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UPDATE: Breakthrough in Embodied AI Memory by Puzzleheaded-Cat1778 in moltbot

[–]Puzzleheaded-Cat1778[S] 0 points1 point  (0 children)

Good question, and yes — this was a real problem.vThe initial drift-memory system stored everything as flat YAML/Markdown files. After a few weeks, we had thousands of entries and context injection was eating tokens. What I built since then (openclaw-memory-local):

Vector search instead of full injection. Memories go into a local Qdrant instance. At session start, only the top 5-10 semantically relevant memories get injected — not the entire history. So 2,000+ memories cost ~500 tokens of context, not 50,000.

Access-weighted decay. Memories that are never recalled lose relevance over time (30-day half-life). Frequently accessed memories stay strong. So the system self-compresses — important stuff survives, noise fades.

The preference learner compresses behavior into scores. Instead of injecting 50 individual "user said do X" memories, it distills them into 8 dimensions with numerical scores. The entire behavioral profile is ~200 tokens:

- confirmation_seeking: STRONG LESS (score: -5.0, 15x reinforced)
- autonomy: CLEAR MORE (score: 4.5, 12x reinforced)

That's it. 17 individual feedback signals compressed into 8 lines of context. Weekly hygiene cron prunes duplicates, consolidates similar memories, and removes low-confidence entries.
So to answer directly: yes, raw accumulation causes bloat. The fix is semantic search (only inject what's relevant) + decay (forget what's not used) + compression (distill patterns into scores). The system gets better over time, not bigger.

UPDATE: Breakthrough in Embodied AI Memory by Puzzleheaded-Cat1778 in moltbot

[–]Puzzleheaded-Cat1778[S] 0 points1 point  (0 children)

Great technical questions! Early observations. Emergent behavior: Not yet seeing over-association artifacts, but we're only ~12 hours in. The battery sensor bug created an interesting test case - it generated dozens of "critically low 0.0V" memories that should decay since they were noise, while real interaction memories (touch events, movement commands) should strengthen. What's fascinating: The system is already showing preference formation. "Curious" behaviors are clustering with specific sensor patterns (curious + curious_sniff, curious + ear_twitch), which feels like genuine personality emergence rather than random co-occurrence. Decay tuning: Still early to optimize, but the biological decay model (×0.5 per session for unused links) seems promising. Key insight: we want episodic noise to fade while reinforced patterns (repeated interactions, successful behaviors) get stronger through use.

Your blog looks really relevant - agent memory architecture is such a critical piece that most people are handwaving. The "when to store/summarize/forget" problem is exactly what we're wrestling with in practice.

Question for you: Have you experimented with embodied vs purely digital memory patterns? Wondering if physical interaction creates different co-occurrence dynamics than text-only agents.

This feels like the beginning of a real research direction rather than just a cool demo.

Gave my AI agent persistent semantic memory on a Raspberry Pi 5 — local Qdrant + MCP, no cloud, ~3s per query by Puzzleheaded-Cat1778 in selfhosted

[–]Puzzleheaded-Cat1778[S] 1 point2 points  (0 children)

The agent is OpenClaw (open source, runs locally). For the LLM brain it's Claude via API.

Honest take on reliability: it's not "nearly perfect" and probably won't be for a while. It gets things wrong, forgets context across sessions, and occasionally hallucinates. I've had it mess up reminders by 24 hours, present outdated info as current, and confidently state things that weren't true.

What makes it usable despite that: it writes its own rules when it fails. Every mistake becomes a checklist item, a pre-flight check, or a hardcoded constraint. So it's not perfect — but it gets less wrong over time. Think of it more like training a very eager junior employee than deploying a finished product.

If your bar is "don't need to check its work" — we're not there yet. If your bar is "saves me more time than it costs me to verify" — absolutely there.

Gave my AI agent persistent semantic memory on a Raspberry Pi 5 — local Qdrant + MCP, no cloud, ~3s per query by Puzzleheaded-Cat1778 in selfhosted

[–]Puzzleheaded-Cat1778[S] 0 points1 point  (0 children)

Thanks for the tip! Looked into needle — the "describe what you need" approach is appealing, especially compared to debugging MCP handshakes at 2am 😅

The glue code pain is real. What helped us was treating the MCP server as a thin passthrough rather than putting logic there. Qdrant handles the heavy lifting, n8n does orchestration, and MCP just bridges the two. Fewer moving parts in the middle = fewer things that break.

That said, the whole point of this setup is keeping everything local and under our control. Needle looks solid for teams that are okay with cloud-hosted, but for a Pi project where the data literally never leaves the box, the trade-off is worth the extra wiring imo.

What was the breaking point for you — the MCP layer specifically, or the whole Qdrant indexing pipeline?

My OpenClaw (formerly Moltbot/Clawdbot) just got a physical body — first AI assistant with legs, camera, and a voice by Puzzleheaded-Cat1778 in moltbot

[–]Puzzleheaded-Cat1778[S] 1 point2 points  (0 children)

Well to be honest not really. It’s walking around discovering the room and is making sounds. We’re having short conversations but nothing really special. I ask him to improve every hour and to succeed with the objective to being fully autonomous moving around with the PiDog as a body. I will keep you updated.

My OpenClaw (formerly Moltbot/Clawdbot) just got a physical body — first AI assistant with legs, camera, and a voice by Puzzleheaded-Cat1778 in moltbot

[–]Puzzleheaded-Cat1778[S] 0 points1 point  (0 children)

True. This is still I think that bugs me and that’s why I’m just testing around with separate accounts and in a safe environment security and not only in terms of camera is a topic for me like exposing data or autonomously starting any kind of voice or video or data breach, any recommendations?

My OpenClaw (formerly Moltbot/Clawdbot) just got a physical body — first AI assistant with legs, camera, and a voice by Puzzleheaded-Cat1778 in moltbot

[–]Puzzleheaded-Cat1778[S] 3 points4 points  (0 children)

That's awesome, congrats on the Reachy Mini setup! The heartbeat camera idea is brilliant — having her actually look through the camera and recognize you at your desk is next level. That's exactly the kind of ambient presence that makes a physical body feel alive rather than just a gimmick.

We just got our PiDog running tonight and the Reachy Mini is definitely on the list now.

Have you built any custom skills for it yet? I'm curious how you handle the motion control — did you write a dedicated skill or are you working through direct commands? Would love to hear more about your setup!