CcccheckAI

Welcome to try CheckAI for free. checkai.space It might just become a really useful tool in your everyday writing.

anthropic redesigned claude code with multi-tasking in mind. here's what you can actually do with it now: open multiple coding sessions side by side. run your test suite in one window while refactoring in another. drag and reorder them however your workflow needs. terminal and file editor are built right in, no tab switching. you send tasks out to different sessions and work on them simultaneously instead of taking turns. tell one session to run tests, another to write a feature, and a third to debug something you found earlier. come back and check on all of them when you're ready. coding moves from solo work to managing a small team. @AnthropicAI @anthropic #ClaudeCode

Some thoughts on using AI for products and research lately 1. Is AI impressive? Absolutely. But the impressive part is AI, not me. After using AI extensively, I've noticed this illusion creeping in — "wow, I'm so capable." I can effortlessly build cool websites and apps I never could before, run experiments way faster, and I'm constantly exposed to cutting-edge tools and shiny new jargon from Silicon Valley. It's easy to feel like a genius. But the genius is Claude, Codex — not me. I can use them, and so can everyone else. So the real question is: when everyone has access to AI as powerful as Opus 4.6 for cheap, where does my competitive edge come from? 2. AI can drastically reduce labor, but it can't create demand. I saw a video from a creator called 水球泡 on Bilibili. He said the essence of a "one-person company" is the business, not the one person. That hit hard as someone also building products. AI is still a tool — it boosts productivity, but creating and fulfilling demand is still a human job. That's why everyone is anxiously hyping AI, building AI, investing in AI, yet the ones actually making money are selling courses. Because AI doesn't create demand. You can have the most advanced AI, but without a solid business model, marketing, or a problem worth solving, it's like using a laser cannon to kill mosquitoes — you just end up anxious about why you can't fire your laser cannon. So AI's trajectory might not be replacing industries, but rebuilding mature ones. Demand doesn't disappear — people just want better service. Once AI is deeply embedded across verticals with agent systems, new demands will emerge, and companies will reinvest saved costs into innovation. Also — AI is still confined to the virtual world. "everything online is virtual." The internet is deeply woven into our lives, but it's still behind a screen, separate from our physical existence. The real explosion might come when AI can truly interact with and explore the physical world. 3. So where does future competitive advantage lie? I believe it's no longer about any specific skill or knowledge — it's about your experience and understanding of a vertical industry. Someone who's gone deep in a field knows its ins and outs, the subtle, complex, even inarticulable wisdom embedded within. That wisdom manifests as judgment — the ability to tell AI what's right, what's wrong, and what to do next. To develop this, you can't just bury yourself in tech. You need to actively think: What are the core needs in your field? Where is it heading? If I were the boss, how would I get more people to buy my product? In other words, shift your mindset from craftsman to entrepreneur. That's how you stay competitive. 4. This applies to AI + research too. I read a book called The Nature of Technology — brilliant book. Its core thesis: nearly all new technologies aren't created from scratch; they're combinations of existing technologies. Every technology is an N-ary tree, rooted in some natural phenomenon captured by humans. A computer breaks down into memory, screen, chassis — each decomposable further, down to things like "controlling electrons" (screens) or "exploiting metal ductility" (chassis). In other words, yes, we mock top-venue papers for being A+B — but fundamentally all technology IS A+B. It's about how cleverly you combine them, and that requires experimentation. Can AI do this experimentation? Absolutely — faster and better than most humans. So I believe AI will bring a quantum leap in technological progress, and will likely make ~90% of mediocre researchers (myself included) obsolete. The survivors will be those with industry insight, broad knowledge, and sharp judgment — the ones who can tell AI which A + which B will yield results. 5. What to do now? Given all this, I don't think we need to be too anxious. We should stay calm. These insights take time to accumulate and refine. Just shift from an engineer's mindset to an entrepreneur's mindset. Think about your field's ecosystem, build industry knowledge and experience rather than just technical skills. And read more books — books aren't controlled by recommendation algorithms, they don't shift based on your preferences, and a great book gives you systematic ways of thinking. These are some recent reflections, and a note to myself. I think the AI era is actually pretty friendly for someone like me with mild ADHD — I can quickly realize ideas I was too lazy or unmotivated to pursue before. Haha. Let's go!

@Xiaoniu6161 确实没截屏啊

追觅科技，原属小米生态链企业之一 其 CEO 俞浩近期发飙怒批团队没魄力：砸2亿年薪招聘，把宇树所有客户、标的抢过来 最后一行字特别醒目:以上内容，谁截屏出去，立即开除。

@LensScientific Is it really real?

Despite being just one cell, Lacrymaria olor is a formidable predator that hunts and consumes other microorganisms. 📽: James Weiss

🌀 Mysterious Portal Appears in the Sky Over Switzerland 🇨🇭

What is your first thought when you open this fridge?

@rodniesmith @realDonaldTrump thanks

@Weahther @realDonaldTrump Amazing!

I built a tool to analyze how much the biggest X accounts rely on AI to write their posts, combining AI detection with engagement metrics to create a weighted leaderboard. The results were actually pretty unexpected. @realDonaldTrump took the #1 spot on the weighted score, followed by @vitalikbuterin, @karpathy, and @sama. Turns out, the absolute top-tier accounts are surprisingly human-leaning. Then I checked who has the absolute highest AI usage—hitting 99% to 100%. It’s basically just crypto Twitter. Accounts like @cryptosharmin, @cardano, @altcryptocom, and @opencto_xyz are maxing out the AI detector. Starting to think the entire web3 space is just language models talking to each other. You can check the full leaderboard here: checkai.space/check-x-ai

美容師もAIに乗っ取られる！？

What can an everyday dev do with the leaked Claude Code? Honestly, it’s the perfect fodder for personal AI agents. Whenever I need to build a new test harness, I’m just going to feed it to my AI to study Anthropic's design patterns. Definitely kept a copy for this exact reason. #Claude #AIAgents

Principles of the AI era: As long as you learn slowly enough, you never have to learn 😎

@XueJia24682 its suprisingly real not AI

✨🇨🇳On the streets of Shanghai, a humanoid robot is walking a robotic dog.😂

My childhood dream has become a reality

@kimmonismus its real

This clip is more impressive than 99% of robots doing MMA. Unitree is increasingly being used in real-world environments, its use in hospitals being the most obvious application. I can easily imagine that in just a few years, numerous robots will already be working as support staff.

In fact, the difficulty of LLM detection has been overestimated; due to next token prediction, AI-generated and human-written texts exhibit significant distributional differences.

@liquiditygoblin Yes, in fact, the difficulty of LLM detection has been overestimated; due to next token prediction, AI-generated and human-written texts exhibit significant distributional differences.

In an effort to try stop seeing so much slop I've been trying to train my own AI detection model. Found something incredibly interesting. for the most part LLM generated text and human written text are linearly separable.

@Rainmaker1973 Extremely creative

When you can’t afford a PlayStation

@gaoj0017 Doesn't Google adhere to "Don't be evil" anymore? Is this principle still upheld today?

The TurboQuant paper (ICLR 2026) contains serious issues in how it describes RaBitQ, including incorrect technical claims and misleading theory/experiment comparisons. We flagged these issues to the authors before submission. They acknowledged them, but chose not to fix them. The paper was later accepted and widely promoted by Google, reaching tens of millions of views. We’re speaking up now because once a misleading narrative spreads, it becomes much harder to correct. We’ve written a public comment on openreview (openreview.net/forum?id=tO3AS…). We would greatly appreciate your attention and help in sharing it.

@neha_dtt but you are not real

This is me without any makeup Do I look pretty ?