In the AI era, defining problems is often more important than solving them; here we explore AI's contradictions, boundaries, and possibilities.
This article defines AI guardrails as runtime constraints that ensure safe, responsible AI operation across data, model, application, and infrastructure layers, detailing the threats they mitigate — like prompt injection and data exposure — and their role in enabling compliant, scalable enterprise AI adoption.
This article defines software reliability per ISO/IEC 25010, distinguishes it from availability and stability, and details five operational concerns — correct user-visible results, change management, blast radius, detection/recovery speed, and failure semantics — with concrete distributed-systems patterns like idempotency, fault domains, burn-rate alerting, and fault injection.
This article explains the concept of an agent harness—a user-owned software layer that provides environment, system prompts, tools, an agentic loop, and a translation layer for AI models—using a climbing harness analogy, and argues that open-source, neutral harnesses like Pi empower user autonomy over AI.
Earendil releases Pi 1.0, a terminal-based agent harness introducing Codemode for JavaScript tool orchestration in a QuickJS sandbox, native MCP integration with five exposure modes, and Pi Durable for persistent multi-session execution with crash recovery.
The article argues that most enterprise AI agents don't need to traverse graph databases; instead, they need curated context (definitions, join keys, governance rules) to write SQL directly. It distinguishes Lookup questions (known paths) from Path questions (where the path is the answer), showing that Lookup dominates and graph traversal adds latency and cost without benefit.
This article walks through the core concepts needed before deploying a large language model on‑premises, covering weight precision, quantization methods, model packaging formats, inference engines, GPU memory considerations, KV‑cache sizing, sampling strategies, optional extensions such as LoRA and RAG, and a step‑by‑step decision workflow to match hardware, model, and deployment goals.
Vector dimensionality reduction in RAG retrieval compresses information and can degrade recall, NDCG or latency, but the impact depends on task, data distribution, and model architecture, so practitioners must measure cost, speed and accuracy on their own datasets using metrics such as Recall@K, Precision@K and NDCG@K.
The article analyzes the concept of an enterprise context layer for AI, breaking down its components—knowledge, expertise, and policies—into AI‑ready data, semantics, and reusable skills, and outlines the five capabilities needed to build, govern, and activate this shared corporate brain.
LangGraph is an open‑source framework that replaces linear LLM pipelines with graph‑based, stateful agents, offering loops, conditional branching, persistent checkpoints, human‑in‑the‑loop support, and built‑in monitoring, enabling complex multi‑step workflows that scale from simple chatbots to enterprise‑grade AI assistants.
The author runs a deepseek‑harness session to analyze its source code and build a custom skin, reporting smooth operation, fast token processing (98.3 M input, 360 K output, 131 tok/s), high cache hit rate, and detailing installation, usage, and encountered quirks.
