181–190 of 561 posts
It is interesting that the updates are largely developer experience updates. It doesn't appear that significant innovations are happening on the core models outside of performance/cost improvements. Both devex and perf/cost are important to be sure, but incremental.
This is kind of the wrong place for this, but given the burst of attention from LLM-loving people: is there any open source chat scaffolding that actually provides a good UI for organizing chat streams and doing stuff with them? A trivial example is how the LHS of the ChatGPT UI only allows you a handful of characters to name your chat, and you can't even drag the pane to the right to make it bigger; so I have all th…
This may not be useful to you, but there are browser extensions that add a bunch of functionality to ChatGPT. The first that comes to mind: https://chrome.google.com/webstore/detail/superpower-chatgpt...
JSON mode is a great step in the right direction, but the holy grail is either JSON-schema support or (E)BNF grammar specification.
The function calling is JSON Schema support but extremely poorly marketed. I am planning on writing a blog post about it.
I suppose it could help to make simpler API calls and save some prompt tokens, but it would definitely need schema support to really be useful.
Earlier quoted context omitted.
Comparing a 7B parameter model to a 1.8T parameter model is kind of silly. Of course it's behind on accuracy, but it also takes 1% of the resources.
The person I replied to had decided to compare Mistral to what was launched, so I went along with their comparison and showed how I have been unsatisfied with it. But, these open models can certainly be fun to play with. Regardless, where did you find 1.8T for GPT-4 Turbo? The Turbo model is the one with the 128K context size, and the Turbo models tend to have a much lower parameter count from what people can tell. N…
Grab an 8K context model, tweak some internals and try to pass 32K context into it - it's still an 8K model and will go glitchy beyond 8K unless it's trained at higher context lengths.
Anthropic for example talk about the model's ability to spot words in the entire Great Gatsby novel loaded into context. It's a hint to how the model is trained.
Parameter counts are a unified metric, what seems to be important is embedding dimensionality to transfer information through the layers - and the layers themselves to both store and process the nuance of information.
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…
> - vectorDB startups -> don't need embeddings anymore they don't provide embedings, but storage and query engines for embeddings, so still very relevant > - file processing startups -> don't need to process files anymore curious what is that exactly?.. > - vertical ai agent startups -> GPT marketplace sure, those startups will be selling their agents on marketplace
My profit margins at https://olympia.chat just got 3x better <3
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Copyright Shield > OpenAI is committed to protecting our customers with built-in copyright safeguards in our systems. Today, we’re going one step further and introducing Copyright Shield—we will now step in and defend our customers, and pay the costs incurred, if you face legal claims around copyright infringement. This applies to generally available features of ChatGPT Enterprise and our developer platform. So essen…