๐ฟ๐ฎ๐บ๐ฎ๐ธ๐ฟ๐๐๐ต๐ป๐ฎโ ๐ฒ/๐ฎ๐ฐ๐ฐ@techwith_ram
open on x โMIT just released a 700-page book that actually teaches machines how to think. Pdf: algorithmsbook.com/files/dm.pdf
related
@techNmakTechniques 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
@Sumanth_077
@GithubProjects
โถ@_avichawla
โถ@abouelatta_aliI've tried all ( 74 ๐ตโ๐ซ ) 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
โถ@coreyhainesco
@LiorOnAI
@Dr_Singularity
@rohanpaul_ai
@tom_doerrI 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 โ 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 โ 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
@emilkowalski
@chrstnerodeโก 11+11
@dr_cintas
โถ@coderabbitai
@radshaan+1
@bertwittโก 12+13Introducing Vibe SDK@rauchg
โถ@prathamgrv
@every
@dhruvtwt_
@bbssppllvv
@PrajwalTomar_
@tom_doerrTo keep analyzing pdfs, some tools are qpdf, pdfalyzer, strings.
If you just want to get text out of PDFs, check out marker - https://github.com/datalab-to/marker .
I'm also interested in any weird edge cases anyone else has found - let me know!@VikParuchuri
@OpenRouterโก 10+2
โถ@iannuttall
@flowisgreat_