It was interesting as a library at the very beginning to see how people were thinking about patterns but pretty useless in production.
It was interesting as a library at the very beginning to see how people were thinking about patterns but pretty useless in production.
But it quickly became obvious that LangChain would be better named LangSpaghetti.
That’s nothing against the authors. What are the chances the first attempt at solving a problem is successful? They should be commended for shipping quickly and raising money on top of it to keep iterating.
The mistake of LangChain is that they doubled down on the bad abstraction. They should have been iterating by exploring different approaches to solving the problem, not by adding even more complexity to their first attempt.
https://blog.langchain.dev/announcing-our-10m-seed-round-led...
Admittedly for anything more than 1-2 joins you are better off hand crafting the SQL. But that is the exception not the rule.
Refactoring DB changes becomes easier, you have a history of migrations for free, DDL generation for free.
In the early 2000 I worked where people handcrafted SQL for every little query for 100 tables and yeah you end up with inconsistent APIs and bugs that are eliminated by code generation / meta programming done by ORMs.
String disagree: if that’s true you likely don’t even need a proper RDBMS in the first place.
An ORM is not a replacement for knowing how SQL works, and it never will be.
Yes; exactly. There's value in a Schelling Point[0], and in a pattern language[1].
> requires literally none
True, yes. There isn't infinite value in these things, and "duplication is far cheaper than the wrong abstraction"[2], but they can't be avoided; they occupy local maxima.
0. https://en.wikipedia.org/wiki/Focal_point_(game_theory)
1. https://en.wikipedia.org/wiki/Pattern_language
2. https://sandimetz.com/blog/2016/1/20/the-wrong-abstraction
my guess is 40% of software engineers did a AI pivot the last 18 months, so there's a massive market for frameworks, and there's an inclination to go beyond REST requests, find something that just does it for you / can do all the cool patterns you'll find in research papers.
Incredible amount of bad info out there, whether its the 10th prompting framework that boils down to a while loop and just drives up token costs, the 400 LLM tokenizer library that can only do GPT-3.5/4.0, the Nth app that took XX ex-FAANG and $XX mil and a year to get another web app, or another iOS-only OpenAI client with background blur,m memory thats an array of strings injected into every call
It's at the point where I'm hoping for a cooling down even though I'm launching something*, and think it's hilarious people rant about it all just being hype and think people agree.
* TL;Dr consumer app with 'chain gui', just hand people an easy to use GUI like playground.openai.com / console.anthropic.com, instead of getting cute and being the Nth team to try to launch a full grade assistant on a monthly plan matching openai pricing, shoving 6000K+ prompts with each request and not showing them
The abstractions are handy if you have no idea what you are doing but it's not groundbreaking tech.