LiteLLM is the most widely used open-source LLM gateway: one OpenAI-compatible endpoint in front of Ollama, vLLM, llama.cpp and the cloud APIs, with keys, spend tracking and fallbacks. It is also a project with a difficult 2026. Two of its PyPI releases were published by an attacker in March, and its GitHub security page lists 17 advisories published since April. That is why … [Read more...] about LiteLLM Alternatives in 2026, and How to Run LiteLLM Safely With Local Models
Coding
OpenCode With a Local LLM: Ollama, LM Studio and llama.cpp Setup
OpenCode is the open-source coding agent that has taken off this year: about 214,000 stars on GitHub, MIT-licensed, and a new release most weeks. It runs in your terminal, reads your project, edits files and runs commands. By default it talks to cloud models, but it can just as easily use a model running on your own machine through Ollama, LM Studio or llama.cpp. Getting that … [Read more...] about OpenCode With a Local LLM: Ollama, LM Studio and llama.cpp Setup
Best Vector Database for Local RAG in 2026: What It Costs in RAM
Quick answer For a personal RAG corpus the database is not the expensive part. Fifty thousand chunks embedded at 768 dimensions need roughly 188 MiB of RAM once index and overhead are counted — less than the embedding model that produced them. Four different kinds of software get called “a vector database”. Choosing the wrong type costs you far more than … [Read more...] about Best Vector Database for Local RAG in 2026: What It Costs in RAM
Five Ways a Real-World PDF Breaks pdftotext (And How to Read It Anyway)
Every tutorial on extracting tables from PDFs uses the same example: a clean, machine-generated invoice where pdftotext -layout returns tidy columns and everyone goes home happy. Then you point the same script at a document somebody actually made, in InDesign or Illustrator, for print — and it returns rows of blanks. Or worse, rows that look correct and aren't. I spent a few … [Read more...] about Five Ways a Real-World PDF Breaks pdftotext (And How to Read It Anyway)
Local AI Pair Programming in 2026: Which Tools Are Still Maintained
Search for an AI pair programmer that runs against your own local model and you will be pointed at the same tool almost every time. It has 49,000 stars, it is genuinely good, and it has not shipped a release in more than a year. Nothing on its page says so. This guide checks which terminal and editor AI assistants are actually still being developed, using repository activity … [Read more...] about Local AI Pair Programming in 2026: Which Tools Are Still Maintained
How to Use Ollama with LangChain in Python (2026)
LangChain is the usual way to put a local model behind real application code — chains, retrieval, tool calls — and Ollama is the easiest way to serve that model. This guide covers the current integration package, a working example of each of the two things you will actually use (chat and embeddings), and the settings that cause most of the trouble. Quick answer Install one … [Read more...] about How to Use Ollama with LangChain in Python (2026)
Best Local LLMs for Coding in 2026: Models by VRAM, Licence and Use
A local LLM for coding gives you an assistant that reads your code without sending it anywhere, never meters your usage, and keeps working offline. In 2026 the open models are good enough to be genuinely useful. The catch is that "best" depends on two things people often mix up: how much memory you have, and what kind of help you want. Tab autocomplete, chat about your code and … [Read more...] about Best Local LLMs for Coding in 2026: Models by VRAM, Licence and Use
Best Local Embedding Models for RAG in 2026: Sizes, Dimensions & Licences
If you are building a local RAG system in 2026, the embedding model matters more than the LLM you plug it into. A weak retriever hands your model the wrong chunks, and no amount of parameters downstream will fix that. The good news is that the best open embedding models are small — most run comfortably in under 1 GB, alongside your main model, on hardware you already … [Read more...] about Best Local Embedding Models for RAG in 2026: Sizes, Dimensions & Licences
How to Run Qwen3.8 27B Locally: Hardware, VRAM & Setup Guide (2026)
Qwen3.8 27B is Alibaba's newest open-weight model, released in August 2026. It's one of the strongest models you can realistically run on your own computer. The short version: you need about 18GB for the model at 4-bit, plus room for context, so a 24GB graphics card or a Mac with 32GB or more of unified memory. This guide covers exact memory needs by quantization and context … [Read more...] about How to Run Qwen3.8 27B Locally: Hardware, VRAM & Setup Guide (2026)
AMD Strix Halo vs Apple M5 Max: The 128GB Unified Memory Local AI Showdown
If you want to run big AI models locally without buying several expensive GPUs, two platforms offer up to 128GB of fast unified memory in a small box: AMD's Ryzen AI Max+ 395 ("Strix Halo") and Apple's M5 Max, in the 2026 Mac Studio and the MacBook Pro. Both can hold 70B-class models and ~120B Mixture-of-Experts models that won't fit on a consumer GPU. This guide compares them … [Read more...] about AMD Strix Halo vs Apple M5 Max: The 128GB Unified Memory Local AI Showdown