Introducing GLM-4.6: Advanced Agentic, Reasoning and Coding Capabilities As our new flagship model, GLM-4.6 brings significant advancements across real-world coding, long-context processing (up to 200K tokens), reasoning, search, writing, and agentic applications. API: docs.z.ai/guides/llm/glm-4.6 Subscription: z.ai/subscribe Weights: huggingface.co/zai-org/GLM-4.6 Try it now: chat.z.ai Tech Blog: z.ai/blog/glm-4.6
@Dr_Singularity
@interjc
@haydenbleasel
@every
@rohanpaul_ai
▶@samhogan
▶@aidenybai
▶@askalphaxiv
▶@CopilotKit
@amilabs
▶@prathamgrv
@dhruvtwt_I 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
▶@TheAhmadOsman
@techNmak
@realmcore_After a couple hours of work, I finally finished developing my first ever skill. :D
Claude’s frontend skill tells the AI to "pick an extreme aesthetic" and "be creative."
The problem tho is LLMs are just based on probability. Without strict rules, they statistically default to the most likely patterns, that's where AI slop comes from.
To get clean and production-grade UI, you need to override these biases with some engineering constraints.
I open-sourced Taste-Skill to fix this. :)
Check it out! https://github.com/Leonxlnx/taste-skill
(still early lots of improvements are on the way)@LexnLin
@LiorOnAI
▶@coreyhainesco
@tom_doerr
@flowisgreat_Introducing Vibe SDK@rauchg
@OpenRouter≡ 10+2@uAghazadae @Zai_org should work perfectly with http://eve.dev / http://ai-sdk.dev@rauchgI've tried all ( 74 😵💫 ) AI Coding Agents & IDEs
[Rork, CodeRabbit, Anima, Zed, Factory, Cursor, Windsurf, Copilot, Lovable, Bolt, v0, Replit, MarsX, Canva, Devin, Github Spark, Vercel, Lindy, Warp, Figma, Cline, Vibe Coder & more]
The most complete list ever made (with demos & notes):@johnrushx
▶@ctatedevTechniques I'd master if building RAG systems that actually work:
Bookmark this.
1. Sliding Window Chunking
2. Semantic Chunking
3. Document Hierarchies
4. Metadata Enrichment
5. Query Expansion
6. Hybrid Search
7. Reranking Models
8. Context Window Packing
9. Lost in the Middle Problem
10. Hypothetical Document Embeddings (HyDE)
11. Multi-Query Retrieval
12. Contextual Compression
13. Sentence Window Retrieval
14. Auto-Merging Retrieval
15. Cross-Encoder Rescoring
16. Temporal Context Decay
17. Negative Sampling
18. MMR (Maximal Marginal Relevance)
19. Graph-Based Retrieval
20. Recursive Retrieval
21. Citation Trackingchunks
22. Context Ablation Testing
23. Adaptive Retrieval@athleticKoder
▶@_avichawla