They are impressive but I often ask myself why so much effort is spend here. While people are curious about results, a bit of waiting for the counts shouldn't matter.
The most "sensible" application would be for betting markets I thought.
One modern need is to avoid people using the well known patterns of vote reporting to inflame conspiracies about "mysterious" and "sudden" reversals in vote tallies.
Trust me, telling people the vote will look weird ahead of time does not quiet the vote consipuracistst. Heck I'd say it gives ammo. They call it predictive programming. The lie is revealed and normalized.
True but for example in California we have it extra bad where it takes weeks for votes to get counted.
Even the most idealistic person with respect to how people should process election results must admit that it taking this long either:
a) Undermines trust or
b) Creates a more fertile ground to undermine trust
It is strange now, but Apple made a big deal of marketing it as UNIX back then. Like at the front-page of apple.com. UNIX had a good reputation as solid and serious.
Looking at the flatpak manifest it has `share=ipc` but no directories shared.. I'm not sure on flatpaks defaults for what it would actually have access to on the host.
Is this widely known? Presumably the jackpots are sufficiently geographically distributed so that a player who observes one wouldn't be incentivised to pause playing?
These are probably the kind of machines you sometimes see in gas stations and bars, there's only a few of them in any location at most so there's never too many players nearby to be dissuaded from playing. Plus, a jackpot isn't the only way to win money.
I don't think it is widely known how much casino gaming machines are allowed to control outcome. Indeed, in some cases they are required by law to control outcome.
toast0's answer below is the correct one. Plus in Australia, gaming/gambling is heavily regulated - my state government didn't even allow poker machines until 1991.
So we never started with a network of disconnected poker machines, the whole thing was built from the ground up with a centralised jackpot system. I don't know what hardware the original jackpot platform ran on but I know it used hardware RNG units. By the time I started there ~2003 it had been migrated to Stratus.
A capability isn't binary. There is a massive difference between can produce an impressive demo and can reliably complete the task without a human babysitting it.
Yup, new SOTA models especially with high/xhigh/max reasoning too often overengineer solutions, good for benchmarks that usually measure task completion, bad for normal development where you don't want 'rewrite in rust and 1k LOC unit tests' style solutions when agent does mundane bug fixes.
When it comes to mundane bug fixes the value is in actually finding the cause of the bug, and I find SOTA models way outperform smaller ones here. I don't care about their output - I can write the correct 5 line patch myself once I understand what's wrong.
It's not this week's change. Fable was the step change for programming. And most of truly useful and powerful capabilities arrived in the last eight months.
AFAIK: Mistral does not even try to compete in this field. There are other use cases for LLMs beside coding. As Mistral AI wrote:
> During the first wave of generative AI, the central question was who could build the most powerful model. Organizations and governments are now asking a different one: how to harness the power of AI for their mission-critical needs without surrendering control over the infrastructure and intelligence loop. Demand for that combination of performance with control, choice and independence is growing internationally, as enterprises and governments weigh the long-term technology dependencies, data governance requirements and deployment choices that come with any AI investment.
> Mistral is the only AI company in the world building the full stack required to answer that question: open-weight models, the infrastructure and the compute capacity they run on, and the products that bring them into production; ensuring that customers are never locked into a single vendor's roadmap, pricing or availability.
> Mistral’s full-stack and open approach also allows organizations to build on it without exposing their most valuable data, workflows and institutional knowledge to anyone outside their own walls. That's what makes Mistral’s stack the sovereign AI layer, meaning retaining control across four dimensions: data that stays inside the organization's boundaries, models that are controllable and customizable, compute that is private and predictable, and systems in production that are fully controllable and auditable.
People said this for Opus 4.6 too. Every release the models get RLHF'ed into accomplishing a new task and the people who need to do this task think there was a step change.
Yeah that's kinda my point. I'm not sure if the models have gotten that much smarter, but they're certainly getting more capable. That's not the same thing though.
There are things GPT 6.0 can accomplish for me that 5.3 was not able to. But there are also things it still fails at, and it doesn't seem to be much better at the big picture. It does spam about 100x more tests though and I wonder if just RLHFing it to test everything constantly is carrying it more. 6.0 writes so many tests and spends so much time verifying it's work in python sandboxes. Slow as hell but it tends to get things right the first time more which is good, I guess. I don't love the thought of a 500loc feature adding +4000loc due to tests though.
Yep same with latest Claude models - code isn't really any better than Opus 4.5/4.6, but use 5x as many tokens doing random stuff that's mostly unnecessary.
And yeah still for some reason they often can't understand how to set up any project locally without handholding, which is something you'd think an LLM would actually be good at
People fawn over AI brands now like cars and it's silly. OpenAI and Anthropic have been flipping spots for best LLM coder for the last two years and to say one is better feels silly; I've been using them both and they're very similar with different personalities. Recently Grok has become competitive in many aspects, and while I don't have much experience with Gemini it seems to come and go in terms of coding quality.
Saying only anthropic models are competitive frontier coding models is out of touch with the space imo
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