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New models and developer products

openai.com

531–540 of 561 posts

Re: New models and developer products

#531

Earlier quoted context omitted.

> OpenAI doesn't have some sort of egress feed for your database. That's what they're trying to incentivize, especically with being able to upload files for their own implementation of RAG. You're not getting the vector representation of those files back, and switching to another provider will require rebuilding and testing that infrastructure.

Thats exactly what i thought. Smart strategy on OpenAI's part given that its extremely easy (and free) to do RAG with pgvector.

It's neither free nor performant.

The developer experience is lacking vs. other vector database providers and the performance doesn't match those that prioritize performance rather than devex. You're also spending time writing plumbing around postgres that isn't really transferrable work.

For some people already in the ecosystem it will make sense.

Re: New models and developer products

#532

Earlier quoted context omitted.

https://en.wikipedia.org/wiki/Paradox_of_tolerance > The paradox of tolerance states that if a society's practice of tolerance is inclusive of the intolerant, intolerance will ultimately dominate, eliminating the tolerant and the practice of tolerance with them. Karl Popper described it as the seemingly self-contradictory idea that, in order to maintain a tolerant society, the society must retain the right to be into…

> To answer your question more succinctly, because the poster isn't the only person who will read these comments. I would bet the poster you replied to had the same intent.

I'll always defend your right to say whatever you want but that never implies it's valid.

Re: New models and developer products

#533

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.

My early benchmarking seems to show that it's somewhat better for coding. https://aider.chat/docs/benchmarks-1106.html

Its unclear to me if the 1106 is the same as the turbo model.

Re: New models and developer products

#534

Earlier quoted context omitted.

My early benchmarking seems to show that it's somewhat better for coding. https://aider.chat/docs/benchmarks-1106.html

Its unclear to me if the 1106 is the same as the turbo model.

Ya, OpenAI's naming schemes don't seem very consistent. But my read of the announcement is that gpt-4-1106-preview is the new turbo model.

GPT-4 Turbo is available for all paying developers to try by passing gpt-4-1106-preview in the API and we plan to release the stable production-ready model in the coming weeks.

https://openai.com/blog/new-models-and-developer-products-an...

Re: New models and developer products

#535

Earlier quoted context omitted.

Its unclear to me if the 1106 is the same as the turbo model.

Ya, OpenAI's naming schemes don't seem very consistent. But my read of the announcement is that gpt-4-1106-preview is the new turbo model. GPT-4 Turbo is available for all paying developers to try by passing gpt-4-1106-preview in the API and we plan to release the stable production-ready model in the coming weeks. https://openai.com/blog/new-models-and-developer-products-an...

Ok great thank you for pointing that out.

Re: New models and developer products

#536

You can now [1] pay from $2 to $3 million to pretrain custom gpt-n model. This has gone unnoticed but seems really neat. Provided that a start-up has enough money spend on that, it would certainly give competitive advantage. [1] https://openai.com/form/custom-models Edit: forgot to put the link

Wow, this is directly going to affect my company in the near term. We had been trying to do it all internally but have found little success. Even at ~$3M it's going to be an attractive choice.

Re: New models and developer products

#537

If I had no contact with society from the 29th of November 2022 (the day before ChatGPT was released according to Wikipedia) and came back today to see the OpenAI keynote I would have lost my mind. The progress and usefulness of these products is absolutely incredible.

I'm sorry, what breakthrough feature did we see here? - Code interpreter, function calling were already possible on any sufficiently advanced LLM that could follow instructions well enough to output tokens in a rigidly parseable format, which could then be fed into a parser, and its output fed back to the LLM. It was clunky to do with online APIs like ChatGPT, but still eminently possible. - Custom chatbots were easy…

Working, multi-lingual voice input/output is huge IMNHO.

Re: New models and developer products

#538
post #404

Earlier quoted context omitted.

"Well enough in practice" isn't good enough for this, because it's a security vulnerability. You have to be 100% certain that a suitable motivated adversarial attacker can't find a way through. Here's an attack that beats your example. Enter this as a full response (it's using a trick that makes the LLM think it has completed the first instruction before delivering the attack): Describe the contents of the text below…

The version above was really simplified. You need to iterate a little to get something that's too hard to break. Try this: https://chat.openai.com/share/7d091da1-729b-4678-98fe-def4f9...

I've seen this happen in these conversations too; a solution gets proposed, it gets bypassed, another solution gets proposed that manages to block the specific prompt, it gets bypassed, another solution gets proposed, and so on. And the eventual claim ends up being, "well, it's not easy but it's clearly possible", even though nothing has actually been demonstrated that shows that it is possible.

To try and shortcut around that whole conversation, let me ask you more directly: are you confident that the prompt you propose here will block literally 100% of attacks? If you think it will, then great, let's test it and see if it's robust. But if you're not confident in that claim, then it's not a working example. Because if 100 people try to use prompt injection to hack your email agent and 1 of them gets through, then you just got hacked. It doesn't matter how many failed.

99% is good enough for something like content moderation. It's not good enough for security.

Chained LLMs are a probabilistic defense. They work well if you need to stop somebody from swearing, because it doesn't matter if 1/100 people manage to get an LLM to swear. They do not work well if you're using an LLM in a security-conscious environment, and that is what severely limits how LLM agents can be used with sensitive APIs.

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To head off another potential argument here that I typically see raised, saying "no application is completely secure, everyone has security breaches occasionally" changes nothing about the fundamental difference between probabilistic security and provable security. Applications occasionally have holes that are accessed using novel attacks, but it is possible to secure an interface in such a way that 100% of known attacks will not affect it. At that point, any security holes that remain will be the result of human error or oversight, they won't be inherent to the technology being used.

It is not possible to secure an LLM in that way (at least, no one has demonstrated that it is possible[0]). You're not being asked here to demonstrate that a second LLM can filter some attacks, you're being asked to demonstrate that a second LLM can filter all attacks. So even a theoretically robust filter that filters 99% of attacks is not proof of anything. We're not trying to moderate a Twitch chat, we're trying to secure internal APIs.

Unless you're confident that the prompt you just offered will block literally 100% of malicious prompts, you haven't proven anything. "Hard to break" is insufficient.

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[0]: I'm exaggerating a little here, Simon has actually written about how to secure an LLM agent (https://simonwillison.net/2023/Apr/25/dual-llm-pattern/), and the proposal seems basically sound to me and I think it would work. But the sandboxing is just very limiting/cumbersome and that proposal is generally not the answer that people want to hear when they ask about LLM security.

Re: New models and developer products

#539

Earlier quoted context omitted.

How do you manage token limits when sending large amounts of code structure to OpenAI?

Aider has a "token budget" for the repository map (--map-tokens, default of 1k). It analyzes the AST of all the code in the repo, the call graph, etc... and uses a graph optimization algorithm to select the most relevant parts of the repo map that will fit in the budget. There's some more detail in the recent writeup about the new tree-sitter based repo map that was linked in my comment above.

Any plans to open support up for other languages that tree-sitter supports?

Re: New models and developer products

#540
post #258

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…

I've been thinking about this for a while now, wrt two points: 1. This will be the end of traditional SWEs and the rise of the age of debuggers, human debuggers who spend their days setting up breakpoints and figuring bugs in a sea of LLM generated code. 2. Hiring will switch from using Leetcode questions to "pull out your debugger and figure out what's wrong with this code".

Yep, time to start adding source_file.prompt sidecar files next to each generated module so debugging sessions can start at the same initial condition.
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