Also wrestling with this challenge at the moment and curious to hear experiences from others. Even though it requires human input, the capture and the way it's updated has to get automated.
Spend time building a test harness and evaluations of whether the solution meets the requirements. Then you don't need to look at the code because those other pieces will bring the necessary guarantees and trust.
Because we all prefer it over Gemini and Codex. Anthropic knows that and needs to get as much out of it as possible while they can. Not saying the others will catch up soon. But at some point other models will be as capable as Opus and Sonnet are now, and then it's easier to let price guide the choice of provider.
Still do. Great for workloads where it's okay to bundle a bunch of requests and wait some hours (up to 24h, usually done faster) for all of them to complete.
You’re ignoring Jevons paradox. Everyone, both people and companies, will be making exponentially more software with these tools. Software that both needs to get created, debugged and updated to realize the intention of it. That’s what our time will be spent on as programmers.
Apple has a solid hardware business and massive profits from their App Store tax, they are not dependent on ad business in the way Google is. Very different incentives.
Sounds neat but what kind of range limits would that impose on each trip? Switching from one means of transportation to another, even if both are buses, increases the total travel time significantly. Not to mention all the hassle involved for passengers.
Hallucinations and output quality are two different problems. Hallucinations are usually expressed in perfect sounding sentences by the LLM, that's what makes it so convincing to end users.
I agree. And I also know how much of that experience comes from having a legal dept. that are collaborative and supportive of what the tech org wants to do. Which I suspect is quite rare.
> I keep hearing that LLMs are trained on "Internet crap" but is it true?
Karpathy repeated this in a recent interview [0], that if you'd look at random samples in the pretraining set you'd mostly see a lot of garbage text. And that it's very surprising it works at all.
The labs have focused a lot more on finetuning (posttraining) and RL lately, and from my understanding that's where all the desirable properties of an LLM are trained into it. Pretraining just teaches the LLM the semantic relations it needs as the foundation for finetuning to work.
Nice unlock to hyperinflate their way to $100B. I'd buy an AGI spoon but preferably before hyperinflation hits. I'd expect forks to outcompete the spoons though.
How is that different from Google Chrome? Not debating that it's scary to have all that context collected in one place, just saying it's already been status quo on the web for years.
In the tech industry, open source has clearly won. You're right that most end users don't particularly care. The engineers building solutions definitely care, and prefer to build on top of open source dependencies.
Man, this hits hard. I've done so much to protect my part of the org chart from the whims of others and the cost-cutting pressure of the organization at large. My team are happy. Personally, I'm burned out to the level that nothing excites me any more and it's really hard to muster the energy to even do what's needed at the job, let alone drive vision and the team forward.
Cutting Google accounts out of your life, however, is an entirely different undertaking that would take much longer and have a big impact on how you use the web.
Data provinence would be neat and a big benefit. But any solution that requires virtually all content publishers to change approach (here: add signing steps to their publishing workflow) is doomed to fail. There is no alternative way to do this than what OP is doing, which is to try to filter the fire hose of content into real vs not.
In the Nordics almost everything that gets passed as law has been thorough studies of impact and consequence first. Takes a long time but means the law has a chance of actually having the intended effect.
> The only thing keeping Anthropic in business is geopolitics. If China were allowed full access to GPUs, they would probably die.
Disagree. Anthropic have a unique approach to how they post-train their models and tune it to be the way they want it. No other lab has managed to reproduce the style and personality of Claude yet, which is currently a key reason why coders prefer it. And since post-training data is secret, it'll take other providers a lot of focused effort to get close to that.
Framework desktop with AMD Strix Halo [1] are getting there as a viable alternative. Offering up to 96 GB of unified RAM at the moment, so still a gap up to the beefiest Mac Studio alternatives though.