We’re rolling out Gemini 4 Argon in Antigravity to paid Google AI Pro subscribers today. Demand has been incredible and we've scaled capacity deliberately to get here. We’ve rolled out a separate weekly-only Argon quota for Pro on top of the existing Gemini bucket for 3.8 Flash so you have the best of Gemini for all your tasks.

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio

AINX Basket management for AI holders is LIVE on LONG. AI holders can now vote on what goes into the basket and earn fees from AINX market activity. Unlocking a new ownership layer for stock pairs. Last week, we launched AINX, introducing a new LongX primitive: actively managed community baskets combining long and short positions on @Lighter_xyz AINX has already reached $250k in TVL and $1.2m in notional value. Now, AI holders get to shape what comes next, together. To participate head to the AI token page and click on the AINX tab. Here’s how it works: 1. Each cycle lasts 7 days. The basket’s positions stay fixed throughout that cycle. 2. AI holders have 30 hours to sign up to vote. 3. A specific basket proposal then opens for voting for another 30 hours. 4. To pass, a proposal needs both a majority and a minimum threshold of AI votes otherwise no change will be taking place. 5. Your voting weight is proportional to your AI holdings. 6. Regardless of outcome. Every voter can claim fees in AINX from its pairs, proportional to their weight, whether they vote for or against. This is an experimental pilot. What we learn will guide new functionality and the rollout of community baskets for more pairs on LONG as soon as next week. LONG.

Currently Trending on Pons $BUN, $PRIORS, $PONS, $DELTA, $ZZZ, $ORBIO

With all the market noise, we haven’t said enough about the actively managed baskets we released on @longdotxyz (more is coming) a lot of our releases so far doesn't require direct user engagement they run on auto pilot. I think this is one of our biggest leaps in fulfilling a bigger vision of stock-fi by enabling stock pairs to express VERY specific narratives and amplify them(remember these are stock based communities that generate flows by being dist engine for stocks and capture these flow into deep stock liq in the pair pool, community vault and external LP) Stocks increasingly represent more than individual businesses. The strongest performers often become proxies for entire categories. But sometimes the category itself is not yet exciting or the stock didn't even recognized in it. Crypto already works this way: zcash → today privacy(in the past a more secure/efficient BTC) HYPE + LIT → onchain perps(maybe 2y ago it was about onchain crypto perps eating CEX perps but now liq market infra/RWAs perps?) Community-owned and managed baskets bring that same thinking to stock pairs, letting communities accrue value from net long bets while amplifying an underpriced narrative behind their stock before it gets massive. AI/compute related pairs → datacenter + supply chain baskets Pharma pairs → GLP / peptide baskets Memory pairs → Leopold/bottleneck style baskets Imagine the NVDA community building a datacenter basket five years ago, while everyone still called it a GPU gaming company. A way to gain exposure to the compute thesis while reinforcing the bigger bet: NVIDIA becoming the backbone of global AI infrastructure. Stock communities can now own the future they see coming. Distribute it with via basket pairs. And even enable multiple diff thematic "bets" via baskets. The design space is infinite. And we’re still completely underestimating how much “delusional,” conviction driven communities on LONG will impact the financial system. LONG.

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies. My observations: 1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively. 2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now. 3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention. 4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is. 5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now. 6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text. They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it. 7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy. 8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies. Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here. 9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens. 10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity! 11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it. 12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable. 13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think. 14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data. 15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open. 16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down. 17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building? 18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone. 19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good. 20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped. 21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense. Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era. Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways. Happy to be home and locked in. Until next time!

lol ...they are becoming more situationally aware... (from Gemini 4 Argon report)

交易员周一备忘录：本周超级宏观周来袭，三个决胜时刻定调大盘 大盘在箱体窄幅震荡，山寨流动性几乎抽干。 如果你觉得盘面毫无波澜，那只是暴风雨前的宁静。 从周三开始，宏观核弹级数据接踵而至，将直接决定 10 月下半场的资金流向与风险偏好。 梳理本周三个核心决胜时刻，以及加密交易员的应对策略： 一、本周关键日程表（北京时间） 1. 周二（10.13）：OPEC 月度原油报告 中东地缘局势未定，油价波动将直接牵动全球通胀预期。 2. 周三（10.14）20:30：美国 9 月 CPI 通胀数据（核心重磅） 直接检验通胀是否具备粘性，是决定美联储 10 月政策节奏的最强裁判。 3. 周四（10.15）全天：PPI 数据 + 美联储主席讲话 + 半导体双雄放榜 晚间公布 9 月 PPI；美联储主席接力登场定调预期； 同时，阿斯麦（ASML）与台积电（TSM）公布 Q3 财报，直接决定全球 AI 与科技股的底气。 二、影响币圈走势的三个深层博弈点 1. 通胀与利率预期的拉锯： 若 CPI 超预期反弹，市场降息预期受挫，美债收益率抬头，高风险资产将迎来一波流动性抽血；反之，若通胀持续温和回落，软着陆交易重启，资金将重新涌入风险市场。 2. 科技股财报的“贝塔传染”： 币圈近期的走势依然紧密锚定纳斯达克。台积电与阿斯麦作为 AI 算力底座，它们的财报指引不仅关乎美股芯片巨头，也将直接传导至链上 AI 赛道的估值中枢。 3. 周内波动率的“前低后高”： 周一与周二属于典型的观望期，做市商通常收紧报价、盘面呈现缩量牛皮市；周三 CPI 公布瞬间与周四美股开盘前后，是全周多空双爆的高频插针窗口。 三、交易员实战操作指南 1. 忌盲目重仓单边博弈： CPI 前后往往伴随剧烈的洗盘和上下插针，胜率极低，切勿用高杠杆赌宏观数据。 2. 盯紧现货溢价与美股盘前： 数据出炉后，关注 Binance 现货溢价及美股科技股的盘前反应，它们往往是 BTC 真实方向的领先指标。 3. 守住本金等待趋势明朗： 缩量周期的生存法则是：让数据落地、让子弹飞一会儿。把仓位留给确认突破后的右侧行情，而不是在迷雾中当对手盘的过桥流动性。