jononor

With enough tokens, all bugs are shallow? :D

Yep that is annoying. There are USB-C magnetic charge adapters. It will prevent shit from getting into the slot, and easy to charge magsafe style. And of course you can easily take it out temporarily to use a standard USBC charging cable.

Time to migrate off Atlassian, and ban it for any use in the company. You cannot just help yourself to customer data like that. The data is not yours, never was, and never will be. Pay for a service that blatantly rips of our company IP? Nope.

Thanks for showing your colors so clearly Atlassian. Good riddance.

A decent amount of software developers and gamers do spend 3000 USD on a PC. That kind of hardware is going go get more and more capable over time wrt genAI models.

Of course there will always be a gap to frontier closed hosted models. It is not an either or proposition.

You are the one that claimed the prices of those shuttle services were lower than that of WaferSpace. 7k USD for 1k chips of 20mm2 at 180nm. Is it not the case?

I went though the list of prices at Europractice. Waferspace is 7000 USD for 1k of 20mm2. That is a per mm2 price of 350 USD. I could not find any offering at Europrice that matches that?

Interesting! Which EDA tool must I use for those, and what is the price of that? Will these services accept a single run of 1k?

What are your thoughts on including a RISC-V hardcore along with the gates? Because for almost all projects I can imagine using FPGA for, I would want a microcontroller also. This might however be slightly colored by me being a software/firmware first type of electronics engineer. Thinking especially for the smaller gate counts, like under 10k - because there a soft CPU takes up very precious resources.

X-ray would be the traditional approach, though quite expensive. IRIS by bunnie is another approach that aims to bring cost way down. Ref https://www.bunniestudios.com/blog/2024/iris-infra-red-in-si...

Nice specs! Looking forward to seeing how this and the other projects on Waferspace goes. Being able to produce 1k chips at a reasonable price will hopefully do wonders for open hardware / open silicon.

Glad to see this. At least there is one player in Europe which does a full vertical integration around LLM/AI - from datacenter to LLM models and applications (Mistral Vibe). On the data center part Europe seems to be doing OK, and also ok on applications. It would be nice to see more players focusing on the LLM model building - though it legitimately seems like a very tough (maybe even bad) business to be in.

Have been playing with Qwen3.5 35B. Runs OK nicely on a RTX5060Ti, though I would have liked to have a bit higher thoughput (a 5080/5090 would do). It is seemingly close-but-not-quite-there for code generation / agentic coding. So I am actually quite hopeful that in a few years time, using local LLM models will be quite feasible.

Very much looking forward to play with the BIO functionality on the Baochips that I have ordered. Thanks for the nice write up! It is fascinating to see how widely applicable the "just throw a RISC-V core or 4 in there" design pattern is. The wide range of CPU designs that are standardized, the number oc mature open source implementations, and the lack of royalty fees, and the ready-to-run programming toolchains really drives this to a new level. And CPUs are small in die area anyway compared to SRAM! Was cool to see on the RPI2350 how they just threw in another two RISC-V cores next to the ARMs.

For these reasons specified above, I think that this trend will continue. For example, in my specialization of edge machine learning, we are seeing MEMS sensors that integrate user programmable DSP+ML+CPU right there on the sensor chip.

Highly recommend Statistical Rethinking for anyone looking for practical/applied/intuitive approach to Bayesian Statistics. For example the 2023 lecture series: https://youtu.be/FdnMWdICdRs?is=KycmwPL-cn8clOK5

Many "subjective" tasks can also be done in an "objective" manner - as long as there is a large enough dataset to estimate what humans would evaluate the outputs - and the evaluators being reasonably consistent. Many human preferences are relatively homogeneous, or sometimes clustered into groups. And there are whole fields of study/practice of such phenomena, such as sensory science - with applications in food, audio, images etc.

Number of machines under control is a measureable target. Quite suited for this concept, at least in theory.

Very nice development towards more open hardware. And super powerful at that. The verification technique is alos super interesting. Have ordered a small set to play with, and to support the project.

There are also several RISC-V microcontrollers on run 1 of Wafer Space, hopefully some of those will also be available online soon. https://github.com/wafer-space/ws-run1

There are easy fixes to get rid of violent and crazy people. Why would a powerful ASI bother with fixing them? A rabid dog just gets put down by humans. Why would we expect anything better of our overlords?

Ouch, that is indeed a risk one must be wary of. Can be a "works for the company but sucks for employees". Which can also drain the company of skilled people, a poor trade in most cases.

Bus factor 1 is rarely enough for "entire business". But if the GPUs are for training models, and their users are the data scientists that are also on holiday around the same times - that might indeed be good enough policy.

The video is pretty hilarious. And the problem a serious one. So I am glad to see that the Norwegian consumer council is taking a stance on it, and not afraid to use humor as a tool.

A stable/predictable base of maybe 50k USD/year is probably more in the range where it could influence someone to be FOSS maintainer full-time.

Altman was just this week quoted as saying things in that direction...

This is basically what happens with the advertising/social media giants (Facebook, Google, etc) because everyone needs msrketing, and mobile companies (Apple, Google) because they handle payments.

It is a lot compared to what? I believe that a LLM capable laptop will cost considerably more than something that is good-enough for non-LLM productivity tasks. At least within the next 5 years. Say that it would cost 600 USD more, that would buy 30 months of subscription. It is this kind of scenario I think many people will favor the subscription.

That something is useful does not necessarily mean that it will be doable for companies the capture enough of value to make up for the billions in investments they have/will have make in the coming years.

Right now the frontier AI companies are explicitly running a kind of chicken race - increasing the burn rates so much that it gets harder and harder. With the hopes that they (and not their competitor) will be the one left standing. Especially OpenAI and Antropic, but non-AI companies like Oracle have also joined. If they keep it going, the likely outcome is that one of them folds - and the other(s) reap the rewards.

Utility (per cost) will go up the tougher the competition. Money captured by single entity possibly down with increased competition.

They are likely aiming to maximize reach/mindshare. Get as many people hooked as possible. More important than minor upside from a few multi-Max users.

EDIT: also, the casual or gym-style members that pay every month but barely use the service are of course very valuable wrt margins

In a hypothetical future with 2026 level LLMs on a (high end) consumer laptop, I still think that majority of buyers would prefer to pay 20 USD/month for a service. Just for the convenience and flexibility.

You can submit analog to TinyTapeout now!

They were caught using all the data on the internet without asking for permission or compensating anyone. And it has cost them nothing and earned them billions so far.