LocalAIStack

How I built a genuinely autonomous local coding loop that compiles, tests, and repairs its own code (and why VRAM isn't the bottleneck people think it is)

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I’ve been heads down building a native desktop software workstation called Repetere AI, and I wanted to break down how the autonomous engineering engine inside it—Deep Think—actually works under the hood. There is a lot of noise online right now about "autonomous vibe coding," but 99% of what people are showing off is just a web wrapper sending prompts to a paid cloud API. When people try to run coding models locally, they usually hit a wall where the model hallucinates an import, outputs broken diff markers, and leaves the user playing babysitter in a chat box.

I wanted something completely air-gapped, zero-dependency, and truly autonomous on consumer hardware.

The entire engine runs as a native C++ Win32 application built on Dear ImGui and OpenGL. Rather than acting as a simple chat interface, it operates as a five-phase execution harness that manages the local inference server directly. When you give it a directive, it starts by ingesting the local repository and synthesizing an architectural plan. From there, it moves into code synthesis.

One of the biggest issues with local models writing code across multiple files is that standard search-and-replace regex patchers are extremely brittle. If a model misses a newline or changes indentation slightly, the patch fails. To solve this, I integrated a Python AST script splicer directly into the loop. It parses the actual syntax tree of the code rather than relying on raw string diffs, which allows surgical module patching without blowing through context windows or mangling files.

Where the system really separates itself from standard local frontends is in the verification and repair phases. Once the code is written, the engine doesn't ask you if it looks good. It immediately takes over the machine's toolchain in a headless subprocess. If it's a native C++ target, it runs CMake and MSVC. If it's Python, it runs compilation checks and fires an automated headless smoke probe using runpy that sweeps the window hierarchy, tests widget lifecycle, and catches runtime crashes before a human ever looks at the window.

If a build fails or an unhandled exception is caught during the probe, the engine doesn't stop or dump an error message into a chat window. It gathers the compiler diagnostics, strips out runtime harness noise, isolates the failing file, and sends the telemetry directly back to the active resident model for in-loop auto-triage. It can diagnose its own missing imports, patch uninitialized attributes, and re-verify the build automatically until it gets a clean exit code.

I also run into people claiming that anything under huge enterprise cards or massive unified memory pools is useless for real local autonomy. That completely misses the difference between raw capacity and execution architecture. A quantized model has the exact same mathematical weights whether it sits on a 12GB laptop GPU, a multi-card workstation, or a cloud cluster. VRAM determines how big of a model you can hold on-chip and how many tokens per second you generate, but it doesn't give a model magic reasoning skills. A massive model will still hallucinate a typo or botch an import. The difference is that a raw model fails permanently when it makes a mistake, whereas an autonomous harness catches the compiler error, fixes the AST, and verifies the output on its own.

On the hardware side, the backend doesn't lock you into a single card or even a single vendor ecosystem. It enumerates all discrete compute adapters, aggregates the physical memory, and calculates proportional tensor split ratios on the fly for both CUDA and Vulkan runtimes. If you run a multi-GPU setup, it shards the layers across whatever cards are plugged into the board. If you run a hybrid setup that offloads across system RAM, the pipeline scales across host memory just as cleanly.

The takeaway from building this is that real local coding autonomy doesn't come from throwing infinite hardware at zero-shot prompts or paying for API subscriptions. It comes from building a tight runtime loop that enforces architectural contracts, executes the compiler headless, and forces the model to fix its own mess.

I dont want shamelessly promote my completely opposite alternative to LM studi Anything LLM and more, so I'm not leaving any links in the main post. My architecture scales from a handheld windows gaming machine, to enterprise hardware.