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Hands-on guides to LLMs, agents, prompt engineering, and the AI tools I run every day for real work, not demos.

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Run Llama 4 Scout Locally: 24GB VRAM, GGUF, Real Speeds

You can run Llama 4 Scout on a 24 GB consumer GPU, but only with an aggressive quantization and some patience. Scout is a 109B-parameter Mixture-of-Experts model, and even its smallest Unsloth dynamic GGUF build is about 32 GB, so a 24 GB card runs it with CPU offload at roughly 20 tokens per second. This guide covers which Llama 4 model fits your hardware, the real VRAM math, and the fastest way to get it running.

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Most Popular

What X and Reddit Users Are Saying about Claude Opus 4.7

What X and Reddit Users Are Saying about Claude Opus 4.7

How power users on X and Reddit reacted to Claude Opus 4.7: praise for agentic coding, token burn concerns, and teams' practical prompting habits.

Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

Gemma 4, Qwen 3.5, and Llama 4 compared on benchmarks, licensing, speed, and hardware so you can pick the right open model fast.

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Alibaba's sparse Mixture-of-Experts: 35B total parameters, 3B active per token. Q4 quantization runs on MacBook Pro M5, matches Claude Sonnet performance.

MiniMax M2.7: Model That Almost Matches Claude Opus 4.6

MiniMax M2.7: Model That Almost Matches Claude Opus 4.6

MiniMax M2.7 review: 230B Mixture-of-Experts reasoning model with strong benchmarks, self-hosting options, and a tenth the cost of Claude Opus 4.6.

Running Gemma 4 26B MoE on 8GB VRAM: Three Strategies That Work

Running Gemma 4 26B MoE on 8GB VRAM: Three Strategies That Work

Run Google Gemma 4 26B MoE with sparse activation on budget 8GB GPUs using aggressive quantization, GPU-CPU layer offloading, and tensor parallelism techniques.

AI Coding Agents Are Insider Threats: Prompt Injection, MCP Exploits, and Supply Chain Attacks

AI Coding Agents Are Insider Threats: Prompt Injection, MCP Exploits, and Supply Chain Attacks

Study of 78 coding agents including Claude Code, Copilot, Cursor: all vulnerable to prompt injection attacks succeeding 85% of the time with adaptive vectors.

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