It’s hard to disagree with the direction of this. But it’s worth being honest that the neuro-symbolic lineage lost to scale for most of the last decade.
At the surface, everything collapses into prose. Fluent text is a poor record of what produced it. Two systems doing completely different work underneath can return the same paragraph. Whether anything symbolic is happening has to be answered below the language layer and not in the prose.
LLMs are excellent at prose, and they’ll remain relevant in that era. But prose is not the thing to lean on once a task needs real adaptation, verification, planning, or abstraction.
The lineage has been kept alive in places with DeepMind’s theorem-proving work among them and it’s finally starting to look right again.
Rei pushed a data pipeline optimization update, which includes better indexing and coverage for onchain and crypto data (demo below)
This lines up with Phase 1 transitioning plans @rei_labs shared previously
Happy Easter! 🐰
Welcome to Nebulyst Season 2.
The largest inference-time training challenge applied to finance. What we couldn't test internally: how much the human on the other end matters.
Training financial AI is a skill. We want to see the ceiling.
Rei Labs cracked what others couldn't in AI.
Core is an architecture designed to sit beneath its products and handle the hard problems: memory, reasoning, and real-time adaptation.
It's not another chatbot. It's the reasoning layer.
That already puts @rei_labs in a different category from most projects in the AI space.
Beta ending soon. Public access incoming.
Self-evolving Units are constantly learning through interactions with their user. The quality of training shapes the trajectory of evolution. With adaptive AI, the user’s ability to teach is the new ceiling.
Great to see experiments and explorations in different specializations.