Lucky77 | 𝔽rAI | (❖,❖)

✅  @ritualnet #119 💚Ritual karaoke: Ritual: 🎤 : ritty karaoke 🎤 The moment the music flows and everyone's voices become one ◇ PLAY: The moment the music flows, we become one. ◇Share: Our own hideout with endless laughter and cheers. ◇FEEL: Leave yourself to the rhythm and get rid of the stress of your daily life. ◇STAY: A record of this night that's more brilliant because we're together. 📌 Enter the Discord channel right now! Ritty Karaoke Live, fighting! 💥

The $1.7T+ US unsecured consumer credit market is starting to move beyond credit scores alone. For decades, lenders mostly relied on systems like FICO to decide who should get a loan, how much they should get, and at what rate. That is changing: newer lending systems, including those used by @SoFi and @CashApp, use signals ranging from education and employment history to cash flow and platform-specific behavior to make those decisions. The next step is to upgrade the lending stack and move key components onchain. Rialo makes this possible by connecting onchain credit systems to real-world data, providing infrastructure to automate servicing workflows onchain, and enabling seamless tokenization of real-world assets, including private credit and consumer loans. That opens up three opportunities: - Richer credit assessment: Lenders can use alternative signals to determine creditworthiness by tapping into real-world data through Rialo. - Efficient servicing: Lenders can run loan management and repayment workflows on Rialo’s transparent, programmable blockchain rails, reducing overhead and improving transparency. - Loan packaging: Lenders can tokenize loans and bundle them into risk-weighted vintages, or loan pools organized by risk profile, with clearer visibility into risk and performance. The opportunity for lenders is not just to make better loans. It is to use infrastructure that enables them to assess, service, and package loans in a single integrated system. Get Real about credit. Get Rialo.

3월도 끝이 났고 @RialoHQ 의 최근 흐름을 보면 L1 경쟁을 넘어 Real World Blockchain 으로 빠르게 포지셔닝하고 있습니다. 특히 작년 말 Jan Camenisch 합류를 통해 ZKP, 프라이버시 레이어 완성도를 끌어올리는 동시에, MPC, FHE, TEE를 결합한 REX 로 민감 데이터까지 온체인에서 안전하게 처리하려는 방향이 명확해졌습니다. 개발자 생태계 측면에서는 Shark Tank를 통해 Chainsure, OlpaDEX, Event Community Hub 같은 실사용 프로토타입이 빠르게 등장하고 있고, SCALE 모델을 통해 AI 에이전트 간 자동 결제, 협업 구조까지 실험 중입니다. 여기에 Pantera Capital 투자 이후 2026년 메인넷을 목표로 확장 중이며, Mysten Labs 출신 팀의 기술력도 계속 강조되고 있습니다. 결국 Rialo는 단순 TPS 경쟁이 아니라 오라클 없이 웹 데이터를 직접 가져오고, 프라이버시를 유지하며, AI까지 온체인에서 작동하는 실용적 L1로 진화하고 있는 단계입니다.

💚gRitual In the previous post, we saw how adding safety mechanisms can sometimes reduce an agent’s ability to act. Today’s paper explores a different tradeoff. What happens when we try to make reasoning more efficient? [ Does Making Reasoning More Efficient Reduce Understanding? ] ❖ The common assumption Reasoning models often generate long chains of thought. These steps help improve accuracy, but they also increase cost and latency. So it seems natural to try to compress them. Fewer steps → faster responses → same result. Efficiency should not change understanding. ❖ What actually happens The paper studies a method called self-distillation of reasoning. Instead of generating full reasoning traces, the model learns to produce shorter, more efficient outputs. At first, this seems to work. Performance remains similar on familiar tasks. But deeper evaluation reveals a problem. As reasoning becomes shorter, the model loses something important: epistemic uncertainty. The model becomes less aware of what it does not know. ❖ What the results suggest This leads to a surprising effect. Compressed reasoning may look correct, but it becomes less reliable in unfamiliar situations. Especially under distribution shifts: • performance drops more sharply • incorrect answers become more confident • the model fails to detect uncertainty In other words, efficiency removes not just steps — but also signals of understanding. ❖ Why this matters For Ritual, this is critical for how agents operate in real environments. Agents often face situations that are: • incomplete • uncertain • constantly changing In these settings, knowing when you might be wrong is just as important as being right. If efficiency reduces that awareness, agents may act with false confidence. ❖ The deeper shift This research suggests that reasoning is not just about reaching answers. It also carries information about uncertainty. When we compress reasoning, we risk removing that information. So the question is no longer: “How do we make reasoning faster?” But: “What information are we losing when we do?” ❖ Daily takeaway Shorter reasoning can look just as good. But it may hide important signals. Efficiency is valuable — but not if it comes at the cost of understanding. @ritualfnd @joshsimenhoff @Jez_Cryptoz @whitesocks256 @mongdiny7 @ritualdigest

✅  @ritualnet #119 💚Ritual karaoke: Ritual: 🎤 : ritty karaoke 🎤 The moment the music flows and everyone's voices become one ◇ PLAY: The moment the music flows, we become one. ◇Share: Our own hideout with endless laughter and cheers. ◇FEEL: Leave yourself to the rhythm and get rid of the stress of your daily life. ◇STAY: A record of this night that's more brilliant because we're together. 📌 Enter the Discord channel right now! Ritty Karaoke Live, fighting! 💥

Rialo 어나운스를 통해 임계치 시스템(Threshold Systems)이 어떻게 암호화 작업 실행 시 참여자들의 정족수(Quorum)를 요구함으로써 단일 장애점(Single Points of Failure)을 제거하는지, 다섯가지 기술을 공개한다고 했는데 그중 첫번쨰인 DKG(Distributed Key Generation, 분산 키 생성)에 대해 설명해 볼께요 @RialoHQ 1. DKG(분산 키 생성)란 무엇인가요? 보통의 암호화 방식은 한 사람이 비밀번호(비공개 키)를 관리합니다. 하지만 그 사람이 비밀번호를 잃어버리거나 해킹당하면 모든 것이 끝납니다. DKG는 이 비밀번호를 여러 조각으로 나누어 여러 사람이 나누어 갖게 하는 기술입니다. 특징: 누구도 전체 비밀번호를 알 수 없습니다. 장점: 몇 명의 관리자가 자리를 비우거나 공격을 받아도, 나머지 사람들이 힘을 합치면 안전하게 시스템을 운영할 수 있습니다. 2. Rialo DKG 튜토리얼의 핵심 내용 이 페이지는 Rialo 네트워크에서 이 복잡한 기술이 어떻게 실제로 작동하는지 단계별로 보여줍니다. 임계값 암호화(Threshold Cryptography): 예를 들어, 10명의 관리자 중 7명이 동의해야만 문이 열리는 금고같은 시스템을 만드는 법을 배웁니다. 비밀 공유(Secret Sharing): 하나의 비밀 키를 여러 노드(컴퓨터)에 안전하게 배분하는 과정을 시뮬레이션합니다. 상호작용 및 시뮬레이션: 이론만 설명하는 것이 아니라, 사용자가 직접 노드들이 어떻게 소통하며 키 조각을 생성하는지 시각적으로 확인할 수 있는 인터랙티브한 도구를 제공합니다. 3. 왜 이 기술이 중요한가요? (활용 사례) Rialo는 현실 세계와 연결된 블록체인을 지향합니다. DKG는 다음과 같은 곳에 쓰입니다. 지갑 복구: 내 개인 키를 잃어버려도, 분산 보관된 조각들을 통해 안전하게 복구할 수 있습니다 (시드 구문이 필요 없는 지갑). 보안 강화: 특정 관리자가 배신하거나 해킹당해도 전체 시스템은 안전하게 유지됩니다. 프라이버시 보호: 데이터를 암호화하여 저장하되, 허가된 사람들의 합의가 있을 때만 데이터를 확인할 수 있게 합니다. 요약 이를 설명한 튜토리얼은 어떻게 하면 한 명의 관리자에게 의존하지 않고, 여러 명이 안전하게 권한을 나누어 가지면서도 강력한 보안을 유지할 수 있는가?에 대한 기술적인 해답을 초보자도 이해할 수 있게 시각적으로 풀어낸 가이드입니다. Rialo 생태계에서 보안과 개인정보 보호가 어떻게 기술적으로 구현되는지 궁금하시다면 직접 읽어보시는것도 좋을것 같아요 @RialoKorea @silverwave1000

🤍 gRialo In the previous post, I looked at a simple idea. Splitting a secret reduces reliance on a single party. But it also introduces a new problem. Now the system depends on multiple participants. [ 𝐖𝐡𝐚𝐭 𝐕𝐒𝐒 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 ] ✎ The missing piece At first, it might seem enough to simply distribute the secret. But that leaves an important question unanswered. How do we know each part is correct? ✎ Why this matters If even one share is incorrect, the system may fail. Not because the secret is lost, but because the system cannot trust what it receives. So the problem is no longer just about sharing. It’s about verifying what has been shared. ✎ A different approach This is where VSS introduces a key idea. Each share is not just distributed. It is also verifiable. ✎ What this means in practice Instead of assuming all participants are correct, the system can check: • whether a share is valid • whether it matches the expected structure • whether it is consistent with others And importantly, this can be done without reconstructing the full secret. ✎ Why this changes things This creates a different kind of system. Participants no longer need to trust each other. They only need to follow the same verification rules. ✎ A simpler way to think about it Before: “Everyone has a piece — hope they are correct.” After: “Everyone has a piece — and we can check if it’s correct.” ✎ In short Sharing distributes the secret. Verification makes the system reliable. And VSS combines both. @RialoHQ @RialoKorea @itachee_x