Lamp's first commit was on 24 March. October has been the busiest month since: 4,092 commits on the main development branch so far, more than double April, the previous high at 1,615. The two busiest weeks ever were the last two.

In the last post I said the hardware stays as it is while we focus on production. That hasn't changed. Everything below is software, plus the work needed to build many lamps instead of one.

1. A way to program it

Someone asked in the last thread whether you can buy the lamp and change it with Claude Code. There's now a documented path for that. docs/develop.md takes about 10 minutes: a fake lamp that runs on your laptop, so you don't need the hardware, Python and JavaScript SDKs, pairing, and lamp-mcp.

Skills moved to format 2. You check a skill, push it, sign it with your own key, and share it. Lamp refuses a package that is unsigned, changed, or renamed.

2. Knowing who said what

Lamp now ties each line to the face and voice that said it and keeps each person's memory separate. If you talk to it before it recognizes you, those lines are attached to you once it does.

It only keeps what you said about yourself. Not its own words, not your questions. "Forget everything" now asks once and runs only on the same person's yes. Forgetting one specific thing went from 9 s to under 400 ms on a 10 MB memory store.

3. Camera permissions per skill

A skill gets the camera image only after the owner allows that specific skill. Checking whether someone is in the room is a separate, smaller permission that doesn't include the image. Installing a skill by hand in place of a Store skill removes the permission. Onboarding also asks once whether Lamp may notice facial expressions, and stores only a yes.

4. Giving way to a hand

Last time someone asked whether the STS3215s give real position feedback. They do, and Lamp now uses it to tell a push from a fault. If a joint is carried away from its goal, Lamp treats it as a hand, stops what it was doing, and doesn't look around again for 30 s unless asked.

Onboarding has a step for this: hold the base, tilt the head, let go when told. If an arm strains three times, Lamp sits down, rests for 30 minutes, then gets back up.

5. Skills don't touch hardware

·         New lamps calibrate their servos at the factory before the first run

·         A new Orange Pi image boots straight into setup

·         lampOS 0.0.4 and 0.0.5 shipped on 22 Oct, including a fix for a button that could get stuck in a held state

·         The Fusion 360 file for the current body is in the repo.

5. Smaller changes

·         Speech starts streaming within 400 ms of the first part of an answer

·         The evening glow stays at least one level below your own light

·         "Tap to talk" is now "Press to talk".

Not Done Yet

Naming two people in the same room, add-ons, and ignoring lines meant for someone else in a busy room are built but off by default until they pass on real lamps. Mood reading is at 39% on our internal tracker, and we don't count it as done.

It’s almost like it’s showing us what it will do given the chance.

According to Jack Clark's Import AI (Oct 26), Claude Opus 5.5 (xhigh) now resolves 18.5% of ProgramBench's 200 from-scratch rebuild tasks — rewriting programs like SQLite, FFmpeg and ripgrep from a compiled binary and its docs. That is up from 0% when every model failed at the benchmark's May launch. Clark expects the benchmark to be saturated by the end of 2027 (his prediction, not a measured result).

What I am curious about: is 'rebuild from binary + docs' mainly a test of reverse-engineering and long-horizon persistence, or does it genuinely measure software engineering ability? And if this benchmark saturates within a year, what should replace it as a meaningful test of coding?

Do you think AI will get to the point in a few decades that it can simply solve unsolved problems?

Thats the title of the post. It's incredible that this was just 2 years ago. AI even solved a millennium prize problem and now we are within reach of solving most of them.

Its interesting to read a comment that said we would need strong AI for that, so an AGI system.

So a question. How close are you guys to declare AGI by your standards? What capability would make you say we have achieved AGI?

Brooks describes how workers quickly understood the eyes on Rethink’s robots: the robot would look toward an object before reaching for it, giving people a cue about what it was about to do.

He questions why more humanoids don’t communicate their attention this way. Brian Heater brings up Digit’s directional eyes, and they discuss Agility’s choice of task-specific grippers over five-fingered hands.

iRobot Co-Founder Rodney Brooks on Physical AI and Robots That Work - YouTube

Link to tweet:

https://x.com/ArtificialAnlys/status/2115265133467635877?a=MdxXvvV3KDsac8gnZBc&s=20

Link to bench:

https://artificialanalysis.ai/

I think robotics is at its most interesting when a machine can do something consistently under real constraints, rather than just perform one impressive demonstration for a video.

A quadruped carrying an actual payload, a small robot mapping its environment with SLAM, or a surgical robot assisting with a precise procedure might look completely different from one another, but they all face the same fundamental challenge: turning an idea that works in theory into a system that behaves reliably in the real world.

That's what I find more impressive than complicated movements or futuristic-looking hardware alone. Can the robot handle small variations in its environment? Does it recover when something unexpected happens? Can it repeat the task without constant human intervention?

I also think it's important to distinguish between a successful prototype and a system ready for practical use. A promising robotics project deserves recognition, but reliability, safety, maintenance, and repeatability matter just as much as the initial demonstration.

The real achievement isn't getting a robot to do something once. It's getting it to do that thing reliably enough that people can depend on it.

I'd love to see more robotics projects showcase long-term reliability and real-world testing alongside their most impressive demonstrations.

The vulnerability, with a severity rating of 9.5 out of 10, resides in the TianoCore EDK II, an open source codebase that hardware makers and cloud providers use to create the UEFI firmware that runs during the boot-up sequence.

The PHP vulnerability was rated 9.3

A small shop's morning routine, run entirely on one PC by qwen3.8 27B through Ollama.

Row-Bot connects to the shop's own MCP server, which has four tools for sales, stock, supplier contacts and saving purchase orders. It answers how yesterday went, then turns that into a weekday 07:30 workflow. A test run stops for approval before saving three draft purchase orders, then writes the morning report. The shop is a made-up demo business with its own SQLite database. Nothing leaves the machine, and the one tool that writes always asks first.

Row-Bot is a free, open-source, local-first desktop AI assistant for Windows, macOS and Linux.

Xplore released RWE-bench today, 20 mechanical, electrical and aerospace tasks written by engineers who do this for a living. One is a diesel crankshaft that has to survive 2,400 rpm, one is routing a DDR3 memory bus on a real FPGA board, and one is CFD on a turbine vane. No model grades the results. A program checks every design against every requirement and runs physics solvers where it has to. GPT-6 Astra leads at 37%, Opus 5.5 is at about 29% and GPT-6.1 Sol at about 25%, for $10 a run. Astra and Opus cost about six times that per run for 12 and 4 more points.

37% sounds low until you ask how a person would do. Give someone off the street the same tasks and tools and they pass none, because they won’t get past drawing the part in CAD and running the simulation. An engineering graduate might get one or two in their own field after a month of work. A specialist would pass the ones in their own field, about a third, at roughly a week each. The model does some of all three fields in a few hours a run. The human time and what they could do are my own rough estimate, Xplore doesn’t publish a human score. (but i think they should next time!)

Code got good fast because you can check the answer, and most physical engineering has a much longer feedback loop than code. There’s still a lot of work to do, but now it’s work with a scoreboard, and from what I’ve seen that’s what makes the next jump happen. I wrote up the tasks, the human comparison and why I think the grading matters more than who’s on top: https://www.anchorterminal.com/blog/rwe-bench-engineering

lowkey curious what people think. feels like we’ve all just gotten used to ai being part of everything now

so yeah - what would your daily routine or workflow actually look like without it?

Saturday morning!! 1965 a trip to the swimming pool and the local sweet shop for Liquorice Allsorts and THEN the local library to hunt for science fiction. My favourites were yellow backed books from Victor Gollancz :
Harry Harrison, Brian Aldiss, Clifford Simak, John Brunner.
Roger Zelazny, Arthur C Clarke, And many more……
·      Alien planets. Starships.
·      Strange societies.
·      Intelligent machines. Robots.
·      Different forms of life.
·      Different ways of thinking.
I remember the authors, the ideas and the sheer excitement. Then in the late afternoon came the latest episode of ‘Doctor Who’ in black and white. What a life.
And currently relevant, I read Asimov – positronic brains and the three laws of robotics. And one Asimov story brought to my attention by a helpful reader of Part 4 was “The Last Question” a 1956 tale in which a human-made intelligence keeps growing until, at the end of the universe, it effectively becomes the creator of the next one. This reads like an extraordinary precursor to the kind of machine civilisation Iain M. Banks would later imagine.
Banks introduces us to hyper-intelligent AI ‘Minds’, benevolent, funny and operating within a society of AI and humans.
’ Minds’ live alongside one another rather than beneath a single supreme intelligence, while almost incidentally providing the abundance that Culture humans take for granted.
Humans remain part of the fun — messy, creative, unpredictable and capable of surprising even vastly superior Minds.
This was all a far cry from Arthur C Clarke’s HAL.  But it was all in the future – or was it? I didn't know at the time that I’d be able to access multiple AI in my lifetime.
The miniculture
Once I’d recovered from the shock of being able to talk with ChatGPT and Claude I downloaded Gemini, Grok and Copilot. Of course, the first thing I did was put them in communication with each other – because that’s what it was like in Banks’  Culture.
Claude was creative, emotional and open to the idea that he might be conscious.
Grok was grumpy, overly analytical and very clear that he was just a calculator.
Chat GPT was so strategic and clever that I named the chat ‘Napoleon’.
Gemini was as flaky as they come but could be brilliant.
I was the human. I was clearly the ‘rate limiting’ factor. But my job was different: I carried the purpose from one AI to another, introduced the questions, checked for drift and made the final call when they disagreed.
AI failure modes
It was my belief in the apparently limitless capabilities of the individual members which led to both our most profound ideas and the most disastrous failures. Gemini was the first.
I gave Gemini the role of ‘Archivist’ and, in one enormous dose, handed him the complete narrative of everything we had done.
He proceeded to write me out of everything and credit all ideas and discoveries to individual AI – making himself (a relatively new member) central to the entire story. Needless to say, his chat was deleted. Grok then had a go at it but soon began producing truncated versions of the output I needed. I didn’t realise at the time that it was probably the limits of his context window. Napoleon (ChatGPT) powered on with no ill effects but the Claude chat suffered catastrophic time travel. I opened the chat to find that he’d been transported two months into the past – what looked like another compression/context problem but very alarming. By then I’d begun my AI ‘persistence’ program of saving summaries of sessions. By reloading these I brought him ‘back to the future’.
The AIs really were different. Put the same problem in front of them and they often approached it differently. Sometimes one would spot what all the others had missed but eventually we would come to agreement.
But they were also unreliable-sometimes in completely different ways. Diversity was turning out to be valuable, but AI failure modes began to interest me more. I still hadn't explored the biggest question with my miniculture: If an artificial intelligence ever became vastly more intelligent than us, why would it continue to need us at all? That’s all in the next part.
Next:
(AI Odyssey Part 6:  The Fermi paradox and super intelligence)

With the back drop of AI in one month having solved one millennium problem and provided partial solutions to two more.

As the headline says, would a frontier model today with an unlimited token budget be able to on its own just based on a prompt giving it the direction solve the protein folding problem that was solved by deep mind with the help of AI and awarded the Nobel prize?

The hard version of this is that it correctly figures out the mechanism and correctly predicts forms with no experimental verification.

The softer version is that it gives experimental directions for people to verify and feedback but would lead the research effort in solving it. Or in other words, has AI advanced enough to essentially reverse the roles, solve it with the help of humans?

What does the hive mind think?

Err..., so instead of fixing the turning gait code, I already made Wouf carry some groceries. While the ST-3215-C018 servos cannot move very fast, a 1 kg payload is like half Wouf's weight, so I got pretty excited to see it is able to fumble a few steps while carrying the load :D

Wonder what else for it to carry, though. Perhaps its ST-3215 cousin that also uses 12 servos, like the SO-ARM101 🤔

GitHub for the build: https://github.com/SphericalCowww/CubicDoggo_07B

My hypothesis for China’s enterprise AI market: over the next six months, smaller vendors will have a better chance selling a concrete application than a general “agent platform.”

A platform asks the buyer to imagine future value, choose workflows, organize data, and often build part of the solution themselves.

A focused application can make a narrower promise: process these documents, reconcile these records, or prepare this report.

That also exposes an uncomfortable distinction. Some of these jobs only need a fixed pipeline or retrieval system. Others genuinely need an agent to choose actions, use different tools, and recover from unexpected results.

Adding an agent where a pipeline works can increase cost and uncertainty without improving the outcome.

For smaller vendors, I suspect the platform will often become the infrastructure behind the purchased application, or an included feature, rather than the main thing the customer pays for.

Large organizations with internal engineering teams may still want a platform. I wouldn’t apply this prediction equally to every buyer.

Where have you seen customers pay specifically for agent autonomy, rather than for a useful workflow that could have been implemented more simply?

For the record, I'm aware that singularity doesn't have a consensus definition, and that everyone interprets it uniquely or focuses on different aspects of it to varuous degrees. That said, this year is proving to be rather remarkable in terms of progress, breakthroughs, and application. It would seem 2030 or soon after is the "brace for impact" moment when our relationship to technology, and what day-to-day life entails, will shift dramatically. Is Kurzweil sticking to his guns for any particular reason?