Privacy Enabled Portable AI Context Memory
Synchronized LLM
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SuperNet is excited to announce that our core context memory technology Atomic Memory is live on GitHub today β
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
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Supernet AI π retweeted
An enterprise agent should remember where the last conversation ended. Continuity makes every interaction more dependable.
Power your agents with Atomic Memory Cloud.
memory.atomicstrata.ai/?utm_β¦
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Supernet AI π retweeted
Let your system run autonomously.
Give your agents memory they can use across sessions, and your team a way to see what was stored, retrieved, corrected, and why.
Configurable memory for AI agents, now in Public Beta.
memory.atomicstrata.ai/?utm_β¦
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Supernet AI π retweeted
At Stanford talking AI context on-chain at the Science of Blockchain conference. Always great to be back on campus.
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Supernet AI π retweeted
llm-wiki v1.0: From Compiler to Programmable Wiki
Renditions of @karpathy's idea is mostly compiled sources into a knowledge base. v1.0 changes the question from "what does it compile" to "what do you want to build?"
Introducing llm-wiki-compiler CLP π§
github.com/atomicstrata/llm-β¦
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Supernet AI π retweeted
Get early access for agent memory that survives the session.
SDK, CLI and Python install paths are ready for builders who want persistent memory across real agent workflows.
Now live for teams building with Claude Code, Cursor, Codex and MCP β¬οΈ lp.atomicstrata.ai/early-accβ¦
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Supernet AI π retweeted
Shout out to our Speakers joining on Narrative Dayππ»π₯
Goldchau (@Goldchau_eth ), Co-Founder of Decentralised Intelligence (@Didotxyz_ ), building infra for autonomous agents at the intersection of AI and decentralised systems.
Aileen (@Aileentech ), Co-Founder of Atomic Strata (@AtomicStrata ), in exploring semantic memory that helps agents maintain context and auditable workflows.
Jordan, Founder of Bored2AI, building fast AI video tools that let creators turn ideas into stories and worlds without a studio.
Gideon (@CrazGideon ), Core Contributor at CreatorFi (@TheCreatorFi ), building the creator economy layer of Web3.
Darren (@Dmeister00 ), CMO of Tomoland (@tomoland_app ), an AI-powered 3D social sandbox turning "build-and-play" into a self-sustaining digital economy.
π§ Together on the panel: AI Frontier β Convergence, infra, and next-gen agents.
πBlackBixon KL
π
28th July 2026, 11AMβ5PM
β οΈRSVP: luma.com/s2klpbyn
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The more agents you run, the more memory matters.
With GPT-5.6 Ultra, one task can now split into parallel agent workstreams that stay active across complex projects. That only works when each agent can share decisions and source evidence while keeping corrections tied to current context.
Agent teams need memory that stays inspectable and revisable as the workflow changes.
Sol, Terra, and Luna, our GPTβ5.6 family of models, are starting to roll out now in ChatGPT, Codex, and the API.
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Self-improving agent loops need real memory discipline.
Each failed run should teach the system something useful for the next attempt, while corrections and review notes keep the workflow improving without flooding the agentβs context.
If your agent cannot remember cleanly, can it really improve?
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Supernet AI π retweeted
Agent memory without grounding just becomes context pollution.
Most memory stacks are append-only. No provenance or way to correct false beliefs, just more text crammed into your context window. Memory needs source tracking and update semantics so you can track each fact's source and edit what your agent believes and why.
Don't build on a black box.
github.com/atomicstrata/atomβ¦
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The teams adopting MCP at scale are discovering that tool access is easy but tool governance is hard.
Opening up every tool in the stack to your agent is a connectivity win but a governance risk. The missing layer is not access, it is audit: knowing which tools an agent reached for, what it remembered from each interaction, and which actions needed approval before execution. MCP is the pipe but you still need the guardrails.
Access and audit are two different problems that get solved at different layers with @AtomicStrata
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Palantir warned that transferring your data may transfer your leverage.
This is why knowing who has access to your data in the first place matters. Most organizations hand their data to their AI systems and then lose visibility into where that data goes. They cannot tell which parts of their data the AI used or who else saw it. Data sovereignty needs a tracking layer that records every access and makes that history visible to you. Without that visibility, you cannot get data sovereignty.
Our thoughts on the importance of AI sovereignty.
1. Your AI sovereignty dictates your institutionβs future. Sovereignty is the precondition for choice. Relinquishing sovereignty transfers the future choices of your institution to others, who are likely to exploit it for their gain and your loss.
2. Data retention is your treasure. Transfer it at your own peril. Your ability to win is dictated by your ability to recognize and use your unique edges, and you keep winning by compounding the underlying data to generate new insights. Transferring that data hands over access to your pre-existing winning plays and yields the means of production for new ones.
3. Tokenmaxxing hijacks your value orientation and decreases your institutional fortitude and intelligence. The pursuit of high token usage incentivizes disposable scripts over robust software β with the addictive feeling of false progress. There is a reason why those selling tokens refuse to charge based on value.
4. Controlling your weights is controlling your fate. Weights are the distilled form of hard-won, accumulated institutional knowledge. If you let others control your weights, you are allowing them to migrate the alpha of your business to theirs.
5. There is no contradiction between sovereignty and alpha. The architecture that maximally preserves sovereignty is one that enables institutions to own their tribal knowledge, and to compound it as alpha.
6. Politicizing the technical issues involving sovereignty is what your adversary wants. Techno-politicization is the wellspring of false sovereignty. Techno-politicization drives decisions that seem to reduce dependency, but ultimately limit agency β especially on the battlefield in the West.
7. Real expertise is existential. Allowing politics or favoritism to determine your technical decisions rewards whoever is best at politics, not whoever is right. Listen to those closest to the problems, not those speaking most compellingly about them.
8. Learn from institutions that are winning or that have consistently delivered. Institutions facing existential threats do not have the luxury of making technical decisions based on political preferences.
9. Only listen to institutions, countries, and people who have a proven record of being right. A track record of correctness is the best and only signal for future correctness. Judging something as right or wrong based on who you like is exceedingly misguided.
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Here is the secret every successful AI-native organization gets right that most miss entirely.
Four important steps you must follow:
1. Humans move up to strategy, taste, and judgment while agents handle the execution.
2. The whole business becomes readable to agents.
3. You point the agents at the right work. Repetitive enough for an agent, complex enough that incumbents never bothered.
And fourth, the company itself becomes the context layer rather than the people.
In the old world, the company was the people.
They held the knowledge, made the calls, and did the work.
In this new world, the people become the creatives and the agents become the labor.
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The more the company depends on agents, the more the accuracy of the context layer becomes crucial.
That is why the context layer cannot just be a database of things that were said. It needs to resolve contradictions before they are stored, flag low-confidence claims before they become policy, and give the team a way to inspect and correct what is in there before the agents act on it.
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An infrastructure problem lies beneath every agent-first company.
At @AtomicStrata we are building the layer that makes the shared brain something a team can actually trust, inspect, and correct before the agents run on it.
github.com/atomicstrata
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Supernet AI π retweeted
Three quiet failure points break almost every knowledge base:
1) Generated content goes wrong because the model can misread a source or connect ideas that were never actually related.
2) An imported content goes wrong because another tool's extraction mistakes come along with it.
3) A page correct in March can be wrong by June if the source underneath it changed.
Most tools trust their own output directory by default, which means all three problems ship straight to the agent unchecked.
Run LLMwiki on your own sources and see the trust signals in action. github.com/atomicstrata/llm-β¦
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LLMs produce confident sounding nonsense on a regular basis.
Review policy in v0.10.0 treats generated pages like code review by holding risky candidates with structured reasons for human approval.
Compile with a safety net: github.com/atomicstrata/llm-β¦
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Your chatbot is faking its memory.
GPT and Claude can answer brilliantly, but by default they donβt fully remember your projects, preferences, workflows, or history. Thatβs why builders create agents with long-term memory: external storage, retrieval, and context that persists beyond one chat
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A chatbot is great for one-off answers.
An agent is built for ongoing work: tracking goals, remembering decisions, using tools, and improving with context over time.
That long-term memory is what makes it useful beyond a single conversation.
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Loop engineering is the next move beyond manual prompting, but nobody is talking about the part that makes it work long term.
A loop running without you only stays useful if it can remember and compound knowledge from previous sessions. Otherwise, you'll end up automating mistakes from a stale memory.
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Once you're designing a loop meant to run with less supervision, the open research question is how to govern what gets stored, what gets retrieved, and what gets discarded.
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Agents must store, retrieve, and update state without accumulating contradictions. Preventing stale or low-quality memory from dominating its decision-making layer is an open problem we are solving in @AtomicStrata
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