Absolutely false. At least when it comes to multimodal inputs, even a simple classifier will outperform the largest LLMs who still hallucinate details or don’t describe audio and images accurately.

And there’s also the issue of cost/inference speed. Running a trillion parameter model for all tasks will be incredibly costly, require a cloud API, while a tiny CNN can be run locally or at a cost multiple orders of magnitude lower.

And yet, Fable and Opus are among the best coding models out there (matched only by GPT5.6 Sol).

It's not about the people there being smart or not, it's about their and the company's priorities, resources and what they choose to focus on.

I'm not sure what you mean? You can see the intelligence of LLMs progress predictably and stably according to scaling laws. LLMs have to encode language in addition to intelligence so there's a minimum bound for them to output sensible text (you can train specialised tiny models to solve basic puzzles without language). Start at around 127M and compare models of increasing parameters and you'll see a clear progression in intelligence.

> It's unintuitive since, to the best of my knowledge, one of the basic tenants of algorithm development was that you can't just brute-force your way towards a solution for some complex problems, e.g. naive sorting algorithms suddenly won't beat quicksort if you put more processing to them

How is that a basic tenet? Simple, easier to parallelise algorithms that have lower memory requirements, or can take better advantage of hardware, or don't hit a plateau the more compute you throw at them, can absolutely beat cleverer algorithms. E.g. brute forcing rendering with Monte Carlo path tracing will give you more physically accurate results than ray tracing or rasterisation algorithms that rely on a bundle of hacks to approximate global illumination, transparency smooth shading, etc.

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

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.

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

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

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.

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.

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.

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.

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.

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.