"Zero power" does not include the power needed to translate information between electronic and optical domains and the light source itself.
Readit Newscpldcpu commented on Show HN: Zero-power photonic language model–code zenodo.org/records/177642... · Posted by u/damir00
cpldcpu commented on I know we're in an AI bubble because nobody wants me petewarden.com/2025/11/29... · Posted by u/iparaskev
What also cannot be ignored, is that transformer models are a great unifying force. It's basically one architecture that can be used for many purposes.
This eliminates the need for more specialized models and the associated engineering and optimizations for their infrastructure needs.
I am not a professional software developer but instead more of multi-domain system architect and I have to say it is absolutely magical!
The public discourse about LLM assisted coding is often driven by front end developers or rather non-professionals trying to build web apps, but the value it brings to prototyping system concepts across hardware/software domains can hardly be understated.
Instead of trying to find suitable simulation environments and trying to couple them, I can simply whip up a gui based tool to play around with whatever signal chain/optimization problem/control I want to investigate. Usually I would have to find/hire people to do this, but using LLMs I can iterate ideas at a crazy cadence.
Later, implementation does of course require proper engineering.
That said, it is often confusing how different models are hyped. As mentioned, there is an overt focus on front end design etc. For the work I am doing, I found Claude 4.5 (both models) to be absolutely unchallenged. Gemini 3 Pro is also getting there, but long term agentic capability still needs to catch up. GPT 5.1/codex is excellent for brainstorming in the UX, but I found it too unresponsive and intransparent as a code assistant. It does not even matter if it can solve bugs other llms cannot find, because you should not put yourself into a situation where you don't understand the system you are building.
Not a big fan of emojis becoming the norm in LLM output.
It seems Grok 4.1 uses more emojis than 4.
Also GPT5.1 thinking is now using emojis, even in math reasoning. 5 didn't do that.
cpldcpu commented on Qualcomm to acquire Arduino qualcomm.com/news/release... · Posted by u/janjongboom
Additionally, they're launching their first joint product, the $44 Uno Q SBC, which has a Dragonwing SoC and STM32 microcontroller on an Uno form factor board[1].
It seems like Arduino will keep their brand, maintain their existing product lines, and continue building devices using other vendor's chips (besides Qualcomm), etc... but as with all acquisitions—I wonder how long that state of affairs will last.
Alternatives like the Pi RP2040/2350 and Espressif's vast selection of boards can fill the gaps, but Arduino was what got me and many people I know into microcontrollers, so I have a special fondness for them!
At this point in time, the shield headers rather look like a trademark than a useful connecter.
cpldcpu commented on Language models pack billions of concepts into 12k dimensions nickyoder.com/johnson-lin... · Posted by u/lawrenceyan
The dimensions should actually be closer to 12000 * (no of tokens*no of layers / x)
(where x is a number dependent on architectural features like MLHA, QGA...)
There is this thing called KV cache which holds an enormous latent state.
cpldcpu commented on SpikingBrain 7B – More efficient than classic LLMs github.com/BICLab/Spiking... · Posted by u/somethingsome
> you can also encode information in the time domain.
Also known as a serial interface. They are very successful: PCIe lane, SATA, USB.
These interfaces use serialized binary encoding.
SNNs are more similar to pulse density modulation (PDM), if you are looking for an electronic equivalent.
cpldcpu commented on SpikingBrain 7B – More efficient than classic LLMs github.com/BICLab/Spiking... · Posted by u/somethingsome
To me it sounds like sparse matrix multiplication repackaged as "event-driven spiking computation", where the spikes are simply the non-zero elements that sparse GPU kernels have always been designed to process.
The supposedly dynamic/temporal nature of the model seems to be not applied for GPU execution, collapsing it into a single static computation equivalent to just applying a pre-calculated sparsity mask.
Perhaps a bit cynical of me, but it feels like wrapping standard sparse computing and operator fusion in complex, biological jargon...
I believe the argument is that you can also encode information in the time domain.
If we just look at spikes as a different numerical representation, then they are clearly inferior. For example, consider that encoding the number 7 will require seven consecutive pulses on a single spiking line. Encoding the number in binary will require one pulse on three parallel lines.
Binary encoding wins 7x in speed and 7/3=2.333x in power efficiency...
On the other hand, if we assume that we are able to encode information in the gaps between pulses, then things quickly change.
cpldcpu commented on SpikingBrain 7B – More efficient than classic LLMs github.com/BICLab/Spiking... · Posted by u/somethingsome
Can you explain more? Why would that be the case? What is being passed from one layer to the next is not a linear value but the delay until the next spike, which is very different.
It was also a question from my side. :)
But I understand that they simulate the spikes as integer events in the forward pass (as described here https://github.com/BICLab/Int2Spike) and calculate a continuous gradient based on high resolution weights for the backward pass.
This seems to be very similar to the straight-through-estimator (STE) approach that us usually used for quantization aware training. I may be wrong though.