I've built the same toy project (personalized bedtime story generator) with lang-chain and lang-graph + pydanticAI. I preferred the latter. With agents right now the priority is to try to wrestle predictability out of the models and working with pydantic backed dependencies and state is great
I am very impressed and I am getting great results for queries like "Show me how to use langchain in python".
However I am disappointed that when I provide a url it can not read the page. Given that this is a search engine I would expect it to be able to read any public URL I provide it. For example I attached a PDF of my resume and provided a link to a public job description and asked it to generate a cover letter tailored to my experience for this position. This is something I have done with easy success with ChatGPT GPT-4o, but Phind throws its hands up. :(
Thank you! Appreciate the feedback. Custom links are something we should be able to do -- we'll run some checks and make sure that feature is working properly. Thanks for letting me know.
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this happens after I've moved everything to height. downside is it was a lot of work. upside is height is amazing and I'm pleased with the choice. anyone else using it?
We are loving it and we aren't even using it fully to its ability. For example we do almost no communication in the 'chat' that exists for each issue (in place of comments) since we are a very small team and still are talking mostly in slack about the issues, but I predict as we grow this will become a useful feature for us.
In the meantime we are loving the 'every issue can have sub-issues' and have customized the fields to our liking.
This is a tool with a lot of power. I can see a well-intentioned PM going crazy with it, but for our needs I was startled with how great it is.
DMNO looks wonderful. I would love to use it for a monorepo which is using TypeScript/React for frontend services and Python for the backend. Native Python support please, `user_demand += 1`
How does that solve the problem that uv isn't a drop-in replacement? Are they going to implement the whole of pip, warts and all? Unlikely, because even though its in rust, they're getting a fair bit of speedup by making assumptions (see their ruff benchmarks, most of pylint isn't implemented), and as we've seen with both poetry and pipenv, those assumptions break down. pixi may get somewhat closer (given their experience with conda, and so familiarity with workflows and needs outside webdev), but I suspect uv will only further add issues and confusion to Python installs (especially if people decide to try aliasing and things break).
I jumped to conclusions in my previous comment, but that was based on a personal experience. Realistically, I've only ever needed 2 commands: `pip freeze > requirements.txt` and `pip install -r requirements.txt`. They don't need 100% API compatibility to deliver value, just "good enough". 80/20 rule and all that.
The issue is, Python has multiple projects that are already doing that, and they've not been adopted (and then everyone complains that Python packaging is a mess...). Maybe uv will be like the easy_install -> pip transition, but I doubt it (the requirements are much higher and more diverse).
Arguably they'd get significantly further choosing a specific and narrow use-cased and nailing it (e.g. deploying WSGI/ASGI apps via OCI containers, with the ability to have live reload during dev), rather than introducing another option whose main selling point is speed, which while nice, is hardly going to resolve why we have the different use-cases and hence solutions.