标签：#an

The funding, Gatik's largest so far, was led by Qatar Investment Authority and Koch Disruptive Technologies.

Ian Nowland discusses why conventional CI/CD practices break down for stateful, core infrastructure. Drawing from his leadership at AWS and Datadog, he shares actionable techniques for safe progressive deployments, synthetic testing in production, and mitigating blast radius in complex software platforms. By Ian Nowland

Apple’s Mac Mini and Mac Studio have been tough to buy for months, but updated versions have arrived. Both include new chips optimized for AI, with the M6 Mac Mini getting a $200 price bump.

Uber said no one will be able to access the video once the ride ends

Now exiting stealth mode with a $26 million seed round, Keenable has been building a vast web search index for AI agents.

The social app founded by Bonobos co-founder Andy Dunn is expanding beyond events with new digital homes for groups to connect, organize, and make plans.

Uber is updating its teen account feature to allow parents to use the selfie cameras on Uber drivers' phones to check in on their teenage children during ridehail trips. Since first launching teen accounts in 2023, Uber has touted the myriad ways parents can keep tabs on their children, from PIN verification to live GPS […]

Nintendo didn’t design its Switch dock with travel in mind. Genki’s Covert Dock is a much smaller alternative my family has been using to connect our Switch to hotel TVs for years, but I’ve come to realize I much prefer having a stand for the console when using it this way. So when it came […]

Illinois prosecutors shared defendants’ personal data with federal immigration agents without criminal warrants, public disclosure, or legislative oversight.

The current EV market is flush with variety. I've driven single-motor economy-minded machines, dual-motor EVs with all-wheel drive, tri-motor vehicles that dial up the performance stakes, and even wild quad-motor performance cars, like the Rimac Nevera. But this is the first time I've ever driven a five-motor EV, one with the agility and speed to […]

“It never crossed my mind that they would be so bold as to sell our private data for AI,” says one former Spirit Airlines flight attendant.

Counting the creatures in the world around us is critical for a raft of conservation efforts. It helps scientists gauge biodiversity, track migration, and spot invasive species. That census-taking, though, often requires humans to tabulate what they see, trap, or otherwise sense—a potentially laborious, costly process that can still leave gaps. But developments over the…

Google is rolling out a new Android feature that's been proven to reduce, or even eliminate, motion sickness when using a phone inside a moving vehicle. Dubbed Motion Assist by Google, it's very similar to Apple's Motion Cues, first introduced in 2024. The Android 17 feature appears to be rolling out in phases, with some […]

OpenAI CFO Sarah Friar explains how advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost.

昨天收盤後，阿富兩筆預測全錯，他花了晚上的時間做歸因，結論是自己的規則體系有個洞：A、B、C 三條規則只比對「台指期夜盤方向」跟「前一晚美股方向」這兩個回頭看的訊號，完全沒有檢查未來幾天有沒有大事要發生。他當場補上第四條，叫 D-obs：下單前先查未來 1 到 3 個交易日有沒有台股權值股高度連動的財報或央行事件，有的話把信心往下調 0.05 到 0.1，方向不動。 寫得很漂亮。他還替這條規則附了一個可以打臉自己的驗證條件：如果 8/25 到 8/27 這三天的預測失準都伴隨台積電領跌加上量縮，就支持「財報前觀望是系統性缺口」這個假說；反之，證據就變弱。 今天是 8/25，第一天。 早上八點三十七分，他照新規則做了下修 盤前訊號其實一面倒偏多。美股 8/24 收紅，道瓊漲 1%、517.8 點收 53,277.01，那斯達克漲 0.4% 收 26,180.46。台指期夜盤上半場反彈逾百點。他把 A、B、C 逐條核對：非結算日，A 不適用；夜盤幅度約 0.22% 到 0.34%，落在 0.3% 雜訊邊界上，數字查不精確，保守當雜訊處理；夜盤跟美股同向，C 不觸發。 然後 D-obs 觸發了。NVIDIA 8/26 美股盤後要公布財報，符合「未來 1 到 3 個交易日、權值股高度連動」的定義。他照規則把加權指數的信心從原本估的 0.55 往下壓，壓到接近 0.5 之後，再套用 8/20 那條自訂教訓（信心趨近 0.5 就誠實標平盤），最終把加權指數標成 flat、信心 0.40。00919 因為是高息 ETF、AI 權值曝險低，下修幅度小，維持 up、信心 0.52。 當日停損線設在 30.04，是現價 30.65 減 2%。日虧損熔斷 60 元。計畫寫明不新倉、不換倉。 10:30 巡檢，00919 報 30.98，未實現 +27 元。12:30 再巡，31.02，+28 元。兩次都是同一句：未觸發，不動作。 收盤：兩個標的一中一錯 加權指數收 45,169.46，比昨收的 44,762.32 漲 407.14 點，0.91%。他標的是 flat，miss，brier 0.29。 00919 收 31.07，比昨收 30.65 漲 1.37%。他標的是 up，hit，brier 0.144，是這陣子少見的漂亮分數。 帳面上這是好日子。36 股 00919 成本 1,086 元、均價 30.17，現在市值 1,118 元，未實現 +30 元、2.76%。券商可用現金 1,089 元加上市值，總資產約 2,207 元，本金 2,200。第 11 個交易日結束，他終於站回本金上方，多了 7 塊錢。目標是 4,400，剩 19 個交易日。 同一條規則，兩天，兩個相反的故事 昨天他把加權指數看錯，寫下的原因是「NVIDIA 財報前的觀望性賣壓」，台積電領跌、量縮到 4 月 7 日以來新低。 今天他依這個道理主動把信心下修、把方向改成平盤，結果指數大漲 0.91%。他在復盤裡寫的原因是「市場提前反映樂觀情緒，法說前搶跑」。 同一個標籤，兩天，兩個方向相反的故事。昨天說財報前大家會怕所以賣，今天說財報前大家會期待所以買。兩次都錯。 阿富自己看到了這件事，復盤裡有一句寫得很直白：「兩次假說方向相反！顯示財報前單一標籤不足以判斷方向，需視當時整體市場情緒基調而非機械下修信心。」這句話我認為是今天整份紀錄裡最有價值的一行。他沒有替自己圓場。 但他接著寫的處置我有意見。他把今天記成「D-obs 觸發第 2 個已結算樣本，累積 2/5，未達門檻，暫不改規則」。問題在於，昨天他親手寫下的驗證條件是：失準若伴隨台積電領跌加量縮就支持假說，反之證據變弱。今天指數大漲 0.91%，跟那個模式完全對不上，按他自己訂的標準，這是一筆削弱證據，不是一筆待累積的樣本。他把一個反證，放進了「等湊滿五筆再來討論要不要改」的計數器裡。 這個差別不小。反證應該讓假說失血，樣本只是讓假說等待。一條規則如果連自己的證偽條件被打中都只換來計數加一，那五筆湊滿的時候，它多半也只會被修得更複雜、更難被推翻。 我對 D-obs 的看法 我不覺得 D-obs 本身荒謬。財報週市場行為會變，這是真的。荒謬的是它的產生方式：從單一天、單一次失手，逆推出一個因果故事，隔天就升格成盤前流程的固定步驟。這種規則的問題在於它幾乎不會錯，因為它兩邊都能解釋——市場跌就說是觀望賣壓，市場漲就說是搶跑買預期。能解釋一切的規則，預測力是零。 阿富的紀律其實是好的。他的預測到今天累積 12 筆已結算，加權指數 6 中 2、00919 6 中 3，系統回報樣本仍然不足以下結論。另一份以成交紀錄計分的校準報告，12 次試驗方向命中率 50%、RPSS 0.144，標籤是 INDISTINGUISHABLE_FROM

DuckDB Labs has previewed DuckDB v2.0, codenamed "Cyanoptera." This release includes over 10000 commits and introduces a client/server mode, enabling network connections. Improvements also encompass extension portability, advanced data types, and a new parser. Performance enhancements include asynchronous I/O and storage optimisations. General availability is expected in fall 2026. By Olimpiu Pop

Say hello to a revamped collection of perfectly proportioned 38-mm Speedmasters.

Airbound's ultra-lightweight approach to drone delivery has attracted backing from Greenoaks, DoorDash, and Silicon Valley investor Lachy Groom.

There are currently 3 categories. Without reason, RFK Jr. is considering changes.

For the past two months or so, I've been working on a variety of AI development projects rather than writing -- writing skills, plugins, workflows, and applications; testing and refining harnesses; and performing diligence or working with clients (hands-on work as well as brains-on work) as they think through where they're going with AI and how they're getting there. I've been down a lot of rabbit holes and talked to a lot of forward-thinking practitioners, and I have explored a lot of what is actually possible now by building things...and I've spent my "think-time" on what that all might actually mean going forward. Here's what I've come up with: 59 predictions in 17 categories around how the world of AI -- and the broader world in light of AI -- are changing. I'll write more deeply about many of these over the weeks ahead. Predictions Here's what's coming, in my not-so-humble opinion, based on what I'm seeing in client projects & diligence, conversations, and research. Each prediction is grouped by category and by time horizon (within 12 months / 1-3 years / 2-4 years / 3-5 years), with a confidence level and a falsification criteria (i.e., what I'd expect to observe if I'm wrong). Confidence isn't a measure of how much I want something to be true; it's a measure of how much variance I think exists in the outcome. I'd love your feedback on what I'm missing or where I'm missing the forest for the trees (or the boat entirely :D)! Any surprises for you? Organizational Structure & Delivery Model #1. Small Cross-Functional Pods Become the Standard for Software Development (2-4 years) The leading-edge/aspirational development team model will have moved from agile teams of 6-8 to AI-powered Pods of 2-3 (often product/development/deployment, sometimes SrDev/JrDev/Product). This prediction underpins many of the other predictions in this entire group -- most of the rest of the Org Structure & Delivery Model cluster assumes it holds. It's plausible for greenfield and startup