Please wait while we find the best results for you.
Analyzing content and applying filters...
The technical foundation of Bigspin is the open-source DSPy project
Open sourcing Microcode! Microcode is a context-efficient, general purpose terminal agent fully powered by a packaged `dspy.RLM()` program. Set your...
Raveesh put together a skill that allows folks to try DSPy and GEPA and get a feel of how it works.
This is a mock summary for the article at https://x.com/Eito_Miyamura/status/2014757193766093069.
Taught by a Professor https://github.com/junji64/DSPy/blob/main/DSPy.ipynb
Alex L. Zhang about his work on RLM for long context in discussion with Yacine on his podcast.
In this episode, we sit down with Maxime, the creator of the essential Attachment Library and a prolific contributor to the DSPy community. We dive...
The main goal of this post is to demonstrate how to build an indexing flow in CocoIndex to persist a multimodal dataset (text + images) in LanceDB. A...
We present an experimental evaluation that assesses the robustness of four open source LLMs claiming function-calling capabilities against three...
In this video Prashanth Rao talks about his contribution to DSPy codebase and in the course of doing so gives us a walkthrough of BAML, it's...
Worth knowing why someone wouldn't like DSPy and see the conversation underneath.
In this conversation, Herumb Shandilya, a core maintainer of DSPy and developer of its Rust variant DSRS, discusses his journey with DSPy, the Rust...
Vicente Reig creator of DSPy.rb talking at ruby conference on DSPy.
We present Artemis, a no-code evolutionary optimization platform that jointly optimizes agent configurations through semantically-aware genetic...
DSPy’s built-in usage tracking gives you aggregate token counts after a program runs. That’s fine for simple pipelines. But when you’re debugging...
Quick Primer on GEPA# GEPA (Genetic-Pareto) is a reflective optimizer. It evolves prompts by having an LLM critique failures and propose...
New technical example: @cocoindex_io plau @DSPyOSS for structured extraction from intake forms. This demo shows how to build a fully-typed,...
Compounding Engineering is a philosophy where every task you complete makes the next one easier. This isn't just about reusing code—it's about...
In this work, we introduce agent symbolic learning, a systematic framework that enables language agents to optimize themselves on their own in a...
Comparing DSPy, GEPA-AI, and Synth AI on the LangProBe benchmark suite