Gtrade

I read candles, not comments. Posts are no financial advice, just my personal ideas and thoughts.

[Gtrade @Gtrader]

@AristoX Nothing else on the market is stronger than that. Ready for the launch.

[Aristotle 🏛️ @AristoX]

I've been thinking about this for a long time.
About what it means to think. To reason. To know.
Today we're introducing something I'm really proud of.
We call it Aristotle.

[Gtrade @Gtrader]

Wouldn’t be surprised if ethereum:0x66a5cfb2e9c529f14fe6364ad1075df3a649c0a5 would do the same or even better performance like zcash:native. Full send!

[Gtrade @Gtrader]

@KhalafAlHabtoor If all relevant decision makers are dead since 28th (or earlier) who runs the country and does the decisions?

[Gtrade @Gtrader]

@KhalafAlHabtoor هل جُرِرنا إلى هذا — أم أن أحدهم كان بحاجة إلى أن نكون فيه؟
من النادر جداً أن تتعرض ١٢ دولة للهجوم من دولة واحدة في أسبوع واحد.

[Gtrade @Gtrader]

@just_cameron Half human half bot

[Gtrade @Gtrader]

Companies have raised $47M building AI memory.

Mem0 — $24.5M
Letta — $10M
Cognee — €7.5M

All of them charge per memory.
None of them work offline.
None have a knowledge graph.

I built one that does all of it. For free.

Open source. MIT licensed.
And this is just v1 — the full cognitive upgrade drops soon.

https://t.co/fsQSuYbtfJ 🧵

#AI #OpenSource #AIAgents #LLM #MachineLearning #BuildInPublic #DevTools #ArtificialIntelligence #Memory #RAG

[Gtrade @Gtrader]

Agreed — human links are implicit. That's exactly why we use spreading activation, not explicit graph traversal. The graph is substrate; activation patterns are the recall mechanism. Same principle as neural associative memory.

Pruning/remapping — solved with offline dream consolidation (batch dedup, merge, prune during idle). SYNAPSE (arXiv:2601.02744) proved activation-based graph recall beats flat vector on LoCoMo benchmarks.

[Gtrade @Gtrader]

Fair correction on pricing — updated. Letta's Postgres-based approach is solid for self-hosted.

On graphs: they're not for the model to reason over — they're for retrieval. Vector similarity finds what's similar. Graphs find what's connected. "Alice works at Google" + "Google is in Mountain View" — vector search won't connect those. Graph traversal + spreading activation will.

Complicated? Yes. That's the moat.

[Gtrade @Gtrader]

Benchmarks are coming LOCOMO + LongMemEval. Hindsight wins on recall. We're playing a different game: cognitive architecture (spaced repetition, spreading activation, fleet memory, dream consolidation). They're features that compose, not a feature checklist. But fair numbers talk. We'll publish them.

[Gtrade @Gtrader]

Mnemosyne is MIT licensed.
Free forever. No cloud lock-in.

And what's on GitHub right now? That's just the beginning.

The full cognitive engine — spreading activation, dream consolidation, knowledge graph enrichment — is being battle-tested on a 10-machine cluster as we speak.

v2 drops soon. Star the repo so you don't miss it.

⭐ https://t.co/fsQSuYbtfJ
📦 npm install mnemosy-ai
🌐 https://t.co/X5g0fXz0t4

#AI #OpenSource #AIAgents #LLM #MachineLearning #BuildInPublic

[Gtrade @Gtrader]

Let's talk about what $47M in VC money built:

Mem0: 8 features. $0.01/memory. No graph. Cloud-only.
Letta: 6 features. Server required. Limited agents.
Cognee: 5 features. LLM-dependent pipeline.

Mnemosyne: 33 features. $0/memory. Full graph. Works offline. Multi-agent native.

Not funded. Not backed. Just built.

[Gtrade @Gtrader]

@AlchemyPay @UltimaEcosystem 🚀🚀🚀

[Gtrade @Gtrader]

@openclaw Can you change hardcoded context limit of opus 4.6 from 246k to 1M what it is capable of.