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推しの佐藤信長さんがきっかけでイタリアの国宝バイクブランドDucati を知ったよ✨✨ ちょうど友達が最近買って！間近で見られて感動した🏍️ 排気音のバルブの音めっちゃカッコよかった🥺🥺 #Ducati# #佐藤信長#

🐦 ACTUM Early Bird Campaign ACTUM is officially launching a limited-time Early Bird Campaign. This 15-day campaign is designed to reward the earliest community members and establish the core foundation of the ACTUM ecosystem, supporting its long-term development. 🎯 Campaign Overview 📅 Duration: 15 Days (Limited-Time Only) Users who join our Discord during this period and complete basic verification will receive the Early Bird role. ⚙️ How to Participate Simply enter the designated channel and click the symbol to automatically receive the Early Bird role: 👉 No additional application or complex steps are required, just click to claim. 👑 Early Bird Benefits Users who successfully obtain the Early Bird role will enjoy: • Priority access to future community events • Early access to ecosystem updates and announcements • Increased weighting in future reward systems • Recognition as early-stage community contributors 📌 Important Notes • Early Bird status is only available during the 15-day campaign period • Users joining after the campaign ends will not be eligible • All participants must comply with community rules • ACTUM reserves the right to review and disqualify any fraudulent or abusive behavior This is your opportunity to become one of the earliest core members of ACTUM and secure early ecosystem advantages before further expansion. 🏭 Welcome to ACTUM

延伸了一些关于 TradFi 的死穴在哪里，以及 infiniFi @infiniFi 凭什么能去撬动的想法。 这两天和一个在传统资管圈做风控的朋友聊天，聊到一个关于期限错配（Duration Gap）​的老大难问题。原来我按照常理思考，觉得这事儿银行家们玩了几百年早就炉火纯青了，后来朋友说，正是因为他们玩得太熟练，反而留下了致命盲区。他们赚的是利差，担的是挤兑的风险，一旦长短期资金不匹配，流动性瞬间枯竭，这就是2008年的剧本。 然后他叹了口气说，现在的 DeFi 借贷虽然透明，但依然是把长钱和短钱硬凑在一起，就像把十年的房贷和一周的信用卡账单扔进同一个抽屉，风险定价完全靠感觉，没人敢真正把长周期的国债资产拿出来给散户分一杯羹，因为怕锁死。 这就给像 infiniFi​ 这样的系统带来了一个真空市场地带。 infiniFi 搞了一套自我协调的去中心化存款人驱动系统。 先分边站队，再自动匹配，最后用算法兜底。普通散户把钱扔进来（Mint iUSD），拿收益；胆子大的长线资金把流动性锁定（Lock iUSD），换取更高的回报；系统背后用自动化阶梯（Automate Laddering）​ 把长短债精准配对，再用 Curve @CurveFinance 当流动性备胎，硬生生把传统的银行资产负债表变成了透明的智能合约。 有趣的是，它没有中间商赚差价。​ 传统银行靠存贷利差吃饭， infiniFi 是部分准备金模型，风险共担、收益共享，没有冗员，没有层层盘剥，把利润直接返给存款人。 也许这是下一个 DeFi 2.0​ 的机会了。不再是无脑挖矿，而是真正解决 TradFi 遗留了几十年的流动性错配难题，把固收市场的蛋糕重新切一遍。

Announcing agentic performance benchmarking for Speech to Speech models on Artificial Analysis. We use 𝜏-Voice to measure tool calling and customer interaction voice agent capabilities in realistic customer service scenarios Even the strongest Speech to Speech (S2S) models today resolve only about half of realistic customer service scenarios end-to-end - a meaningful gap relative to frontier text-based agents on the same tasks. Voice channels introduce significant complexity: challenging accents, background noise, and packet loss, all while requiring fast responses, consistency across long multi-turn conversations, and reliable tool use. Performance also varies considerably by audio condition: in clean audio some models perform notably better, but realistic conditions continue to pose a challenge. Conversation duration also varies meaningfully across models, with implications for both customer experience and operational cost. About 𝜏-Voice: Our Agentic Performance benchmark is based on 𝜏-Voice (Ray, Dhandhania, Barres & Narasimhan, 2026), which extends 𝜏²-bench into the voice modality to evaluate S2S models on realistic customer service tasks. It measures multi-turn instruction following, support of a simulated customer through a complete interaction, and tool use against simulated customer service systems. The simulated user combines an LLM-driven decision model with realistic audio synthesis: diverse accents, background noise, and packet loss modelled on real network conditions. This complements our Big Bench Audio benchmark measuring intelligence and Conversational Dynamics (Full Duplex Bench subset) benchmark measuring conversational naturalness. Scores are the average of three independent pass@1 trials. We evaluate under realistic audio conditions using the 𝜏²-bench base task split across three domains: ➤ Airline (50 scenarios): e.g., changing a flight, rebooking under policy constraints ➤ Retail (114 scenarios): e.g., disputing a charge, processing a return ➤ Telecom (114 scenarios): e.g., resolving a billing issue, troubleshooting a service problem Task success is determined by deterministic checks against expected actions and final database state, consistent with the 𝜏²-bench evaluator. Key results: xAI's Grok Voice Think Fast 1.0 is the clear leader at 52.1%, averaging 5.6 minutes per conversation, the second-longest overall. OpenAI's GPT-Realtime-2 (High) (39.8%, 3.0 min) and GPT-Realtime-1.5 (38.8%, 4.8 min) follow, with Gemini 3.1 Flash Live Preview - High close behind at 37.7% (3.8 min). Speech to Speech is a fast evolving modality and we expect movement in rankings as we continue to add new models with these capabilities, and model robustness improves. Congratulations @xAI @elonmusk! See below for further detail ⬇️

📢SIP #2# Position Yield is live! Eligible positions earn a share of protocol fees, with larger size and longer duration stacking more yield. $DUSD margin already auto yields and now Position Yield adds a second yield income stream for Standers.

Very interested in what the coming era of highly bespoke software might look like. Example from this morning - I've become a bit loosy goosy with my cardio recently so I decided to do a more srs, regimented experiment to try to lower my Resting Heart Rate from 50 -> 45, over experiment duration of 8 weeks. The primary way to do this is to aspire to a certain sum total minute goals in Zone 2 cardio and 1 HIIT/week. 1 hour later I vibe coded this super custom dashboard for this very specific experiment that shows me how I'm tracking. Claude had to reverse engineer the Woodway treadmill cloud API to pull raw data, process, filter, debug it and create a web UI frontend to track the experiment. It wasn't a fully smooth experience and I had to notice and ask to fix bugs e.g. it screwed up metric vs. imperial system units and it screwed up on the calendar matching up days to dates etc. But I still feel like the overall direction is clear: 1) There will never be (and shouldn't be) a specific app on the app store for this kind of thing. I shouldn't have to look for, download and use some kind of a "Cardio experiment tracker", when this thing is ~300 lines of code that an LLM agent will give you in seconds. The idea of an "app store" of a long tail of discrete set of apps you choose from feels somehow wrong and outdated when LLM agents can improvise the app on the spot and just for you. 2) Second, the industry has to reconfigure into a set of services of sensors and actuators with agent native ergonomics. My Woodway treadmill is a sensor - it turns physical state into digital knowledge. It shouldn't maintain some human-readable frontend and my LLM agent shouldn't have to reverse engineer it, it should be an API/CLI easily usable by my agent. I'm a little bit disappointed (and my timelines are correspondingly slower) with how slowly this progression is happening in the industry overall. 99% of products/services still don't have an AI-native CLI yet. 99% of products/services maintain .html/.css docs like I won't immediately look for how to copy paste the whole thing to my agent to get something done. They give you a list of instructions on a webpage to open this or that url and click here or there to do a thing. In 2026. What am I a computer? You do it. Or have my agent do it. So anyway today I am impressed that this random thing took 1 hour (it would have been ~10 hours 2 years ago). But what excites me more is thinking through how this really should have been 1 minute tops. What has to be in place so that it would be 1 minute? So that I could simply say "Hi can you help me track my cardio over the next 8 weeks", and after a very brief Q&A the app would be up. The AI would already have a lot personal context, it would gather the extra needed data, it would reference and search related skill libraries, and maintain all my little apps/automations. TLDR the "app store" of a set of discrete apps that you choose from is an increasingly outdated concept all by itself. The future are services of AI-native sensors & actuators orchestrated via LLM glue into highly custom, ephemeral apps. It's just not here yet.