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The article analyzes the evolution from Prompt Engineering to Context Engineering and finally Harness Engineering, explains why each layer is needed, provides concrete code examples, diagnostic scripts, and practical guidelines for building reliable AI coding agents.
This article compares three popular vector databases—Pinecone, Chroma, and Weaviate—explaining how they store embeddings for RAG systems, showing Python setup code, and outlining each solution's architecture, scaling limits, cost considerations, and ideal use cases.
The article introduces instinct, a confidence‑driven memory framework that lets AI coding agents automatically observe, consolidate, and suggest reusable patterns across sessions, using SQLite for storage, MCP for integration, and a Python API for extensibility.
The article explains why good features matter more than fancy algorithms and walks through practical techniques—scaling, log transforms, binning, interaction, various encoding schemes, datetime extraction, text statistics, geospatial distances, aggregation, feature selection, and automated feature generation—illustrated with concrete pandas and scikit‑learn code examples.
The article explains ADK's three core orchestration modes—SequentialAgent for ordered pipelines, ParallelAgent for independent concurrent tasks, and LoopAgent for iterative quality‑control loops—detailing their suitable scenarios, state‑flow mechanisms, and how to build a complete order‑to‑delivery workflow without writing explicit orchestration code.
This article walks through constructing Mini-vLLM, a from‑scratch LLM inference engine that tackles the O(N²) attention cost with KV‑cache, boosts throughput via dynamic batching, adds observability with Prometheus/Grafana, supports gRPC, and scales across multiple workers, with benchmark numbers demonstrating its CPU‑only performance.
The article analyzes how traditional vector database pipelines use external filters for context constraints and proposes the Aspect Database’s multi‑aspect embedding approach, which encodes contextual attributes directly into similarity vectors to enable unified, context‑aware retrieval for AI systems.
The article details how speculative decoding—using a fast small model to draft tokens and a large model to verify them—overcomes the memory‑bandwidth bottleneck of autoregressive inference, introduces SSD’s self‑draft and tree‑verification stages, presents real‑world benchmark gains, and shows how to enable it in vLLM.
The article presents ten built‑in Pandas vectorized operations—such as np.select, assign, cut/qcut, melt/pivot_table, describe, query, transform, to_datetime, explode, and string accessor methods—showing concise one‑liners, their verbose equivalents, and the typical speed gains they deliver on large DataFrames.
The article presents a privacy‑first system that extracts numeric telemetry from each LLM interaction, discards raw text, and evaluates whether detection of prompt injection and jailbreak attacks remains effective, showing only a 1.4 F1‑point drop when using solely text‑independent features.
