Algorithms are also improving. I believe it's very unlikely for these two improvements together to not result in one to two orders of magnitude cheaper cost per "intelligence". Of course, that might just make use cases that are too expensive today viable and thereby increase usage further.
LLMs don't tend to help much when solving challenges beyond their skill level. Either they one-shot a challenge, or thei are almost useless as a companion for them.
It is a hard requirement. Once you reach higher levels of challenges you spend most of your time reading through RFCs, web sepcs, Github issues, mailing lists, papers, random bugtrackers and library/framework code. There is no way to create a whitelist for that. Besides, a firewall won't stop good hackers.
Normal CTF workflows can involve a lot of research but that's not the point. You can design self-contained challenges with offline solving in mind, and bundle any truly necessary docs/src/etc. with the challenge download.
Qwen3.6 9B is as good as GPT-4o and runs on my M2 MacBook Air. Models are getting stronger and less costly at the same time, but these are somewhat separate branches of research. Frontier labs are spending more because they are still getting marginal returns and there is more capacity to spend than there was a year ago.
You are right, I was mistaken about the version. I evaluated it in general chat assistant prompts plucked from my history across a range of topics but did not use it for coding - there was never a time when I thought 4o was “good enough” for agentic coding.
They are intrinsically linked beyond a certain point. If we're making progress but costs are spiraling exponentially then it stands to reason that we will soon reach a point where we can no longer afford the increasing costs and thus progress will slow.
(barring some breakthrough that reduces costs, which of course may happen, but for which recent model improvements are not strong evidence of)
I guess within the domain of AI, a pertinent question would be: "do I want to use anything but the best?" The errors older models give being directly analogous to being stupider in my eyes.
Depends — many tasks in various pipelines have a reasonable Pareto frontier and diminishing returns after a certain level of performance. You may just have a high budget constraint (say like YouTube computing ASR subtitles; they are not going to be using the best ASR models because it’s expensive). If it’s myself, with a coding agent, I’m going to get the best thing I can afford.
If higher bandwidth networking consisted primarily running more and more ethernet lines in parallel, you would most certainly agree that "networking has stagnated".
"Reasoning" and now "Agentic" AI systems are not some fundamental improvement on LLMs, they're just running roughly the same prior-gen LLMS, multiple times.
Hence the conclusion that LLM improvement has slowed down, if not stagnated entirely, and that we should not expect the improvements of switching to these "reasoning" systems to keep happening.
“ChatGPT came up with an idea which is original and clever. It is the sort of idea I would be very proud to come up with after a week or two of pondering, and it took ChatGPT less than an hour to find and prove”
Until you or I can actually use Mythos in Claude without an nda or other strings attached, Mythos is not released and is just an effective marketing tool for Anthropic.
At least to me this is a pretty sour grapes take. There are all kinds of released products that are expensive or need an NDA. You're just too poor to afford it. But make no mistakes there are governments using this in mass and likely against you.
Self hosting at a reasonable scale is much cheaper than people think. I am running clusters of DGX Spark machines with BiFrost load balancers in our company and for client projects. They work flawlessly!
128 GB unified memory, Nvidia chip and ARM CPU for just around 3k€ net. They easily push ~400 input and ~100 output tokens per second per device on say gpt-oss-120b. With two devices in a cluster, thats enough performance for >20 concurrent RAG users or >3 "AI augmented" developers.
It is definetly cheaper now. What I want to say with this, is that token costs rising so dramatically that AI usage becomes uneconomical is not a high probability future. Even if AI subscriptions were sold heavily below cost (which is also unlikely, after R&D).
The party is called the "Christian Democratic Party" but in practice pushes no christian policies. 47% of germans are legally atheists. Only 5% regularly visit mass.
All very true, but I don't think it contradicts my point. Perhaps the lower prevalence of religious participation in Germany makes German secularists more comfortable with religious symbols and practices.
vLLM isn't suitable for people running LLMs side-by-side with regular applications on their PC. It is very good at hosting LLMs for production on dedicated servers. For the prod usecase ollama/llamacpp are practically useless (but that's ok - it's not the projects goal to be).
Northdata [1] does basically this, but mostly ingests European data currently. Perhaps they will expand to ingest US data as well at some point. Not affiliated with them in any way. I just use them to look into company structures every now and again.
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