CosmoFxcker

@Alpha_InsightX Im gonna fxck around!

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The problem is not just what your agent forgets. Wrong claims silently get stored all the time, poisoning what your agent believes. Atomic Memory asks before it commits. See what AUDN catches before it reaches your memory storage. github.com/atomicstrata/a…

A big update just landed for your compiled wiki 👀 v10 of LLMWiki ships review policy, Open Knowledge Format export and import, and full documentation. Watch the feature breakdown below ↓

Google has built the ultimate blueprint for an AI agent's digital brain. llm-wiki-compiler just became one of the first tools to support @Google's Open Knowledge Format, a standard for exchanging compiled knowledge between systems. Interoperability is a principle we build around at Atomic Strata. Our tools are designed to work with the broader ecosystem, not be the only thing you need. Say goodbye to lock-in; this is a step toward AI memory that belongs to you. github.com/atomicstrata/l…

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Switch from OpenAI to Anthropic or any open-source models without losing your agent’s memory. 🧠 Memory layers are often coupled to the model they were built around, so if you swap your LLM, you have no choice but to start over. The way to switch providers without losing a single memory is to store it independently. This is made possible through our open-source Atomic Memory. Your agent's memory, claim history, lineage, and trust scores all carry over untouched. github.com/atomicstrata/a…

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 ⬇️

We built two open-source memory tools for AI agents, both developed by @AtomicStrata LLM-Wiki Compiler is your persistent knowledge base - Durable markdown compiled from your sources and built to compound over time. Atomic Memory is your agent's persistent working memory - It only finds the specific facts that it needs, and you can directly correct its knowledge when needed. Each remains valuable on its own, but even stronger together. Available at: github.com/atomicstrata

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

Andrej Karpathy's pattern is to compile knowledge once and grow smarter for every query you make, which is what we built with LLM-Wiki Compiler. We extended that idea to AI agent memory. Once you've fed information to your agent, it's tracked as claims with evidence and lineage. When something changes, only what needs to change gets revised. Nothing gets silently replaced without a record of what it used to believe. The repo is open and we are actively building. Come contribute! ⬇️ github.com/atomicstrata/a…

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. github.com/atomicstrata

@AtomicStrata Fuck openclaw. Hermes all the way @Teknium

Run a Smarter Memory Layer at a Lower Cost for your AI Agents Your agents inject their entire memory file into every prompt, whether it is relevant or not. Hermes native memory includes the full MEMORY.md every turn. OpenClaw carries full cross-channel context on every query. That is a fixed token cost you pay regardless of whether any of what it retrieves is useful. Atomic Memory sits underneath both Hermes and OpenClaw and changes how memory gets injected. Retrieving only the facts the current query actually needs. Benchmarks show it does this at a lower cost per query than tools with comparable retrieval accuracy, which enables precise context injection without the token overhead.

@c9_cinema Lezduit

The ones who matter most are never the loudest in the room. They move quietly because they already know. You found this. This is not for everyone.

@AtomicStrata @_HermesAgent Still considering this vs others, the only part for me its the token consumption which i have to compare it

This is @_HermesAgent backed by Atomic Memory in a real work setup. Every decision, update, and correction your team makes gets organized and stays inspectable across sessions. Atomic Memory improves your Hermes agent by replacing the 2.2KB native memory cap with unbounded, per-turn memory that resolves contradictions before anything hits storage. The memory layer your team actually needs. github.com/atomicstrata/a…

@c9_cinema cinema?

Whoever controls the narrative controls reality 🎭