Just add a --verbose flag that shows the stacktrace when there is an error. Then add a footer message when an error appears in non-verbose mode that invites the user/agent to use --verbose to get the full picture.
It obviously may end up in thousands of tokens burned through though (you can also fix that adding different levels of verbosity), but hopefully errors are not common.
You don't need SOTA-level LLMs to create value with AI. Hell, you can build good solutions with a simple small finetuned models.
> When models are good, expectations are adjusted accordingly to deliver things on par with the whole industry, you can't just say, I have built my own Intel Pentium II, now I will try to use it to compile Electron App and run 3DS Max there.
I know you are taking your analogy to its breaking point but it really depends on what you are doing. I know people that use 10+-year old thinkpads and they do just fine.
That's convenient accounting. The reality is that they can't stop training since they risk losing customers if they do so. So they shouldn't factor it out of profitability analysis.
Ah, come on. I remember the scalping of GPUs due to crypto-mining and then all the things Nvidia did to market segment crypto out of the regular (gaming) consumer space. AI is much worse because the scale is OOM greater, but crypto/blockchain effects on the market weren't harmless either.
AI as a tech is fine. But disliking it and the social/economic effects around it is fine too, people should be allowed to feel however they want to feel about certain techs and situations.
To recommend people to suck it up is not the answer I wish in the society I want to live in.
> I know artificial analysis quite well as the gold standard in llm evals.
I also know them, but it took me a while to realise you were publishing their data in that table. I don't think it was clear.
> The age is important because new techniques keep being developed and so it is a very rough indicator of the size/cost/efficiency trade-off.
Yes but you are already including the name of the model, your potential public for the table already know about model's release history and therefore each model's age, at least roughly.
It is kind of noisy because the release recency, which is what your "age" column actually represents, is not important data for the comparison you are trying to make.
Also what message we should get from that table is not really obvious.
There is now an Antigravity CLI which will replace Gemini CLI. Gemini CLI is going to be EOLd by June 18th afaik. Antigravity CLI and GUI share the same agent harness, so it might do the same task.
Well, we were overall better for a couple of centuries after abolishing all-powerful kings + some welfare laws here and there (ymmv, maybe serfdom sounds nice to you). So those changes can work for a while, big emphasis on can and for a while.
Greedy accumulators always end up ruining things for societies when it gets into ridiculous extremes (and there is a part of society that notices and gets fed up).
It is difficult to believe that you can cobra effect yourself into greatness. I'd rather say the most useful perk for companies doing this is the AI-washing adoption metrics they can report, which will hopefully (for them) increase valuations.
I still don't know how to reconcile these reports with what other people say about GenAI-agentic assisted engineering being the only way of working nowadays, especially in startups.
Probably there is no dichotomy going on and it depends on multiple factors, but it seems so weird to see reports that are so different between each other.
It obviously may end up in thousands of tokens burned through though (you can also fix that adding different levels of verbosity), but hopefully errors are not common.