QUIC for dynamic content delivery: Cubic, BBRv1, and BBRv2 compared

Legal text is dense and context-heavy, perfect for testing vector search at scale. I built a semantic search system over 143,000 legal texts, compared multiple embedding APIs, and tuned USearch to deliver millisecond-level retrieval on CPU. No GPUs, no SaaS dependencies, just fast retrieval for real-world legal data. Full write-up: https://lnkd.in/gsTTsQ_4

Designing Production-Ready RAG Pipelines: Tackling Latency, Hallucinations, and Cost at Scale: ... data quality standards. The quality of data affects user experience through hallucinations which occur when poor data quality exists. System ...

A highly informative article that concisely describes the intricacies of how Tailscale succeeds to universally and holistically perform NAT traversal in common and rare network constellations by using various protocols, such as STUN, TURN or UPnP, an algorithm derived from the birthday paradox to increase connectivity success, its proprietary DERP relay technology as fallback, and much more. I can only recommend. https://lnkd.in/e-pb_qSb

REST vs gRPC: What actually changes on the wire? Choosing an API style isn’t about “what’s cooler”—it’s about fit for purpose. Our one-page visual breaks down how requests travel, payload formats, and when to reach for each. When REST wins - Human-readable JSON and browser-native tooling - Plays nicely with CDNs, proxies, and caching - Best for public/partner APIs and broad client support When gRPC shines - HTTP/2 multiplexing with compact Protobuf - Lower overhead, strong contracts, and built-in streaming - Ideal for microservices/internal RPC and low-latency paths Quick pick - Browser-first or heavy caching? → REST - Need streaming / tight SLAs / high throughput? → gRPC At Codemia, we teach the trade-offs you’ll defend in real reviews—latency budgets, payload shape, caching layers, and how to evolve contracts without breaking clients. 👉 Save the graphic for your next design doc. 💬 Which side do you use more today—and why? #SystemDesign #APIs #gRPC #REST #Microservices #BackendEngineering #Codemia

The Google Spanner paper is a 10/10 read. The most interesting bit? TrueTime, an API that gives time results with error bounds, and one that guarantees <= 7 ms of clock skew. Impressive! The downside? Not OSS. Even if it were, would require reproducing the timing hardware. Read it here: https://lnkd.in/g_w2XcKS

I just completed a very cool project combining semantic caching, intelligent agents, and production guardrails into a single system. It is a production-ready RAG system with semantic caching, LangGraph agents, and always-on guardrails for student loan assistance as part of my 16th assignment for AI Makerspace Cohort 8. Here is what I learned: ✅ Semantic caching (vector similarity) is 5-20x faster than exact string matching and achieves 70-99% cost savings on repeated queries ✅ Guardrails must be production-safe by default (always-on) with an explicit flag to disable for testing edge cases, not the other way around ✅ Multiple processing modes (direct/agent/evaluated) let users choose speed vs intelligence based on their specific query complexity and requirements Things I Want to Try Next: 🔍 Implement hybrid caching strategies combining semantic similarity with intent classification to improve cache hit rates across paraphrased queries 🔍 Explore streaming responses for long-running agent workflows to improve perceived latency and provide real-time feedback to users 🔍 Build custom domain-specific guardrails with layered validation (basic/strict/compliance modes) for financial aid regulation enforcement and audit trails Successfully tested all 3 processing modes, semantic cache hits (6x speedup), guardrails blocking (off-topic/PII), and multi-tool agent coordination. Repo: https://lnkd.in/eJVAw4_T Video: https://lnkd.in/eVY2-br4 #AI #MachineLearning #RAG #LangGraph #ProductionML #SemanticCaching #Guardrails #AIEngineering

Curious about the limits of n8n's performance? This article dives into rigorous testing to uncover how far you can push n8n before it falters. I found it interesting that understanding these limitations is crucial for running mission-critical workflows efficiently. What strategies do you implement to ensure your workflows remain resilient under pressure?

🚀 Our updated deep dive into WHIP & WHEP is here, and it’s more relevant than ever. https://hubs.la/Q03NMRMH0 We’ve revisited this post to highlight how these protocols simplify WebRTC signaling, reduce round‑trip chatter, and streamline connection setup. With WHIP/WHEP, a single HTTP request handles both SDP and ICE exchange — cutting latency and complexity. 🔍 Plus: we break down how Red5’s architecture supports these protocols in real‑world streaming and cluster setups. If you care about speed, scalability, and modern WebRTC architecture — this is worth a read.

Having LLMs to think more & longer to improve performance with more compute yet more latency, is an incentive for inference-time fine-tuning techniques. Here's part 1: Prompting techniques, aiming at scaling token budget while resucing token cost. Here's the article on medium, exploring them: https://lnkd.in/dV6QH_dA

💸 Making Agents Faster and Safer w/ Caching + Guardrails 🚂 This week, I focused on taking an AI agent from prototype to production-ready by tackling two of the biggest challenges in real-world AI systems: performance and safety. Using LangGraph, I built a pipeline that: ⚡ Implements caching to reuse embeddings and responses instead of recalculating them. 🛡️ Integrates the Guardrails API to validate every input and output — filtering out jailbreaks, PII, and off-topic requests. 🧠 Connects RAG, Tavily Search, and Arxiv tools to reason across documents and sources intelligently. 🎬 Demo: https://lnkd.in/etwE_tVJ 👩🏻💻 Github: https://lnkd.in/eu7yiETy 💡 One experiment I did: I ran a cache performance test on repeated document queries: ⏱️ First run: 577 ms ⚡ Cached run: 278 ms — nearly 2× faster, and with zero additional API calls. Then, I stress-tested safety by sending edge cases: ❌ Off-topic and jailbreak prompts were blocked. 🔒 Personal Identifying information inputs were automatically redacted. ✅ Legitimate questions passed through normally. 📈 Overall, caching is about efficiency and predictability at scale, while guardrails transform AI from “smart” to safe, compliant, and trustworthy. Keeping Agents safe w/AI Makerspace 👩🔬 #LangGraph #GuardrailsAI #Caching #RAG #AIEngineering #LLMOps #AIMakerspace