Phoenix𝕏@Xaraphim
open on x ↗these clankers are getting much better than I thought You can just link your models to OpenFoam lol.
related
@OpenRouter≡ 10+2
▶@meodai
▶@TheAhmadOsmanTop 5 local LLMs:
1. GLM-4.5-air: best agentic/coding model that runs on consumer hardware at very decent speeds. Rivals Claude 4-sonnet.
2. Nousresearch/hermes-70B: the only model that will do whatever you ask, and tell you whatever you want to know. Literally critical to have.
3. GPT-OSS-120B: very intelligent it’s like having 4o at home, great context window, great agent
4. Qwen3-coder-30B-3A-instruct: very good coding agent, excellent workhorse, incredibly fast
5. Mistral-magistral-small: very fast, excellent agent, great coder, multimodal, punches way, way, above its size.
I would be okay never using a proprietary llm, although given the subsidised compute I will continue to use them since I’m getting free leverage.@0xSero
▶@bestdesignsonx
▶@arlanoska≡ 2
▶@samhogan@wesbos definitely @mobbin https://mobbin.com/
also UI library showcases like @chakra_ui is an underrated place to look!@jeffzxh
@bbssppllvv
@DhravyaShah@uAghazadae @Zai_org should work perfectly with http://eve.dev / http://ai-sdk.dev@rauchgbig thanks to all the supporters
of my wiggle bones library
https://wiggle.three.tools/
it is being put to great use@KMkota0
▶@StarfishStudios
▶@ultramock
▶@AkshitVrma
@flowisgreat_
@JohnPhamousI took the @karpathy autoresearch loop and pointed it at markets.
25 AI agents debate macro, rates, commodities, sectors, and single stocks daily. Every recommendation scored against real outcomes. Worst agent by rolling Sharpe gets its prompt rewritten by the system. Keep or revert. Same loop, prompts are the weights, Sharpe is the loss function.
Trained the agents on 18 months of market data. 378 iterations. 54 prompt modifications, 16 survived.
The system learned which agents to trust using Darwinian weights — geopolitical, commodities, and the @BillAckman quality compounder rose to the top.
The agents even figured out their own portfolio manager was the weakest link before we did!
Deployed the trained agents. +22% in 173 days. Best pick: AVGO at $152, held for +128%.
The final prompts are evolutionary products — shaped by market feedback, not human intuition. Now running live with my own capital.
https://github.com/chrisworsey55/atlas-gic
Part hedge fund, part research experiment :)@Chris_Worsey
▶@stephenhaney≡ 3
@Dr_Singularity
▶@StarfishStudios▶@ultramock▶@AkshitVrma@flowisgreat_@JohnPhamousI took the @karpathy autoresearch loop and pointed it at markets.
25 AI agents debate macro, rates, commodities, sectors, and single stocks daily. Every recommendation scored against real outcomes. Worst agent by rolling Sharpe gets its prompt rewritten by the system. Keep or revert. Same loop, prompts are the weights, Sharpe is the loss function.
Trained the agents on 18 months of market data. 378 iterations. 54 prompt modifications, 16 survived.
The system learned which agents to trust using Darwinian weights — geopolitical, commodities, and the @BillAckman quality compounder rose to the top.
The agents even figured out their own portfolio manager was the weakest link before we did!
Deployed the trained agents. +22% in 173 days. Best pick: AVGO at $152, held for +128%.
The final prompts are evolutionary products — shaped by market feedback, not human intuition. Now running live with my own capital.
https://github.com/chrisworsey55/atlas-gic
Part hedge fund, part research experiment :)@Chris_Worsey▶@stephenhaney≡ 3@Dr_Singularity
@dhruvtwt_
▶@emilkowalski≡ 2
▶@argofowl≡ 2
▶@thdxr
@matsugfx@derekmeegan i make all of these on http://shadertoy.com!@instance_11
▶@pheralb_
▶@flornkm@DaxterOttselDev everything you need can be found in this video!!
i went outside the box a little and figured out how to integrate it directly with my website so the stats stay updated :D
https://www.youtube.com/watch?v=gYv7D83u7yQ@sylviiuIntroducing Vibe SDK@rauchg