Raksa

You are going to be able to run Fable 5 locally on your desk In 2 years Apple will be coming out with Mac Studios with 1.5TB of memory With just 300gb of memory you can run Opus 4.8 level intelligence Think of what you can do with 5x that You need to be preparing for this now Start getting familiar with local AI technology Go to your Hermes/OpenClaw and use this prompt: “I am brand new to local AI and want to get familiar. Look at the computer you are currently on. Understand the specs. Then go on Huggingface and find the best models I can run on it. Then, walk me through how these models work, how they will run locally, and use cases I can do with them. After walking me through all of that so I’m educated, you can then load it onto this computer and build an interface so I can use them” In 2 years EVERYONE on Earth will have a local model running on their desk The people who start preparing now will be WAY ahead of everyone else

We're extending Claude Fable 5 access on all paid plans, as well as keeping Claude Code’s weekly rate limits 50% higher, through July 19.

try claudex, one shot, my 5h limit is gone

If you aren't yet bold enough to install the Codex app, you can stay in the presence of your orange crab and point it at GPT 5.6 Sol. Takes 5 minutes. Kudos to Theo for explaining one of the ways to get this done. Step 1: Install CLIProxyAPI Step 2: Connect Step 3: Define following alias and enjoy claudex ``` alias claudex='CLAUDE_CODE_SUBAGENT_MODEL=gpt-5.6-sol \ CLAUDE_CODE_ALWAYS_ENABLE_EFFORT=1 \ CLAUDE_CODE_MAX_TOOL_USE_CONCURRENCY=3 \ ENABLE_TOOL_SEARCH=false \ claude --model gpt-5.6-sol' ``` If this gets blocked, I owe you a reset.

A 744-billion-parameter AI model running on a regular computer with only 25 GB of RAM—and no GPU. 🤯 Colibrì is a lightweight, open-source inference engine written in pure C that can run GLM-5.2 on consumer hardware. Instead of loading the entire model into memory, it keeps RAM usage low by streaming only the required Mixture-of-Experts components directly from an SSD while generating each token. The impressive part: ⚡ Pure C 📦 Zero runtime dependencies 💾 Around 25 GB of RAM 🧠 744B total parameters, with roughly 40B activated 🚫 No expensive GPU required There is an important trade-off: this is not fast inference, and the quantized model files still require hundreds of gigabytes of SSD storage. But as a technical proof of concept, it is remarkable. It shows that running enormous AI models locally may depend as much on clever memory management as it does on expensive hardware. Tiny engine. Massive model. Very smart engineering. 🐦💻 Repository link below 👇 The model is officially listed as a 744B-parameter MoE with about 40B active

Fable 5 vs Muse Spark 1.1 on Maxfusion AI MCP Both videos generated with Maxfusion AI X Claude