joefourier

I stopped using Cursor because of how terribly optimised it is (worse than VSCode despite being a fork). It would routinely take up 50% of the CPU resources on my MacBook M4 and gigabytes of RAM for absolutely no reason.

I switched to Zed, and I'm never going back to Electron/non-native IDEs.

Why is it unethical? I'm both a freelance engineer and a business owner that sells software, and I've both sold my labour for equity/revenue share, and for a flat hourly rate.

If I charge a client $50k for some software and they made $1 million profit from it, good for them? As long as they pay our mutually agreed upon rate on time and there was no hostile negotiation, why should I feel suddenly entitled to more money if that wasn't in our contract? How do I know how much of the value is from my work and not their marketing or idea?

What you're saying seems as crazy as me saying that someone who bought my software for $99 and used it on a multi-million dollar project is being unethical unless they give me more money. How on Earth does that make sense? Should I be forced to switch to a royalty model? What if I make more selling copies at a flat rate, what if I don't want to have to investigate the finances of thousands of customers and have to deal with that whole trouble?

For me it's the same thing regardless of whether I'm selling my labour or a product. I can choose whether to accept a flat hourly rate, equity, or a mix of both, and usually the better deal is the hourly rate.

If I find a way to hire a software engineer for market rates (say, $200k/year in the US) and get $2M revenue from their work, good? They can ask for a raise or a bonus, we can renegotiate, they can leave if they're unhappy, but I'm not obligated to give them more money than was in our agreement anymore than they're obligated to give me their salary back in the project fails.

My response is “great! Let businesses take a lesson here: give all your employees a chunk of the company. Let’s all share in the success!”

Don't >95% of tech companies offer stock options or equity, from startups to FAANG?

Yeah most of the performance increases have mostly been from architectural improvements like reduced precision tensor cores. AFAIK FP4 is basically the limit for floating point matmuls, after which you need to switch to integer addition if you want to reduce bits, and I don’t think we’ve figured out 1-bit LLMs just yet.

Demand is so high and supply so low customers will go to anyone that has any gear, period. Anthropic is paying xAI for GPUs from 2022, not the latest Nvidia release.

And yet Anthropic is paying xAI over a billion dollars a month for those out of date GPUs in their first datacentre (H100s being nearly 4 years old at this point).

Even A100s are still barely available on the major clouds despite being 6 years old.

Do you think the work will still apply to speculative/alternative decoding methods like MTP and block diffusion, which are making batch=1 decoding less memory bound? Kernel launch overhead and memory transfer become less and less significant as a % of time when computing multiple tokens at once.

You'd be surprised, people are somehow buying Tesla P40s and M40s on eBay for almost $300 and $180 respectively (M40 being the same gen as GTX 950). Google Colab still offers T4s and it's taken them years to add modern GPUs. Hope they're powering them with renewables at least.

And people in general are holding on to their old machines for very long periods of time now, especially CPUs. I've had to support first gen Intel i7s at work! That's pre AVX.

Outside of training the biggest LLMs at big labs, GPU lifespan isn't as short as the OP made it out to sound. A100s are 6 years old and still a reliable work-horse, and the 80GB version hasn't depreciated that much on the used market. On the consumer side, 3090s are actually still selling for very close to 2020 MSRP.

Even the ancient V100 (soon to be 10 years old!) had somewhat of resurgence on the second-hand market, with a healthy market for interconnects in China.

If I had a datacenter and power consumption was not a concern, I'd be holding on to my A100s for years at least for inference.

And why is V100 even used? V100 is four generations old and not even supported anymore.

It wouldn’t surprise me that due to bureaucratic processes, it’s still somehow the most readily available GPU for Apple researchers despite being almost 10 years old now. I recall even last year seeing V100s used by Microsoft researchers who weren’t working on LLMs.

Not a single new 64GB GPU, but multiple used GPUs.

They’ve significantly increased in price (so much for hardware depreciation…) but you can still get a modded 22GB 2080 ti for $320, or a Mi50 32GB for ~$450 each (used to be $150 a few months ago, alas), or a Mi50 16GB or <$200 but you’d need to stack 4 of them.

There’s also some more exotic configurations but those are probably the simplest options. You won’t get the performance of an RTX Pro 6000 Blackwell of course, and the power consumption will be pretty high so it’s only worth it if you have cheap electricity. But it is possible.

What quant? You should have no problem running it at Q4 with 256K context, Q5 or Q6 even although maybe not at full context. I can run Q4 on a 4090 with just 24GB VRAM.

Who is going to buy a $4299 M5 Max MBP with 64GB of RAM just to run Gemma 4 31b? Firstly you don't need 64GB for that model. Secondly if you want a machine that sits in the corner and does nothing but LLM inference, you don't buy a MacBook Pro, you buy some GPUs which are going to cost you a fraction of that (~$1k for ~64GB of VRAM is possible). The people buying Apple Silicon for inference general aim for the Mac Studios with enormous amounts of RAM (128-512GB), to run very large models.

The idea is obviously to be running the LLM on your work laptop. As a developer I'd need a laptop with 24GB of RAM for work anyway, and 48GB, which is enough for a very good quant of Gemini, is just $400 extra.

Why didn't you take into account batching, input tokens, different costs of electricity, and the fact that a laptop can still hold a decent % of its resale value, and is useful for many other tasks than running an LLM?

I'd compare it to OpenAI 5 years ago except I think even then OpenAI had way more!

Say what? 5 years ago OpenAI had received around $139 million in funding, and they’d just come out with GPT3 with 175B parameters, a 2048 context window, trained on 300B tokens on a 10,000 V100 cluster which would have cost maybe $4-13 million at the time for their training run.

Meanwhile Deepseek V3’s famously frugal training was $5M, and Chinese AI companies are raising billions in funding. Sure American AI companies are raising tens (and maybe hundreds in the case of OpenAI, if you count their circular funding rounds) of billions but they’re grossly inefficient, and we’ve already hit the limits of the scaling laws where there’s little point in increasing the number of parameters of a model.

It's incredibly common all over Europe, not just Switzerland. Not only the metros but the trams and even buses often rely on this system where there's no turnstile or barrier, you just walk in.

Not sure it's about being a high trust society or not, there's frequent inspections where they block the doors, and you get a hefty fine if you're caught without a valid ticket. I certainly wouldn't call Prague or Rome or Dublin high trust societies on par with a Swiss city.

Personally I feel like it would be less undignified and infantilising to have a machine take care of my basic bodily functions than a human being. There's no feeling of judgement or being shamed in front of someone else, and the machine could even restore a feeling of autonomy since it would feel like you're using a tool instead of being helplessly reliant on another person's help.

I also used dedicated servers in the late ’90s (and they still offer great value today). But before AWS, provisioning new hardware typically took days, not minutes.

VPSes and non-custom configs for dedicated servers were pretty instant as far as I know, I think the advantage of AWS was more that you could scale up and down much more easily since you weren’t locked down in a monthly contract, and that you could automate server provisioning through an API.

Cloud computing was an absolutely mind blowing revolution - suddenly your startup could run its own computer systems in minutes without need to install and run your own systems in a data center. This was an absolute game changer, and I really drank the AWS Kool Aid down to every last drop then I licked out the cup. I was all in on AWS in a big way.

Am I the only one who remembers that VPSes and dedicated hosting services were a thing before AWS came around? Yes you had to pay for a month at a time and scaling wasn’t as instant, but it wasn’t like the only option before cloud computing was having to drive to the datacentre and install your own server.

In every country, men commit almost all violent crimes. In school, boys physically bully other boys. Hence the physical punishment for them.

As I've said, and @echoangle repeated, caning is used for cyberbullying, which girls do too (at a rate relatively close to boys actually). If the law was caning in response to physical bullying, and it just so happened that the vast majority of offenders were boys, I would not object on the basic of sexism (I still would not approve of schools being allowed to physically punish students).

Yes, for homo sapiens, the female is more fragile than the male. This is basic biology. I'm sure that in praying mantis society, females get harsher punishments.

There's no way the typical 16 year old girl is more fragile than the typical 9 year old boy, yet only the latter is subject to this punishment. Until children reach the age of 12 or so the strength difference is quite minor (and there's even a brief period where girls are taller and heavier).

Also it's absurd to punish demographics differently based on their statistical averages. Redheads are less sensitive to pain, should your hair colour determine how many strokes of the cane you get?

Boys and girls being different does not mean one sex deserves corporal punishment and one does not. Girls are equally capable of cyberbullying (which is covered by this law), why should they only get detention while a 9 year old boy has to suffer physical violence? What does this teach girls - that they can get away with more? That they're more fragile than even a prepubescent boy?

If the law punishes one demographic less severely for the same actions, that's injustice. No different in principle from pre-modern practices where if a noble maimed a commoner, they'd just need to pay a fine, while if a commoner did the same, they'd be put to death.

There he was, a short, beefy guy with a goatee and a Red Sox cap and a thick Boston accent, and I suddenly learned that I didn’t have the slightest idea what to say to someone like him. So alien was his experience to me, so unguessable his values, so mysterious his very language, that I couldn’t succeed in engaging him in a few minutes of small talk before he got down to work.

I'm a self-taught software developer with no university education and I too am socially awkward in front of tradespeople in my house. I don't think this is about Ivy League degrees, just being a nerdy intellectual who's bad at small talk and doesn't have any topics in common with a blue collar worker.

Calling anything "large" in computing is problematic since hardware keeps improving. GPT-1 was an LLM in 2017 and had 117M parameters, when did it stop being large?

GPT would have been a better term than LLM, but unfortunately became too associated with OpenAI. And then, what about non-transformer LLMs? And multimodal LLMs?

Maybe we should just give up, shrug and call it "AI".

Local models sound great until you realize you dont get alot of the features that we implicitly expect from hosted models. Many things would require additional investment into the operations and setup to get to a comparable system. We ended up wanting things that would require us to roll our own memory system, harnesses for the model, compliance needs, and security.

That's not local models vs hosted models, that's using the enterprise services from Anthropic. Any local LLM inference engine such as VLLM gives you an OpenAI compatible API with the exact same features as a hosted model.

I'm not sure what your use case is, but I personally found Anthropic's offerings lacking and inferior to open source or custom-built solutions. I have yet to see any "memory" system that's better than markdown files or search, and harnesses for agentic AIs are dime a dozen.

AGI means artificial general intelligence, as opposed to artificial narrow intelligence. General intelligence means being able to generalise to many tasks beyond the single narrow one that an AI has been designed/trained on, and LLMs fit that description perfectly, being able to do anything from writing poetry, programming, summarising documents, translating, NLP, and if multi-modal, vision, audio, image generation... not all to human-level performance, but certainly to a useful one. As opposed to previous AI that was able to do only a single thing, like play chess or classify images, and had no way of being generalised to other tasks.

LLMs aren't artificial superintelligence and might not reach that point, but refusing to call them AGI is absolutely moving the goalposts.

There's also a difference between having no immediate use, and having no reason to exist. From what I understand, sexual differentiation works by having the Y chromosome act as a switch, and both sexes have to share the same blueprint with hormones guided the development of their organs.

For males not to have nipples, they'd need to be actively destroyed, which poses a risk for females to also not have nipples, which is much worse than males having harmless, inactive nipples.

They would honestly have been better off refusing customers if compute is so limited. Degrading the quality leads to customers leaving in the short term, and ruins their long term reputation.

But in either case, if compute is so limited, they’ll have to compete with local coding agents. Qwen3.6-27B is good enough to beat having to wait until 5PM for your Claude Code limit to reset.

1. Improving the colourisation algorithms has value, it might be that the available colourised photos of celebrities have inaccurate colours or are of poorer quality than say, one done with a diffusion model that can be instructed about the colours of certain objects

2. Don’t forget about B&W films! Getting automatic methods to be consistent over a long length is still not 100% solved. People are very interested in seeing films from WW1 and WW2 in colour, for instance.

3. Plenty of people (myself included) have relatives in their 80s or 90s. Or maybe someone wants to see their ancestors from the 19th century in colour for whatever reason?

Too bad "tiny screens" pretty much do not exist anymore. Screens with hundreds of pixels on each side are very cheap already.

Find me a 0.66" OLED display for ~$1 that has hundreds of pixels on each side then.

It reminds me people who research "colorizing grayscale photos", which do not exist anymore either (if you want a color photo of someone you met in your life, there probably exists a color photo of that person).

What train of thought led you to think people are primarily researching colorising new B&W photos? As opposed to historical ones, or those of relatives taken when they were young? You can take a colour photo of granddad today but most likely the photos of him in his 20s are all in black and white.

Hetzner also offers a VPS with superior specs to their old DO server for €374.99/month, or €0.6009/hour. They could just switch to a VPS temporarily while waiting for the hardware fix.

Although since they were running a LEMP server stack manually and did their migration by copying all files in /var/www/html via rsync and ad-hoc python scripts, even a DO droplet doesn't have the best guarantee. Their lowest-hanging fruit is probably switching to infrastructure as code, and dividing their stack across multiple cheaper servers instead of having a central point of failure for 34 applications.