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dspy discussion
dspy reviews and mentions
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Show HN: Instantly visualize any codebase as an interactive diagram
I have been attempting to do this and failed. Good implementation, but still fails in a lot of cases like my implementation. For example fails for this: https://github.com/stanfordnlp/dspy
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Universal Personal Assistant with LLMs
LLM answer quality directly relates to its given prompts, and therefore, effective prompt engineering is necessary. The landscape of prompt managing platforms and libraries increased manifold. Some tools now actively incorporate specific tweaks of the most recent commercial models, enabling the formulation of prompts that are injected with model-specific formulations. Example libraries are dspy, LMQL, Outlines, and Prompttools,
- Stanfordnlp: DSPy: The framework for programming–not prompting–language models
- DSPy – Programming–not prompting–LMs
- DSPy – open-source framework for programming not prompting LMs
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Swarm, a new agent framework by OpenAI
>People really don't understand how much better LLM swarms get with more agents. I never hit a point of diminishing returns on text quality
Could you elaborate please ?
One use for swarms is to use multiple agents/prompts in place of one single agent with one long prompt in order to increase performance by splitting one big task into many. It is very time consuming though, as it requires experimenting to determine how best to divide one task into subtasks, including writing code to parse and sanitize each task output and plug it back into the rest of the agent graph.
Dspy [1] seems to target this problem space but last time I checked it only focused on single prompt optimization (by selecting which few shots examples lead to the best prompt performance for instance), but even though I have seen papers on the subject, I have yet to find a framework that tackles the problem of agent graph optimization although research on this topic has been done [2][3][4]
[1]DSPy: The framework for programming—not prompting—foundation models: https://github.com/stanfordnlp/dspy
[2]TextGrad: Automatic 'Differentiation' via Text -- using large language models to backpropagate textual gradients: https://github.com/zou-group/textgrad
[3]What's the Magic Word? A Control Theory of LLM Prompting: https://arxiv.org/abs/2310.04444
[4]Language Agents as Optimizable Graphs: https://arxiv.org/abs/2402.16823
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Open Source Frameworks for Building Generative AI Applications
DSPy
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Impactful AI Research
At least some of it comes from "hype" too. The author of Dspy (the writer) (https://github.com/stanfordnlp/dspy) should know this, given that Dspy is nothing more than fancy prompts optimizing prompts to be fancier according to prompt chains described in papers (i.e. Chain of thought, Tree of thought, etc). Textgrad (https://github.com/zou-group/textgrad) is an even worse example of this, as it makes people think that it's not just a prompt optimizing another prompt
Dspy has 17k stars, meanwhile PyReft (https://github.com/stanfordnlp/pyreft) isn't even at 1200 yet and it has Christopher Manning (head of AI at stanford) working on it (see their paper: https://arxiv.org/abs/2404.03592). Sometimes what the world deems "impactful" in the short-medium term is wrong. Think long term.
- Notes on OpenAI's new o1 chain-of-thought models
- Show HN: AdalFlow: The library to build and auto-optimize any LLM task pipeline
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A note from our sponsor - CodeRabbit
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Stats
stanfordnlp/dspy is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of dspy is Python.