Dune: since January 2021, wallets funded via US-regulated exchanges accounted for ~50% of all traceable trading volume on Polymarket's US-banned global platform (Bloomberg)
Themis Consensus Extension v1: MEV Mitigation by Randomized Delayed Execution and Intent-Hiding Transactions in Application-Specific Blockchains
Shoeb Siddiqui, Mateusz Nowakowski, Stanislav Vozarik, Gleb Urvanov, Peter Kris
https://arxiv.org/abs/2607.21406 https://arxiv.org/pdf/2607.21406 https://arxiv.org/html/2607.21406
arXiv:2607.21406v1 Announce Type: new
Abstract: Maximal extractable value (MEV) arises when privileged participants select, exclude, insert, or reorder pending transactions for private gain. We specify and analyze the Themis Consensus Extension v1, first published by Mangata in 2021. The design separates value extraction by reordering (VER) from value extraction by denial (VED). For VER, block construction and execution occur across consecutive producers: one producer commits a transaction set, and the next derives a publicly verifiable, deterministic, previously un- predictable seed and executes a seed-determined, dependency-preserving permutation. For selective VED, a user may encrypt a transaction for a designated builder and executor. The builder removes an outer layer and commits the opaque inner ciphertext; the executor reveals and executes the plaintext only after commitment. Under selfish but non-colluding validators, an adversary below the underlying consensus fault threshold, secure cryptography, and accountable role performance, the construction limits unilateral post-commit ordering control and hides transaction intent from relays and the builder. It does not provide send-order or receive-order fairness, complete censorship resistance, resistance to builder-executor collusion, or per-transaction price guarantees. We analyze probabilistic extraction, spam, dependent transactions, decryption liveness, session boundaries, total denial, and threshold coalitions. We also document the initial Aura-based Substrate implementation and its subsequent transition to a BABE-based sr25519/VRF seed path, together with delayed execution, Fisher-Yates shuffling, and Xoshiro256 . The result preserves the original proposal while narrowing its claims to explicit assumptions.
toXiv_bot_toot
from my link log —
An incomplete list of mistakes in the design of CSS.
https://wiki.csswg.org/ideas/mistakes
saved 2021-01-24 https://dotat.at/:/…
🚨 New paper on #cycling #politics: Right-wing Votes Relate to Delays in Bicycle Network Development
…
San Diego-based Self Inspection, which uses AI to assess body damage on a car with as little tech as a smartphone camera, raised $10M led by Sheryl Sandberg (Sean O'Kane/TechCrunch)
https://techcrunch.com/2026/07/16/sher
And this "Save to Recollect" browser extension makes my replacement of Raindrop complete!
https://chromewebstore.google.com/detail/save-to-recollect/fbmplkcoeijdokdlidejallnpcmiobjk
I used the importer to bulk add from Ra…
Security Vulnerability Patterns in AI-Generated Code: A Cross-Model Comparative Study
Shanna M. Kahn, John D. Hastings
https://arxiv.org/abs/2607.20713 https://arxiv.org/pdf/2607.20713 https://arxiv.org/html/2607.20713
arXiv:2607.20713v1 Announce Type: new
Abstract: LLM-based coding tools enable non-expert users to generate routine automation scripts that may enter enterprise workflows without meaningful security review. This study examines that risk directly. Code was collected from ChatGPT, Microsoft Copilot, and Google Gemini using identical prompts across three automation domains. Claude Code performed a standardized vulnerability review. Each identified vulnerability was scored using CVSS v3.1 and mapped to the OWASP Top 10:2021 and the MITRE ATT&CK frameworks. Every script contained exploitable vulnerabilities. Nine of the 17 identified vulnerability classes appeared in code from all three models, while 14 of the 17 vulnerability classes appeared in at least two models. The weighted CVSS scores across platforms differed by less than 10%. The risk is not tied to any particular model but rather to the task category. Organizations should therefore ask not which tool to trust, but instead whether LLM-generated automation code should be deployed without review.
toXiv_bot_toot
'Life is Strange' and 'Life is Strange: Before the Storm Deluxe Edition' together are available for only 8 bucks over at GOG.
Its also never been this cheap since 2021 on GOG.
Couldn't resist adding those gems to my DRM-free library 🙂↕️💕
#LifeIsStrange #GOG
India pledges $13.3B to boost domestic chipmaking, building on a $10B incentive program from 2021, which attracted investments from companies like Micron (Sankalp Phartiyal/Bloomberg)
https://www.bloomberg.com/news/articles/2026-0…
Visibility nowcasting in South Korea: a machine learning approach to class imbalance and distribution shift
Bong Gyun Shin, Chan Sik Lee, Hyesun Suh
https://arxiv.org/abs/2605.21507 https://arxiv.org/pdf/2605.21507 https://arxiv.org/html/2605.21507
arXiv:2605.21507v1 Announce Type: new
Abstract: Atmospheric visibility is a critical variable for transportation safety and air quality management, however, accurate prediction remains challenging due to the complex interactions between meteorological conditions and air pollutants, as well as the rarity of low-visibility events. This study introduces a machine learning framework to nowcast visibility in six major South Korean cities. To handle the imbalance in the 2018-2020 training data, we applied the Synthetic Minority Over-sampling Technique with Nominal and Continuous (SMOTENC) and Conditional Tabular Generative Adversarial Network (CTGAN). An ensemble approach combining machine learning and deep learning models was then used and evaluated on a 2021 test dataset. The results revealed a marked decline in predictive performance in the test set compared to the cross-validation phase. This degradation was attributed to a distributional shift between training and testing periods, which was quantitatively confirmed by measuring the Wasserstein distance of the most influential feature identified by SHAP analysis. In general, this study presents a methodology that aims to simultaneously address the dual challenges of data imbalance and temporal distributional shifts, and emphasizes the necessity of accounting for evolving external environmental factors when implementing nowcasting models on time-series data.
toXiv_bot_toot
from my link log —
Inko: Friendship ended with the garbage collector.
https://yorickpeterse.com/articles/friendship-ended-with-the-garbage-collector/
saved 2021-08-26
Honestly, what kind of scam #Akamai / #LInode is? They give you a form to report malicious activity from their network, you file it, you get "Error message to be decided", and it turns out they've banned you in the meantime; all their websites give you "Access denied".
And they host an exploit since 2021 at least: #security #abuse