DSPy

707 members Est. Jun 18, 2025 Updated Apr 1, 2026
Picassio @ocbieuvang · Feb 4
Checkout my rlm-dspy cli project. It is using dspy-rlm for explore and working on local codebase. Included GEPA/SIMBA for RLM instruction optimizing. It also has buildin sementic search with embedding and tree-sitter.

https://t.co/vUNFQWjWM7
0
0
1
497
0
Tom Dörr @tom_doerr · Jan 22
Had to reread the post and documentation multiple times to figure out that they aren't doing DSPy-style agent optimization, they are just chatting with Claude and Codex https://t.co/n8gKVCbn0N
Tweet media
2
0
30
12.3K
13
Tom Dörr @tom_doerr · Jan 21
Recursive Language Model implementation in DSPy

https://t.co/WoeyaEcWqh https://t.co/7hm8BUmnol
Tweet media
1
0
11
3.5K
9
luis.poe @_luispoe · Jan 14
I’ve been living under a rock since Nov '25.
Given how fast things move, I assume everything has changed. What are the must-know dspy updates? Hit me! 🥊
0
0
0
475
0
Tom Dörr @tom_doerr · Jan 13
https://t.co/Jkd0Ll2SUt
Tweet media
0
0
0
177
1
Tom Dörr @tom_doerr · Jan 13
Brutal to call DSPy a “prompt-optimizer research artifact” https://t.co/eir0OkmtvJ
Tweet media
1
0
0
1.3K
1

Tom Dörr

@tom_doerr

Follow for posts about GitHub repos, DSPy, and agents Subscribe for top posts DM to share your AI project (Due to volume of DMs I'll prioritize subscribers)

161.0K Followers
4 Contributions

Picassio

@ocbieuvang
13 Followers
1 Contributions

luis.poe

@_luispoe
0 Followers
1 Contributions
707
Total Members
+ 0
24h Growth
1
7d Growth
Date Members Change
Apr 1, 2026 707 +0
Mar 31, 2026 707 +0
Mar 30, 2026 707 -1
Mar 29, 2026 708 +0
Mar 28, 2026 708 +0
Mar 27, 2026 708 +0
Mar 26, 2026 708 +0
Mar 25, 2026 708 +0
Mar 24, 2026 708 +0
Mar 23, 2026 708 +0
Mar 22, 2026 708 +0
Mar 21, 2026 708 +0
Mar 20, 2026 708 +0
Mar 19, 2026 708

No reviews yet

Be the first to share your experience!

Programming—not prompting—LMs

Community Rules

Information flow drives great AI

Information Flow is the single most key aspect of good AI software

Make LLM calls functional & structured

Interactions with LLMs should be Functional and Structured

Make inference strategies polymorphic modules

Inference Strategies should be Polymorphic Modules

Decouple behavior spec from learning paradigms

Specification of your AI software behavior should be decoupled from learning paradigms

Steer learning via natural‑language optimization

Natural Language Optimization is a powerful paradigm of learning