标签：#science

Quick answer Craigslist search pages ship two copies of every listing: a static HTML list, and a JSON-LD <script> block with images, currency, and geo-coordinates. The obvious move is to join them by ID. Don't — Craigslist's JSON-LD carries no shared identifier at all , not a bare post ID, not a URL, not a SKU. The only field both copies reliably share is the listing's title, and titles repeat. We joined by title through a per-title FIFO queue and measured it recovering 290 of 325 listings (89%) end-to-end on a captured 298-item page. That number is the ceiling of what a title-only join can do on this page shape — plan your field completeness around it, don't assume 100%. Why can't you just match the JSON-LD by ID? 🧩 When we built the Craigslist Multi-City Listings Scraper , the first design assumed what almost every JSON-LD block on almost every e-commerce-shaped site provides: a productID , a sku , a url , an @id — something that lines up a JSON entry with its DOM counterpart deterministically. Live inspection of a captured Craigslist search page found none of those. Each itemListElement entry has exactly name , image , offers , @type , and a position field that looks like it should solve the problem — until you check it against the static list past the first ~18 entries, where some static-list rows have no JSON-LD counterpart at all and the position numbering drifts out of alignment. So the join key that's actually usable, live, is title — and titles aren't unique. The fix is a FIFO queue per title: walk the static <li> list in document order, and for each title pop the next unconsumed JSON-LD entry with a matching name. # actors/craigslist-listings-scraper/src/search_parser.py def _parse_ld_json ( tree : HTMLParser ) -> dict [ str , deque [ _LdEntry ]]: by_title : dict [ str , deque [ _LdEntry ]] = defaultdict ( deque ) for list_item in data . get ( " itemListElement " , []): entry = _ld_entry_from_item ( list_item ) title = list_item . get ( " item " , {}). get

We maintain a corpus of 456 role-specific resume examples in TypeScript. Someone asked me what a good bullet point actually looks like, and rather than answer from taste I decided to measure the thing I already had. Fifteen minutes later we had a script, a set of numbers, and a conclusion. The conclusion was wrong, because the script had silently read about twelve percent of the data. This is a post about that failure mode, and then about the numbers I got once the script worked. The corpus Thirty-one TypeScript files, each exporting an array of role objects. One role looks roughly like this: { slug : ' cloud-architect ' , title : ' Cloud Architect Resume ' , category : ' Information Technology ' , sampleData : { summary : ' ... ' , experiences : [ { company : ' Amazon Web Services ' , position : ' Senior Cloud Architect ' , description : ' - Designed multi-region architecture... \n - Led migration of... ' , }, ], skills : [...], }, tips : [...], } The interesting field is description . It holds a newline-delimited list of bullets as a single string, so the whole corpus of bullets is sitting there in source, greppable, without a database or an export step. Version one const descs = [... text . matchAll ( /description: ' ((?:[^ ' \\] | \\ . ) * ) '/g )]. map ( m => m [ 1 ]); Nothing exotic. Match description: , then a single-quoted string, allowing escapes so an apostrophe inside the text does not terminate the match early. It found 199 description strings. I did not question that, because I had no prior for what the number should be. 199 sounded like a lot of text. We computed medians off it, looked at the opener distribution, and started writing. The number that saved me was on a different line of the same output: roles 456 . The slug count was fine. So 456 roles between them had 199 job descriptions, which would mean the overwhelming majority of roles had no work history at all. I knew that was false, because I had rendered these pages. Why it read twelve percent

If you're in the market for a tiny power station that punches well above its size and weight then have a look at EcoFlow's new fourth-generation River series. The River 260 Gen4 features a 256Wh capacity battery while the 520 Gen4 packs in 512Wh - storing about 2.5x and 5x the energy of the largest […]

Dark energy might be getting weaker. Scientists are wondering if interactions with dark matter in a “dark dimension” may be responsible.

The intricate, 5.6 trillion-pixel map covers approximately 75 percent of the sky and includes about 4 billion celestial objects.

The science curriculum the industry is offering to schools casts plastics as safe, recyclable, and a source of good jobs. Critics say it’s propaganda.

Ever woke up feeling like a truck hit you, only to realize your Apple Watch had been screaming "Warning!" via your data for the last 24 hours? Heart Rate Variability (HRV) is the "canary in the coal mine" for our bodies. It's a powerful metric that tracks the variation in time between each heartbeat, serving as a direct window into your Autonomic Nervous System. In this guide, we are going to build a real-time HRV anomaly detector using wearable data analysis , Scikit-learn , and AWS Lambda . By applying machine learning to time-series health data, we can identify physiological stress, potential infections, or overtraining before physical symptoms even manifest. If you’ve been looking to dive into anomaly detection in time-series or want to master health data engineering , you’re in the right place! The Architecture: From Heartbeat to Alert 🛠️ To achieve real-time monitoring, we need a pipeline that moves data from your wrist to a cloud-based inference engine. Here is the high-level flow: graph TD A[Apple Watch / Wearable] -->|Sync| B(Apple HealthKit) B -->|Webhook/Hook| C[AWS API Gateway] C --> D[AWS Lambda - Inference] D -->|Fetch History| E[(DynamoDB / S3)] D -->|Isolation Forest| F{Anomaly?} F -->|Yes| G[Push Notification / Alert] F -->|No| H[Log & Silent] Prerequisites 📋 Before we start coding, ensure you have the following: Python 3.9+ Scikit-learn & Pandas for data crunching. AWS Account (for Lambda deployment). An app to push HealthKit data (like Health Auto Export or a custom Swift hook). Step 1: Understanding the Data 📊 HRV data is tricky because it’s highly personalized. What is "low" for an athlete might be "high" for someone else. This is why we use Isolation Forest , an unsupervised learning algorithm that excels at detecting outliers in multi-dimensional datasets without needing labeled "sick" vs. "healthy" days. Step 2: Building the Anomaly Detection Logic Let's write the core logic using Scikit-learn . We’ll use the Isolation Forest algorithm becaus

German company Isar Aerospace has successfully launched Europe's first entirely commercial orbital rocket. It attempted to achieve the feat last March, but that lasted all of 30 seconds before the vehicle crashed into the sea and exploded. This time the company managed to get the two-stage Spectrum into low Earth orbit from a Norwegian spaceport. […]

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on e-bikes, power stations, and how to work anywhere, follow Thomas Ricker. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started With a deep breath, I took […]

"We achieved within a few years what had taken the European space industry decades before."

The team embedded electrodes into live cockroaches, then outfitted them with cameras and injection devices to deliver medication via remote control.

The shift in the fish’s diet is having significant downstream impacts on the environment and climate.

Polls show that overwhelming majorities of Americans hate data centers. China makes a perfect scapegoat for tech leaders and their allies—the only problem is a lack of evidence.

Introduction Jumia is one of Africa's leading e-commerce platform that manages millions of transcations with a diverse products from electronics,beauty products and many more categories.Therefore,tracking key perfomance indicators is essential for supply chain operatios and profit optimization Objective My project aim is to build an interactive excel dashboard using Jumia transactional data.I aim to convert disorganized data into an interface that can help in decison making,identify trends and monitor products. Dataset description Data Cleaning and preparation process Raw data mostly contains inconsistency and errors that may occur that may interfere or give the wrong output. An example of a raw dataset In the example above we can see inconsistent and missing data that we need clean in order to have an effective output. First step is to format the prices from text to currency format and replace the before since excel will in order to calculate the discount eg below The image above is the discount price which was obtained by finding the difference between the old price and the new price. The image below is an example of the formula to categorize the prices whether high,low or medium.I used the IF,AND functions.Another example of a logical combination would be the us of OR . The difference when using the IF(AND function is that all the conditions must be met while in the IF(OR ,only one condition has to be met. In the image below i used logical combination of that are IF and AND for the discount category. In the image below i also used the IF AND functions to in the ratings category. After removing duplicates,removing inconsistent data eg texts in numbers columns.Below is an image of the cleaned version of the Jumia dataset. An example of a clean dataset Descriptive Analysis To calculate the average current price of products i used the average formula and highlighted the cells eg =AVERAGE(B2:B113) .The average old price of products was obtained by the same formula but

The research could do more than help unlock the mystery of caterpillar hearing. It could also help make microphones better.

Code: Megapixel99/lambda-language lm is a small low-level language I wrote: static types, explicit memory, no closures, no garbage collector, and four independent backends that emit C, WebAssembly, ARM64 and bytecode for a VM. Its compiler is about 4,400 lines of JavaScript. The obvious next question is whether the language can compile itself, and the obvious first step is the lexer, which is 129 lines. In lm the same lexer is 355 lines. That ratio is the finding, because almost none of it is lm being a verbose language. Six specific absences account for nearly all of it, and writing them down was a planned milestone rather than an afterthought: the point of porting the lexer first was to find out what the language could not do while the port was still small enough to abandon. The one that cost the most src/lexer.js has a single advance(n) that moves pos , line and col together, called from 14 places. lm had no way to take the address of a scalar local, so a function could not mutate a caller's variable, and a function returning three values would need a struct allocated on every call. So advance does not exist. All 14 sites write pos += 1; col += 1; inline, and the newline case writes the three-line variant. That is the single largest source of the size difference, and it also caused the only correctness bug in the port. Column counting inside a string literal has to skip UTF-8 continuation bytes, and because the logic is inlined rather than centralised there is no one place to fix it. The two comment scanners over-count a column in exactly the same way. They get away with it only because a comment always ends at a newline, which resets the column before anything reads it. That is worth sitting with. A centralised advance would have been fixed once and been right in all three places. Instead the code is right in one place by correction and in two others by luck, and the luck is load-bearing: change what terminates a comment and two latent bugs become live ones. Dup

Ev Ağı Nasıl Çalışır? Bir ev ağını en basit haliyle şöyle düşünebiliriz: Internet │ ISP ağı │ Ev Router'ı │ ┌─────────────┼─────────────┐ │ │ │ Laptop Telefon NAS Ev router'ı burada iki farklı dünyayı birbirine bağlar: ISP üzerinden ulaştığı dış ağ ve evdeki cihazların bulunduğu yerel ağ. Bu basit topolojinin arkasında LAN, WAN, subnet, DHCP, routing, NAT, firewall, bridge ve VLAN gibi kavramlar birlikte çalışır. 1. Ev ağı, ISP ve internet tarafı LAN ve WAN LAN — Local Area Network , router'ın yerel ağ tarafıdır. Evdeki laptop, telefon, NAS, televizyon gibi cihazlar genellikle bu tarafta bulunur. Örneğin router'ın LAN adresi: 192.168.1.1/24 olsun. Cihazlar da: Laptop 192.168.1.20 Telefon 192.168.1.30 NAS 192.168.1.50 adreslerini kullanabilir. Bunların tamamı aynı: 192.168.1.0/24 yerel IP ağına aittir. WAN — Wide Area Network ise router'ın kendi yerel ağı dışındaki bir upstream ağa bağlandığı taraftır. Tipik bir evde: Internet / ISP │ WAN │ Router │ LAN │ Ev cihazları şeklinde görünür. LAN ve WAN, Ethernet kablosunun fiziksel türünü tanımlamaz. Aynı standart Ethernet bağlantısı bir router için LAN, başka bir router için WAN rolünde olabilir. Örneğin: Internet │ Upstream Router LAN: 192.168.1.1 │ │ Ethernet ▼ Downstream Router WAN: 192.168.1.50 LAN: 192.168.10.1 Buradaki 192.168.1.0/24 ağı: upstream router açısından LAN, downstream router açısından WAN tarafıdır. Dolayısıyla LAN ve WAN kavramları hangi router açısından baktığımıza göre anlam kazanır . Upstream ve downstream Ağda internet veya daha üstteki ağa doğru olan yön upstream , son kullanıcı cihazlarına doğru olan yön ise downstream olarak adlandırılır. Internet │ Upstream Router │ Downstream Router │ Laptop Downstream router'ın internete doğru bağlandığı router onun upstream router'ıdır. Bu terminoloji özellikle evde bir modem/router arkasına ikinci bir router bağlandığında kullanışlı hale gelir. ISP'nin rolü ISP — Internet Service Provider , ev ağını daha büyük internet altyapısına bağlayan servis sağlayıcıdı

Other archives and libraries around the world may also contain genetic traces of past disease outbreaks.

Every neuron and connection in the brain of a fly has been mapped—twice.

Two of Europe's leading launch startups announced new fundraising this week.