ACCount39

Yeah, it's not talked about too often but RPi 1 was basically Broadcom's "useless SoC recycling program".

If not for the success of Raspberry Pi, Broadcom would have zero SBC presence today. Like many companies chasing the high volumes and high margins, they don't see the market value in a segment like this. Thus things like poor docs and weird blobs. Raspberry Pi is open to the degree it is despite Broadcom, not because of it.

Ironically, that Broadcom tie could have caused RPi to eventually lose ground to other SBCs - because the likes of Rockchip just deliver more bang for buck in SoC dept. Broadcom doesn't seem to be investing in staying competitive in the segment. So RPi's edge is just the ecosystem being more important than raw price/performance.

For now though, that's pretty much on hold, because SBC price tags are increasingly DRAM dominated instead.

Biology doesn't have particularly good simming, but what it does have is massive parallelism.

So as long as you can test things on cell culture level? You can do a lot with a high throughput setup.

Blue pigments used to be extremely rare and precious. Then some chemists found the infinite blue glitch.

It is - anything short of HBM can be replaced that way.

The issue, of course, is just how much time, effort and skill is required to do it.

It might become a viable commercial process outside third world countries if RAM prices spike even more - or if someone finds a way to teach an off the shelf $1000 robot arm advanced autonomous microsoldering.

Then there's the problem of forcing the device itself to recognize the new RAM. It's a device-specific trick. RAM sticks have a little tag chip that tells a PC "how much RAM this stick has", but RAM chips themselves don't - so the device has to infer its RAM capacity from elsewhere. Like strap resistors on the board. Which you have to find and adjust for the new RAM capacity. That, or fix the RAM values in firmware, which is also device-specific.

Honestly, reasonable.

I've been eyeing LPCAMM2 interposers for non-RAM uses - as an off the shelf connector for stacked PCB designs. The conclusion was that it wasn't produced or used at sufficient scale for it to work.

It's not about "teaching style". It's about having intrinsic motivation vs having to be motivated with a stick and threat of failure.

Less "storage server" and more "attention server".

A server that both holds KV cache in its own memory, and uses its own compute to perform attention operations. That way, you don't have to transfer cache entries server to server for every attention sweep - only the "requests" and "responses". Attention bandwidth stays server-local.

"Lockdown Mode" is something the user enables. If the user wants to trade functionality for security, it can happen. Good.

The "no root access, no bootloader unlock, no installing your own Apple-unapproved software" bullshit is mandated by Apple, with no opt-out.

Create a problem, and then heroically fight to make a solution.

This shouldn't exist in the first place. There would be no need for it if Apple didn't insist on "we own all the iPhones, our users just get to tap at them".

If you could just unlock the bootloader, re-enable SWD and do what you want with an iPhone that you actually own, there would be no need for this restrictive program at all.

AI price-performance is improving rapidly. AI usage, whether in tokens used, in cash spent, or any of the above performance-adjusted, is curving upwards too.

Causality is uncertain, but the "Jevons paradox" interpretation is certainly not contradicted by the available data. Anecdotes of per-employee AI spend rising, and multi-agent freerunning AI tasks becoming more common are also plentiful.

Personally, I think that the "Jevons paradox" interpretation is basically correct. Humanity doesn't have a limited demand on intelligence - but humans are expensive, so it's supply-constrained. The cheaper intelligence gets, the more of it ends up used. As AI gets cheaper and more capable of substituting for human intelligence, usage will rise.

It is an appealing target for LLMs? Well, LLMs do make "handroll your hot paths in assembly for better performance" much more viable.

But that's a bit of a problem too?

Because, unlike humans, LLMs have no trouble whatsoever recalling all the mnemonics. Hell, they can often recall all the opcodes and encodings too and disassemble from hex "manually".

So if the primary advantage of this syntax is better human-readability and human-understandability? The need for it in a future where low level assembly routines are written by LLMs in a perf evaluation loop isn't that great.

This "symbolasm" looks exactly like what Blackfin did.

I think it's a very promising approach - it makes assembly a lot more intuitive, especially to people with background in higher level languages. It makes listings far more immediately readable, even without memorizing every instruction mnemonic. But it just came in too late. By the time Blackfin happened, coding in assembly was firmly an afterthought.

I'd like it to succeed, but I have my doubts that it will.

We seriously need some "you own your hardware" laws - before the likes of Google tighten every bolt and implement enough "security" to firmly lock the users out of their own devices.

It's dead obvious that companies like Apple and Google and Sony and John Deere lock their hardware down to protect their revenue streams - not to help the users in any way. And yet, they're peddling the lie of "security" - and have been getting away with it for decades.

Now that AI is there to give even an average user a leg up when it comes to things like "I don't want to see ads in my TV's menus, can you do something about it", the companies will see more and more reasons to implement more "security" to stop it. And modern "security" measures are hard to defeat with technical measures - so this has to be curbed with legal action instead.

Your reply to my comment downstairs got chewed up by HN. Maybe some sort of LLM detector thought you're speaking too much like an AI?

The reason why I'm not "team sensors" is that we're nowhere near to being able to as much as squeeze out even one mid-range camera sensor for all its worth. Things like AI assisted scene reconstruction show just how much data is there in even a small series of photos - when the AI is there to back the raw measurements with sensible assumptions.

The difference between "a plausible reconstruction" and "an accurate measurement" is academical rather than practical. You never get perfectly accurate measurements (because real world sucks), so a well designed "plausible reconstruction" will often have better error averages AND bounds than a naive measurements-first pipeline - simply by taking better advantage of spatial and temporal redundancies. Adding more assumptions to the pipe from there gives you even better performance.

I expect approximately none of the near-term gains in robotics performance to come from the sensors themselves. There's still room for improvements there, mind. There is some cool sensor tech that could result in a better performance ceiling. But we're nowhere near that "sensor ceiling".

Today's robotics sensors are so far ahead of what robotics AI can reliably utilize that trying to push for "we need better sensors" is a mistake in nearly every case.

Oh, it gets extremely annoying when you try to use those kinds of indirect, noisy, smeared, nonlinear, relative measurements for absolute sensing.

The power of modern AI robotics is that, much like the biological mess it's trying to replicate, it doesn't care that much about absolute sensing.

Imperfect sensors fused by a high performance control model with a good foundation of world dynamics and self dynamics? Gets you impressively close to "perfect sensors" in practice. Being able to take a big bundle of noisy nonlinear ambiguous measurements, and implicitly reference them off each other and over time to get to a usable control loop is powerful.

It's the kind of power human nervous system wields to force the horrid mess that is the human body to perform.

For "detect when you've accidentally picked up two items from a bin instead of one", a wrist camera is a far more natural sensor than a bundle of actuators - especially for lightweight items. Wrist cameras are awesome. But even a bundle of actuators, if fused well enough, can often do it.

The problem is that designing algorithms to sensor fuse to this level by hand is pretty much a no-go. The kind of sensor wrangling hell I would only wish upon an enemy. Which gets us right back to general purpose robotics being an AI problem at its core.

Human "dynamic range" is a hack - the limits look impressive, but they're only reachable through a lengthy adaptation process. Modern camera sensors with HDR, backside illumination and analog gain controls get pretty close to that - and much, much faster.

In practice, if you want really good performance across an extreme dynamic range, it's usually easier to stack multiple specialized sensors within a single sensor unit. A filter-free reduced resolution sensor for high QE in low light conditions (or at high framerates), and a daylight-optimized sensor that gets high resolution, full Bayer, IR-cut and a polarized glare filter to get you as much data as it can in high light environments. This gives you straight up superhuman range with no adaptation time.

Do the raw low light images look nice? No. But human brain itself takes a lot of postprocessing for "looks nice" to happen. Salvaging good images from series of noisy measurements is an AI problem. We've had some pretty capable sensors for a while now, but it took AI-powered computational photography to truly make low light photography happen on a smartphone.

I'm aware of the "detection of individual photons at above-chance rates" experiments with human vision. But modern backside illuminated CMOS, if plugged into the same setup, would also be able to detect photons at above-chance rates. Not to mention the actual photon event cameras. Very niche items, but they can detect photons pretty reliably - not merely "more likely to be right than wrong".

There are also thermal imagers and image intensifier stages, if you want to take the "niche extreme sensors" route. Not commonly done. But goes to show just how much ass engineered sensors can kick.

The limits aren't in the sensors - it's in making sense of the data they give you.

"Estimate of weight and rigidity" is exactly the kind of thing that's performed rather well by low-dimensional sensing of actuators themselves.

Backdrivable actuators know how much current are they sinking - and thus, how much force are they facing. So even the simplest two finger gripper can easily "know" that one object is squishy and the other is not. The response curves are different. Same can apply to arm actuators for weight estimation.

Now, what this doesn't actually get you is an estimate of how hot/cold an object is, and how moist an object is.

Where cameras come in is specifically the "guide a grasp" part. Force sensing alone can tell a robot "the grasp is failing". A camera pointed at the grasped object can tell "why".

Sure, olfactory sensing sensing is "just" sensing of volatile molecules. And we can build plenty of different molecular sensors. Including for things humans can't detect naturally.

But, just like being able to build a load cell and a thermometer doesn't translate to "skin-like sensing", we can't go from "chemical sensors" directly to smell.

For one, things like sensor diversity, density, longevity and manufacturability matter a whole lot. Can you give a robot a weather station type "pressure + temperature + humidity + PM etc" air quality sensor? Yes. Can you give that robot a sensor with enough molecular coverage and fidelity to replace the human sense of smell? Almost certainly not. Can you even get close on a sensible budget? Not with the tech we have.

Getting a system to perform molecular sensing with discrimination against many molecule classes - the kind of operation human smell performs - is very much not trivial.

Now, whether that's worth pursuing at all - I'm still leaning towards a "no". It's a very niche capability.

Embodiment for general purpose goals like "grounding, alignment, and AI safety" just seems to be one of those things that some people want to be true. There's no actual reason for it to be true. There are no findings showing it to be true. People just want it to be true anyway.

I'm very skeptical of that. Wishful thinking is a treacherous thing.

LLMs provide some incredibly solid "general purpose commonsense reasoning" primitives. And the likes of Astra, Opus 5.5 and Gemini 4 have demonstrated generational leaps in "spatial problem solving" - including robotics tasks. Those don't even have dedicated "sensor input embedding" and "action output diffusion" ends rolled into them at model level - but you can give them control over robot arms, and they'll have a decent chance of, slowly, finding a way to complete new tasks you'd previously would have to tune a robotics model for.

Previously, I have considered "generative video models" to be a decent candidate for the foundation of future robotics models - they, by necessity, perceive video, learn the kind of world dynamics required to plan and execute actions in real world environments. And they can begin to learn those dynamics off embodiment-free general purpose web-scale datasets.

But non-robotics LLM generalists have come to show they're no slouches either. They're not at all optimal for things like rapid precise manipulation. But they find more and more use in robotics pipelines anyway. Makes me wonder what something like "Astra reasoning engine but with robotics architectural extensions" could accomplish.

"Matching the dexterity" is a false goal. It's focusing on the wrong things.

As teleoperation readily shows, even a very dumb primitive hand can be salvaged if you get a very advanced control policy to drive it.

Sure, it would still take a lot longer for a well tuned robot with just a pair of UMI-style grippers to tie shoe laces than it would a practiced human. But a robot doesn't have to work faster than a human. "It can do the same task for cheaper" is often good enough.

The right thing to focus on is: AI. We already have robot bodies that are good enough to do many economically valuable tasks. We don't have the AI that would make those bodies perform to their potential. Modern robotics is an AI problem above all.

Human "sense of balance"? Human vestibular system is a sad joke if you compare it against any off the shelf smartphone IMU. Proprioreception is a joke when you compare it against current sensing in every motor and absolute encoders in every joint. Human hearing loses to a half-decent microphone array.

Even human vision, the one perception channel evolution has put an awful lot of weight on, is beat pretty resoundingly by the kinds of camera units that ship in smartphones nowadays.

The two senses robots don't have an edge in are: touch and smell. But I'd struggle to make the case that smell is critical at all, and touch? Hard to beat it directly. In practice, however, it's already partially compensated for.

Sure, a robot with a basic UMI-style gripper doesn't get rich tactile feedback - only a few channels of feedback from arm joints and finger motors. But you know what those actuated UMI gripper get that humans don't? Wrist cams. A robot can see the object it's manipulating - right near the point where the manipulation happens. That matters a lot for precise manipulation tasks.

Humans don't have wrist cameras not because they're undesirable, but because evolution sucks at wiring. We don't suck at wiring.

The fact that humans can pull off general purpose manipulation tasks with the bodies they have, and the best of robots can't? A testament to how much of a gap is there between modern robotics AIs and the evolved wiring of human brains. The intelligence edge is what makes the human body, flawed as it is, work as well as it does. Everything in the human body is half-assed and then salvaged by the brain in post. But that gap is closing as AI tech advances.

Very similar. Might even say it's the same thing viewed at a different angle.

We don't know the details of OpenAI's implementation, mind. But the "limited layerwise depth" problem was known for a while now, and both OpenAI's approach and this are getting at it. And that's far from being the first attempts to address it.

Training and stability were the main issues that stopped it from being adopted rapidly and widely the way something like MoE was. But I think it was always a matter of time.

What do you think I mean by "stagnated"?

Nuclear isn't uncompetitive due to some inherent technological reason. It's uncompetitive due to overregulation and gold plating.

Solar used to be completely uncompetitive, but improvements in technology and increases in scale made the economics work. Nuclear got kneecapped before that could have a chance of happening.

It is a shame that nuclear power has stagnated - we could have been moving towards fossil fuel free grid half a century ago. We've lost decades of progress there. But it's a good thing that solar power is coming into its own more and more.

Late is better than never.

It's possible that this "phrase hash" method was used for a different case - like another, unrelated and possibly unreleased algorithm. And then it was confused for the one used for those curves.

I know the LTE modems had ADCs and DACs in SoC - ZIF with analog signal running straight between the tuner and the SoC. But 5G systems rolled ADC/DAC into the transceiver chip itself - with only the digital processing living in SoC.

Is the "QLink" setup they use in 5G systems now just a thin protocol running over LVDS and streaming I/Q between the transceiver and SoC DSP as digital signals?

Never actually went that far, but I did wonder if you could plug a QLink tuner chip into an FPGA to use it as a broadband SDR frontend.

I know for sure that Qualcomm LTE/5G modems are also SDRs under the hood.

Like Wi-Fi chips, they don't use SDR mode during most of the normal operation. They have dedicated hardware units that do a lot of the decimation, conversion, modulation and demodulation efficiently. But the "SDR mode" is still there for things like factory calibration.

Makes me wonder how many other common MCUs and SoCs are just waiting for someone to unlock SDR in them.

TX is less "not possible" and more "this dev doesn't want to enable it" from what I gathered. Probably for "I don't want people using this to shit all over the spectrum" kinds of reasons. So you can get TX - it'll just require extra hacking.

When the solution becomes "you need a nanometer precision stepper and an electron beam to modify the SoC's circuitry and dismantle the 'root of trust' that's making your hardware betray you to the corporate interests", you've lost.

We can't rely on hardware staying hackable. We couldn't ever since "secure boot" became a thing.

Why the fuck is it normal for a device bought and owned by you to be inaccessible to you?

Companies say "security" and mean "security of our business model".

Solar panels are getting cheap enough for grid hook-ups, installation and regulatory approval to become the bottlenecks.

Plug-in solar is suboptimal in many ways, but it does sidestep a lot of those hurdles. Not too surprised to see it on the rise.

Another thing that might be worth keeping in mind is that plug-in solar panels are often installed vertically - i.e. balcony solar. In which case the peak of the panel's generation is offset to the edge of the typical "duck curve". If it gets popular enough to affect the grid, it might get the solar-induced demand slump larger, but make the edges even sharper.

Would that make grid scale battery storage with its instant response even more important?