Yeah but then they not going to have a fancy blog about how they hit the AWS traffic limit to VPC resolver! Now days a tech blog like this is gonna be good tech PR for the company.
That's... quite the interpretation. Do you really think that Stripe's intention is to "encourage people with low skills to apply" by writing a blog post about monitoring DNS?
From a developer with extensive database ETL experience, is it still necceary to learn or use LangChain? Would it be just easier to load directly to vector db?
It was never necessary to use it. It's handy for exploratory work and studying thee patterns of how they glued stuff together behind the scenes, but once you know what you want to build it's another bloated abstraction that's just in the way.
I agree with this, but would argue it's not even useful for exploratory work. Most of it's function can be generated in a single prompt for your use case.
LangChain was the first big attempt at a cohesive LLM application framework. As a result, it's terrible. If someone is seriously suggesting using it, they aren't keeping up.
If you are using Python, check out the package I've been building, magentic https://github.com/jackmpcollins/magentic It supports structured outputs and streaming, and aims to avoid making unnecessary abstractions (but might require some more understanding of LLM patterns as a result).
Also recently released is pydantic-ai, which is also based around pydantic / structured outputs, though works at level of "agents". https://github.com/pydantic/pydantic-ai
Frankly, just Python. LLM generation is just a function call, fetching from a vector db is just a function call.
LLMs are hard to tame at scale, the focus is on tightly controlling the LLM inputs, making sure it has the information it needs to be accurate, and having detailed observability over outputs and costs. For that last part this new wave of AI observability tools can help (Helicone, Langsmith, W&B Weave...).
Frameworks like LangChain obscure the exact inputs and outputs and when the LLM is called. Fancy agentic patterns and one-size-fits-all RAG are expensive and their effectiveness in general is dubious. It's important to tightly engineer the prompt for every individual use-case and to think of it as a low-level input-output call, just like coding a good function, rather than a magical abstract intelligent being. In practice, I prefer to keep the control and simplicity of vanilla Python so I can focus on the actually difficult part of prompting the LLM well.
It has caught my attention, I keep hearing about it as a new industry standard, I keep meaning to try it.
The reason why I keep procrastinating it is that, again, experience has shown me that LLMs are not really at a point where you can afford to abstract away the prompting. At least in the work I have been doing (large-scale unstructured data extraction and analysis), direct control over the actual input string is quite critical to getting good results. I also need fine-grained control over costs.
The DSPy pitch of automagically optimizing a pipeline of prompts sounds costly and hard to troubleshoot and iteratively improve by hand when it inevitably doesn't work as well as you need it to out-of-the-box, which is a constant with AI.
But don't get me wrong, I know I sound quite skeptical, but I intend to keep giving all these advancements a serious try, I'm sure one will actually be a big upgrade eventually.
In my opinion, utterly useless and I put it in the same bucket as Langchain. Lots of grandiose claims but doesn’t actually solve any problems people have.
I think we are at a stage where people are so eager to build something around LLMs to become the next shovel-maker, that a lot of what is being built doesn’t actually serve anyone’s needs.
To be honest, I think a lot of people realize that what Langchain is doing is providing a small amount of value in the form of a huge amount of abstraction, which means it can be convenient for very simple off the shelf solutions, and a huge headache for anything else. Most people realize the value add if Langchain can be recreated with a few lines of code, and end up just building their own.
pgadmin is GOAT, i hope this tool lives upto expectation, i have some time today will probably add some more features like export setting and audio support to it.
Would really really be nice to have the
-user management -> registering -> device management
A hello world example for v3 that will be great. To understand how to actually register a new account on the matrix server with Registration Token enabled server.
