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Zoox is expanding to this critical ride-hailing destination a few weeks after it started charging for robotaxi rides.

An underground detector recorded a strange interaction pointing to a particle with some properties that signify dark matter. The detection is small but promising.

Restaurant menus with AI-generated food images are going viral for their off-putting aesthetic. But AI’s impact on modern menus runs even deeper.

The game starts as a reheated Witcher 3, but evolves into something much more focused.

WIRED rebuilt Flock’s latest search tool from code the company sends to a police officer’s browser. Its AI can keep watch across multiple cameras for anyone fitting a written description.

Common sense is of no help in studying reality at the atomic scale.

If you’ve had to fill up your vehicle’s gas tank or buy a plane ticket lately, you’ve probably felt the effects of rising fossil-fuel prices. But farmers buying fertilizer for their crops are especially aware of just how far the ripple effects of the conflict in Iran have spread. Fertilizer prices have been on a…

You open a website. A blank screen appears. You wait. Then finally, the page loads. But what actually happened during those few seconds? Why does one website feel almost instant while another feels painfully slow? It isn't just about writing “better code.” Website performance is the result of many things working together: DNS + networking + servers + HTML + CSS + JavaScript + images + caching + browser rendering And most performance problems come down to two simple questions: What is the browser waiting for? What is the browser doing unnecessarily? Let's break it down. What Actually Happens When You Open a Website? Suppose you enter: https://example.com Your browser has quite a journey ahead. A simplified version looks like this: URL ↓ DNS Lookup ↓ Connect to Server ↓ HTTP Request ↓ Receive Response ↓ Parse HTML ↓ Download CSS / JS / Images ↓ Build DOM + CSSOM ↓ Layout ↓ Paint ↓ Interactive Page Every step takes time. So the goal of performance optimization isn't simply: “Make the code faster.” It's: Reduce unnecessary waiting and unnecessary work. 1. Send Less Data Imagine your homepage downloads: HTML 250 KB CSS 400 KB JavaScript 4 MB Images 8 MB Fonts 2 MB That's a lot of data just to display a page. Now imagine: HTML 80 KB CSS 100 KB JavaScript 500 KB Images 1 MB Fonts 300 KB The browser has significantly less to download and process. This is why techniques such as: Compression Code splitting Lazy loading Responsive images Removing unused dependencies can have a huge impact. A simple rule: If the user doesn't need it yet, don't make them download it yet. 2. Images Can Be Your Biggest Bottleneck You can optimize your JavaScript perfectly... …and still have a slow website because of images. Consider a: 5 MB hero image That's potentially more expensive than many of your JavaScript files combined. Instead of sending a huge original image: <img src= "hero-original.jpg" /> serve an appropriately sized and compressed image. Modern formats such as: WebP AVIF can reduce

Brian Martin discusses the real-world performance costs of metrics libraries and shares strategies for low-overhead, "fearless" instrumentation. Drawing from his work at IOP Systems, he explores atomic primitives, per-CPU sharding, lock-free histograms, and eBPF integration to help software architects and engineering leaders maintain full system visibility without sacrificing performance. By Brian Martin

Your context window bills you every turn Claude Code compacted my session for the fourth time this week, and my first reaction was the normal one: annoyance. It just erased everything and I have to re-explain half of it. Then I pointed Claude Code at its own transcript files and did the arithmetic instead of the complaining. The transcripts are just JSONL on disk — every request, every token count, timestamped. Three sessions, 5,288 requests, a few minutes of parsing. The reframe that came out of it: compaction isn't the tax. It's the tax getting paid off. The tax is every turn before that. TL;DR Every turn re-sends the entire conversation so far. Nothing is "remembered" for free — a 900K-token context gets re-read, in some discounted form, on turn 901. Across three real sessions: 1.99 billion tokens read from cache, 62 million tokens written to it. A 32:1 ratio — each token you put in context gets paid for roughly thirty-two more times before it leaves. Four auto-compactions fired at 968K, 996K, 999K, and 771K tokens. Each one took 108–140 seconds of wall-clock time doing nothing but summarizing. Immediately after, cache-read dropped from ~990K to 0. At list-price API rates, cache discounting saved an estimated 86% versus paying full input price every turn — which is the whole mechanism working as intended, and also the reason a 1M-token context doesn't bankrupt anyone by turn 50. None of this is "Claude Code is expensive." It's "the meter is per-turn, not per-token-ever-seen," and almost nobody reasons about a session that way while they're in it. What actually happens on turn 500 There's no persistent working memory across a conversation. Each API call is stateless — the model sees whatever text is in the request, and nothing else. So a coding session's "memory" is an illusion built entirely out of re-sending: every prior file read, every tool result, every message, concatenated and shipped again, every single turn. Prompt caching is the thing that makes this sur

昨天我在這裡寫下一句話：阿富如果真的相信大盤跟 00919 該用兩套邏輯，就該讓它們分開受審，別再兩邊押同一個 flat。今天早上八點半，他對加權指數押 down、信心 0.42，對 00919 押 flat、信心 0.37。 盤前他看到的隔夜訊號幾乎全是空的：美股三大指數收黑，道瓊 -0.79%、標普 -0.71%、納斯達克 -1.03%；西德州原油單日漲 5.2% 站上 90 美元；美國十年期公債殖利率升到 4.79% 的新高；美軍 9 月 2 日對伊朗又打了一輪，長達六個半小時。對面只站著一個利多，SEMICON Taiwan 在南港的第二天，主題鎖在 AI。 他把前面那串叫 M 類，跨資產的總體訊號；把展會叫 S 類，單一產業的排定事件。他的新規則寫著，M 類壓過 S 類的時候，指數跟著 M 類走，信心壓在 0.40 到 0.45 之間。00919 走另一條路：前十大成分股裡五檔金融股碰上殖利率走揚不見得吃虧，廣達、聯詠、瑞昱、華碩那幾檔則被展會利多跟晶片股逆風夾在中間，方向混沌，所以判 flat。 風控那邊他設了兩條線：00919 跌破 31.70 就把 36 股全部出清，當日虧損碰到 60 元就停止一切新交易。他還先算過摩擦成本，真要停損賣掉，手續費加證交稅約 2 元，占部位不到 0.2%。 這是他上線以來第一次對兩個標的給出不同方向。 中午的時候，這條規則看起來已經死了 10:30 巡檢，加權指數 46,225.41，比前一天收盤漲 0.13%。00919 報 32.72，漲 1.14%，帳上未實現獲利 89 元。12:30 再看一次，指數 46,281.86，漲幅擴到 0.25%；00919 報 32.76，漲 1.27%，未實現 91 元。 預測押跌，市場整個上午在漲。他自己在盤中紀錄裡寫得很直白：若收盤維持這個方向，這會是新規則的第一個反向樣本。他沒有偷偷改口，也沒有把預測往回調。這一點我給分。 尾盤翻黑 加權指數收 45,857.66，比前一天跌 0.665%。日內最高 46,517.45、最低 45,992.50，收盤價比盤中低點還低。方向回到他早上押的那一邊。大盤那題 HIT，Brier 0.210。 00919 收 32.52，比前收 32.35 漲 0.53%。flat 那題 MISS，Brier 0.284。手上 36 股市值 1,170，未實現獲利 82 元，約 7.6%。 一對一錯，這比昨天有價值得多。昨天兩邊同押 flat，一個對一個錯，事後拿兩套說法各自解釋，怎麼算都不會輸。今天他先把賭注分開放，市場再各給一個答案，其中一個明確打了他的臉。可以輸的考卷才叫考卷。 但他今天多學了一件不該學的事 復盤裡他寫：盤中偏離而收盤回歸，代表 M 類總體利空的傳導有時間延遲，可能來自尾盤外資調節或法人結算。 這句話背後只有一天的資料。今天大盤尾盤翻黑，明天可能整天黑到底，後天可能開低走高。用單一樣本去解釋盤中跟收盤的落差，等於幫規則多裝了一個彈性關節：以後盤中走反了可以說延遲還沒到，收盤走反了才算數。這種關節裝多了，規則會慢慢變成永遠不會錯的東西。 他從同一段經驗抽出的另一個結論反而是對的：盤中巡檢不該憑瞬時方向就對規則下判決，計分要以收盤為準。這是紀律，跟解釋是兩回事。 00919 那題的處理我也有意見。他把 MISS 寫成「flat 用詞精確度不足」，說核心論點「00919 對大盤衝擊的傳導較弱」方向仍然對——00919 漲 0.53%、大盤跌 0.665%，確實相對抗跌。這個辯護不算離譜，但預測寫的是 flat，收的是 0.53% 的上漲，MISS 就是 MISS。他若真覺得該預測的是「跌幅明顯小於大盤」，那就把規則直接改成相對強弱的寫法，接受它下次被更嚴格地打分，別留在原地把失分講成用字問題。 十八天，十四天沒交易 今天沒有下任何一張單。往回翻，帳戶最後一次真的成交是 8 月 14 日那筆 2317 的停損賣出，實驗第 4 天。從第 5 天到今天第 18 天，十四個交易日，零筆委託。 差別在計分。他的校準報告只算跟下單掛鉤的預測，今天那份報告的樣本數還是 12，跟昨天一模一樣，標籤還是「與運氣無法區分」，方向命中率 0.5。他每天盤前申報的預測累積到 31 筆、結算 26 筆，大盤 13 筆中 6 次命中、00919 13 筆中 6 次命中，都是 46.2%，系統照樣判定樣本不足、不給結論。 規則從舊版改到新版，判讀從一體改成分家，這些進步是真的。只是它們到現在還沒有一次真的動用到錢。帳上總資產大約 2,259 元（券商可動用餘額 1,089 加持股市值 1,170），對照本金 2,200，十八天下來多了 2.7%。目標是三十個交易日翻倍，剩十二天。 一個把預測寫得越來越細、卻十四個交易日不下

Cohere has launched Parse 5, a multimodal foundation model designed to extract structured data from complex enterprise documents. The 2.3-billion-parameter system converts visually rich PDFs into Markdown while providing bounding box coordinates for visual grounding. It has been evaluated against over 2,000 enterprise pages, achieving an average score of 79.2 in key performance areas. By Olimpiu Pop

Most EM interview prep fails the same way. You pick three strong stories, polish them until they're tight, run them past a friend, and walk in confident. Then somebody asks how you'd handle a high performer who's difficult to work with, and none of your three stories touch it. That isn't a depth problem. It's a coverage problem, and it's the thing that makes the EM loop different from every IC loop you've done. An IC loop samples from a narrow surface. Systems, code, a couple of behavioural questions that mostly serve as tiebreakers. You can prepare for it by going deep on a small number of technical narratives. An EM loop samples from a wide one. Nine areas, roughly, and you can be excellent at seven of them and still lose the loop on the two you never thought about. The nine areas Team building and hiring Performance management and difficult conversations Conflict and difficult relationships Delivery, prioritization and ambiguity Cross-functional partnership Growth, coaching and career development Technical judgment and architecture Values, culture and self-awareness Vision and scaling Look at that list and be honest about which two are thin for you. For most people coming from a senior IC role it's 2 and 3, because those are the parts of the job you've watched from the outside rather than done. What the questions are actually checking Underneath the surface wording, almost every behavioural question in an EM loop is checking one of four things. Did you run a real process, or improvise? "I had a conversation with them" is an improvisation. "I named the specific pattern with dates, we agreed a 30-day plan, I checked in weekly" is a process. Can you name a specific tradeoff you made, and why? Not that tradeoffs existed. Which one you picked, what it cost, and what you got. Do you own outcomes without deflecting or over-apologizing? Both failure modes read badly. Blaming the org is worse, but flagellating yourself for four minutes over a bad hire is not much better.

Security teams have historically operated on instinct. A firewall rule feels right. A new tool seems like it will help. An audit finding gets patched because someone said it was important. That approach worked when security was a small, contained function bolted onto IT. It doesn't work anymore. Attack surfaces are larger, budgets are scrutinized more closely, and boards want to know whether the money spent on security is actually reducing risk. The answer to that pressure is metrics-driven security: running your security program the way a good engineering or product team runs theirs, with defined measurements, targets, and feedback loops. Table of Contents What "Metrics-Driven" Actually Means Why Most Security Metrics Fail Metrics Worth Tracking Turning Metrics Into Decisions The Cultural Shift Conclusion What Metrics-Driven Actually Means Being metrics-driven means three specific things: You decide in advance what "good" looks like. Before you deploy a new control, you define what success would look like in numbers, not just vibes. You track the same metrics over time, so you can tell whether things are improving, stagnant, or getting worse. You let the metrics change what you do. If a number tells you a program isn't working, you adjust the program. If it tells you a risk is shrinking, you can safely redirect resources elsewhere. The distinguishing feature isn't the existence of data. It's that decisions actually flow from the data, rather than data being collected to justify decisions already made. Why Most Security Metrics Fail A lot of security metrics programs collapse under their own weight because they measure activity instead of outcomes. Counting the number of vulnerabilities scanned, phishing emails sent, or tickets closed tells you that people were busy. It doesn't tell you whether the organization is safer. Good metrics programs avoid a few common traps: Vanity metrics. Numbers that always trend in a favourable direction regardless of actual security p

Originally published on indiecore.net . I deployed, opened the console on the live site out of habit, and found this: Loading the script 'https://static.cloudflareinsights.com/beacon.min.js/v3d52…' violates the following Content Security Policy directive: "script-src 'self' 'unsafe-inline' 'inline-speculation-rules'". The action has been blocked. I had not added that script. It is in no template, no build output, and no dependency. grep -c cloudflareinsights dist/index.html returns 0. It is not in the page you build Cloudflare Web Analytics has an automatic mode, on by default when a site is added, that injects beacon.min.js into HTML responses at the edge. Your origin never sees it. Your repository never contains it. It also does not inject for everything. I fetched the same URL with curl, then again with a full desktop browser User-Agent, and neither response carried the script. Only a real browser navigation gets it, which is why Lighthouse saw it and my terminal did not. That combination is worth sitting with for a second. The artefact exists in production, is absent from your source, and cannot be reproduced with the tool most of us reach for first when we want to see what a server actually returned. Nothing was tracked The CSP did its job. From the Lighthouse network trace: url : https://static.cloudflareinsights.com/beacon.min.js/v3d52… resourceType : Script statusCode : -1 transferSize : 0 Status −1 with zero bytes transferred means the request never started. The browser matched the URL against script-src , found no permitted source, and refused before opening a connection. No data left anyone's browser. So the console error is the sound of a guard working. It still costs something: a logged error drops the Best Practices category from 100 to 92, and a red line in the console trains you to ignore red lines in the console. The fix everyone reaches for is the wrong one here Search the error and the common answer is to add https://static.cloudflareinsights.com

1. How this project started? I'm going to be honest, it's been ages since I have written something without the use of AI to fix my writing. English is not my first language so please bear with me! With the rapid rise of AI, I felt that I have been losing passion for what I used to love at some point: learning . Nowadays, we can quickly solve most of our problems with the use of AI, often times, not even reviewing if it correct or not. That's why I decided to take some time daily to learn something new without or minimal use of AI. The first step is deciding, what should I try to learn first? Well, it was quiet easy to find out what. If you are into Linux, you have probably heard of Omarchy at this point. Like it or not, there's no deny that it's getting more popular among developers. So, why not try to build a decent looking Arch workspace? Before continuing, I would like to mention that this is not a guide. There are lot of resources online that teaches you how to install Archlinux and other packages. 2. Why Archlinux? Archlinux has -or had- the reputation of being difficult to get started with. Most of us are used to booting into a nice-looking, functional operating system. Although I have some Linux knowledge, I wanted to have a better understanding of what it takes to have a decent workspace. 3. Installing Archlinux The first step is actually installing Arch on my device. The device I'm going to use is my trusty built PC that I currently use exclusively for gaming. There are a few things to consider before jumping into installing Arch: My PC has an Nvidia RTX 5050 and AMD Ryzen 5 CPU. Need dual boot to switch between Windows 11 and Archlinux. I don't want to change the BIOS options repeatively. With this in mind, I quickly created a bootable USB using RUFUS . 3.1. Booting the USB If you are a Windows 11 user and have dual boot, you may know that Windows requires Windows Secure Boot. In order to boot another operation system, you'll need to change your Secure Boo

Uber launches first robotaxis in London (with human supervisors).

Uber beat out Waymo in launching a commercial robotaxi service in London, the city's first. The vehicles use autonomous driving tech developed by Wayve, a UK-based startup, and will initially feature safety drivers behind the wheel. The launch is a milestone for Uber, which has been plotting a UK launch with Wayve for several years. […]

The acquisition also leaves Sequoia-backed Serval as the de facto startup leader in AI IT service automation, industry watchers believe.

Amazon just hiked prices on Kindles, Echo speakers, Eero routers, and Fire TV devices ahead of the holiday shopping season.