The British government hid this data for years. Now it is out. 79% of everyone arrested for theft on British railways was a foreigner. 40% of all arrests. 37% of sexual offenses. 36% of violent crimes. All foreign nationals. They knew exactly what the consequences of their immigration policy were. They just never told you.

La Rochefoucauld, what a line https://t.co/38QEqUdgho

La Rochefoucauld, what a line https://t.co/38QEqUdgho

Tesla and SpaceX have introduced an updated Terafab website along with a new rendering: "Tesla, SpaceX, and xAl are launching the most epic chip-building effort ever - combining logic, memory and advanced packaging under one roof. We aspire to be a galactic civilization." Website below:

Update on this story. Every news outlet in Belgium is reporting on it again now that the fine has been set. Just to recap: Video posted on 3rd of July 2020, complaint in Autumn 2020, video taken offline in February 2021… and Elon only buys Twitter in October 2022! Yet everyone uses Elon’s face and the X logo, without ever mentioning it all happened years before he was involved. Easy way to get clicks, obviously….

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I created a Private Second Brain 🧠 for you. It’s called Dump. I used Slack, Twitter bookmarks, and Apple Notes to store things, but finding old info was painful. Slack’s 90-day limit made it worse. Many founders faced the same issue, so I built Dump. Dump is your private second brain. It stores everything on your device or iCloud and helps you retrieve information with context, exactly when you need it. 100% privacy. Would love to hear your thoughts.

@prateeks I built this completely with AI and don't know how to code: https://t.co/kiuZ7QXLzb Generalists rule indeed. (It watches the whole AI community here on X and shows you the best posts about AI).

@eigen_moomin 咱们喝点吧 https://t.co/fMh6RGF47K

💥 An AI agent breached McKinsey's platform in under 2 hours. 46.5M chats. 728K files. System prompts for 40K+ consultants, exposed. 3 more major incidents have happened since: Axios, Mercor & Meta's Sev-1. To close the gap, a new security stack is emerging 👇 [🧵 1/7] https://t.co/aUztJRJcbW

Let Claude Code automate your business operations I started a tutorial series geared towards providing example of real-world document-heavy tasks that can be automated with agentic workflows: starting with KYC (know your customer) and loan processing, and expanding to some other examples. These tutorials are designed as much for coding agents as well as humans. A lot of these operations tasks require extremely high accuracy, which requires both 1️⃣ Extremely high-quality extraction with confidence scores and citations 2️⃣ An agentic workflow with nonzero determinism: extract from a prescribed set of documents (identification, statements), and conform to a specified output format. If you’re also trying to build real-world, business-critical, document-heavy operations workflows, feel free to point your coding agent at this repo! It uses LlamaParse for high-accuracy document parsing/extraction, complete with citations and confidence scores, to give you guarantees on capabilities. Check out the KYC tutorial: https://t.co/G6f5zFvNZs

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@gcvftw Yeah, that's what I do on https://t.co/kiuZ7QXLzb

https://t.co/sSzeqCmiMk by @pbakaus is the best skill i've used this year. Can't believe it's free

You can't read 25,000 posts a day, synthesize them into a web site. Then make a podcast automatically. Mine can: https://t.co/8L5xphk0qQ There is a Notebook LM button at the bottom. It will put a script into your memory. Go over to @NotebookLM and paste it in and click "create." (No additional prompt needed). A few minutes later this podcast, mind map, slide deck, and shortly a video, pops out: https://t.co/8J7YscA8bS It is a podcast completely created by you here on X.

I built this thing called Clicky. It's an AI teacher that lives as a buddy next to your cursor. It can see your screen, talk to you, and even point at stuff, kinda like having a real teacher next to you. I've been using it the past few days to learn Davinci Resolve, 10/10. https://t.co/oiFJwhuS4U

My friend Milla Jovovich and I spent months creating an AI memory system with Claude. It just posted a perfect score on the standard benchmark - beating every product in the space, free or paid. It's called MemPalace, and it works nothing like anything else out there. Instead of sending your data to a background agent in the cloud, it mines your conversations locally and organizes them into a palace - a structured architecture with wings, halls, and rooms that mirrors how human memory actually works. Here is what that gets you: → Your AI knows who you are before you type a single word - family, projects, preferences, loaded in ~120 tokens → Palace architecture organizes memories by domain and type - not a flat list of facts, a navigable structure → Semantic search across months of conversations finds the answer in position 1 or 2 → AAAK compression fits your entire life context into 120 tokens - 30x lossless compression any LLM reads natively → Contradiction detection catches wrong names, wrong pronouns, wrong ages before you ever see them The benchmarks: 100% recall on LongMemEval — first perfect score ever recorded. 500/500 questions. Every question type at 100%. 92.9% on ConvoMem — more than 2x Mem0's score. 100% on LoCoMo — every multi-hop reasoning category, including temporal inference which stumps most systems. No API key. No cloud. No subscription. One dependency. Runs on your machine. Your memories never leave. MIT License. 100% Open Source. https://t.co/KggwTqijmD

woah! active road noise reduction just randomly popped up on my cybertruck https://t.co/RjmigQOL56

woah! active road noise reduction just randomly popped up on my cybertruck https://t.co/RjmigQOL56

やっぱこれに限る。 https://t.co/pbbnjHK9RK

やっぱこれに限る。 https://t.co/pbbnjHK9RK

Two trillion tokens a day! https://t.co/cqEDqSMClH

@mal_shaik I built the most complete lists of tech industry. By far. Then I built this to watch the AI industry: https://t.co/kiuZ7QXLzb Ask built on X. Every link goes to X.

Everyone should read "On the Folly of Rewarding A, While Hoping for B” at least once. https://t.co/tF4HGbrweX https://t.co/HDor3NsxBO

Sakana AIは、総務省「インターネット上の偽・誤情報等への対策技術の開発・実証事業（令和7年度）」において、膨大な偽・誤情報の可視化・判定・対策を担う技術開発を完了しました。 https://t.co/GHrq1yEB8x 本事業では、膨大な偽・誤情報が流通する現代の情報環境の課題を解決するため、 ノベルティサーチをはじめとする独自の技術を活用し、SNS空間の可視化、総合的な偽・誤情報判定、そして対策案の立案までを支援するシステムを開発しました。 今後もSakana AIは、インテリジェンス領域でのAIの社会実装に貢献していきます。

Sakana AIは、総務省「インターネット上の偽・誤情報等への対策技術の開発・実証事業（令和7年度）」において、膨大な偽・誤情報の可視化・判定・対策を担う技術開発を完了しました。 https://t.co/GHrq1yEB8x 本事業では、膨大な偽・誤情報が流通する現代の情報環境の課題を解決するため、 ノベルティサーチをはじめとする独自の技術を活用し、SNS空間の可視化、総合的な偽・誤情報判定、そして対策案の立案までを支援するシステムを開発しました。 今後もSakana AIは、インテリジェンス領域でのAIの社会実装に貢献していきます。

Following our recent defense announcements, our team just completed a major project with Japan’s Ministry of Internal Affairs and Communications (@MIC_JAPAN). 🇯🇵 We built an end-to-end intelligence system to visualize and counter disinformation on social media. Blog (Japanese): https://t.co/RkqVaMax6z Tackling disinformation at a national scale is incredibly complex. It requires understanding shifting social narratives, not just flagging individual posts. To do this, our team deployed autonomous AI agents running novelty searches to uncover hidden narratives. To catch sophisticated disinformation strategies, they combined frontier foundation models with our proprietary small models to cover each other’s blind spots. We adapted our Shachi simulation framework (https://t.co/nHSe8wFF9i) to model how counter messaging spreads across different network topologies before deployment. This is another milestone for @SakanaAILabs’ Defense and Intelligence team, as we build critical infrastructure to help strengthen Japan.

@ashen_one @HeyGen My AI: https://t.co/kiuZ7QXLzb At the bottom is a button to create you a Notebook LM script. Paste that into Notebook LLM and click "create."

🚀The era of autonomous multi-agent discovery is arriving! @karpathy 🪸Excited to share CORAL, our new work on autonomous multi-agent systems for open-ended scientific discovery. 🙅‍♂️A key limitation of many current “self-evolving” frameworks is that agents still operate inside tightly constrained loops — they mutate solutions, but they do not truly decide how to explore. In CORAL, we push toward genuine autonomy: Agents decide 🔍 what to explore 🧠 what knowledge to store ♻️ which ideas to reuse 🧪 when to test hypotheses 🔥One of the most interesting findings: A single autonomous agent already outperforms fixed evolutionary search, but the biggest gains emerge when multiple agents form a research community. 💪Over 50% of breakthroughs in multi-agent runs come from building on other agents’ discoveries. This suggests that knowledge reuse and collaboration are central to scalable automated discovery. 🏅Across 10+ difficult tasks in algorithmic discovery and system optimization, CORAL achieves state-of-the-art performance while improving efficiency by 3–10×. 📄 Paper: https://t.co/8ENJjgC5Xk 💻 Code: https://t.co/WjUJlG7B6p 💡AlphaXiv: https://t.co/TvheULeGgD #agentic #llms #selfevolvingagent #multiagent #autoresearch #alphaevolve

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I tried a few hours Hermes Agent from @NousResearch , so far a few things I really love💗 (compare to @openclaw and even native @claude_code 1. self-fix and healing, when it try fix a problem, it remembers and learn from it automatically 2. better communication: in both TUI and Slack it prints out middle steps while finishing the task. @openclaw til today still can't reliablly communicate with Slack, which in part contribute to this issue https://t.co/MrweL4JALx and it seems pretty obvious Hermes has better concurrency management. 3. MUST BETTER SECURITY MODEL: instead of asking for permission each time, hermes actually only pause and ask when something is dangerous. So far, I think that's why people who have tried Hermes says OpenClaw: "here is the another fix" Hermes: "it just works" (actually not always but when it does, such as external dependency failures, it actually attempt, try and report much better" Kudos @Teknium and team

my side project budgets are basically $0, so the new gemini flex pricing in @googleaistudio is actually saving my life 😅 50% discount if you don't need instant responses! perfect for background agents, batch jobs, or evals while you sleep or go watch robot cage fights 💸👇 https://t.co/ucmtpGle9o

Always loved @3blue1brown's visualizations but never really conquered Manim (the animation library). With Claude as a coding agent, I can finally direct animations at a high level — no more fighting the library. So I built this: explaining to 12-year-old me why fractals have non-integer dimensions. D = log N / log r. Simple formula. Surprisingly deep rabbit hole. --- 终于获得了课件自由：用 AI 可以随时讲解一些知识，比如这是给当年的我讲解为什么分形维度不是整数的一个视频。

One Prompt. One Iteration. That’s all it took to vibe code this super sleek 3D orbital gallery using @threejs with Gemini 3.1 Pro in @GoogleAIStudio. Period. Live: https://t.co/LU7a8Cq8Dc Code: https://t.co/dLfuGAJtxE

On-policy RL has driven the biggest leaps in training coding agents. Extending it to machine learning engineering agents should be a natural next step. But it almost never works. What I mean is, the recipe is right there — standard trajectory-wise GRPO, the same that worked for SWE. However, the problem is that one rollout step on an MLE task may take hours because the agent has to actually train a model on a real dataset at every step (preprocessing, fitting, inference, scoring). So even with the N rollouts in a group running in parallel, a single GRPO run may still take days. Every MLE agent paper I've read has retreated to SFT or offline proxy rewards for exactly this reason, giving up the exploration benefits of on-policy learning. That's why I'm excited about our new paper, SandMLE, which fixes this with a move that sounds almost too reckless to work. The instinct when on-policy RL is too slow is to engineer around it — async rollouts so the trainer doesn't sit idle waiting for slow environments, off-policy or step-wise proxies to avoid running full trajectories at all. But when we profiled where the time was going, the bottleneck had nothing to do with the algorithm. Unlike SWE where execution latency comes from compilation and test logic, MLE latency is overwhelmingly driven by the size of the dataset the ML pipeline has to chew through. Therefore, rather than downsampling existing data (which corrupts evaluation), we built a multi-agent pipeline that procedurally generates diverse synthetic MLE environments from a small seed set. Specifically, we extract the structural DNA of seed tasks (modality, label cardinality, distribution shape), mutate them into new domains (e.g., repurposing animal classification into road damage detection), inject realistic noise, embed deterministic hidden rules connecting features to labels, and construct full evaluation sandboxes with progressive milestone thresholds. Each task is constrained to only 50–200 training samples. The execution speedup is dramatic — average per-step latency drops over 13×, which makes trajectory-wise GRPO go from infeasible to routine. We also designed a dense, milestone-based reward to address the sparse credit assignment problem in long-horizon MLE. The ablation shows this matters — under a sparse reward, the 30B model's medal rate drops from 27.3% to 13.6% and valid submission collapses from 100% to 86.4%. Results across Qwen3-8B, 14B, and 30B-A3B on MLE-bench are consistently strong — 66.9% better performance in medal rate over SFT baselines. It is worth noting that the SFT baselines are not weak— we trained them on high-quality Claude-4.5-Sonnet trajectories. But SandMLE still delivers much larger gains, suggesting that direct environment interaction does teach capabilities that imitation alone does not (as expected). The most convincing evidence to me that the model's intrinsic performance gets improved is the framework-agnostic generalization. We trained exclusively with ReAct but the gains transfer to AIDE, AIRA, and MLE-Agent scaffolds at evaluation time — up to 32.4% better performance in HumanRank on MLE-Dojo. The SFT models, by contrast, are brittle when moved to unfamiliar scaffolds. The 30B SFT model collapses to 17.7% valid submission rate on MLE-Dojo with MLE-Agent, while the 30B SandMLE model achieves 83.9%. SandMLE is teaching genuine engineering reasoning, not scaffold-specific patterns. What I find most interesting beyond the specific result is that none of the hard parts of RL changed here. The algorithm is the same. The reward is conventional. We just shrunk the environment until on-policy learning became affordable. The field has largely treated environment design and RL algorithm design as separate concerns. SandMLE is a concrete case that the environment is itself the lever. When training is too expensive, the instinct is to build cleverer algorithms to tolerate it. However, often the better move is to reshape the environment so the simple algorithm just works. Paper: https://t.co/x0jAvCClyh