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llama.cpp, the open-source engine behind nearly every local AI tool in existence, just joined Hugging Face. Georgi Gerganov and the founding ggml.ai team announced on February 20, 2026 that they are moving to Hugging Face as full-time employees — bringing together the model distribution layer (Hugging Face Hub) with the local inference layer (llama.cpp) under one roof. The projects remain fully open-source. Here is what this means for anyone who runs AI on their own hardware.

OpenClaw personal AI assistant is one of the biggest self-hosted AI projects right now, and for good reason: it lets you run a single gateway that connects your existing chat channels to one always-on assistant. Instead of opening a separate app, you can message your AI through WhatsApp, Telegram, Discord, Slack, iMessage, and more. If you want control, flexibility, and a privacy-first setup, OpenClaw is worth understanding.

GPT-5.3 Codex and Claude Opus 4.6 both dropped on February 5, 2026 — within fifteen minutes of each other. Two frontier models, two very different strategies, one shared message: AI coding agents have arrived. Here’s how they compare, what each does best, and what these cloud giants mean for anyone who cares about running AI locally.

Apple’s FoundationModels framework gives every Swift developer direct access to the ~3 billion parameter language model that powers Apple Intelligence — entirely on-device, with no cloud dependency. Shipping with iOS 26 and macOS Tahoe, this framework means any app can now offer AI features that run locally, respect user privacy, and work offline. Here’s what it offers, how it works, and why it matters for the future of private AI.

The start of 2026 has brought a wave of exciting developments for local AI. From groundbreaking hardware announcements at CES 2026 to new lightweight models designed for mobile devices, the industry is making it clearer than ever: powerful AI no longer requires the cloud. Here’s what happened in December 2025 and January 2026—and what it means for privacy-focused users on iPhone and Mac.

If you’ve ever wondered which GGUF quantization to choose—Q4_K_M, Q5_K_M, or Q8_0—this guide gives you practical, device-specific advice for iPhone and Mac. We’ll keep it simple: what the names mean, how they impact memory, speed, and quality, and clear presets to get great results in Enclave.

Local large language models took another leap this summer. Beyond faster chips, we saw real progress in platform support, in‑browser inference, friendlier tools, and model efficiency. Here’s what changed—and how to take advantage of it on iPhone and Mac.

Choosing the “right size” model has changed a lot since last year. In 2025, training recipes, data quality, and smarter architectures often matter more than raw parameter count. This guide updates our most-read piece with the latest facts, practical device picks, and a simple decision flow you can use today.

The AI world was rocked this week when OpenAI officially announced their most surprising move yet: GPT-OSS, a family of powerful open source AI models that marks the company’s dramatic return to its open roots. After years of keeping their most advanced models behind closed doors, OpenAI has revealed what could be the most significant open source AI breakthrough since Meta’s Llama series.

Just when the AI world seemed to have settled into a rhythm of bigger and bigger transformer models, Liquid AI has once again shaken things up. Following the success of their first-generation models, the company has just unveiled the LFM-2 series—a new family of Liquid Foundation Models that represents a major leap forward in efficient, powerful, and private AI.