Open-Source

You’ve seen what GitHub Copilot and Cursor can do. Now you want that experience — fast inline completions, multi-file chat, agent-style edits — without routing your proprietary code through someone else’s cloud. That’s not a niche concern anymore. Regulatory requirements, IP agreements, air-gap environments, and straightforward cost math are all pushing teams toward self-hosted AI coding assistants. The good news: the tooling has matured significantly. In 2026, you have three serious, production-ready options that cover very different operational profiles: Tabby (the turnkey team solution), Continue.dev paired with Ollama (the flexible developer stack), and Void Editor (the open-source IDE-first approach). This guide walks through all three — setup steps, hardware requirements, honest trade-offs — plus a comparison table to help you match the right tool to your situation. ...

Cloud-based AI coding tools have transformed how developers write code. But not everyone can — or should — send their code to a third-party server. Regulated industries, security-conscious engineering teams, and developers who simply value their privacy are driving a real and growing interest in self-hosted alternatives. This guide covers the leading self-hosted AI coding assistants available in 2026: Tabby, Ollama paired with Continue.dev, LocalAI, Fauxpilot, and LM Studio. I’ll give you an honest picture of hardware requirements, integration quality, and where each tool fits best — with no invented benchmarks. ...

Cursor is excellent. But at $20–$60 per month, and with your code routed through proprietary servers, it’s not the right fit for everyone. Whether you’re a solo developer on a budget, an enterprise with strict data-residency requirements, or simply someone who prefers open systems you can audit and control, there are now real open source alternatives worth using in 2026. I’ve tested the major contenders. This guide covers six of the best — Continue.dev, Aider, Tabby, Void Editor, Cody/Amp, and FauxPilot — with honest assessments of what each does well and where each falls short. No invented benchmarks, no sponsored rankings. ...

Open source LLMs (Large Language Models) have transformed from research experiments to production-ready alternatives to proprietary APIs in 2026. The best open source LLMs—DeepSeek-V3.2, Llama 4, Qwen 2.5, and Gemma 3—deliver frontier-level performance in reasoning, coding, and multimodal tasks while enabling self-hosting and customization. Over half of production LLM deployments now use open source models rather than closed APIs like GPT-5 or Claude. The “DeepSeek moment” in 2025 proved that open source LLMs could match proprietary model capabilities at dramatically lower costs. Organizations choosing open source LLMs prioritize data privacy, cost predictability, fine-tuning flexibility, and independence from API rate limits. Evaluating DeepSeek vs Llama vs Qwen requires understanding model architectures, licensing restrictions, and deployment options. Open source LLMs excel in domains requiring data residency, custom behavior, or high-volume inference where API costs become prohibitive. ...