Since I discovered and started reading Lesswrong a couple years ago, I keep seeing dismissive or sneering comments across the (technical) internet, usually with very little substance - like this one (what does it even mean? I find the article rather reasonable and on point). I know that yudkowski's more recent stance is controversial (I haven't started reading his work on how AGI will kill us all so I'm suspending judgement ), but is there a valid reason for the widespread smugness or is it just a bandwagon effect?
How do the economics of your statement work out? Clearly inference providers don't have a time to ROI of 10 years on their hardware costs; and that's without even taking ongoing energy costs into account. What's missing here?
I very begrudgingly started paying for grok for this exact reason. They nailed the voice UI and it works incredibly well with android auto unlike Claude&Gemini (which don't work with android auto at all) and chatgpt (which works well but has hardcored system instructions that make it's voice mode feel like a dopamine deprived Gen Z)
Reddit absolutely has algorithmic feeds since it ipo'ed (maybe earlier but I used third party apps so I wasn't subjected to them). 90% of my home page is bullshit I didn't ask for.
What does "ai in production" even mean?
Writing production code ? Depending on how it gets reviewed and the qa mechanisms in place, could be stupid or not.
Read only access to production systems and data? Again it depends on safeguards, but probably not stupid, it can be very useful for debugging.
Unsupervised write access to production data or infrastructure? Incredibly stupid, but I dont think anyone serious does this.
Aside from display quality I wonder if humans are more perceptive to changes in specific colors? Towards the end of the game some examples felt impossible while others were trivial .
Thanks! You mean to be able to search for movies that occurred in a specific set of locations? That's a really cool idea, I hadn't thought about it. The data is there already; it's just not searchable in that way. I might give it a go next week. I need to think of a nice UX for it.
Thanks a lot for the usability feedback! I'm trying to improve it based on this and a few other early comments.
Regarding your question, one limitation I'm aware of is that my data consists mainly of movies _made as historical movies_, not movies that just happen to be set in the contemporary period of when the movie came out. Unfortunately there doesn't seem to be an easy way to identify this latter type of movies based only on wikipedia metadata/categories; so the only way I can think of would be to process _all_ movies on wikipedia and infer whether they are in a contemporary setting... but that's prohibitively expensive. I'll think about it more.
Thanks a lot for the feedback, this is what I was hoping to get. After working on something for too long the UX becomes so obvious that it's hard to imagine it from the perspective of someone seeing it for the first time. I'll work on the onboarding. And try to find some history subreddits where I can share this. Thanks for the encouragement!
I do it sometimes (even just through the openai playground on platform.openai.com) because the experience is incredible, but it's expensive. One hour of chatting costs around 20-30$.
What's funny/interesting from a psychological perspective is that several of these made me click (and discover genuinely interesting content) on links that I ignored in the real version. Could you do this everyday please?
I wonder why the focus on replaying UI interactions, rather than just skipping one step ahead to the underlying network/API calls? I've been playing around with similar ideas a lot recently, and I indeed started out in a similar approach as what is described in the article - but then I realized that you can get much more robust (and faster-executing) automation scripts by having the agents figure out the exact network calls to replay, rather than clicking around in a headless browser.
The data might be the limiting factor of current transformer architectures, but there's no reason to believe it's a general limiting factor of any language model (e.g. humans brains are "trained" on orders of magnitude less data and still generally perform better than any model available today)
As the other commenter said, prompting is everything, and most LLMs are sycophants and will try to do anything you tell them without pausing to tell you "why the hell are you trying to query an API with SQL? That's not what SQL is for". While it's possible to build stuff with llms with little to no technical knowledge, it's still very hit and miss.
With that said, the space is moving incredibly fast and the latest Claude/GPT-o1 are far ahead of anything that was available 3-6 months ago. Unfortunately Claude doesn't allow sharing publicly like ChatGPT, but here is a gist of Claude's answer for +- the same question your friend asked:
I'm on mobile so it wasn't handy to quickly paste an example API request/documentation for the LLM to follow; so there's a chance it might have hallucinated some if the API parameters - but if I included that; in my experience the code would work on first shot 90% of the time.
Regarding your second query, I'm too unfamiliar with clojure and the two solutions you mentioned to really understand what you were trying to achieve, but if you explain just a little bit more, I'm happy to record a screencast of me figuring it out with llms/genai tools from the ground up. What do you mean with "a container solution that allows for live coding"?