Was non-trivial (e.g. days and days of cumulative work) but worked with Codex to build up a parametric generator where I could adjust the star size, number of rings, leds per ring. It did all the LED-to-LED routing, panel layout with mouse bites, placed decoupling caps. At the time Codex wasn't as good at spatial reasoning, I expect that Astra would do this much faster.
4 x layer PCB with GND and 5V inner layers. WS2816B LEDs (not available in strips yet). 3D printed a frame so that two stars could be opposite each other (video shows only one side). ESP32 S3 on a 3.3v to 5v shifter board sandwiched in between. ESP32 wifi antenna pokes through and sits on a keep out section. Received control signals from a central controller via ESP Now.
Technology is stacked sigmoids. There's a lot more to unlock.
What's becoming more important in human's relationship with technology is restraint. Dopamine comes from the journey, not the destination, and for a journey to be meaningful, there needs to be some amount of challenge / struggle. The increasingly important skill to lead a fulfilled existence is learning how to optimally balance ease vs. challenge.
There’s still 50-500x cost reduction in “this is only an engineering problem” low hanging fruit from specialized chips to run the models + improved distillation.
Entirely feasible that by 2031, Fable 5 (or greater) intelligence level models will run cool on smart phones, if not sooner.
You're betting on getting getting ridiculously powerful chips to run on batteries in a tiny housing without cooling, while we can't even get enough RAM? It would be a terrible waste of resources. Now we already have TFLOPs wasting in our pockets and backpacks, then we'll have PFLOPs idling, because there's so much time between prompts. Much more efficient to batch it on a server.
At some point we'll have enough RAM, surely. The incentives to produce more are huge and all the fabs are booked out.
Maybe it'll take 10 years or 20 years. <5 years is not long enough for manufacturing to catch up.
Not much of a comment on the phone stuff but I'd caution against suggesting technology will never be good enough to do X. Maybe it'll be horrendously wasteful but it might happen.
If fable 5 could eventually run on a smartphone, I wonder what we'll get out of datacenters. Musk and others are trying to build 100gw of compute by 2030. Will we get something 1-10 million times better than Fable 5, or will the parity gap between local and datacenter capability pair down enormously?
Yep, switched from CC to 5.6 Sol. Not using it in my day job (only side projects), but I can crank 5.6 Sol on Extra High "fast mode" and never have to worry about credits.
Not doing crazy multi agent swarms, but have yet to hit any limits during pretty intense weekend sessions.
The impact on the psyche on some Mathematicians of this AI progress must be pretty brutal. To me, it breaks the mystique of Mathematics a lot.
You still need a lot of skill to digest and understand the proofs, but "this is the worse it will ever be." I'd imagine part of the motivation of a large set of mathematicians is to be the "first" or to crack the nut that others couldn't. If Mathematics becomes working with an AI to get a Lean certificate, and then essentially reverse engineering that into something digestible, then it's fundamentally a different pursuit.
Software Engineering feels a little less impacted? Though if you identify with loving coding, then perhaps similarly? I've always liked the outcome of what writing code can do, and enjoyed the craft hand coding for the past ~30 years. But I haven't once ever missed writing code by hand since Opus 4.6, I couldn't go back.
My motivation to do mathematics is some combination of wanting to understand the system of mathematics deeply and enjoying the craft and puzzle of working on research problems. If I never had to publish again and the computer was 1000x better than me, so that I can live on my 20k a year UBI, then I’m fine with that. The anxiety is that this isn’t realistic at all so I’ll likely have to spend my life doing something different than pondering math now.
Software Engineering feels a little less impacted?
I don't think so - software engineering has already been completely up-ended; we are showing mathematicians what is next for them.
Sure though wrt your point about mystique there is a difference in psychological importance and cultural meaning - maths at the highest level is far more intellectually challenging and even "glamorous" and represents one of the peaks of human achievement. Ironically though if we allow the invention of AI belongs to our (software engineering) field, this is the first time we've matched those peaks.
I agree with you on not missing manual coding, which in some way surprises me - but it's been a long time since I had the passion of my youth for it.
I am a mathematician and I never believed in this kind of mystique (maybe in other, more resilient mystiques).
I have always expected machines will be able to do math. Since late 2018, I expected them to be able to do math long before physical stuff (basically Moravec paradox).
it does not in any way break the mystique of mathematics for me, though that mystique continues to attach to proofs that are in some way beautiful or elegant, whether produced by humans or machines.
as for software engineering, I've definitely used claude to help with both complex problems that I could have worked through myself but with greater expenditure of time and effort, and with problems that I would not have been able to do without spending a lot of time learning my way around a whole new domain, but in both cases what I am most keenly aware of is that I am benefitting from some human (or many humans) having solved this problem before.
I am ABD in mathematics. That was a long time ago. I taught math for many years at a community college. My amateurish but knowledgeable perspective is that these developments shatter the mystique for me.
What AI is showing is that mathematics is mostly just pattern searching and AI can do this far better, faster, and with a much wider base than humans can. When I was working on my thesis problem I realized that I worked much less than my fellow students. I was an average student in my program but even for the best students they had to spend a lot of time thinking about stuff. They put in a lot of effort.
Is the difference between me and Tao mostly effort and that he has a much better memory of mathematical facts than me?
This is only the "problem solving" side of mathematics. Completely missing the theory builders who have completely reshaped the world of mathematics (and far beyond). AI is a long way from matching original thinkers of the calibre of Euclid, Al-Khwarizmi, Newton, Leibniz, Euler, Galois, Riemann, Cantor, Hilbert, and Grothendieck. Or Turing, Gödel and Von Neumann?
I lean more towards the "proofs from the book" mindset - if there's a really beautiful proof of some theorem out there I would be happy to see it no matter whether it was discovered by a human or a computer, and conversely if a computer has churned through a search space and generated a clunky proof in 100000 lines of lean code that just means that people trying to find a nice proof can do so with the assurance that the theorem is true. note that mathematicians didn't give up on trying to find a better proof of the four colour theorem once a clunky computer assisted proof showed the theorem was true.
The question is, will universities continue to fund graduate programs to the extent they currently do so that people can find elegant proofs of AI derived theorems? I’m skeptical. A lot of good mathematicians tried to prove or disprove the Jacobian Conjecture and AI did easily. The value added by human mathematicians seems to be low return on the investment.
Luckily never been bitten by component orientation issues. But every time I get a PCB assembled at JLCPCB with WS2812b or WS2816b LEDS they always email me to confirm LED orientation. I'm assuming customers have messed this up frequently enough to require a customer ACK.
They will send this email for all LEDs and generally all polarized components (capacitors, diodes, etc.)
JLC is also extremely unique because they offer the component orientation configuration GUI. This GUI's output is likely used for PNP tape orientation configuration as well.
All LEDs are like this. It's horrible. The only good thing about it is that every professional in the entire industry has come to expect that if it's an LED, something somewhere will be wrong with it.
By all LEDs do you mean addressable and other multipin LEDs? Never seen anything weird with classic 5mm/3mm. The flat side+leg length is quite reliable. 0805/1206 smd LEDs also seem mostly fine.
The cathode marks are basically randomly assigned. This is because each company more or less designs one leadframe/package structure per size or type of LED. Sounds reasonable, right? But LED dice aren't made of silicon. They're made of weirdo high-bandgap materials, and every color is different in its own way. From physics alone, some of the colors end up with cathodes on top of the dice and some of the colors end up with anodes on top. They all get bonded in to the package the same way. So... that marking built in to the package? That's always the "top of die" wire next to it. But "top of die" might be cathode, or it might be anode. Flip a freaking coin!
If you want to see this clearly, find a datasheet that covers an entire series of LEDs. Vishay often does this, here's one: https://www.vishay.com/docs/82437/vlmo1300.pdf . They'll tell you the marking locations for each color individually. Notice how they're all completely different? Yeah.
Could the manufacturers do better? Yes. Yes, they could. We all wish they would do better. But they do not. So LEDs are the stuff of nightmares for people who assemble boards.
Through-hole parts are somewhat better (but there are certainly rogues!), but this is 2026: the volume's in surface mount.
Multipin LEDs are no better. The SMD no-lead versions are again fairly randomly marked. The PLCC-4 types are usually "cutoff corner near pin 1", but I've seen it as pin 4... or pin 2... constant vigilance is the only solution. Constant vigilance!
If you just say “implement this” you’re going to have a bad time after a certain point.
You really need to add yourself as a human in loop to be in the middle of design choices. That is, ask the model for a plan, what the trade offs are, should parts of the code be refactored before the next feature, etc.
Also, there’s an element of reading the code and ordering refactors. I’ve noticed that for embedded code codex loves to to do everything in a main.c and too many compiler defines. Asking it to propose a refactoring into modules helps a lot.
It’s just like writing real code, if you don’t do design up front and don’t aggressively refactor as the requirements change, it becomes a mess.
Ideally you ask the agent the thing you want to know / business question you want answered.
An agentic loop then runs. The agent can look at the schema, look at any existing SQL scripts available that query similar tables, run a few limit 10 probe queries. Analyze the data, do some joins, check the data again. Show you the data, ask for feedback, etc.
Pure zero / one shot SQL generation isn’t the solution and isn’t how humans do things. We look at the schema, run some queries, do some joins, spot check the totals / row counts, etc.
I think/hope that most benchmarks have moved to an agentic loop - I'd still call that 'text to sql', since you're going from the business question to one or more SQL queries that provide the answer.
With a loop you can get extremely high results on a clean DB with a clear question. (I'm usually seeing high 9s accuracy.). A messy/ambiguous DB schema then degrades that again (which is what companies actually have, where you get closer to 20-30% rates pre-context engineering), which a clean semantic/presentation layer can bring back up. Then you're just left with the problem of underspecified/poorly formulated question - which you can partially solve with the agent pushing back, but also can be solved by improving the human side of things.
Was non-trivial (e.g. days and days of cumulative work) but worked with Codex to build up a parametric generator where I could adjust the star size, number of rings, leds per ring. It did all the LED-to-LED routing, panel layout with mouse bites, placed decoupling caps. At the time Codex wasn't as good at spatial reasoning, I expect that Astra would do this much faster.
4 x layer PCB with GND and 5V inner layers. WS2816B LEDs (not available in strips yet). 3D printed a frame so that two stars could be opposite each other (video shows only one side). ESP32 S3 on a 3.3v to 5v shifter board sandwiched in between. ESP32 wifi antenna pokes through and sits on a keep out section. Received control signals from a central controller via ESP Now.
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