is about making the cortex bigger. This one is about making the spinal cord cheaper.

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.

That is not a typo. It is the finding of a paper published in ACS Nano in March 2026, and it matters because power is the wall that edge AI keeps running into (or falling off?).

Why Power Is the Whole Game

Most of the interesting AI you read about lives in a data centre. It has a wall socket, a cooling system, and an electricity bill measured in millions. Edge AI is what happens when you try to put that intelligence into a hearing aid, a pacemaker, a drone, a soil sensor, or a pair of smart glasses. You are running off a small battery, or whatever energy you can scavenge from sunlight or vibration.

In that world, every microwatt counts, and there is one component that has been quietly eating the budget for decades: the analog-to-digital converter (ADC). Sensors produce continuous signals, but computers think in ones and zeros. Something has to translate between the two, and that translator is the ADC. It is usually the single biggest line item in a battery-powered device’s power budget.

The Michigan team’s trick is to skip the translator entirely.

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Filed Under: AI, Embedded, Robotics Tagged With: embedded AI

June 1, 2026 by David Such Leave a Comment

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.

Along the way, we examine NVIDIA’s Cosmos Reason 2 model, the EmbodiedGovBench governance framework, emerging standards efforts, and the practical realities of deploying foundation models on embedded platforms. We also ask whether traditional functional safety concepts such as SIL and ASIL can adequately address the unique challenges posed by robots whose actions are selected by large vision-language models.

The broader question is one that every roboticist, embedded engineer, and AI practitioner will soon face: when intelligence becomes local, autonomous, and physically embodied, what mechanisms ensure that accountability remains local too?

Listen to the Podcast…

Filed Under: AI, Embedded Tagged With: embedded AI, podcast

May 28, 2026 by David Such Leave a Comment

The Godfather of AI Claims a Multi-modal AI had a Subjective Experience

The Prism, the Pointing Arm, and What Hinton Got Right (and Wrong).

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.”

Hinton’s claim is that the chatbot, in saying this, is using the phrase “subjective experience” exactly the way you and I use it. Therefore the chatbot had a subjective experience. Therefore the line we draw between human and machine experience is, in his words, rubbish.

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.

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Filed Under: AI, Embedded, Title Post Tagged With: embedded AI

May 28, 2026 by David Such Leave a Comment

Creating an Index for a Technical Book using AI

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.

One of the more tedious aspects of putting together a book is index tagging. The publisher can index for you, but it will cost around $4 per page and this comes out of your royalties. Not many people get rich writing a book, but there is no point throwing away hard earned royalties! There are lots of things you have to do manually when creating a book (like writing), but this feels like a task that a Large Language Model (LLM) should be good at.

NSP expects the finished index to weigh in at 5 to 8 percent of the manuscript word count, so for a 100,000-word book you are building something in the range of 5,000 to 8,000 words. This is a substantial document in its own right.

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Filed Under: AI, Embedded, Title Post Tagged With: embedded AI

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Recent Posts

  • When Fable 5 Broke, Anthropic Did Not Change the Constitution July 2, 2026
  • Your Brain Does Not Sleep. It Runs Maintenance June 28, 2026
  • Who Is Accountable When the Robot Decides? June 23, 2026
  • Cutting Claude’s Token Bill by Converting PDFs to Markdown June 7, 2026
  • Why PrimalBot Needs to Sleep June 7, 2026

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When Fable 5 Broke, Anthropic Did Not Change the Constitution

July 2, 2026 By David Such Leave a Comment

Fable 5 is back baby! On June 12, three days after launch, the US government applied export controls to Claude’s Fable 5. Amazon researchers had found a way to prompt the model past its safeguards: it identified a set of previously known software vulnerabilities and, in one case, produced code demonstrating how one of them […]

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