> Custom OAuth implementation with user registration/login
Please don't. At 2 minute glance you are allowing empty state (csrf) and bearer tokens in query string[0], not checking if token is expired or not[1], storing secrets in plain text / not salting[2], missing PKCE Validation, debug mode always on, redirect URL only checking if includes (127.0.0.1.evil.com works)[3] so much...
Please, please, please don't recommend this for any production usage.
I've been about it as "throwaway software." Why bother searching for someone else's mediocre LLM generated software when I can just as easily (and hopefully as cheaply) generate the same thing, but it just works for me
Kindle has been doing this for years and has really made me a loyal customer to them. Always surprised the penny pinchers at Amazon haven't killed it yet.
The demos I see for these types of tools are always some toy project and doesn't reflect day to day work I do at all. Do you have any example PRs on larger more complex projects that have been written with codebuff and how much of that was human interactive?
The real problem I want someone to solve is helping me with the real niche/challenging portion of a PR, ex: new tiptap extension that can do notebook code eval, migrate legacy auth service off auth0, record and replay API GET requests and replay a % of them as unit tests, etc.
So many of these tools get stuck trying to help me "start" rather than help me "finish" or unblock the current problem I'm at.
My favorite example is the asana loader[0] for llama-index. It's literally just the most basic wrapper around the Asana SDK to concatenate some strings.
gpt3.5 turbo is (mostly likely) Curie which is (most likely) 6.7b params. So, yeah, makes perfect sense that it can't compete with a 70b model on cost.
Curious to know what value you've seen out of these clusters. In my experience k means clustering was very lackluster. Having to define the number of clusters was a big pain point too.
You almost certainly want a graph like structure (overlapping communities rather than clusters).
But unsupervised clustering was almost entirely ineffective for every use case I had :/
Definitely a difficult problem you're taking on here, but I don't see anything specific to LLMs here? How or why are you marketing towards LLMs?
How do you compare to the larger players here already Nango[0] and Merge[1] ?
I'm curious how you're thinking about data access / staleness? It's great that you're handling the oauth dance, but does that mean every end user of the product has to auth every product they interface with or are you handling this all at the super admin / enterprise level?
Right now I think there's too much emphasis on the "data loading" aspect of LLMs. I expect to see a swing back into using 3rd party API's SDKs. Interested to hear your thoughts on the Google API, it's absolutely massive and trying to shoehorn that into a unified API scares me.
The only real player that I could see to launch something like this and be successful is Okta.
I had interviewed with Uber's ATG years ago and their pitch, even then, was that they were building a platform for whoever won the autonomous game to be available on uber, not just their own cars.
Interesting they kept that strategy even after spinning out that group. Curious if they managed to keep anyone from that team to help with this product.
I played with building out a graphql mesh [0] of a few different APIs as I was curious to see if I could build one schema (and subsets of it) and have GPT interface over that. Turns out, it did a pretty good job if you can provide it the right portions of the schema it needs.
It also helped out when I was struggling to reconcile with how large of JSON payloads I was getting. The REST endpoints are just killing the prompt size, but having the model choose the fields it needed from GraphQL really helped out there.
Put it down for a while until I can get access to the plugin fine-tuned version of chatgpt and see if there's still a need or if it is additive still.