There's a guy on Telegram selling fake USDT. To prove he's real, he pins a receipt to his channel — an actual transaction, "100,000 USDT, confirmed onchain," with a link you can click and verify. That's the trick. The receipt is real. What he sells you isn't. I clicked the receipt. Mostly to see what a scammer's wallet looks like. It wasn't his wallet. It was a 2-of-3 multisig — the kind of setup a company uses, three keyholders, two signatures to move anything. Weird thing for a Telegram scammer to be touching. So I pulled its history. Fifteen months. $812 million in. $807 million out. I read that twice. The wallet keeps almost nothing. It's not a wallet — it's a doorway, and money only ever walks through it. Somebody went to the trouble of institutional-grade key management to build a doorway. I wanted to know who was feeding it, so I went one step upstream to the wallet pouring money in. Except it wasn't a wallet pouring money in. It was 69,591 of them. Almost seventy thousand addresses, a couple thousand dollars each, all draining into one collector that bundles them and sweeps the total forward in $900,000 gulps. That's not customers. That's a funnel. And if each of those is a payment, a lot of them are people who got the exact scam I started from. At this point I'm just following the water downhill, and downhill is the cash-out. The money fans out and lands in a handful of addresses. Arkham has labels on three of them. OKX. Kraken. Binance. I want to be careful here, because it matters: those exchange labels come from Arkham, not the exchanges, and I'm not saying anyone at those companies knew anything. B ut the path is there. Every hop, every hash, on a public ledger, sitting in the open the whole time — the same open ledger those exchanges are legally required to watch. I opened a scammer's receipt because I was curious what was behind it. What was behind it was seventy thousand victims, a doorway nobody owns, and a clean exit through three of the biggest companies in crypto. All of it, every address: https://bitquery.io/investigations/812m-ghost-wallet-nobody-flagged submitted by /u/buddies2705 [link] [Kommentare]

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]

'There's a rot at the core of Tinseltown...'

Human DNA can survive on cave walls for thousands of years - shedding light on prehistoric human activity even where bones, sediments or artifacts are

Singapore leads the world in per capita adoption of Anthropic's Claude AI model, reflecting a rapid integration of AI in business. GIC's senior VP Dominic Soon highlights the massive benefits of responsible AI deployment at a recent GIC-Anthropic event. With a US$1.5 billion investment in Anthropic, GIC underscores its commitment to AI development.

It has 205 miles of bare-bones range.