标签：#cad

Trump admin's propaganda "arcade" hits some apparent legal limits.

Interesting research: “ Stealing Reasoning Traces from Proprietary LLM APIs “: Abstract: Leading large language model providers now conceal their models’ step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the client passes back with each subsequent request. Building on prior research, we identify an architectural vulnerability: these encrypted blocks are fully compatible and interchangeable across different sessions, users, and models within a provider’s ecosystem. We exploit this compatibility to develop a scalable decryption jailbreak. By injecting an encrypted reasoning trace from a given model into a weaker, and less safeguarded model from the same provider, we force it to decode and output the trace verbatim in plaintext, without ever jailbreaking the more capable model directly. This vulnerability enables four distinct attack vectors. First, it circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model’s reasoning, as we demonstrate across Anthropic, OpenAI, and Google. Second, it allows for large-scale private data extraction. Developers frequently share session logs publicly, unaware of contents of the encrypted blocks. By decoding 315,320 reasoning blocks scraped from public repositories, we recovered 367 Personally Identifiable Information (PII) artifacts and 182 credentials. Third, it inadvertently reveals hazardous information hidden within the reasoning process, even in cases where the model’s final, visible output safely rejects a malicious request. Fourth, attackers can leverage this flaw to execute invisible prompt injections, embedding malicious payloads entirely within encrypted blocks to poison public agentic rollouts. Following responsible disclosure, we propose concrete cryptographic and system-level mitigations to secure client-side reaso

Most countries tax immovable property, and most of them argue about it badly. The argument usually skips the part that decides the answer: before any map is coloured, someone has to choose what is added up, what it is divided by, over which territory it is aggregated, and which cases are left out . Change any of those and the map can change while the underlying data stay identical. This post works through that problem with Chilean data, because Chile happens to publish the pieces needed to do it honestly: a national cadastre of every taxable property, and an official index that ranks small civic territories by socio-territorial vulnerability. The mechanics, though, are not Chilean. Any jurisdiction that assesses property for tax and then maps the result against a deprivation measure faces exactly the same four choices. The question fits in one small fraction: territorial indicator the unit you compare it against the total you want to describe ​ Adding up the assessed value inside a territory answers how much administrative value was allocated there. Dividing that same total by households, by residents or by square metres answers different questions. None of them is «the correct one» by nature; the error appears when one is presented under another's name. The arithmetic is usually innocent. The narrative is not always. Reading contract I cross two Chilean administrative registers: the real-estate cadastre of the Servicio de Impuestos Internos (SII) —Chile's tax authority, roughly the counterpart of the IRS or HMRC— and the Índice Global de Vulnerabilidad Socioterritorial (IGVUST) , a socio-territorial vulnerability index published by the Ministry of Social Development and Family. The unit of analysis is the neighbourhood unit , not the parcel, the household or the person. A word on that unit, because it has no clean equivalent elsewhere and it drives half of what follows. A Chilean unidad vecinal (UV) is a civic territory drawn for neighbourhood organisation and loca

"We raised at a peak, but sold at a fraction of that," Unacademy co-founder and CEO Gaurav Munjal wrote. "I'm not going to dress these facts up."

Contents Motivation and Purpose Using an Animation to Visualize Key Concepts Working With the Model 1. Get the Files 2. Choose the Dimensions 3. Inspect the Assembly 4. Export the MakerWorld Plates 5. Attach the Hoses Create the Model Animation Reference Links Motivation and Purpose I made a parametric bayonet connector for AC hoses , to attach such hoses to a mobile AC unit and a typical window kit taking the hot air outside. It is part of my Air Conditioning Collection on MakerWorld . And of course, I prefer code over using some GUI CAD application, leading straight to OpenSCAD as the established standard for 3D-models-as-code. It is also one of the few ways MakerWorld models can be made customizable by the end user. The connector allows attaching a hose with a simple push and a short twist, instead of needing a threaded joint or tools every time the hose is removed. The design has three printable parts: A female connector with bayonet slots on the inside. A male connector with matching lugs. A female adapter with a wider fitting section for joining to an existing tube, like the one on an air intake cover. The male lugs fit into the slots in the female connector. Push the parts together, twist them, and the lugs travel along the horizontal parts of the L-shaped slots. This is the same basic idea used by bayonet light fittings, camera mounts, and other quick-release connectors. See the Bayonet mount overview for useful background. The hose itself is held on the printed connector with a worm-drive hose clamp. A screw on the clamp pulls the perforated band tight around the hose. See the Hose clamp reference for an explanation of that mechanism. Using an Animation to Visualize Key Concepts The OpenSCAD file contains code that creates a 10-scene animation. It shows the female connector turning to expose the slots, the hose and male connector moving into position, the male part twisting to lock, and the parts separating again. It also emphasises the parametric nature of

"It's called the US Space Academy. That's a big deal."

Interesting paper : Abstract: With entrepreneurial fraud cases on the rise, we investigate how entrepreneurs carry out criminal deception , employing deceptive means to defraud audiences. Analyzing court data from Silicon Valley ventures and their founders prosecuted for fraud between 2000 and 2023, our findings reveal that entrepreneurs carry out criminal deception through a process of façading : Entrepreneurs construct, perform, and protect illusory appearances (façades) that externally project high-growth performance to audiences while masking ventures’ actual underperformance. We identify three forms of façading—­surface, reinforced, and deep façading­—that are contingent on the severity of the gap that entrepreneurs face between audiences’ performance expectations and ventures’ performance reality. Our theoretical framework captures how entrepreneurs facing minor, wide, and extreme expectation-reality gaps engage in evermore sophisticated efforts to detach the venture’s externally projected appearance from its actual operational reality. Practically, we propose several approaches to deter and detect criminal deception, including the extension of U.S. Securities and Exchange Commission surveillance and whistleblower program, investor due diligence reform, and dedicated entrepreneurship education interventions that clearly demarcate when entrepreneurs transgress into criminal deception. We make contributions to literatures on cultural entrepreneurship, organizational wrongdoing, and the social effects of entrepreneurship. ...

I have been thinking a lot about AI and integrity. Part of that is contextual integrity. I recently found two papers on the topic. “ CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs “: Abstract: Large Language Models (LLMs) increasingly use persistent memory from past interactions to enhance personalization and task performance. However, this memory introduces critical risks when sensitive information is revealed in inappropriate contexts. We present CIMemories, a benchmark for evaluating whether LLMs appropriately control information flow from memory based on task context. CIMemories uses synthetic user profiles with over 100 attributes per user, paired with diverse task contexts in which each attribute may be essential for some tasks but inappropriate for others. Our evaluation reveals that frontier models exhibit up to 69% attribute-level violations (leaking information inappropriately), with lower violation rates often coming at the cost of task utility. Violations accumulate across both tasks and runs: as usage increases from 1 to 40 tasks, GPT-5’s violations rise from 0.1% to 9.6%, reaching 25.1% when the same prompt is executed 5 times, revealing arbitrary and unstable behavior in which models leak different attributes for identical prompts. Privacy-conscious prompting does not solve this—models overgeneralize, sharing everything or nothing rather than making nuanced, context-dependent decisions. These findings reveal fundamental limitations that require contextually aware reasoning capabilities, not just better prompting or scaling...

Block Blast!, the most downloaded mobile game of 2025, is coming to Apple Arcade in September where it will be ad-free.

Interesting empirical research: “ Black Box Warfare: Human Judgment and Military Decision-Making in the Age of AI .” Abstract: How is AI transforming decision-making in modern conflict? This study provides a unique empirical window into that question by deploying a high-fidelity replica of an AI decision-support system (DSS) used in military targeting. After reconstructing the interface and functionality of the real-world system, we tested its impact on combat decisions in two experiments involving 2,015 Israeli military personnel. Contrary to widespread fears of automation bias, we find strong evidence of algorithmic aversion, especially in scenarios involving high collateral damage. Yet we also show that integrating “explainable AI” features reduces algorithmic aversion and promotes more thoughtful evaluations of algorithmic recommendations. These findings challenge prevailing assumptions, revealing that trust in military AI is dynamic, varying with individual predispositions, perceived operational stakes, and the informational features of the interface. By grounding normative concerns in empirical evidence, our study offers critical insight into the integration of AI in warfare and underscores the enduring importance of human agency in high-stakes military decision-making...

Originally published on Loop & Retry — field notes on building LLM agents that survive production. Here's the failure mode that surprises people who've only reasoned about agents statistically. You measure a per-step error rate — say 10% of steps produce something wrong — and you assume errors are independent, so a wrong step is a wrong step and the rest of the run is fine. Then you watch a real trajectory and see something else: step 4 gets a fact slightly wrong, step 5 reasons on top of that wrong fact and commits harder, step 6 takes an action premised on both, and by step 8 the agent is confidently executing a plan that was doomed at step 4. One mistake became five. The errors weren't independent — they were coupled through the context , and coupling is what turns a 10% step-error rate into a run that's wrong far more than 10% of the time. This is the cascade : a single fault amplifying down a single trajectory. It's distinct from the failure I wrote about in distributed retry patterns , where the problem is one bad condition hitting many workers at once — that's a blast radius, a horizontal spread. The cascade is vertical: it spreads through time within one run, because an agent's own past output is its future input. This post is about the vertical kind, why it's structural rather than bad luck, and where you can cut it. Why coupling is the default, not the exception A stateless function that fails just returns an error. An agent that fails does something worse: it writes the failure down where it can read it again. The mechanism is the same one that makes agents work at all — the transcript accumulates, and every step conditions on everything before it. That's a feature for carrying intent forward. It's also the exact channel a mistake travels down. Three ways a single fault propagates through the context: Poisoned premise. The agent derives or retrieves a wrong fact — a misparsed tool result, a hallucinated ID, a stale value — and it lands in the transcript a

There’s new benchmark measuring AI’s ability to perform mathematical cryptanalysis. Anthropic’s frontier model actually found new attacks. The benchmark: “ CryptanalysisBench: Can LLMs do Cryptanalysis? ” The idea is to benchmark the ability of LLMs to discover new mathematical cryptanalytic attacks against a series of historical algorithms. Abstract: Cryptanalysis—the task of finding attacks against cryptographic schemes—its at the intersection of mathematical reasoning and cybersecurity, two areas where LLMs have advanced fastest. Cryptanalysis represents both a clean testbed for frontier reasoning (as practical attacks can be automatically verified) and a domain with unusually high stakes, since the primitives under study underpin our digital security. In this paper we ask whether LLMs can do cryptanalysis, and find that the answer is increasingly yes. We introduce CryptanalysisBench, 191 tasks across six families of cryptographic primitives (block ciphers, hash functions, etc.) drawn primarily from four NIST standardization competitions. Our benchmark consists of three tiers: (i) primitives with known practical breaks; (ii) primitives with no known practical break, evaluated both at full strength and as scaled-down variants; and (iii) a challenge set of production primitives at the frontier of cryptanalysis. Five frontier models (Claude Opus 4.8, Sonnet 5, Mythos 5, GPT-5.5, and the open-weights GLM-5.2) break 65%­86% of Tier 1 schemes, 6­12 Tier-2 schemes at full strength, and 24­61 across all scaled-down variants. Beyond deriving known results, models produce novel cryptanalysis, such as a key-recovery attack that exploits a design flaw in the SpoC AEAD and an error in KINDI’s published CCA-security proof, both to the best of our knowledge not previously known...

New paper: “ Encryption and Globalization 15 Years Later: End-to-End Encryption and the Third Round of the ‘Going Dark’ Debate “: Abstract : This Article updates and expands on 2012 research on encryption and globalization, analyzing what the authors call “Round 3” of the Going Dark Debate: the current controversies over end-to-end encryption (E2EE). Governments around the world have proposed, and in some cases enacted, laws limiting E2EE for law enforcement and national security purposes. This Article explains the underlying technologies and market developments for a law and policy audience to assess those proposals critically. The Article proceeds in three parts tracking three rounds of the Going Dark Debate. Round 1 covers the Crypto Wars of the 1990s, when U.S. export controls on strong encryption ultimately fell in 1999. Round 2 covers the period roughly 2010 to 2015, when encryption-in-transit became widespread but lawful access remained available through cloud providers, giving rise to what the authors called a “golden age of surveillance” rather than a period of going dark. Round 3 addresses the current debate over E2EE, where no entity between sender and recipient can read the plaintext...

Most bibliography failures show up the night before arXiv or the journal deadline: placeholder DOIs, year pasted into volume= , inverted page ranges, invented case reporters. The fix is a paper repo that fails closed from day one . One command pip install https://github.com/SybilGambleyyu/citesure/releases/download/v0.5.68/citesure-0.5.68-py3-none-any.whl citesure init my-paper cd my-paper citesure gate . --preset ci citesure gate . --preset arxiv citesure init writes refs.bib , pre-commit hooks ( gate --preset ci + soft-lint), .github/workflows/citesure.yml , and a short CITESURE.md for coauthors. Empty bibliographies skip hard-ID floors until entries appear. What the gate checks Soft-lint — placeholder number/issue, inverted pages, year-like volume/month/edition, unsafe keys, all-caps titles, missing venues, duplicate DOIs/titles Health — hard-ID coverage floors Promote dry-run — DOIs still buried in url= Live verify — Crossref, doi.org, arXiv, PubMed, Europe PMC, DataCite, OpenAlex, CourtListener Domain packs Fifty-five live-clean packs (demography, sociology, political science, anthropology, ML, law, ecology, …): citesure packs --gate-all citesure packs --run anthropology-classics Evidence: 256/256 integrity · 209/209 claim pairs · 55 packs. Source: github.com/SybilGambleyyu/citesure · Demo: citesure.sybilgambleyyu.workers.dev

a16z Speedrun, Ada Ventures, and Snowball VC have invested in Cascade's $3.5 million seed round.

Daniel Solove argues in the Wall Street Journal (alternate link ) that giving people control of their personal data is not an effective way to regulate privacy in this era. Instead, we need to hold companies accountable for their actions, similar to what we do with food and drug companies. Measures such as rigorous data minimization, fiduciary duties, liability for negligent or reckless technological design, liability for algorithms that cause harm, and multi-stakeholder review of technologies will be far more effective. Paper .

Amazing : Researchers from ETH Zurich in Switzerland, however, managed to create a new type of pixel that can simultaneously do both. This hypercharged pixel, called a Fourier pixel, can generate and sense arbitrary light fields and tap into a pixel’s full potential for carrying information by manipulating light’s intensity, oscillation phases, and polarization. The team reported its findings in a paper published yesterday in Nature. We are one step closer to 1984 technology: The telescreen received and transmitted simultaneously. Any sound that Winston made, above the level of a very low whisper, would be picked up by it; moreover, so long as he remained within the field of vision which the metal plaque commanded, he could be seen as well as heard. There was of course no way of knowing whether you were being watched at any given moment...

Learn how to use ChatGPT, start your first conversation, and discover simple ways to write, brainstorm, and solve problems with AI.

Interesting paper: “ Cybersecurity Mission Creep .” Abstract: Cybersecurity is experiencing mission creep. Policymakers are casting more and more problems as issues of cybersecurity. So reframed, wildly different policy issues, from misinformation, to child social media safety laws, to antitrust regulations, to alleged journalist misconduct, to anti-sex trafficking statutes become what this Article calls “cybersecuritized.” Before this reframing, these issues present as important but not existential. But once cybersecuritization positions the issues as threats intensified by their technological nature, they gain access to the politics and law of urgency and exceptionalism and invite troubling governance responses...

A pending report on climate attribution may be setting the stage for conflict.