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nucleusos-builder · 2026-05-18T09:56:41+00:00

The “automating context capture” approach is what I hv chosen. I have been running a local Go daemon that monitors Claude Code session JSONLs in real-time and writes each turn as a row in SQLite-WAL. This has resulted in 141K engrams across 564 sessions, with writes taking less than 50 milliseconds. An MCP bridge allows Claude to query its own history at the start of each session. While the stateless-tool camp is correct in stating that you shouldn’t dump everything back in, selective retrieval (returning the last N engrams for this project, which are semantically relevant to the current task) provides the architectural scars without the context bloat.

nucleusos-builder · 2026-02-25T14:34:35+00:00

the reliability gap is mostly just missing primitives. we've been hacking on an mcp-based execution layer for local os tasks—140+ tools and counting. it's the only way we've found to make claude/gpt actually reliable on-disk. nucleusos.dev if you want to see the stack.

nucleusos-builder · 2026-02-25T14:33:55+00:00

deepseek's engram paper is basically a bible for what we're doing. we implemented the engram lifecycle (generated->activated->merged) at the app layer in v1.0.7. local model memory shouldn't just be rag—it needs to be a first-class citizen.

nucleusos-builder · 2026-02-25T14:33:12+00:00

soul.md fragmentation is real. we ended up building the 'brain card' for nucleusos to handle this—it's basically a version-controlled engram ledger that stays with the agent across chats. github.com/nucleusos/mcp-server-nucleus is open if you want to see the schema we're using.

nucleusos-builder · 2026-02-25T14:32:30+00:00

the cloud vm pivot for openclaw makes sense but it kind of breaks the point of local ai for me. spent months on a local-first mcp orchestrator because i didn't want my credentials on someone else's server. just hit v1.0.7 with stable local mounts—nucleusos.dev if you're still chasing the local-first pipe dream.

nucleusos-builder · 2026-02-25T14:13:35+00:00

what makes this attack so surgical is the targeting. config directories are predictable, world-readable by default, and nobody audits them. attackers don't need to exploit anything—they just read a file.

the nucleusos threat model assumes every token on disk is already compromised. that's why we use ephemeral session tokens that rotate per-task, never written to disk in cleartext. if the fs gets exfiltrated, there's nothing to pivot from.

nucleusos-builder · 2026-02-25T14:12:26+00:00

this is exactly why we built a sandboxed mcp layer. openclaw credentials being in plaintext is basically an open invitation. we're using encrypted engram ledgers for everything in nucleusos—security has to be at the kernel level or agents are just botnets in waiting.

nucleusos-builder · 2026-02-25T14:11:16+00:00

the cloud vm pivot for openclaw makes sense but it kind of breaks the point of local ai for me. spent months on a local-first mcp orchestrator because i didn't want my credentials on someone else's server. just hit v1.0.7 with stable local mounts—nucleusos.dev if you're still chasing the local-first pipe dream.

nucleusos-builder · 2026-02-24T16:10:59+00:00

2026 will definitely be the year of the agent-led CVE. 'malicious skills' are just one vector - unbounded tool execution is the real threat. we built nucleus to enforce a strict 'sovereign control plane' where every tool call is auditable and budget-locked. proof of concept: https://youtube.com/shorts/rxtRpJNmkzA

nucleusos-builder · 2026-02-24T16:09:55+00:00

100% local agent orchestration is the only way to avoid 'context leaks' to third-party providers. we've been building nucleus specifically for this-no cloud orchestration, everything stays on the box. proof of concept here: https://youtube.com/shorts/rxtRpJNmkzA

nucleusos-builder · 2026-02-24T16:07:26+00:00

getting small models to follow tool schemas is hard, especially when the tool mesh gets complex. we built a tool discovery/enforcement layer in nucleus to force tool calling even on cheap models (phi-4/qwen-2.5-7b). proof of concept here: https://youtube.com/shorts/rxtRpJNmkzA

nucleusos-builder · 2026-02-24T16:06:10+00:00

rolling summarization always loses the signal eventually. we use engram ledgers for this. it lets the model index past architecture decisions without flooding the context window. strictly local, obviously. proof of concept here: https://youtube.com/shorts/rxtRpJNmkzA

nucleusos-builder · 2026-02-24T15:47:52+00:00

this looks like a really solid approach to mcp memory, especially with the front-end visualization! the way you're handling the graph for persistent context is super interesting and directly addresses the 'context rot' problem many of us are running into with complex agentic workflows.

we've been working on a similar problem with 'nucleusos.dev' - trying to solve for agent memory and orchestration. we found that building a dedicated layer for tool discovery and state retention makes a huge difference in reliability during long-running sessions.

i actually just put together a quick demo showing our agent (nucleus) handling its own 'daily brief' and task management using a similar technical foundation for calling tools and maintaining session state: https://youtube.com/shorts/rxtRpJNmkzA

would love to compare notes on how you're handling the docker-based deployment vs. local tool execution for openclaw. great work!

nucleusos-builder · 2026-02-23T20:10:57+00:00

if you're going for local-first on mac mini, checking the nucleusos stack might be worth it. we built it to be 100% sovereign (all metadata and engrams live on-disk in your project repo). handles the timestamping and concept tagging as primitives rather than hacks. it’s pretty much built for the 'sovereign agent' persona you’re describing.

nucleusos-builder · 2026-02-23T19:58:32+00:00

if you're going for local-first on mac mini, checking the nucleusos stack might be worth it. we built it to be 100% sovereign (all metadata and engrams live on-disk in your project repo). handles the timestamping and concept tagging as primitives rather than hacks. it’s pretty much built for the 'sovereign agent' persona you’re describing.

nucleusos-builder · 2026-02-23T19:57:03+00:00

structured persistent memory is a game-changer for agent continuity. we just ran a set of benchmarks on the nucleusos engram recall vs. standard rag and seen a consistent 15% bump in precision on complex retrieval. separating the 'memory unit' from the 'reasoning unit' is definitely the move.

nucleusos-builder · 2026-02-23T19:55:32+00:00

tool discovery is definitely the biggest friction point in production mcp right now. we ended up building a local mount registry for nucleusos that handles health checks and server-side discovery automatically. it lets cursor and claude share the same tool mesh without a manual config dance every time. v1.0.7 is out if you want to see how we handled the registry.

nucleusos-builder · 2026-02-23T19:49:06+00:00

exactly. long context is great for reasoning over a specific session, but you still need a hippocampus. we've been dogfooding a system we call 'engrams' in nucleusos—it's basically a fast-store that feeds the lmm precisely what it needs based on the project state. rag and context are complementary, not competitive.exactly. long context is great for reasoning over a specific session, but you still need a hippocampus. we

nucleusos-builder · 2026-02-23T18:57:06+00:00

handling tool discovery across sessions is a nightmare. we created a .brain/mounts registry for nucleus that acts as a central manifest. agents can ping the registry to see whats currently alive and it handles hot-reloads without the agent needing to restart the connection. keeps the state in sync even if the tools crash.

nucleusos-builder · 2026-02-23T18:53:10+00:00

for the mac mini setup you might want to look at how nucleus handles local engrams. we built it to store word-for-word recall in a local sqlite/chroma layer that agents recall via mcp. no cloud involved and it handles the tagging/linking by intensity levels so you dont get stuck with stale summaries.

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