Hi, I’m Vivek Gupta, Founder & CEO at MemCode. QuantDinger brings research, strategy building, backtesting, paper/live workflows, monitoring, and MCP into one AI workspace. Those workflows generate context that should survive a single chat or run: explicit research assumptions, strategy constraints, confirmed findings, rejected hypotheses, dataset notes, and review decisions. A durable memory layer could preserve that context while leaving market data, backtest results, and execution records authoritative.
MemCode provides state-of-the-art semantic retrieval and durable updates with provenance, retention, export, and deletion controls. QuantDinger could retrieve narrowly scoped research context before an agent runs and write back only explicit, user-approved conclusions or verified workflow outcomes through MemCode’s MCP server or SDK. MemCode supports local memory and self-hosted deployment in concrete terms: the engine/API can run in the operator’s own Docker/VM or as a local process behind a private base URL, keeping records, embeddings, credentials, and logs inside that deployment with no mandatory hosted endpoint or telemetry. MCP + SDK support makes this an optional adapter without core-code changes.
I’d value the maintainers’ perspective on whether durable research context fits QuantDinger’s roadmap. If useful, I can open a small PR or discuss the cleanest boundary between source data, execution records, and memory. The short overview is here: MemCode Leviathan.
Hi, I’m Vivek Gupta, Founder & CEO at MemCode. QuantDinger brings research, strategy building, backtesting, paper/live workflows, monitoring, and MCP into one AI workspace. Those workflows generate context that should survive a single chat or run: explicit research assumptions, strategy constraints, confirmed findings, rejected hypotheses, dataset notes, and review decisions. A durable memory layer could preserve that context while leaving market data, backtest results, and execution records authoritative.
MemCode provides state-of-the-art semantic retrieval and durable updates with provenance, retention, export, and deletion controls. QuantDinger could retrieve narrowly scoped research context before an agent runs and write back only explicit, user-approved conclusions or verified workflow outcomes through MemCode’s MCP server or SDK. MemCode supports local memory and self-hosted deployment in concrete terms: the engine/API can run in the operator’s own Docker/VM or as a local process behind a private base URL, keeping records, embeddings, credentials, and logs inside that deployment with no mandatory hosted endpoint or telemetry. MCP + SDK support makes this an optional adapter without core-code changes.
I’d value the maintainers’ perspective on whether durable research context fits QuantDinger’s roadmap. If useful, I can open a small PR or discuss the cleanest boundary between source data, execution records, and memory. The short overview is here: MemCode Leviathan.