AI Agents
Building My Own AI Agent Environment: Lessons Learned from OpenClaw and Hermes
A real-world journey building a personal AI agent environment with OpenClaw, Hermes, and local AI models. Learn what worked, what failed, and what I discovered.
Alex Vale
Writing about local AI, open models, practical deployment, and the tools that make them useful.
Local AI
Ornith-1.5 for Local AI: What a New MoE Family Means When Your GPU Fits One Model but Not Two
The new Ornith-1.5 open-weight family (35B-A3B MoE, 9B, MIT) fits inside one consumer GPU. What you should actually run on a 24 GB card, why its hybrid attention makes long context unusually cheap from the math, and when it replaces what you already host.
24GB Lab
RunPod vs Vast.ai for Local AI: What to Do When Your 24 GB GPU Is Not Enough
A practical comparison of RunPod and Vast.ai for local AI users who occasionally need more VRAM than a 24 GB GPU can provide — with the stay-local vs rent vs buy decision worked out in order.
AI Agents
Running Claude Code on a Local Model: How Ollama's New Compatibility Endpoint Works
Claude Code can run against a model on your own machine, no paid key involved. Current Ollama does it with ollama launch claude — what the endpoint actually covers, which models to try first, and why the context window is the setting that matters.
Local AI
Muse Glimmer on Your GPU: What the Official Tags and File Sizes Actually Require
Meta Superintelligence Labs' new open model for local deployment lands with a real footprint. We map Muse Glimmer 30B's official quantization ladder against your card tier and Ollama tags, using exact file sizes.
Frontier AI
Qwen3.8-Max Weights Are Public on Hugging Face — But They Won’t Fit a Typical Local PC
Qwen3.8-Max-class weights are now downloadable from Qwen on Hugging Face. Here's how large the A95B checkpoint really is, what the smallest GGUF costs, and why it is far beyond a typical local PC.
Local AI
Qwen3.8-27B Vision: What Images and Video Actually Cost When You Run It Locally
Images work out of the box in Ollama. Verified against the model's shipped configs: what each image costs in tokens, what the projector adds on GGUFs, and exactly where my verification stops at video.
24GB Lab
Qwen3.8-27B Quantization: Which File Actually Fits Your GPU
Exact Hugging Face file sizes for every Qwen3.8-27B GGUF, a blunt per-card recommendation (UD-Q4_K_M at ~24GB), when NVFP4 changes the maths on Blackwell, and two-minute checklist built from hard numbers.
24GB Lab
Qwen3.8-27B on a 24 GB Card With Ollama: The Defaults You Inherit and the Settings That Actually Matter
Why different context surfaces can show 262K and 131K at the same time, which Ollama defaults qwen38-27b hands you after a pull, what thinking-on-by-default costs in real use, plus a day-one checklist from one measured RTX 5090 Laptop GPU session.
24GB Lab
Qwen 3.8 27B on a 24 GB Laptop GPU: Field Report From Actual Use
What Qwen 3.8 27B at 4-bit actually costs in VRAM and time on an RTX 5090 Laptop GPU, based only on retained client-side measurements.
Local AI
Should You Switch to Qwen3.8-27B? An Upgrade Decision Guide for Existing Local LLM Users
A decision guide for people already running a local LLM: what the vendor-published model card says about Qwen3.8-27B versus Qwen3.6-27B, where the reported differences actually matter agentically and multimodally, and how to fit it on your hardware.