anshuman · pinion

Techniques 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

anshuman@athleticKoder

Techniques 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

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@bertwitt≡ 12+13

@OpenRouter≡ 10+2 To 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

▶@abouelatta_ali

@techwith_ram Top 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

@rohanpaul_ai Best Model Per Use-Case Presentations - Gemini 2.5 Full-stack apps - GPT-5 Codex, Sonnet 4.5 Docs - Gemini 2.5, GPT-5 thinking Videos - Sora 2 Images - Nano Banana Coding - Sonnet 4.5, Grok Code Fast Browser use - Sonnet 4.5 Doc Processing - Gemini Flash Enterprise Search - Sonnet 4.5 Data analysis (complex) - Opus 4.1 Agentic workflows - Sonnet 4.5, Haiku 4.5@bindureddy I'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

▶@askalphaxiv

@Dr_Singularity

▶@argofowl≡ 2

As a developer, Please Slap yourself if you are unable to explain even 10 from below terms : Indexing Clustering Denormalization Normalization Read replicas Leader–Follower replication Multi-leader replication Quorum Consensus CAP theorem BASE ACID Eventual consistency Strong consistency Snapshot isolation MVCC (Multi-Version Concurrency Control) Two-phase commit (2PC) Three-phase commit (3PC) Write-ahead logging (WAL) Checkpointing Compaction Rebalancing Resharding Data locality Hot partition Split-brain Failover High availability (HA) Horizontal scaling Vertical scaling Load balancing Connection pooling Caching Materialized views Secondary indexes Composite index Covering index Bloom filter LSM tree B-tree Query planner Cost-based optimizer Deadlock Lock escalation Optimistic locking Pessimistic locking Dirty read Phantom read Read skew Write skew Data skew Backpressure Circuit breaker Throttling Rate limiting CDC (Change Data Capture) Logical replication Physical replication Geo-replication Federation Data lake Data warehouse Columnar storage Row-based storage Time-series partitioning Hash partitioning Range partitioning Consistent hashing Data migration Schema evolution Schema registry Idempotency Exactly-once semantics@SumitM_X 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

▶@coreyhainesco

▶@hyperbrowser