A practical guide to RAG chunking strategies for ML engineers: recursive fixed-size chunking with token-aware overlap, semantic chunking via sentence-level similarity breakpoints, hierarchical parent-child chunking for precision-plus-context retrieval, document-aware splitting for structured corpora, and how to choose chunk size empirically using RAGAS context recall.
A practical guide to summarising meeting notes and transcripts locally with Ollama: a structured summarisation prompt with sections for overview, discussion points, decisions, action items, and open questions, transcribing recorded meetings with Whisper then summarising, extracting action items as structured JSON, generating follow-up emails from the summary, a complete command-line tool with argparse, choosing between Llama 3.2, Mistral Nemo and Qwen2.5, handling variable note formats, and the privacy case for keeping sensitive meeting content off cloud services.
A practical deep-dive into Mamba and state space models for ML engineers: the SSM recurrence and linear-time scaling, Mamba selective state spaces with input-dependent parameters, parallel scan CUDA kernels, Mamba vs transformer performance tradeoffs on recall and throughput, Mamba 2 and SSM variants including RWKV and Griffin, and when to reach for Mamba over a transformer in production.
A curated guide to the best Ollama models in 2026 by use case: Llama 3.2 8B as the best all-around daily driver, Qwen2.5-Coder 7B for coding and debugging, Gemma 3 4B for constrained hardware with multimodal capability, Mistral Nemo 12B for long documents with 32K context, nomic-embed-text for RAG and embeddings, Qwen2.5-VL 7B for structured image analysis, Gemma 3 27B and Llama 3.3 70B for Apple Silicon with large unified memory, multilingual options, and a quick reference table for all use cases.
A practical guide to RAG evaluation for ML engineers: decomposing retrieval and generation quality, RAGAS metrics including context precision, context recall, faithfulness and answer relevancy, diagnosing low retrieval recall with chunking and re-ranking fixes, diagnosing generation faithfulness failures, and building an automated production eval pipeline with online and offline metrics.
A practical comparison of Continue and GitHub Copilot for VS Code developers: setup requirements and time to first completion, completion quality for everyday tasks vs complex problems, chat features including Continue’s @codebase semantic search across your entire project vs Copilot’s open-file context, privacy implications of cloud vs local processing, cost breakdown for individuals and teams, the hybrid Continue+cloud API approach, IDE support across editors, and guidance on which tool to choose based on your specific priorities.
A practical guide to transformer-based time series forecasting: Temporal Fusion Transformer for multivariate problems with rich covariates and probabilistic output, PatchTST for long-horizon univariate forecasting via patch tokenisation, iTransformer for dense multivariate problems via inverted attention, when to use each, and why you should always benchmark against simple baselines first.
A practical guide to running Google’s Gemma 3 locally with Ollama: the 1B, 4B, 12B, and 27B variants and their VRAM requirements, native multimodal image analysis at every size above 1B, CLI and Python usage including image inputs, how Gemma 3 4B compares to Llama 3.2 8B on reasoning tasks, the 12B as a multimodal sweet spot, 27B for frontier-class local quality on Apple Silicon, configuring a 32K context Modelfile, strong multilingual support, and how to choose between Gemma 3 and other local model families.
A practical guide to LLM guardrails for production: input guardrails for PII detection and prompt injection blocking, output guardrails for policy compliance and schema validation, Guardrails AI and NeMo Guardrails frameworks, latency-aware architecture with layered synchronous and async checks, and tool call validation for agentic systems.
A practical guide to summarising YouTube videos locally with Ollama and the YouTube Transcript API: fetching transcripts without an API key, basic summarisation in bullet points, extracting video IDs from any URL format, six summary formats including TL;DR, study notes, and Q&A generation, handling long videos with chunk summarisation, a complete command-line tool with argparse and streaming output, working around videos without captions using Whisper, and choosing the right model for different content types.
