AI subscriptions add up faster than you notice. I'm paying $236/month across seven AI tools — and every company I'm giving money to says they're losing money on me. The math only works because of VC subsidies that won't last. When the bill catches up, you'll wish you'd built your own stack. This playlist tracks what AI actually costs, why it's about to cost more, and what happens when you stop renting and start owning your compute. Topics covered: • What you're really paying every month (the subscription audit) • Why "$20/mo" turns into $200/mo (the enshittification cycle) • Self-hosting the AI tools you actually use • Replacing SaaS with a homelab — what works, what doesn't • When AI is genuinely cheaper than humans, and when it isn't If you've ever stared at a credit card statement wondering when "just one more subscription" became $100+/month, this playlist is for you.
Curated by: Codacus (15 videos)
Everyone benchmarks Local AI using token generation speed. I did too. Then I built a real coding agent and realized something: The agent wasn't slow because of decode speed. It was slow because of prefill. In this video I build a complete local AI coding agent stack using an RTX 3060 12GB, REAP MoE models, llama.cpp, Pi Coding Agent, and Tailscale — then tune prompt processing all the way up to 1,142 tokens/sec. Along the way we'll cover: • Why prefill matters more than decode for agent workloads • REAP models and MoE efficiency on 12GB VRAM • KV cache compression with TurboQuant • Pi Coding Agent setup and model hot-swapping • Running your local AI agent from anywhere with Tailscale No API keys. No subscriptions. No rate limits. Just Local AI. ━━━━━━━━━━━━━━━━━━━━ 📚 Chapters 00:00 Cold Open 00:58 Hardware 03:30 Best Local AI Models (REAP + MoE) 07:35 llama.cpp Optimization (Prefill Tuning) 11:48 Pi Coding Agent Setup 13:55 Tailscale & Remote Access 16:19 Final Build & Takeaways ━━━━━━━━━━━━━━━━━━━━ 🔧 Models Used Qwen3.6-28B-REAP20-A3B-GGUF https://huggingface.co/barozp/Qwen3.6-28B-REAP20-A3B-GGUF GLM-4.7-Flash-REAP-23B-A3B-GGUF https://huggingface.co/unsloth/GLM-4.7-Flash-REAP-23B-A3B-GGUF ━━━━━━━━━━━━━━━━━━━━ ⚡ TurboQuant Fork Used https://github.com/TheTom/llama-cpp-turboquant ━━━━━━━━━━━━━━━━━━━━ 🛠️ Stack • RTX 3060 12GB • llama.cpp • TurboQuant • Pi Coding Agent • Tailscale • Qwen3.6 REAP • GLM-4.7 Flash REAP ━━━━━━━━━━━━━━━━━━━━ #localai #llamacpp #aiagents #qwen #glm #codingagent #rtx3060 #selfhostedai #homelab #opensourceai #moe