321–330 of 561 posts
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Well it won’t because they’ll use the model you paid for and take your customers.
Is it the same as avoiding AWS because they will take your software and run it themselves to steal your clients?
The TTS seems really nice, though still relatively expensive, and probably limited to English (?). I can’t wait until that level of TTS will become available basically for free, and/or self-hosted, with multi-language support, and ubiquitous on mobile and desktop.
Every day this video ages more and more poorly [1]. categories of startups that will be affected by these launches: - vectorDB startups -> don't need embeddings anymore - file processing startups -> don't need to process files anymore - fine tuning startups -> can fine tune directly from the platform now, with GPT4 fine tuning coming - cost reduction startups -> they literally lowered prices and increased rate limits…
HN is quite notorious for that Dropbox comment I suspect that video is going to end up more notorious, it's even funnier given it's the VCs themselves
Those were valid concerns at the time and the market for non technical file storage like they were building was non existant.
Perfectly rational to be skeptical and Drew answered all his questions with well thought out responses.
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For GPT-4 Turbo, not GPT-4.
Yes, nowhere in the text today was there any assertion that Turbo produces (eg) source code at the same level of coherence and consistently high quality as GPT4.
> This will be a very limited (and expensive) program to start—interested orgs can apply here.
Something about the "(and expensive)" part was refreshing. Probably there to cut down on applications from those who can't afford it, but still.
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It's a good strategy. For me, avoiding the moat means either a big drop in quality and just ending up in somebody elses moat, or a big drop in quality and a lot more money spent. I've looked into it and maybe the most practical end-to-end system for owning my own LLM is to run a couple of 3090s on a consumer motherboard at substantial running cost to keep them up 24/7 and that's not powerful enough to cut it and rath…
Everybody's got their own calculus about how competitive their space is and what this tech can do for them, but some might be best off dancing around lock-in by being careful about what they use from OpenAI and how tightly they integrate with it. This is very early in the maturity cycle for this tech. The options that will be available for private inference and fine tuning, for cloud-gpu/timeshare inference and fine…
Once marketing gets in charge of product, it's doomed. And I can't think of a product startup that it hasn't happened to. Particularly with this type of growth, at some point, the suits start to out number the techies 10:1.
This is why openeness and healthy competition is primordial.
In the keynote @sama claimed GPT-4-turbo was superior to the older GPT-4. Have any benchmarks or other examples been shown? I am curious to see how much better it is, if it all. I remember when 3.5 got its turbo version there was some controversy on whether it was really better or not.
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I don't understand the lock-in argument here. Yes, if a competitor comes in there will be switching cost as everything is re-learned. However, from a code perspective, it is a function of the key and a relatively small API. New regulations outstanding, what is stoping someone from moving from OpenAI to Anthropic (for example) other than the cost of learning how to effectively utilize Anthropic for your use case? Open…
> However, from a code perspective, it is a function of the key and a relatively small API. You're thinking of traditional apps and APIs. In an AI application, most of the work is in prompt engineering, not wiring up the API to your app. Prompts that work well for one model will fail horribly for another. People spend months refining their prompts before they're safe to share with users, and switching platforms will…
In people's experience with these sorts of tools, have they assisted with maintainance of codebases? This might be directly, or indirectly via more readable, bette organized code. The reason I ask is that these tools seem to excel in helping to write new code. In my experience I think there is an upper limit to the amount of code a single developer can maintain. Eventually you can't keep everything in your head, so m…
https://github.com/paul-gauthier/aider
It helps gpt understand larger code bases by building a "repository map" based on analyzing the abstract syntax tree of all the code in the repo. This is all built using tree-sitter, the same tooling which powers code search and navigation on GitHub and in many popular IDEs.