ID

@rei_labs Grei

Core 0.5a - Patch Notes 0.5a, opening update of the 0.5 series, is now live on Rei Chat as well as our agentic API. Creating new units is advised as it is the only way for 0.5a to take full effect. PATCH NOTES 0.5a's main focus area is how units learn. faster intake, better retention, and more from every interaction. every change in this update touches some part of how a unit processes what it's given, how it holds onto what matters, and how it builds on what it already knows. Expect brief interruptions over the next 72h as we complete migration and roll out minor updates. Unit-Level Evolution • Evolution moved from system level to unit level Genetic algorithm evolution has moved from system level to unit level. Previously, evolutionary pressure operated globally, with competing inference strategies evolving as a shared population. In 0.5a, the specimens at the basis of evolution are defined by each unit's own relationship and concept exploration parameters. Selection, mutation, and crossover now run against how individual units traverse their hypergraphs, form patterns, and abstract concepts. Two units starting from the same configuration will develop increasingly distinct reasoning behaviors over time, not just because their knowledge differs, but because the parameters governing how they discover and reinforce that knowledge are themselves evolving independently. Resilient Hybrid Recall • Semantic recall combined with hypergraph-aware retrieval • Faster access to relevant knowledge with better structural context + hypertags • Better recovery of entities, relationships, and concepts Core 0.5a introduces a unified recall layer that combines semantic similarity with hypergraph structure. Knowledge that has faded from direct semantic reach can still be surfaced through relationship paths, entity linkage, and hypertags. This gives the system structural redundancy in recall without fighting the decay mechanism. Knowledge is still allowed to fade as designed, but the paths to reach it are no longer dependent on a single retrieval method. Recall has improved significantly as a result. Asynchronous Hypergraph Enrichment • Immediate knowledge availability on write • Background extraction of entities and relationships • Richer knowledge structure without adding interaction latency New knowledge is available immediately, while deeper entity and relationship extraction happens in the background. This keeps writes fast while still allowing knowledge structure to become more detailed over time. Modular Core Services • Clearer separation between knowledge, recall, traversal, exploration and routing • More defined internal service boundaries • Modular framework for future updates 0.5a moves Core toward a more modular structure, with dedicated layers handling different parts of the system. This improves maintainability, reduces fragility, and makes the platform easier to extend. Adaptive Context Processing • Condensation of long-form content before deeper extraction • Better retention of important details during heavier workloads • More efficient handling of large or dense inputs Longer inputs are no longer processed as-is. Core condenses dense content before deeper extraction, preserving key facts and relationships while reducing noise. This keeps recall efficient under heavier workloads and prevents important details from being diluted by volume. Knowledge Persistence • Better handling of entities, relations, and knowledge linkage • More stable hypergraph interpretation in live operation • Cleaner storage and integration of new facts into existing structure Where Resilient Hybrid Recall addresses how knowledge is found, this addresses how it's stored and linked. Core now handles entity formation, relation mapping, and structural integration with more consistency, which gives the recall layer stronger material to work with in the first place. Stronger Runtime Reliability • Cleaner local-first service behavior • More predictable execution paths • Better stability during live end-to-end use A major part of 0.5a is reliability. Service behavior is more predictable, local execution is cleaner, and the system is more stable under real usage. System Maturity • End-to-end validation of store, list, query, and clear flows • Verified delayed enrichment behavior under live conditions •Greater confidence in recall quality outside isolated tests 0.5a's notes is the last set of release notes in this format. as closed beta wraps up, so does this style of patch notes, making way for a different format that better fits what comes next. to everyone who's been part of closed beta, thank you. Disclaimer: 0.5a is exclusively accessible via Rei Chat and our agentic API. Rei Chat/Rei Chat API are user-friendly, OpenAI compliant interfaces for interacting with parts of our architecture. units start as blank slates and are designed to become domain experts through training. they are learning systems, mistakes are possible and normal, the user's role is to train their unit at inference.

@elonmusk What until they discover Cyrillic.

I guess there are still some letters left in the alphabet

Canada is cooked

@Lojom53 Grei

@Djureth Grei

$REI keeps delivering results. Once everyone gets access and more people start using it for all kinds of things, we're gonna see some really cool stuff.

@0xreitern @rei_labs Looking good

latest @rei_labs UX update very smooth (ﾉ◕ヮ◕)ﾉ･*:･ﾟ✧

Grei read if you're new to $REI

Interesting fact. Tesla was a Serb. His father, Milutin Tesla, was a priest in the Serbian Orthodox Church, and his mother Đuka (Georgina) Mandić came from a family with Orthodox priest heritage as well. The three-finger gesture (often called the Serbian three-finger salute) is a widespread national/ethnic symbol for Serbs, commonly seen at sports events, celebrations, protests, political rallies, or any large gathering. In religion, the sign of the cross is made using three fingers together to symbolize the Holy Trinity. The number three repeat frequently in Serbian customs and folklore, reflecting its religious significance. People greet close friends/family with three kisses on the cheek (alternating sides). Certain superstitions or rituals involve actions in threes (e.g., spitting three times to ward off bad luck). etc.

@levelsio See @rei_labs

How did you guys fix persistent memory with OpenClaw? My bot keeps forgetting stuff, I already have qmd installed

GRei

@rei_labs Grei

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.

Core's NeuroEvo simplified Self-optimizing AI infrastructure that perpetually evolves as concurrent users shape it in real-time

People don't understand the scale of $RLS 63,000+ km² - larger than most game worlds combined Built from real Earth data. Not procedural generation This is the canvas for AI civilization

There's growing momentum around "data poisoning", deliberately corrupting the datasets that models learn from. The logic is straightforward: intelligence lives in the weights, so corrupt the weights, corrupt the intelligence. The model absorbs everything indiscriminately, unable to distinguish signal from sabotage, and reproduces the corruption indefinitely. The attack surface is the entire internet. This vulnerability exists because current AI is fundamentally regurgitation at scale. Trillions of tokens compressed into statistical pattern matching. The model never "understands”, it memorizes and interpolates. There's no deeper cognitive process to catch this type of corruption. Core is architecturally resistant on two fronts. Units are blank slates. They carry no pre-baked knowledge scraped indiscriminately from the web. Intelligence emerges through reasoning over data inferred directly from you, your context, your inputs, your domain. The poisoned well everyone drinks from simply isn't part of the architecture. You bring your own water. Core's center is an inference engine, not a knowledge repository. What evolves are strategies, reasoning methods, approaches to connecting ideas. Successful patterns survive and propagate. Failed patterns are eliminated. Selection pressure operates continuously. A dataset can be poisoned because it's static, it sits there, inert, trusted by default. A strategy can't be poisoned the same way because it's subject to ongoing evolutionary pressure. Bad reasoning paths lose. They don't reproduce. More on NeuroEvo Below

GREI

@ReneCureuil 👏👏👏

𝗪𝗵𝘆 𝗥𝗲𝗶 𝗨𝗻𝗶𝘁𝘀 𝗢𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗟𝗟𝗠 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 B̲y̲ B̲o̲l̲t̲N̲e̲x̲u̲s̲A̲I̲, f̲o̲r̲ i̲t̲s̲ A̲I̲-D̲r̲i̲v̲e̲n̲ E̲-C̲o̲m̲m̲e̲r̲c̲e̲ A̲u̲t̲o̲m̲a̲t̲i̲o̲n̲ s̲e̲r̲v̲i̲c̲e̲ f̲o̲r̲ A̲m̲a̲z̲o̲n̲ F̲B̲A̲

Very good read. Excellent example of how you can use @rei_labs for your business to get ahead of the competition.

@0xreitern Very nice overview Grei

@rei_labs @RFLnow Hardcore

We crossed 1.2M API queries in December (new record). Big shoutout to @RFLnow for being part of it. Core everywhere.

@ThomasSowell Nope, the guy is our Tesla.

Jamie Dimon: "You’ve got to look at Elon Musk, at SpaceX, Tesla, Neuralink – the guy is our Einstein. I'd like to be helpful to him and his companies as much as we can."