step 1: build a computer use harness for system one
step 2: train a system one model to output text so it can search and fill in text fields
step 3: rl the model on cua trajectories
step 4: let it rip
We're announcing Mo-2.0 a frontier System One model with 8 output types for deciding and extracting.
- choice
- noul
- score
- box
- span
- text
- number
- datetime
The more I play around with GEPA the more I realize its not just a prompt optimization algorithm.
Its really the most efficient way to have an LLM explore a massive dataset and and make useful insights.
This visualizer alone shows just how cool that process is.
The more I play around with GEPA the more I realize its not just a prompt optimization algorithm.
Its really the most efficient way to have an LLM explore a massive dataset and and make useful insights.
This visualizer alone shows just how cool that process is.
We put together a guide for @DSPyOSS using @mintlify to help beginners quickly learn the patterns for using DSPy in production.
https://t.co/ZhpWStzHue
Here are some of the highlights that'll help you get past the DSPy learning curve faster 🧵
Next episode in the DSPy Series will be with two young entrepreneurs @FaroukAdeleke3 and @ty_todd1 building @modaicdev which makes distributing your DSPy code easy.
Subscribe https://t.co/rNmnowDqDW to get the updates
Inspired by the DSPy Codex, I built a @DSPyOSS scaffold for Claude Code on @modaicdev ! It shares full feature parity with Claude Agent SDK, including MCP servers, tool use, hooks, etc. but with a DSPy signature interface. Links below.
https://t.co/Vr38ZVMmuF
Modaic and Weaviate
1. Modaic and Weaviate: Load the `CrossEncoderRanker` program from the Modaic Hub, as well as `PromptToSignature` (https://t.co/wXDKtik9iJ)