Neon

Run Local LLMs on Apple Silicon with MLX vs llama.cpp

A developer guide comparing MLX (Apple's ML framework) and llama.cpp for running local large language models on Apple Silicon Macs. The article shows a five-minute MLX quick start (pip install mlx-lm) and example commands to run a 4-bit quantized 3B model, explains when to choose MLX versus llama.cpp, links a ready-to-run GitHub starter repository, and points to a paid deployment playbook hosted on Gumroad. The piece emphasizes privacy, offline usage, and cost benefits of running LLMs on-device and was published on 2026-08-07.

Why Most Teams Don't Need Real-Time Streaming

Lucas Ehara argues that many organizations overvalue millisecond-level real-time data pipelines and should instead consider simpler, cheaper batch or micro-batch approaches. The article recommends asking whether the business can act in milliseconds before adopting streaming, highlights streaming's operational complexity and higher cloud costs, and proposes hourly or 15-minute micro-batches as a pragmatic middle ground. The author advises starting with day‑lag (D-1) pipelines and only moving to streaming when measurable business impact justifies the added cost and engineering effort.

RAGnarok: Scoping an Enterprise RAG System

A developer-published walkthrough launching a public series called RAGnarok that outlines the scope and architecture for an enterprise Retrieval-Augmented Generation (RAG) knowledge assistant. Part 1 describes the problem (scattered internal documentation), a proposed tech stack (Sentence Transformers, ChromaDB, LangChain, OpenAI/Ollama), a project folder structure, and a four-phase build plan from ingestion to production hardening. The author notes Part 2 will cover the ingestion pipeline (extractor.py, chunker.py, embedder.py, loader.py) and says code and a repo link will follow once Phase 1 is implemented.