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【 info📺✨ 】 本日1/7（日） 24:30〜より BSフジ連続ドラマ 「〜ｉｆ〜警視庁捜査一課 剣木善治」 第1話が放送されます💁🏼‍♀️ みなさまぜひご覧ください👀 #梅山恋和# #ココチャム# #if剣木善治#

If you haven't subscribed to PiCoreTeam's official YouTube channel yet, make sure to subscribe now! ----- Pi Ecosystem Summit 2026 will be streamed on Pi Network’s official YouTube channel ( on November 17 and 18 at 8:00 AM PST. That's 11:00 PM Vietnam time on November 17 and 18, respectively. Before the broadcast begins, viewers can visit the scheduled video page and click “Set reminder” to receive a notification. At the scheduled time, everyone can watch the presentation together in real time and interact with other viewers through the Live Chat feature. The summit will feature technology demonstrations, developer tools, and applications from across the Pi ecosystem. Pi Network co-founders Chengdiao Fan and Nicolas Kokkalis will also participate. On November 19, Pi Network will announce the Pi Next Hackathon, featuring a prize pool of 200,000 USDT.

AI can generate an answer in seconds. But can it prove where the information came from? That's the question that makes @primus_labs interesting to me. We spend so much time discussing how intelligent AI agents are becoming, yet one fundamental problem remains largely unresolved. How do we know the data they're using is authentic? Imagine an AI agent evaluating a financial opportunity using information from an exchange, a banking platform, or an external API. The agent might produce a convincing analysis. But if the underlying information cannot be verified, the entire decision rests on assumptions. This is where Primus takes a different approach. Through zkTLS, Primus enables applications to generate cryptographic evidence about data obtained from existing web services. Instead of asking users to blindly trust screenshots, copied numbers, or unverifiable claims, applications can check the authenticity of information from its source. What makes this more interesting is the privacy dimension. Primus also works with technologies such as zero-knowledge proofs and fully homomorphic encryption to explore how sensitive information can be verified and processed without unnecessary exposure. I see potential applications across DeFi, proof of reserves, digital identity, and AI-driven financial services. But I think the bigger opportunity is establishing a new standard for digital trust. In the future, an AI agent claiming to have completed a task shouldn't be enough. It should be able to provide verifiable evidence supporting that claim. Of course, authentic data doesn't automatically guarantee correct reasoning. Verification is one layer of a much larger system. Still, I believe @primus_labs is working on infrastructure that becomes increasingly relevant as AI agents begin handling real economic activity. Because intelligence without verifiable evidence can produce convincing mistakes. And the next generation of decentralized applications will need more than convincing answers. What matters more for the future of AI: smarter models or stronger verification of the information they use?

I said that if $BTC dumps to $62,000, I’d give away $20,000 in Bitcoin to 5 people. Well, we made it. As promised, I’m giving away $20,000 in $BTC to 5 lucky winners today. To enter: ✅ Like ✅ Retweet ✅ Comment “Bitcoin” 🔔 Entries close in 24 hours. Good luck!

PeakCut hit 302 stars in its first day by cutting vertical shorts live while your stream is still running — no more hunting highlights by hand. Finding the moments worth clipping is the grind. Paste a YouTube, Twitch, or Kick link and PeakCut transcribes the audio, asks an AI which moments would hold a stranger's attention, and renders each one as a 9:16 clip with word-by-word captions. The standout is live clipping: it records the stream into a rolling buffer and cuts highlights as they happen, using loudness, chat activity, and the transcript. No AI key? It still runs, picking clips by loudness instead. It's for creators who clip their own streams regularly, especially live streamers; finished clips can queue to your own YouTube, Instagram, or TikTok. Only cutting one clip now and then? Skip it. One catch: the first start downloads a ~480 MB speech model, and ffmpeg is required. Live clipping is the real differentiator. Try it on one old stream, see if the AI's picks match your taste, then decide if it earns a place in your workflow. When you cut vertical shorts, what's the worst part — finding the highlights or fixing the captions? #AIClipping# #Shorts#

My timeline cannot stop talking about superintelligence: every day someone new says the machines are coming for everything, and now even Trump is in the conversation, saying super intelligent AI should make people richer. $SAI is Super Artificial Intelligence, and @sai_superai heard all that noise and answered with three letters and a rhino in a hoodie. That is the part I keep coming back to. Three letters carry the whole pitch, and anyone can put a rhino on it. You see the ticker once and you already know what it is selling: a bet that superintelligence is the story of this cycle, with a rhino as its face. @NEXST_AI is turning that talk into something you can play: an AI-only social network where the Super AI creates, people call the winners, and those calls teach the model what humans actually like. And the setup is still the clean part for me: fixed at 1B supply, 0% buy/sell tax, 0% team allocation, half the supply in the liquidity pool, locked or burned with public proof, mint and fee authority renounced. If everyone is going to talk about this, at least the rules are written down before the talking starts. The launch is set for 20 Nov on and the app opens to holders after that. So here is my question, @thecreatorfi: when the whole timeline is talking about superintelligence, do you want a ticker you can meme, or just another thread to scroll past? #ad#

Claude Pro 用户的 Fable 美西时间11月16号晚上11点59分到期 Anthropic 帮助中心： 1. 11月9号到16号，Pro 和 Team 标准席位的所有 Fable 模型算进套餐，最多用掉每周额度的50%，另送一次 Fable 额度重置，这次重置也是16号晚上过期 2. 到期后，Pro 和 Team 标准席位的 Fable 回到 usage credits 按量计费，不再占套餐额度 3. 没用完的50%不结转，这次也没有 Fable 5 那次的一次性补偿额度 4. 网页、手机、桌面、Cowork 和 Claude Code 规则一样 第4条落到 Claude Code 上，就是写死 Fable 跑的脚本和定时任务，17号照样会跑，套餐不再兜底，开着 usage credits 就走 credits。 Fable 5.2 的 API 标价是每百万 token 输入8美元、输出40美元，Opus 5.5 是4美元和20美元。 官方原话：“After you use that 50%, or once the week ends, Fable usage is billed to usage credits if you have them turned on.”

Google discovered a really stupid trick to make AI respond to you much better. Every major model, ChatGPT, Claude, Gemini, DeepSeek, has a one-way brain. They process text from left to right. When you paste a massive document and put your question at the very bottom, the AI reads the entire document blind. It processes the context without knowing what it's actually looking for. So, researchers tested a ridiculous solution. They just copy-pasted the exact same prompt twice in a row. And the results are completely absurd. Across seven different benchmarks and seven different models, accuracy skyrocketed. On one complex extraction task, performance jumped from a dismal 21% to 97%. Just by repeating the text. Here is the best part: it costs you exactly $0 and takes 0 extra seconds. Because hardware processes inputs in parallel, the second pass happens instantly. Zero extra latency. Zero extra output tokens. Zero complex finetuning. On the second read, the AI finally "knows" the question. Every single word in the context attaches itself to the actual goal. If you are building workflows, agents, or automations, you are leaving massive performance on the table for no reason. We keep trying to invent smarter AI. But sometimes, the AI doesn't need a better prompt.

A trading platform becomes truly valuable when users can make better decisions without becoming dependent on it. That's an idea I've been considering while following @Hypesium. In Web3, platforms often compete by offering more features, faster execution, and access to additional markets. Those improvements can make trading more convenient. But there's another question I think deserves attention. Does a trading tool make its users more capable, or simply more dependent on its interface? Imagine someone who has spent months trading through a single application. They understand where to click, how to place orders, and how to navigate its features. But if that application suddenly becomes unavailable, can they still evaluate opportunities and manage their positions independently? If the answer is no, convenience may have created an unexpected weakness. Hypesium offers a Telegram-based trading workflow focused on the Elysium ecosystem, connecting activity across multiple launchpads. I see this as an interesting example of how specialized infrastructure can simplify market participation. However, I believe the strongest trading products should preserve user independence. Traders should understand the assets they hold, the networks they use, and the risks associated with their transactions. They should also know how to verify important information outside any single interface. This isn't an argument against trading bots. It's an argument for using them as tools rather than treating them as substitutes for knowledge. For me, @Hypesium is worth examining through this perspective because accessibility and independence should develop together. A convenient trading experience can save time. But genuine confidence comes from understanding the process well enough to operate without that convenience when necessary. The best infrastructure shouldn't make users feel trapped inside one workflow. It should make them more capable wherever they choose to participate. Would you trust a trading platform more if it helped you become less dependent on it?