Dropping, around or below OECD average:
Iceland, Israel, Malta, Serbia, Chile, Bulgaria, Kazakhstan, Argentina, Malaysia, Romania, Peru, Ukraine, North Macedonia
Mm, yes, normies who hang out in terminals and run curl|bash scripts to install services on their computer and such, typical "normie" activity...
"normies" don't know what OpenClaw, "AI assistant" or what anything else than "ChatGPT" is, and point them to the landing page of Grok/Claude and they'll say "Yeah, that's ChatGPT".
I tried it with a less typical case (the Warsaw, Poland transit app).
I'm impressed that it worked at all and got quite far. Though it is still broken in many ways (random geo locations), the menu bar on top...
The problem is that many web apps are quite simple, while native apps get trickier when it comes to UX. The LLM doesn't work well if it has to interpret animations or cross-correlate map data with locations on a map.
AWS rarely acquired startups, though leveraging someone else's tech is a typical pattern. It did license ParAccel and sold it as Redshift. It also runs Athena, which is based on Trino (a Presto fork).
Now, with Snowflake and Databricks earning big bucks in the intelligence era, time to gain market share by having DuckLabs under its ownership?
I wish AI leaders would fund more data archival projects, instead of hoarding it themselves. They could build a lot of goodwill, but the current state of the art is destroying old books and denying anyone access to them.
These archival projects have an enormous impact, though they are chronically underfunded. I wish there were a strong economic incentive to support them.
Anthropic just learned to the tune of a 1.5 billion dollar settlement that making available for human consumption, even internally, instead of hoarding for AI training exclusively is a fraught exercise.
Two events: Anthropic did end up paying a $1.5B settlement for a case involving the use of pirated books. See Bartz v. Anthropic.
Second, apparently archiving content and providing it to others is risky business. It is often ruled as piracy, see Hachette v. Internet Archive. It went badly for Internet Archive, against common sense.
Everything is "chronically underfunded". There has never been a thing in the world that was "well funded" because people's objectives increase with the amount of money they have. No one has ever said "Wikipedia has enough now" or "The Internet Archive doesn't really need any more money" or "Firefox has collected enough money from Google that it can now operate independently forever".
Besides, this so-called goodwill is an illusion. Opposition to AI in the PR space is entirely dominated by motivated reasoning. Attempting to satisfy people with random things is simply going to shift complaints to "take the commons, chop it up, and give it back to us"; "Should AI companies be the arbiters of what is worth preserving?"; and other such things. These kind of public-relation gains are entirely fictive and neither the supposed beneficiary nor the supposed benefactor is fooled by the theory.
"Oh so they suck up all our water and burn our planet down and we're supposed to be happy that they chop up people's books (that humans spent years creating) just so they can give us digested slop back?". Everyone knows that this sentence is waiting in the wings, and that the entire effort is clearly bait so that someone is foolish enough to pull a Google Books and get sued into oblivion for it.
And that's saying nothing about this so-called "hoarding" of things in a world where we pulp tons of these hoarded things every day and no one cares to scan them.
Hmmm, there are definitely people saying "Wikipedia has enough now". They have enough cash to keep the site going for a long time, but they still beg for donations.
Firefox is similar, people complain about Mozilla's side projects distracting from firefox development all the time, and that they could runa lot leaner if they just focused on the browser.
This isn’t limited to large system prompts. Coding-agent harnesses are also becoming more aggressive about using tools, even for trivial requests. In our tests, prompts such as “Hey” or “commit” sometimes triggered 30+ tool calls:
I often find myself annoyed when Opus fixes a typo in a comment and decides to run tests, lints and whenever else it can find to run. Often it will start by stashing current changes just to preemptively check if all tests were passing before.
And I can blame myself a bit because my rules do say: verify all changes with tests. But as there is that I in AI that is hyped which you’d think means it knows not to put tomatoes into fruit salad …
> [..] my rules do say: verify all changes with tests
I am a bit surprised that you're disappointed that it does exactly what you told it to - people usually have the opposite complaint.
If you're using it interactively and watching what it changes, I'd trigger the tests when you think it's needed. And if you want to go more hands-off, why not add try to encode the same nuance you'd use into the rule?
Maybe, depends on their workflow. In my human workflow, I tend to use commits as checkpoints and then squash before pushing. I'd usually only run time-consuming tests before squash+push.
But yes, anything you want to ensure really needs to be a hook.
edit: realizing with "precommit" you probably meant a git hook not one in their harness. I'd have written the same response more or less though. :)
Following rules like "verify all changes with tests" down to a tee is usually a desirable trait in LLMs. Personally I'd leave that behavior there (just like with humans for some tasks like aviation you have them go through checklists even if some stuff you can infer is not needed). But otherwise just make it "always run tests unless you're absolutely sure they can be skipped".
That's one of the reasons I started https://beolis.com. Now I have a workflow that says to do things in a TDD and run only new tests and related tests but NEVER run the full suite and then when it finishes the workflow runs all the tests -- if it works, great, if it doesn't then the workflow continues, feeds the output to a cheaper LLM to summary the errors and then get another run to actually fix it, based on the failures and the context based on what should be implemented.
-- note: I've been full time in Beolis for some months already, feedback welcome ;)
> prompts such as “Hey” or “commit” sometimes triggered 30+ tool calls
I read that this is because it wastes time looking through past conversations and other context to figure one what you might want it to do - a less ambiguous prompt would be better.
It's rather hard to do at the proxy level with agentic coding, such as Claude Code or similar. These are long-chained sessions of tool use that heavily rely on prompt caching. Changing mid-flight is costly.
It looks like much more context is required to decide on the best model (e.g., summarizing logs might use a cheap model, whereas you likely want Opus/Mythos/GPT 5.6 to debug multithreading logic). In an agentic system, a decision about the model may be embedded in the decision to orchestrate the model.
But intuitively I think it makes sense that a model can learn what model to route things to if it has all the relevant info, and experimentally it works pretty well in our experience
Sometimes creating less verbose variants yourself (a simple script, build.sh, with pointers to logs) can be a quick win.
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