Meta Muse Glimmer might be good? (First thought)
The pitch: a real open-weight agent that fits in your PC
Meta is back with an open-weight model that actually matters for builders. Muse Glimmer is a 30 billion parameter multimodal agent model, distilled from Muse Spark 1.2, and it runs on a single consumer GPU. It won't beat Frontier APIs on raw intelligence, but that's not the point. Most of the work in agent pipelines is cheap execution: tool calls, retries, structured outputs. If Glimmer can handle that for less than your current API bill, it becomes infrastructure you own.
We have never been fans of local AI on this channel. Three months ago we would have told you to stick to the cloud. But the inconvenient parts of cloud setups add up: SSH tunnels, reverse proxies, latency. A local model that just sits there on your machine changes the math. The 18GB file takes up real disk space, and our test PC might not have room without cleanup. That's the first practical hurdle.
What it's actually good at
From an agent perspective, three things matter. First, it's distilled from a closed frontier teacher, Spark 1.2. It is not competitive with smart models like Fable or GPT-5.6 Sol, but it inherits a lot of that model's judgment in a runnable size. Spark 1.2 itself is decent. Second, Glimmer is explicitly tuned for multi-turn, tool-heavy workflows and failure recovery, not just chat. Third, it ships with Llama, Ollama, and vLLM integrations, so it drops into existing harnesses like Hermes or custom Python/Node stacks without rebuilding your pipeline.
The strongest use case is tech support for your own PC. Normally, when something breaks, you copy specs, screenshots, and error messages into a cloud chat and then walk through fixes. That takes time. A local model with direct access would be faster. But there is a misconception: a local model is still a brain in a jar. It runs on your PC, but it cannot see your PC. It knows only the text you paste into the chat window plus what it learned in training. To give it hands, you have two realistic options.
One is LM Studio's MCP support. Recent versions let you attach tool servers for file access and shell commands to the chat window. The other is pointing Hermes at Muse Glimmer through LM Studio's local server at localhost:1234. Then Glimmer gets Hermes's full tool set: terminal, files, browser. That is the always-on local agent setup a lot of people describe as AI tech support, and we are going to set it up in part two.
Keep expectations in check
Marketing materials show Glimmer building apps and websites. We would not use it for that. Its strengths are mundane, repetitive tasks. Meta highlights multi-step planning on high-end PCs, but we would not give it planning at all. Give it a plan and let it execute. Multimodal input is a plus because it can take screenshots of your PC. The reasoning strength is configurable, so you can set it to be a fast medium rather than ask it deep questions. If a really hard problem comes up, Glimmer can act as a communicator and relay the issue to a cloud AI using the same SSH trick.
Reception is mixed, and understandably so. Llama 4 was underwhelming enough that Meta went closed. Glimmer is a distillation of Spark 1.2, and quality is decent, so now is the time to experiment and record results. There is also Zuckerberg's 14-page policy letter asking for reduced US restrictions on training data, continued silicon export controls, and shared intermediate checkpoints with the government. The framing question: is this a PC play or a policy play? It's both. Open-weights are Meta's differentiation against OpenAI, Anthropic, and Google. If you can't beat them on intelligence, beat them on accessibility.
Against Chinese open models like Qwen and DeepSeek, Meta has one clear advantage: resource use. Glimmer is 30 billion parameters, so it's light. Your PC fans won't scream the way they do with bigger local models. That matters for a model you leave running all day. We will test exactly that: point Glimmer at Hermes and turn it into this PC's tech support.