Two mini PCs can support a local coding-assistant workflow, but they do not automatically combine into one larger computer. Repository indexing can find relevant code to include as context for a question or review; a multi-machine router can send each inference request to one eligible machine. Those are separate jobs, and neither means every file is loaded into a model at once.
What “reads my whole codebase” means
A code assistant can index a workspace so it can retrieve relevant passages when you ask a question or request a review. For example, twinny documents workspace indexing with embeddings, keyword search, and a local reranker. Its review features can examine a working tree, a branch, or a GitHub pull request. This is context retrieval, not proof that every source file is inserted into every prompt. See twinny’s documentation.
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Indexing and inference are distinct: the index helps locate project context, while a model server generates a response from the context it receives. twinny can use a model server on the same computer, another device, or a hosted API. That choice affects where code is processed, so “local AI” describes the configuration only when the selected model endpoint is actually local or on a machine you control.
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What two machines contribute
A two-PC arrangement may separate the editor from model serving, or let multiple machines handle independent requests. NVIDIA Personal AI Router (PAIR), for example, pairs compatible machines on a local network and exposes an endpoint that compatible applications can use. It routes each request to an eligible node running a compatible inference engine with the requested model prepared on that node. The client points to the endpoint on the machine where the client runs. NVIDIA’s PAIR overview and setup guide describe the architecture and setup.
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The key limitation: PAIR handles a request whole on one node. NVIDIA says, “It does not pool GPU memory or make several GPUs act as one larger GPU.” It does not split a model or an in-flight request across computers, nor does simply adding nodes make one request faster. More nodes can instead serve separate requests, subject to model and engine availability on each eligible node.
How the software roles differ
IDE assistance and code review
twinny is a VS Code coding assistant. Its documented features combine workspace context retrieval with review of local changes, branches, or pull requests; its model server can be local, remote, or hosted. The documentation also describes a Devices feature for connecting a user’s computers over an encrypted link. These capabilities make it an example of IDE-centered assistance, not evidence of any particular two-mini-PC build or measured review quality.
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Pull-request automation
Mira is a self-hostable pull-request reviewer that indexes repositories and supports GitHub, GitLab, and Forgejo. Its documentation lists direct native adapters for Ollama and vLLM as planned additions, so they should not be treated as currently supported integrations on that basis. Mira’s documented workflow is repository and pull-request focused rather than the same IDE workflow described for twinny. Read Mira’s documentation.
What you need to verify before building
- Model fit: Each node expected to serve a request needs compatible inference software and the requested model available locally. Whether a mini PC can run that model depends on its memory and graphics resources; the available documentation does not establish a tested specification.
- Network and endpoint: Confirm that the machines can pair or otherwise reach the model server, and configure the coding client to use the correct endpoint.
- Review scope: Decide whether you want feedback on current working-tree changes, a branch, or a pull request. These are different scopes, not interchangeable guarantees of a complete repository audit.
- Privacy boundary: Check where the chosen endpoint runs. A locally indexed workspace does not by itself ensure code stays on your machines if requests are sent to a hosted API.
The sources do not identify the machines’ models, processors, memory, graphics, operating systems, selected model, repository size, or observed speed and accuracy. There is therefore no substantiated parts list or performance result to attach to this headline.
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What a two-mini-PC setup can and cannot promise
The defensible claim is that a local coding workflow can index repository content for retrieval and send model requests to an eligible machine; a router such as PAIR can distribute independent requests among compatible nodes. That is different from loading an entire repository into one prompt or merging two computers’ hardware into a single larger model. The quality and usefulness of a review depend on the code context retrieved, the model and configuration, and the changes being examined; the cited product documentation does not establish benchmark results for this particular setup.
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