synquid VS dspy

https://www.youtube.com/watch?v=HnOix9TFy1A

Links to more projects and papers by Prof. Polikarpova: https://cseweb.ucsd.edu/~npolikarpova/

I think this is one of the main ones she discusses in the talk: https://github.com/nadia-polikarpova/synquid

My first post was poorly written. I didn't mean to imply that taking pride in a job well done was bad. I don't think it is. I think programmers have plenty of reason to be happy when they do good work[1]--what I think is foolish is having visions of grandeur when it comes to programming, which in my opinion is what _why seemed to have based on his reasoning for quitting. I could be way off mark, but that's my take.

I mentioned this in another comment, but I think it also has to do with a confusion of categories. _why seemed to want recognition akin to that received by, e.g. Thomas Bernhard, Kafka, for something like shoes or his other software/libraries or general contributions to computing. But the issue is these things will always be utilities for specialists, and any aesthetic properties they have (elegant design, expression, etc.) are secondary to their functioning and they'll always be relegated to the dusty realm of specialists since the code is not the product--the software is. One can write code to create an aesthetic object that is enjoyed and revered by the masses, but I have a hard time envisioning a future in which the masses will ever enjoy and revere code or engineering for its own sake.

Pride was the wrong word to use and one I lazily reached for. After reading your comment, you've helped me realize that what I advise against is misapplication of expectations to different categories of things. _why seemed to want an aesthetic reception and legacy on a general, popular scale for work that is ultimately only a utility to the vast majority of the population and indeed, not even accessible to the population, and even if it were, I don't think many people would admire programming libs for fun--such a hobby will remain the lot of only enthusiasts. There is no pop coding like there is pop music.

[1]: Though I'd also argue that much of what you state taking pride in is not programming--which is just expressing ideas in programming languages--what you are talking about is engineering/design, which can be done perfectly well and separately from the programming part. we just happen to solve a lot of problems with computers these days so we need to express solutions for computers to consume and we tend to blend those responsibilities (we'll one day get to a point where the computers do most of the programming and we just design https://github.com/nadia-polikarpova/synquid)

Synquid synthesizes programs from refinement types, which are very similar in that you express a type-level predicate on the output using an expression which involves the input.

Legal practitioners often need to find specific cases and clauses across thousands of dense documents. While traditional keyword-based search techniques are useful, they fail to fully capture semantic content of queries and case files. Vector search engines and large language models provide an intriguing alternative. In this talk, I will show you how to build a legal search application using the DSPy framework and the Milvus vector search engine.

I’ve observed that Pydantic - which we’ve used for years in our API stack - has become very popular in LLM applications, for its type-adjacent features. It serves as a foundational technology for prompting libraries like [DSPy](https://github.com/stanfordnlp/dspy) which are abstracting “up the stack” of LLM apps. (some opinions there)

Operating AI apps reveals a big challenge, in that debugging probabilistic code paths requires more than the usual introspective abilities, and in an environment where function calls can have very real monetary impact we have to be able to see what’s happening in the runtime. See LangChain’s hosted solution (can’t recall the name) that allows an operator to see prompts and responses “on the wire”. (It just occurred to me that Langchain and Pydantic have a lot in common here, in approach.)

Having a coupling between Pydantic - which is *just about* the data layer itself - and an observability tool seems very interesting to me, and having this come from the folks who built it does not seem unreasonable. WRT open source and monetization, I would be lying if I said I wasn’t a little worried - given the recent few months - but I am choosing to see this in a positive light, given this team’s “believability weight” (to overuse Dalio) and history of delivering solid and really useful tooling.

DSPy is the best tool for optimizing prompts [0]: https://github.com/stanfordnlp/dspy

Think of it as a meta-prompt optimizer, it uses a LLM to optimize your prompts, to optimize your LLM.

Isn’t this just a very naive implementation of what DsPY does?

https://github.com/stanfordnlp/dspy

I don’t understand what is exceptional here.

Have you done any comparison with DSPy ? (https://github.com/stanfordnlp/dspy)

Feels very similiar to DSPy except you dont have optimizations yet. But I like your API and the programming model your are enforcing through this.

I'm interested in hearing if anyone has used DSPy (https://github.com/stanfordnlp/dspy) just for prompt optimization for GPT-3.5 or GPT-4. Was it worth the effort and much better than manual prompt iteration? Was the optimized prompt some weird incantation? Any other insights?

You should check out x.com/lateinteraction's DSPy — which is like an optimizer for prompts — https://github.com/stanfordnlp/dspy