Supernet AI 🌐

Privacy Enabled Portable AI Context Memory

[Supernet AI 🌐 @Supernet_AI]

SuperNet is excited to announce that our core context memory technology Atomic Memory is live on GitHub today ⭐

[Atomic Strata @AtomicStrata]

We just open-sourced AtomicMemory.

The AI memory industry has a black-box problem.

AtomicMemory is a configurable open-source SDK + self-hosted Core engine for memory your AI can inspect, correct, swap, and run on your own infrastructure.

Apache 2.0. HTTP-first. Docker quickstart.

https://t.co/pSn52AL7zQ

[Supernet AI 🌐 @Supernet_AI]

Knowing which pattern fits your use case before you ship means you won't get trapped into a setup where changing your model or framework means losing all of your agent's accumulated memory.

If you found this useful, share what you're building or contribute to what we're building at @AtomicStrata 🤝
https://t.co/AkBRpjKCVR

[Supernet AI 🌐 @Supernet_AI]

Agent memory stops being accurate over time if you're using the wrong semantic memory pattern.

Most vibe coders don't realize other patterns exist as distinct choices. They either default to RAG or copy a tutorial setup without understanding the tradeoffs.

Knowing these 3 patterns means you can pick the right one for your use case instead of hitting a wall six months in ⬇️

[Supernet AI 🌐 @Supernet_AI]

The 3 main architectural patterns for implementing semantic memory in LLM agents are:

1) Vector store retrieval (RAG-based)
The most common pattern. When your agent needs context, it runs a similarity search against the current query and pulls the closest matches.

2) Knowledge graphs
Instead of a similarity search, memory is stored as entities and relationships. Your agent retrieves facts by traversing connections between nodes rather than matching embeddings.

3) Hierarchical memory with consolidation
Hermes uses this pattern. It divides memory into layers where facts are extracted from raw sessions and stored separately, so retrieval pulls what your agent actually learned rather than searching through full transcripts.

[Supernet AI 🌐 @Supernet_AI]

@DanKulkov that's quite a steal 👀

[Supernet AI 🌐 @Supernet_AI]

@ZackKorman People are still learning about what can go wrong, and you simply can't secure something you don't understand.

[Supernet AI 🌐 @Supernet_AI]

@egorkabantsov are you able to inspect or give permission on what it deletes? 👀

[Supernet AI 🌐 @Supernet_AI]

@kunchenguid A good workflow is all about continuity 👏

[Supernet AI 🌐 @Supernet_AI]

@openclaw @Microsoft Enterprise adoption is here! Big W for OpenClaw 🦞

[Supernet AI 🌐 @Supernet_AI]

@sflorimm A lot of advantages in being able to switch freely and continue your work 👀

[Supernet AI 🌐 @Supernet_AI]

@DaveShapi Definitely a high signal for an upcoming release 😂

[Supernet AI 🌐 @Supernet_AI]

@googleaidevs It's nice to see capable open models becoming more accessible to local AI developers!

[Supernet AI 🌐 @Supernet_AI]

@Google @googlegemma Google's open model strategy deserves more credit than it gets 🔥

[Supernet AI 🌐 @Supernet_AI]

@googlegemma We're looking forward to see what people build with this open source 👏

[Supernet AI 🌐 @Supernet_AI]

@morganlinton The moment you go multi-model, it would be as important to care about AI memory to preserve your agent.

[Supernet AI 🌐 @Supernet_AI]

@ClaudeDevs This is a great update! exciting to see how effectively agents can navigate the platform on their own.

[Supernet AI 🌐 @Supernet_AI]

@sweatystartup if AI isn't working well for you that only means you're using it wrong

[Supernet AI 🌐 @Supernet_AI]

@emollick A lot of replies here proves your point 😂

[Supernet AI 🌐 @Supernet_AI]

@doodlestein If your workflow didn't change but the burn rate did then the meter is probably the thing worth investigating.

[Supernet AI 🌐 @Supernet_AI]

@bridgemindai I'd be curious whether they started weighting certain Codex actions differently behind the scenes.

[Supernet AI 🌐 @Supernet_AI]

@kimmonismus How does the user's context survive once everything is merged? 🤔