field notes
Your code calls one OpenAI-compatible endpoint, but each request quietly walks three stages — edge cache, your own GPU, then capability-aware cloud failover. Here's the whole decision path, and the settings that shape it.
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Swapping models on the fly — for cost or for resilience — quietly breaks any request that needs vision or tools, if the substitute can't do them. Here's how the gateway routes by what a request actually needs, and how to set a vision model and backups.
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Point can1357's oh-my-pi (omp) terminal agent at hardware you own: install it, declare a WideAreaAI provider in models.yml, set it as the default model, and code against your own Gemma node with zero token costs.
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Aider is a terminal AI pair programmer that edits your code and commits the diffs. Point it at a coder model running on your own GPU through Wide Area Intelligence and you get git-aware AI pairing with no per-token bill, no rate limits, and full privacy.
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Continue is the open-source Copilot alternative — chat, autocomplete, and inline edit in VS Code and JetBrains. Wire it to a Qwen Coder model on your own GPU and pay nothing per request, with no code leaving hardware you own.
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Cline is an autonomous VS Code agent built for frontier models — so most local LLM setups disappoint. Here is what actually works: a 32B coder model, a big context window, and your own GPU behind one endpoint.
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A no-fluff cost breakdown: real 2026 API prices, real electricity math for an RTX 4090, honest break-even points, and the hidden factors a calculator won't show you. With a verdict for every usage level.
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Stop guessing whether a 70B model fits your GPU. The real formula is weights + KV cache + overhead — here are the numbers for every popular model, every context length, and every GPU tier, with no marketing rounding.
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Open a GGUF repo on Hugging Face and you get 20 files. This is how the naming works, how much quality each quant really loses, why smaller is faster, and which one to actually download.
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Four ways to run the same open-weights model on your own hardware — and why they're different jobs, not competitors. An honest breakdown of where each one wins, with a full feature comparison.
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Your model runs great on localhost — until you're at work, on your phone, or shipping an app. A clear-eyed comparison of port forwarding, Tailscale, ngrok, and Cloudflare Tunnel, plus the auth architecture that makes a public endpoint safe.
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A coding agent uses your GPU for two hours a day. The other twenty-two, $1,600 of silicon sits idle. Here's how to queue overnight batch jobs — dataset labeling, embeddings, summarization — and let your nodes chew through them while you sleep.
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Point Alibaba's open-source coding agent at hardware you own: deploy a Qwen Coder model to a node, wire up three environment variables, and code with zero token costs and full privacy.
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