SumitM · pinion

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@SumitM_X

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

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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@athleticKoder Low-key websites I quietly rely on 1) http://roadmap.sh Gives you a brutally clear learning path for roles like frontend, backend, DevOps, etc No fluff, just “learn this → then this → then this”. 2) http://playcode.io An online playground to quickly test HTML, CSS, JS without setting up anything locally Perfect for quick experiments and debugging ideas 3) http://usehooks.com A collection of reusable React hooks with real use cases Saves time and helps you avoid rewriting the same logic again and again 4) http://devhints.io Concise cheat sheets for languages, frameworks, and tools. Ideal when you forget syntax and don’t want to read a 20-minute blog 5) http://jsoncrack.com Turns messy JSON into a clean visual tree Makes understanding large APIs and configs way easier than staring at raw text 6) http://realtimecolors.com Lets you generate and preview color palettes instantly Useful when you want decent UI colors without guessing or copying blindly 7) http://regex101.com Build, test, and debug regex step by step with explanations Honestly, the fastest way to stop hating regex 8) http://bundlephobia.com Shows how big an npm package really is before you install it Helps you avoid bloating your app with “tiny” libraries 9) http://caniuse.com Tells you which CSS/JS features actually work across browsers Essential before using shiny new features in production 10) http://toolbox.googleapps.com Google’s own diagnostics tools for DNS, email, headers, and network issues Surprisingly useful for debugging real-world problems 👉 Which one of these do you already use and which one did you not know existed?@shekhu04

@Dr_Singularity

@bertwitt≡ 12+13 Average UI isn’t our competition. It’s what we replace. https://ui.watermelon.sh/@vanshdevx 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

▶@DataChaz 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

Vercel Ship (so far) ▲ Vercel Services — Run full stack on Vercel — Microservices on Vercel — Private service comms ▲ eve — The framework for building agents — Durable agents with one folder — Sandboxing, approvals and evals ▲ WebSocket support — Persistent connections ▲ Vercel Functions — Dockerfile support — 5 GB packages, 30 min — Zero-config Node servers ▲ Vercel Agent in Dashboard — Autonomous investigations — Plan-based permissions — Fixes surfaced as PRs ▲ Vercel Container Registry — Container images on Vercel — OCI push, pull and tag ▲ Vercel Sandbox — Custom images — Persistent sandbox storage — 24 hour runtime + expiration ▲ AI SDK 7 + agent harnesses — Toolkit for building agents — Coding-agent harnesses ▲ Chat SDK — Multi-channel agents ▲ Access & security — Vercel Passport: OIDC access — Vercel Connect: external services — OIDC audiences + Audit Logs ▲ Enterprise apps and agents — Security posture dashboard — Vercel Functions in your cloud ▲ AI Gateway — Voice, speech and transcription — API key budgets ▲ Vercel Blob — Private blob storage — Signed URLs and OIDC auth ▲ Builds & deployments — 500 concurrent builds for Pro — Vercel Drop — Elastic builds ▲ Vercel CLI — Speed Insights + Web Analytics — Trace any request — Deployment limits removed ▲ Observability — Trace eve sessions — Trace Workflow runs ▲ Workflows — Inflight cancellation — Nitro v3 + TanStack Start ▲ SDK-free Vercel Flags — No SDK keys@ctatedev

▶@_avichawla 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 http://x.com/i/article/2032605528149352448@SHL0MS

▶@coreyhainesco

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

@OpenRouter≡ 10+2