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Memory is not a log. It is a story rewritten after you learn how it ended. You will remember being less certain than you were. You will remember seeing the risk you missed. That is not dishonesty. That is how remembering works. Which is why you cannot learn judgment from memory alone. The correction is unglamorous. Before the decision, write down what you expect. Not a plan. A prediction. We are choosing this because we believe that. We think it takes three weeks. We think the load arrives by March. If it does not, we were wrong about the growth, not about the design. Four lines. A date. Somewhere you will find it again. Add the number you least want to write. How sure are you. Seven out of ten is a real answer. Certain is almost never a real answer, and writing it down makes you notice how often you reach for it. Then set a time to come back. The value is not in the writing. It is in the reading, six months on, when the outcome is known and the note is not. You find out something nobody else can tell you. Whether you run early or you run late. Whether your confident calls land better than your hesitant ones. Whether you are wrong about people more often than you are wrong about systems. That is calibration. It is the whole difference between ten years of experience and one year lived ten times. It changes rooms, too. A review where somebody can produce what they actually believed in March is a different meeting from one where everyone remembers agreeing. Hindsight makes us unfair in a peculiar direction. It makes the past look obvious, and the people standing in it look careless. They were not careless. They were standing exactly where you are, with less information and no ending. So write down where you are standing. Your future self will not be able to reconstruct it, and your future self is the one who has to get better. – Serguey Asael Shinder

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

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

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.

I spent Friday night staring at a mock municipal utility shutoff notice. The text was dense. The language was punitive. The deadline was buried in a block of legal code on page two. Generosity usually shows up as time or money, and that kind of giving matters. I think it can also look like removing friction. Millions of vulnerable and non-native speaking families receive legalistic notices, like eviction warnings, utility shutoffs, medical bills, or benefit discontinuances, written in adversarial legalese. The emotional and cognitive weight is massive. These notices are dense no matter who is reading them. I still read some of them twice, and most people meet one while already having a hard week. What I Built I directed the build of Charitas Clew . It is an open-source, zero-judgment paperwork engine for public notices. Charitas Clew ingests overwhelming institutional notices and uses Google AI to decompress the legal gravity into plain-language clarity. Instead of a generic chat interface, it outputs a strict Action Protocol: The Actual Meaning : Demystified in plain, dignified language. Key Dates and Timelines : Pinpoints critical statutory deadlines and grace periods. Simple Next Steps : 2 to 3 actionable, reassuring instructions. Personal Speaking Script : A first-person script the user can read out loud when calling or visiting a clerk, caseworker, or counselor. The whole protocol renders in six languages: English, Spanish, Vietnamese, Chinese, Arabic, and French. A notice written in adversarial English comes back as plain language in the language spoken at that household's kitchen table. Charitas Clew joins the Clew Suite , my portfolio of civic tech tools focused on making complex systems more inspectable. Demo Live Production Instance: charitas-clew.web.app Firebase Hosting serves the frontend. Every AI call routes through the Express gateway on Cloud Run. Paste a notice or upload a photo of one, pick a language, and read the result. Code earlgreyhot1701D /

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.

Security Bolted on at the End Is Not DevSecOps The most common failure pattern in teams that claim to "do DevSecOps" looks like this: build the pipeline, ship the feature, and run a security scan right before release — treating security as a checkbox at the end of the process instead of something built into every stage of it. That's not DevSecOps. That's a security review with extra steps. Real DevSecOps means security is woven into the pipeline itself — scanning dependencies on every commit, catching misconfigurations before they're deployed, and treating a vulnerability the same way you'd treat a failing test: something that blocks the pipeline, not something reviewed manually after the fact. This guide is for people who already have some DevOps or backend foundation and want to understand what it actually takes to move into a DevSecOps-focused role — not just "add security" to an existing skillset, but understand the mindset shift that makes the discipline distinct. This post originally appeared on the Ciphemic Academia blog . What "DevSecOps" Actually Requires DevSecOps sits at the intersection of three skill areas, and a real role expects working competence across all three, not deep expertise in just one: DevOps fundamentals — CI/CD pipelines, infrastructure-as-code, containers, the same core skills a cloud/DevOps engineer needs Application security — understanding common vulnerability classes, how to find them, and how to actually fix them, not just recognize their names Security automation — the specific skill of embedding security checks into a pipeline so they run automatically, consistently, on every change That third point is what actually distinguishes DevSecOps from "a DevOps engineer who also cares about security." It's specifically about automation and process — making secure practices the default path, not an extra manual step someone has to remember to do. Step 1: Confirm Your DevOps Foundation Is Solid DevSecOps is not an entry point into DevOps —

A six-week journey through source builds, CI pipelines, and one package pacman never mentioned. TL;DR: pacman -S whisper-cpp ggml-vulkan . That's it. That's the whole answer. Here's why it took me several weeks to find it. The setup I use whisper.cpp for local speech-to-text and as a part of my projects. I have a GPU utilization monitor permanently visible in my GNOME panel via the Vitals extension — so when whisper.cpp started detecting my GPU but running everything on CPU anyway, I noticed immediately. Went to fix it. What followed was several weeks of googling, building from source, writing a custom PKGBUILD, setting up CI, publishing an AUR-style repo — and eventually discovering that the actual fix is a single extra package that pacman never once mentioned to me. The investigation First thing I checked: is the official extra/whisper-cpp package compiled with Vulkan support? All search results said no — -DGGML_VULKAN is explicitly OFF, GPU code is absent from the binary. The app sees your GPU through vulkan-icd-loader but has no code to actually use it. That matched exactly what I was seeing. So the binary itself was the problem. At the time, a separate whisper-cpp-vulkan package had existed in the repos but kept appearing and disappearing — and right then it was gone from both extra and AUR. AUR pushes were also temporarily restricted due to a supply-chain incident. So the "just install the vulkan variant" path was closed, though it used to be available sometime. The obvious move: build from source with -DGGML_VULKAN=ON , package it up, done. I published whisper-cpp-vulkan-arch with a PKGBUILD and prebuilt binaries, wired up CI to track upstream releases automatically and rebuild correspondingly, and wrote a Reddit post explaining the situation. The post was dated August 13, 2026. The twist A few weeks later, someone commented on the post. They suggested installing ggml and vulkan-icd-loader . I started writing a detailed reply explaining why this was wrong: gg

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

I kept pricing off gut instinct + one competitor for way too long. You know the move. competitor is $29 so you're $19. or you're $49 because "premium." neither is a strategy. it's just… anchoring with extra steps, so i did something kind of dumb and kind of useful: opened a bunch of public pricing pages and wrote them down. analytics tools, email tools, form builders, newsletter stuff, hosting, that kind of indie/bootstrappy SaaS. no login walls. if i couldn't see a number, i didn't invent one. Sticker prices were less useful than noticing the shape: where free cuts off what the mid tier is actually selling (usually "remove friction," not "more features") whether the unit is seats, usage, projects, subscribers, etc. copying one competitor's number skips all of that. if you want the sheet I packaged it as an excel workbook (pricing tab + a patterns tab + a blank experiments sheet). yellow/uncertain cells mean i refused to make a number up. free 12-row sample: https://payhip.com/b/0Ubzu full ~100 comps: https://payhip.com/b/l72jY ($19) otherwise curious how people here actually did comps when they first shipped. interviews? gut? one rival's page? something else? ended up with ~100 rows in a spreadsheet. product, category, url, model (flat / seat / usage / freemium+paid / whatever), entry/mid/top when it was on the page, free tier y/n, annual discount if obvious, a short note, date checked. what got boring after enough of them some of it stops being interesting once you've seen it twenty times: usable free tiers that do one real job, then you pay to remove branding / raise limits / unlock collab. not the "7-day demo of everything" free. annual is weirdly consistent. like, ~two months free / ~15–20% off shows up a lot. a ton of indie entry prices live in this unglamorous $9–25 band. not always, but enough that "$79 starter" starts looking like a choice, not a default. not everything is per-seat. flat-by-project / flat-by-workspace shows up more than i expected. i wasn't