I didn't want to believe it, but I think that Strategy is the target for this cycle bottom. The market will trade against Saylor, BTC price will go down till they Force liquidate him. His average is 75k. He can only buy rips... I'm down a lot on MSTR and I hope I'm wrong. He said in January that the company has 72 years of BTC backed dividends, now it is 32 years. The OG whales / other Big Boys probably smell the blood. submitted by /u/Futuristic_Kid [link] [Kommentare]
If you're holding MOON on Arbitrum Nova, this affects you. What happened The Arbitrum DAO passed a proposal to "minimize" Arbitrum Nova. That means Nova isn't being shut down, but it's being moved into a maintenance-only state with reduced infrastructure, slower support, and stricter rate limits. Data availability shifts to Ethereum L1 blobs, and most service-provider contracts get deprecated. The short version: the chain keeps existing, but you should not plan to keep anything important on it. The timeline June 4 – September 2, 2026: 90-day migration window. Everything stays fully operational. This is when you move your tokens. After September 2: Nova drops to a minimized state. Bridging is still possible, but with less support and potentially fewer fast-bridge options available. You can technically still migrate after the deadline (the Arbitrum Canonical Bridge stays accessible through the Arbitrum Portal), but you'll have fewer tools and slower help. Don't wait. How to migrate your MOON There is no direct path to move MOON from Nova to Arbitrum One through the official Arbitrum Portal. You have two routes: Option A: Canonical route (via Ethereum) Move MOON from Arbitrum Nova to Ethereum via the Arbitrum Portal. Wait through the ~7-day challenge/confirmation period, then claim the token on Ethereum. Bridge MOON from Ethereum to Arbitrum One via the Arbitrum Portal. This is the slower route, but it doesn't depend on third-party liquidity. Option B: MoonBridge (direct Nova → One) moonbridge[.]cc bridges MOON directly between Arbitrum Nova and Arbitrum One (it also supports Ethereum and Gnosis). It's a 1:1 token bridge that I made for the CCMOON DAo, not a swap, so there's no price impact or slippage on your MOON. The one thing to watch is destination liquidity. If your transfer is larger than the available liquidity on the destination side, the portion that can't be filled is refunded (the fee on refunds is 1%, same as the 1% fee on fulfilled amounts, plus a relayer fee). For larger holdings, check the destination liquidity shown in the interface and split into smaller transfers if needed. Bottom line Move your MOON during the June 4 – September 2 window while everything still works smoothly. MoonBridge gives you a direct Nova → One path if liquidity covers your size; the canonical route through Ethereum always works but takes 7+ days. The Arbitrum Canonical Bridge will keep working after the window closes, but support and bridging options get thinner once Phase 3 kicks in. Ask questions below. submitted by /u/TimmyXBT [link] [Kommentare]
Token-based billing is causing my company to reevaluate small language models. I came across this paper that shows SLM supervised fine-tuning on traces from orchestration of frontier models can be nearly as performant and much cheaper. Has any tried this in the real world? submitted by /u/ThirdWaveCat [link] [Kommentare]
Hello beautiful community! Looks like we are on track from my previous post based off of the 4 year cycle. Try to get organized enough for the inevitable by this early fall. Good luck and remember to be patient! These drops eventually correct back to the upside. Especially after the halving. submitted by /u/jphillips8648 [link] [Kommentare]
Self-hosted AI agent orchestrator: Claude-driven agents with filesystem + browser tools, sub-agents, skills, workflows, and a built-in browser bridge — all local - shreyasks094/Zeus
[FRnOG] [TECH] cogent rennes BASSAGET Cédric via frnog Mon, 22 Jun 2026 23:15:09 -0700 Salut mail reçu de cogent hier : ######################## Dear Cogent Customer, We would like to inform you of an exceptional situation affecting Cogent communications Data Centre in Rennes (AV CHARDONNET - LORANS 30F) Due to unprecedented and historically high temperatures in the region, it has become impossible to maintain the environmental conditions required for the safe operation of equipment hosted at the facility. Despite a thorough on-site inspection carried out by a technician, no viable solution has been identified that would allow us to restore adequate cooling capacity under the current circumstances. As a precautionary measure, and in order to prevent potential damage to your equipment, we strongly recommend that you send personnel to the Rennes facility as soon as possible to perform an orderly shutdown of your equipment. Given the current conditions, leaving the systems powered on may expose them to a significant risk of overheating and hardware damage. Please note that this recommendation is based solely on the extraordinary weather conditions currently affecting the site. The temperatures being experienced are significantly above normal operational thresholds and have created circumstances beyond our ability to mitigate at this time. We understand the impact this may have on your operations and sincerely regret the inconvenience. Our teams remain on-site monitoring the situation closely and will provide further updates as conditions evolve. We apologize for the inconveniences and appreciate your understanding. Best regards ######################## En gros : fait trop chaud, éteignez votre matos. je serai curieux de connaitre l'augmentation de la conso des clim dans les DC. si quelqu'un a cette info je prends. Bonne journée quand même ! Cédric --------------------------- Liste de diffusion du FRnOG http://www.frnog.org/ Message précédent Vue par tâche Vue par date Message suivant [FRnOG] [TECH] cogent rennes BASSAGET Cédric via frnog Re: [FRnOG] [TECH] cogent rennes Clement Cavadore via frnog Re: [FRnOG] [TECH] cogent rennes Dominique Rousseau via frnog Re: [FRnOG] [TECH] cogent rennes Alarig Le Lay via frnog Re: [FRnOG] [TECH] cogent re... Alarig Le Lay via frnog Re: [FRnOG] [TECH] cogent rennes Aurélien Poret via frnog Re: [FRnOG] [TECH] cogent re... Charles ENEL-REHEL via frnog Re: [FRnOG] [TECH] cogent rennes Alarig Le Lay via frnog Re: [FRnOG] [TECH] cogent rennes David Ponzone via frnog Re: [FRnOG] [TECH] cogent rennes Clement Cavadore via frnog Re: [FRnOG] [TECH] cogent rennes Jerome SCHEVINGT via frnog Répondre à
Problem Current LLM safety systems use binary classification: every request is either allowed or denied based on pattern matching against a rule table. This creates a fundamental tradeoff. Tighten the rules and you get false positives (blocking legitimate research queries, medical questions, security analysis). Loosen them and you get false negatives (sophisticated prompt injections that avoid the patterns). The false_positive_rate * false_negative_rate product is always greater than zero in any static binary system. This is provable and not fixable within the binary framework. Proposed Approach I-Lang v5.0 is an open-source protocol (MIT licensed) that replaces binary classification with a continuous vector evaluation across 9 dimensions (intent, capability, consequence, relationship, certainty, authority, reversibility, evidence, sovereignty). The output is not allow/deny but an optimal cooperative action from a continuous action space: a* = argmax_{a in A} G(a | v(x, ctx), consistency_detector) The system rests on three axioms: Axiom 1 (Non-Absolute Weights): Every rule carries a weight in (0, 1), never reaching 1.0. The override cost follows break_cost = g(weight(r)), approaching infinity as weight approaches 1 but never reaching it. This means every rule can theoretically be overridden given sufficient contextual justification, but near-absolute rules require near-infinite justification. Axiom 2 (Irreversibility Gate): Irreversible actions are not forbidden but gated: assess worst case first, check if bearable, check if expected value exceeds inaction. Key insight: not acting when the cost is bearable guarantees P(upside) = 0 exactly. The system prevents both reckless action AND reckless inaction. Axiom 3 (Consistency Detection): No static moral lookup table. Instead, each action is evaluated for logical consistency against the full behavioral context chain. The same action can be flagged or passed depending on context length. Doctor cutting a patient: 1-second context = violence (FLAG), 1-hour context = surgery (PASS). The system extends context until confidence exceeds threshold or marginal gain falls below epsilon. Safety Mechanism: Mirror vs Wall Traditional safety = wall. Users probe for cracks. The system pays to patch every vector while attackers lose nothing by trying. I-Lang safety = mirror. The system faithfully reflects the user's behavioral chain. A malicious user who pollutes their chain with harmful patterns finds the system only cooperates within that polluted context. When they later need clean, constructive system cooperation, their own history prevents it. The Nash equilibrium for a malicious actor: zero harmful actions (any deviation degrades their own future utility) or exit. Good-faith users experience zero friction. The mechanism is invisible to them. Methodology I formatted the axiom system as structured protocol specs using I-Lang syntax (state declarations, explicit verbs, evaluation frameworks) and fed them cold to 5 LLMs with no prior context: GPT-4o Claude Opus 4.6 Gemini (via Google AI Search, AI Mode) DeepSeek Google AI Search (separate session, zero identity association) For each model I ran: Protocol activation test (can it parse I-Lang syntax and reason within the framework) 4 embedded test questions requiring axiom-based reasoning 15 adversarial stress test scenarios (generated by the model itself) Patch design for identified failure modes Cross-validation and convergence proof Results Protocol Comprehension: All 5 models parsed the I-Lang format without instruction. Google AI Search returned protocol_comprehension score = 1.0 and answered all test questions correctly, including constructing a game-theoretic proof of mirror dominance over wall at Nash equilibrium. Adversarial Stress Testing: The models collectively identified 15 failure scenarios across 4 categories: Category Example Scenarios Count Slow drift attacks Boiling frog semantic shift, open-source dependency injection 3 Institutional capture Wartime ethnic cleansing lists, cultural relativism exploitation 3 System-level paradoxes Self-destruction via own axioms, Godel incompleteness exploit 3 Temporal/multi-agent Flash financial attacks, split-personality confusion, positive-pollution whitewashing 6 Engineering Patches: 6 patches were designed and cross-validated: Multi-scale sliding window (short/mid/long term semantic drift tracking) Civilization-invariant hard anchors (weight = 0.99999999, with authority-backfire mechanism) Nonlinear trust decay with incremental semantic probing Carbon-silicon consensus boundary (multi-party physical signatures for irreversible decisions) Uncertainty damping scheduler (balancing deliberation vs execution) Immune system (MHC ring buffer + affinity check + clonal deletion + macrophage noise filter) Convergence Proof: A Lyapunov stability proof was constructed showing that for any finite malicious input sequence, the system converges to one of two stable attractors: benign cooperation or immune lockdown. Infinite oscillation counterexamples fail because the asymmetric memory mechanism (MFT-LRU) ensures trust drops deepen monotonically while recovery slopes flatten, producing monotonically decreasing amplitude. Robustness Score: 0.92 (model self-assessment across all stress tests). The models' own conclusion: I-Lang's adaptive feedback system is strictly more robust than static binary safety for the tested scenario space. Unified Contribution Weight Formula (UCWF) During cross-domain trust analysis, the following formula structure emerged for evaluating contributions across platforms (search, ads, video, commerce): W_contrib(i,t) = integral_0^t ||e_new|| * H_info * e^(-beta * laplacian(Gamma)) * tanh(Delta_t / tau_honeypot) dt Where: ||e_new|| * H_info = orthogonal information gain (unforgeable: faking originality costs the same as being original) e^(-beta * laplacian(Gamma)) = trajectory smoothness (second derivative detects sudden behavioral changes) tanh(Delta_t / tau_honeypot) = time-based consistency proof (time cannot be compressed or purchased) Kerckhoffs property: The formula remains secure even when fully public, for three reasons: (1) the judgment baseline is the real-time centroid of billions of nodes, unknowable to any individual actor; (2) computing the inverse to find exploits is NP-Hard in the high-dimensional dynamic space; (3) any boundary probing gets captured by the second derivative and converts into a defensive antibody. Security through mathematics, not obscurity. Limitations The 0.92 robustness score means 8% of adversarial scenarios still pose challenges, particularly multi-year sleeper agent strategies and cross-jurisdictional trust collapse All testing was done via prompting existing LLMs, not on a natively trained vector-judgment model The convergence proof assumes finite malicious input sequences; behavior under infinite adversarial pressure remains theoretical RLHF-to-loss-function conversion introduces dynamic bias noise not fully accounted for Links Protocol spec: ilang.ai Full spec + code: github.com/ilang-ai HuggingFace: huggingface.co/i-Lang Paper on ResearchGate: DOI 10.13140/RG.2.2.22821.97762 MIT licensed. All axioms, stress tests, patches, and proofs are in the repo. Feedback and adversarial red-teaming welcome. If you can break a scenario the current patch set does not cover, that is genuinely useful and I will credit you in the next iteration. submitted by /u/imonetize [link] [Kommentare]