A technical analysis maps emerging AI tools—Tessl, Goose, Archestra, Kestra, Modelplane, RAG, and MCP—onto distinct layers of an agent loop architecture, revealing that while tools will change, the un…
A new analysis of retrieval-augmented generation (RAG) systems argues that the optimal retrieval depth k* should be calibrated per pipeline stage and reliability axis, not guessed as a single hyperpar…
AI agents suffer from limited memory due to the stateless nature of large language models, but retrieval-augmented generation (RAG) offers a solution by offloading long-term memory to external storage…
An AI second brain knowledge base stores information so agents can retrieve it by meaning using semantic search, not keywords. Building one with Claude Code involves setting up automated hourly ingest…
ARMOR, a new method for retrieval-augmented generation in telecom question answering, prioritizes retriever-side tuning over generator adaptation, showing significant performance gains on benchmarks. …
Senior AI/ML interviews now focus on real-world production challenges rather than theoretical knowledge, with interviewers asking candidates how they would handle system failures at 2am, latency spike…
A developer returning to maintain protobuf.js discovered that large language models (LLMs) were recommending a competitor's library, protobuf-es by Buf, based on a README that contained selective and …
A developer explains four core concepts—tokens, embeddings, transformers, and Retrieval-Augmented Generation (RAG)—that software engineers need to understand to build scalable, reliable, and cost-effe…
Microsoft researchers introduced Memora, a scalable memory system for AI agents that decouples stored content from retrieval mechanisms, achieving state-of-the-art performance on long-horizon benchmar…
A new analysis argues that retrieval-augmented generation (RAG) pipelines frequently fail in production due to retrieval irrelevance and context poisoning, with enterprise implementations showing a 72…
A developer reflects on the pitfalls of trying to learn everything at once in software engineering, realizing that constant context-switching and overplanning led to mental exhaustion and unfinished p…
A developer outlines a production readiness checklist for AI systems after proof-of-concept, emphasizing that infrastructure layers—data pipelines, architecture, and monitoring—are more critical than …
A developer argues that Cache-Augmented Generation (CAG) offers a simpler alternative to Retrieval-Augmented Generation (RAG) for grounding large language models (LLMs) with external knowledge. CAG lo…
A researcher updates their AI's Wick-Ledger study, mapping the runtime framework to familiar AI concepts like LLMs, chain-of-thought, self-consistency, and RAG. The framework reorganizes these techniq…
A new interview guide for senior Go engineers focusing on AI platform engineering has been published on Leanpub. The book covers LLM gateways, RAG, vector search, Kubernetes, and production failure mo…
A developer building an AI memory system called MemStrata found that it comprehensively beats RAG on mutating code content, as detailed in a paper on arXiv. The developer emphasizes the importance of …
A developer argues that the Model Context Protocol (MCP) is more valuable for distributing context, rules, and operating contracts to AI clients than for remote procedure calls (RPC). By using MCP to …
A technical writer argues that diverse technical systems—from data engineering pipelines to RAG-based AI products—share a common underlying structure, using stages of refinement to transform raw input…
System design interviews are evolving to include AI-specific questions on topics like ChatGPT, RAG, LLM inference, and AI agents. A developer outlines key differences from traditional systems, such as…
A developer who spent years building complex RAG systems found that adding Model Context Protocol (MCP) support to their knowledge base project Papers made traditional RAG obsolete. By replacing 2,000…