Run a private coding assistant that never sends code to an outside cloud service.
Convert CUDA code into ROCm code for AMD GPUs with an applyable diff.
Operate fully air gapped with all queries forced onto local AMD hardware.
Try the agent loop offline with a mock model, no GPU or API keys needed.
| srkrz23/repomind-v3 | abhishek-kumar09/mern-live-js-june-2021 | abhishek-kumar09/z | |
|---|---|---|---|
| Stars | 1 | 1 | 1 |
| Language | HTML | HTML | HTML |
| Last pushed | — | 2021-09-13 | 2019-12-23 |
| Maintenance | — | Dormant | Dormant |
| Setup difficulty | hard | moderate | easy |
| Complexity | 5/5 | 2/5 | 1/5 |
| Audience | ops devops | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Full deployment needs Docker plus either a Fireworks API key or an AMD MI300X GPU, an offline mock mode needs neither.
REPOMIND v3 is an on premises coding assistant meant for companies that cannot send their code to an outside cloud AI service, such as regulated banks or firms with strict data policies. Instead of calling a service like OpenAI or Anthropic, it runs entirely on AMD hardware, using open weight models such as Gemma, GLM, and Qwen3 Coder, either through AMD Developer Cloud, AMD hosted Fireworks AI, or fully on premises on an AMD Instinct MI300X GPU. The system routes questions based on size. Shorter questions, under about 8,000 tokens, go to a smaller Gemma model for a quick first answer. Larger questions get a two step process: Gemma first looks at a short summary of the codebase's files and picks out which ones are actually relevant, then a bigger model reasons deeply over just those selected files instead of the whole codebase, which the README says reduces wasted computing time. There is also an air gapped mode that forces every query to stay on the local AMD hardware and blocks any outside connection entirely, intended for the strictest compliance settings. Beyond answering questions about a codebase, it includes a tool that converts CUDA code, the kind written for NVIDIA GPUs, into ROCm code that runs on AMD GPUs, using a set of mapping rules for memory, streams, events, and kernel launches, and it outputs the change as a diff you can apply directly. It also keeps a tamper evident audit log using cryptographic signing and hash chaining, and supports an API key requirement for its endpoints. To try it, you clone the repository, copy an example environment file and add a Fireworks API key if you have one, then run it with Docker Compose. There is also an offline demo mode that runs the whole agent loop against a mock model with no GPU and no API keys required, useful for trying the interface without any cloud account. This is described as version 3 of the project, building on an earlier version that was benchmarked directly on AMD MI300X hardware.
An on-premises AI coding assistant built for regulated companies, running only on AMD hardware and open-weight models instead of cloud AI APIs.
Mainly HTML. The stack also includes Python, Docker, vLLM.
The README excerpt does not state license terms.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly ops devops.
This repo across BitVibe Labs
double-check against the repo, no cap.