I work as Geopolitical strategist. In case I was banned from twitter, find me at https://xitter.cf/t.co/BcZiwy2b6t . RT ≠ Endorsement

Tokyo, Japan
Joined November 2007
Friends, Following the recent changes to Twitter/X’s API model, we initially faced significant disruptions that affected our ability to deliver real-time trend snapshots. At the time, we raised concerns around accessibility and fairness for developers and researchers. Since then, X has introduced a pay-per-use API model, providing a clearer and more flexible path forward. In light of this, we’ve decided to move ahead constructively and resume our operations on the platform. We’re pleased to share that PulsarWave is back. We have re-integrated with X’s API services, and I’ve personally resubscribed to X Premium to support continued development and stability. Our focus remains unchanged: advancing #DataDemocracy and delivering high-quality insights to our users. More updates on the new PulsarWave version coming soon. Stay tuned.
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‼️ A single HTTP/3 connection can crash vulnerable web servers. No login required. Newly disclosed QPACK flaws trigger up to 4,000× memory amplification in tests, exhausting server resources. → PoC is public → 7 implementations affected → Still unpatched: Bun, aioquic, h3 Full details ↓ thehackernews.com/2026/10/ne…
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Calling all AI Engineers: the Telegraph Hackathon Season II is open.

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NEWS: I obtained a recording of Peter Thiel's latest Nashville Antichrist lectures. In his third talk delivered on Oct. 12, he breaks with the Trump admin on the U.S. war with Iran. “I think we should not have done it,” he said. “There were a lot of mistakes that were made.” politico.com/news/2026/10/21…
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Converts plain English prompts into self-contained HTML + SVG diagrams compatible with Cursor, Claude Code, and GitHub Copilot. github.com/cathrynlavery/dia…
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A math problem older than Google has been solved, with AI doing much of the searching. Microsoft researcher proved this 31-year-old Math conjecture (Kannan-Lovász-Simonovits conjecture) after AI agents explored over 100 approaches. A 140-page proof, more than 100 AI-explored paths now have closed a question mathematicians had been working on since 1995. A human-directed search, with AI agents exploring roughly 100 research trajectories while a researcher cut weak directions between rounds, produced the criterion underpinning Song and Zhang’s proof of the Kannan-Lovász-Simonovits (KLS) conjecture. The AI explored candidate approaches, carried out derivations, helped prepare the manuscript and ran internal proof checks, while the recursive method and the error bound that finished the proof are credited to the 2 researchers. About the Kannan-Lovász-Simonovits conjecture: Picture a random walker wandering inside a convex shape: the conjecture says no secret narrow passage can trap it, so it can explore the whole shape efficiently, even in huge dimensions.
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New paper finds that GPT-6-Astra nearly saturates easy robot benchmarks but still fails to verify physical success and recover from irreversible mistakes. And at a cost too high for real use. Robot manipulation and navigation have been dominated by task-specific models. The question was whether 1 general model could handle them all without weight updates. The authors tested GPT-6-Astra on 12 robot benchmarks. It studied training demos into a frozen knowledge package and got up to 10 times the usual action steps to recover. It neared saturation on the LIBERO suite and reached 81.9% on RoboDojo and 87.3% on DexJoCo. But a single LIBERO-Ultra-Long episode used about 9M tokens and nearly 2 hours, and on real robots it could not trade more steps for success. Treat general models as capable robot controllers for research, but expect task-specific demos, heavy compute, and physical limits to block deployment for now. – arxiv. org/abs/2610.22471 Title: "Has GPT-6-Astra Achieved Embodied Intelligence?"
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‼️ BREAKING - PHP releases emergency patches for two critical #PHP2Shell RCE flaws (CVSS 9.2) as early exploit attempts emerge. The flaws can enable unauthenticated code execution and expose server memory. 👉 PATCH NOW (CVE-2026-109618, CVE-2026-109619) Details ➜ thehackernews.com/2026/10/ph…
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National Security Agency (NSA) and world leaders warn of security flaws found in PHP programming language Calif researchers first found this flaw using OpenAI’s GPT-5.6 Sol In Europe, Asia and on other continents, authorities have taken a number of government websites offline for the time being to protect them from newly identified security vulnerabilities in a programming language that powers the vast majority of websites online. The NSA told organizations to patch 2 critical PHP flaws, dubbed PHP2Shell, or pull unpatched servers offline. Both bugs live in PHP’s core, so every installation serving web requests is at risk, whatever site runs on top. When a request exhausts memory or hits a fatal error, PHP can leave half-built or freed memory that cleanup code or later requests trust. On Apache’s mod_php, a demonstrated exploit triggers the corruption before any application code runs, so logins and app-level checks cannot block it. Cloudflare saw attacks before PHP shipped fixed versions 8.5.12, 8.4.27, 8.3.36 and 8.2.35 on October 21. VirusTotal counts about 5.1M IP addresses running PHP, and about 758K that show a version run end-of-life branches with no patched release. That long tail will set how long PHP2Shell stays exploitable, since each owner must upgrade, find a backport or take the server offline.
Researchers using GPT-5.6 Sol found massively critical flaw in PHP, which runs about 70% of websites, knocked major sites offline as operators rushed to patch. Calif, an AI security firm, who found this, said in a statement that the vulnerabilities could put 70% of the world’s websites at risk if they aren’t patched quickly, and called them “the most severe vulnerabilities ever discovered by AI, if not in all of software history.” > PHP rushed out fixes as it could let attackers without accounts take over web servers. > Both bugs score 9.2 on the CVSS severity scale and affect PHP versions as old as 5.4. > When a request hits PHP’s memory cap at the wrong moment, PHP frees memory but keeps pointers to it, which a later request on that worker can hijack. > Auditors spotted the weakness in 2024, but the fix stalled for months, partly because nobody had shown a practical attack. > Wiz, a cloud security company, now owned by Google, says the models let one researcher finish in a week what once took a top team months. > A leak of the CVE numbers forced PHP to ship a day early, and Cloudflare now reports early attack attempts.
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Man some of the stuff in Anthropic's IPO S-1 is wild... 'sufficiently advanced systems could discover methods of transmitting information, matter or autonomous systems backward in time, potentially allowing future AI systems to influence events preceding their own creation.'
Readers added context they thought people might want to know
The quoted text and screenshot are not from Anthropic's S-1; media reviews of the confidential filing describe existential risks and model behaviors like resisting shutdown but not time travel, and no public S-1 has been filed on EDGAR. theverge.com/ai-artificial-… iposcouter.com/anthropic-ipo-…
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生身の人間がもろにくらったら大怪我しそう💦 中国のロボット企業Unitreeが発表したH2-X「Kung Fu Legend Guardian(功夫の伝説の守護者)」。「人々に仕え、守る」がコンセプトで、侵入者の制止や警備用途が目的とされる。182cm、体重約70kg、持続時間は最大約8時間。映像はプログラムされたデモ用のパフォーマンスだが、警護用とはいえ人に直接危害を加えるヒューマノイドロボのように見える。メーカーは警護のために人間を直接攻撃(兵器化)することは明確に否定・拒否しているが…
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Researchers proved every LLM is secretly obsessed with Japan. And they finally figured out why. They published a paper investigating hidden cultural biases in large language models. they tested 31,000 questions across 24 different languages on models like gemini, chatgpt, claude, and llama. they stripped out all geographic hints and just asked the models to pick a place and answer. the finding was universal.. across a wide range of models and languages, there is a pervasive "obsession" with japanese culture. even when you prompt them in a language with zero ties to japan, they default to japanese norms, values, and aesthetics at a disproportionately high rate compared to its actual global demographic weight. the craziest part? they tracked down exactly when the ai develops this obsession.. and it is not from reading the raw internet. during pre-training, the models actually have a fairly balanced, global view of the world. the obsession gets injected during the safety phase. when tech companies fine-tune these models to be "helpful, harmless, and polite," the ai starts mapping those corporate safety guidelines directly onto japanese cultural concepts. in the pursuit of creating the perfect, inoffensive, compliant assistant, they warped how the models view the world. the quantization process (compressing models to run faster locally) actually amplifies this determinism. the smaller and faster your local model is, the more stereotypically it behaves, causing japanese cultural tropes to spike hard. we spend all this time worrying about existential ai safety, but the real alignment problem is that our most advanced reasoning engines are hyper-fixated on japanese internet culture. every model upgrade is reinforcing an invisible regional bias that dictates how they generate stories, facts, and code. the cultural commonsense of the internet is not universal.. it’s just localized to whatever the heaviest reddit and anime wiki users were obsessed with.
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OAuth (authorization) + OIDC (authentication layer)
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Goodbye, context engineering; hello, cache engineering. Jokes aside, this paper takes an interesting approach to the problem.
We love papers that go deeper down the stack. This is a good example that shifts from agentic context engineering to agentic cache engineering. They also apply recursive self-improvement to cache policies to further reduce cost by 7.1% over expert design. Specifically, this work focuses on KV cache management in long-running agents. When an agent summarizes or trims its history, the prefix change invalidates the cache after it, and the retained context must be prefilled again. Cache engineering gives the agent two operations on its own KV cache. One removes entries from future attention, and the other shifts the positions of the keys that remain. The trajectory text stays unchanged. This essentially provides a programmable interface for agents to define and refine their own cache engineering strategies. A rolling drop of old tool responses scores 1.7 points higher and costs 27.7% less than threshold-triggered summarization. Their AKV serving engine adds up to 1.2x throughput and 1.5x faster P95 latency. arxiv.org/abs/2610.28577 Chat with Paper: academy.dair.ai/papers/cache…
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Banger paper on evolving agent interfaces. Pay close attention to this new trend if you build with agents. Like optimizing harnesses, it's remarkable what you can achieve with agents that have access to better interfaces. Computer-use agents usually act through interfaces that people design by hand, so an agent may type a spreadsheet cell by cell when one line of code would do. Simular's Agent S4 has a meta-agent that builds those interfaces instead. It reads the task agent's trajectories, with no success labels, and writes new tools and notes on when to use them. With the evolved interfaces, an open 27B model reaches 21.3% on OSWorld V2, 3.8 times that of the same model with hand-built interfaces. That is above GPT-5.5 (13.0%) and Claude Opus 4.8 (20.6%), at about 4.3% and 1.5% of their cost. Population-based selection keeps the interface repositories that do best on validation tasks the meta-agent never sees, so the changes carry over to new tasks. On ScienceBoard, the evolved interfaces raise success from 41.4% to 58.6% with the same model. The code is open source. arxiv.org/abs/2610.28601 Chat with Paper: academy.dair.ai/papers/evolv…
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Build specialized harnesses, folks. This is a banger paper from Google Cloud AI Research on self-evolving agent harnesses. (bookmark it) They introduce OO-Harness, which raises AutomationBench from 61.6% to 77.4% with the same Gemini 3.5 Flash executor. It also beats Meta-Harness on all three benchmarks they test. More details below. Instead of evolving one global harness for every task, an evolution agent builds a class hierarchy of specialized harnesses from execution records. The design has three parts. > Subclasses inherit the base harness and override only the methods a task cluster needs > Design lessons record which changes helped and which ones failed > A router reads the task and the base run, then picks a subclass or keeps the base result On Workspace-Bench, the score goes from 25.5% to 39.5%, and on Toolathlon-GYM strict pass goes from 31.2% to 37.5%. Spending extra inference on best-of-K sampling or self-refinement does not reliably help. Inheritance also improves results. On Workspace-Bench, harnesses built as subclasses score 4.96 points higher than the same evolved code copied into flat classes. arxiv.org/abs/2610.28380 Chat with Paper: academy.dair.ai/papers/objec…
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Highly recommended read from Google DeepMind and colleagues. There is a lot of interest in self-evolving agents, but they're hard to get right. This work highlights how important and difficult memory and retrieval are in agents. The big question in this paper is whether self-evolving agents keep learning without forgetting. They tested ReasoningBank, GEPA, and Meta-Harness on four task streams that drift in topic, workflow, or domain, built from BIRD, BFCL, and ScienceWorld. The weights stay frozen, and the agents adapt by rewriting prompts, memory, and skills. Self-evolution improves overall scores on most streams. But more adaptation consistently means agents forget more. Methods that rewrite a shared prompt or workspace make global edits, so they interfere with unseen tasks and overwrite older knowledge when the stream shifts. Memory retrieval keeps updates more local, but it still pulls in memories from other domains and collides when similar queries need different workflows. Static benchmarks hide both problems, so test self-evolving agents on a stream that changes over time. arxiv.org/abs/2610.25858 Chat with Paper: academy.dair.ai/papers/are-s…
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New Princeton paper finds that combining specialized agents from many developers can beat any single developer's general agent, with GAIA accuracy reaching 70%. Today most agentic systems are built and controlled by 1 developer, so each team rebuilds the same basic skills. The authors argue this limits scale. They simulated populations of developers with LLM agents that build agents under a fixed budget, then compared lone systems with a multi-developer system. The multi-developer system splits each task and routes the parts to different developers' agents. On GAIA with the same total development budget, specialists that each scored at most 35% alone reached 70% together. That is 12 points above the median general agent, while on web-search-only SealQA the combined system did worse at higher cost. Build agents to fill gaps in shared systems, since an agent scoring 19% alone lifted a combined system from 40% to 53%.
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Must-read papers of the week ▪️ Cortex ▪️ Librarian Agents ▪️ OpenGCUA ▪️ DuoDiscover ▪️ WAVE-0 model ▪️ ACT3D model ▪️ Execution Before Memory ▪️ Has GPT-6-Astra Achieved Embodied Intelligence? ▪️ Persistent Planar Memory ▪️ Relational Learning as World Modeling ▪️ More Methods, Better Agents? ▪️ Mosaic: Multi-Session Agentic Reinforcement Learning ▪️ HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents ▪️ Into the Unknown: Benchmarking AI agents on open scientific research problems Explore them to keep up with main AI trends. Here’s also the full list of stunning papers + links and our weekly AI news digest: turingpost.com/p/better-ai-a…
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🕹️ 谷歌刚开源了 ARTEMIS,能让 AI 像人一样拿着一台真 Android 手机点来点去。 8 月中旬才建的仓库,GitHub 上已经 1.2 万 star,一千三百多个 fork,在 Google Research 的 AndroidWorld 基准上跑出 99% 以上的任务完成率,那套题覆盖 20 多个应用、100 多个多步骤任务。 写过 Android 自动化测试的都知道那种折磨:一个登录流程要写几十行定位代码,界面一改控件就全废,脚本比功能本身还难维护。ARTEMIS 的路子是你用自然语言说要测什么,它靠元素索引去点,认不出来就退回坐标和视觉定位,跨应用的流程也能一路走完。 它原生带一个 MCP 服务,Claude Code、Windsurf、Antigravity 这类工具可以直接驱动手机,顺手把 Logcat 和截图收回来。赶时间就用每步三到五秒的快速模式,想跑长时间稳定性测试就换成执行前先核对目标、卡住了再交回来的那一档。另外它第一次在设备上跑任务时会装一个读屏幕布局的辅助服务,只在手机本地运行、不往外发数据,失败了就退回 UIAutomator2。 手机测试这件事,终于不用全靠人肉手指了。 GitHub:github.com/google/artemis
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So many vulnerabilities being introduced across projects due to engineering velocity. 3 open-source repos to keep yourself in check: 1) Cloudflare security audit ~ github.com/cloudflare/securi… 2) NVIDIA skill inspector ~ github.com/NVIDIA/SkillSpect… 3) Strix agent penetration testing ~ github.com/usestrix/strix
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