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Three years ago we had our first real outage. Six hours of downtime. Thousands of angry users. Multiple executives on the call. Here's what we did right, what we did wrong, and what we'd do differently. What we did right 1. Communicated immediately. The moment we knew we had a problem, we updated the status page and emailed our biggest customers personally. Not when we had answers. When we had a question. 2. Had a single incident commander. One person making calls. Not a committee. When the CEO tried to direct technical work, the IC politely rerouted and told her where her help was actually needed (talking to customers). 3. Took care of our people. During hour 4, I ordered food. During hour 5, I forced the primary engineer off the call for 20 minutes to walk outside. Long incidents destroy people. You have to feed them and force them to rest. 4. Wrote it down as we went. We had a shared doc with a live timeline. When the post-mortem came, we had every decision captured. What we did wrong 1. Tried to fix the root cause during the incident. For the first 2 hours, we were digging into why the database was struggling. We should have been mitigating (rolling back) first. 2. Let too many people 'help.' By hour 3, we had 12 engineers in the call. Half of them were useless. The IC should have kicked people out sooner. 3. Gave optimistic estimates. 'We'll be back in 30 minutes.' We were not back in 30 minutes. That miscommunication was worse than saying 'unknown.' 4. Didn't prepare the executive communication. The CEO had to answer customer questions in real time with no script. We should have drafted talking points for her after hour 1. What we'd do differently Mitigate first, investigate second. Always. Cap the number of active engineers at 4 during an incident. Others go on standby. Default to 'unknown' for estimates. Only give a number when we're sure. Assign someone explicitly to 'executive liaison.' Their job is to keep the C-suite informed without interrupting the tec

I got fired from my first job, took down a database server with a badly written query, and was...

Citizen Sleeper creator Gareth Damian Martin has revealed their latest game, Signet City. The new RPG casts players as a fungal parasite in a dying city.

We're likely to see more price increases as the big AI companies plan to go public.

Head's true genius—and that of his character, Giles—lay in quietly filling in the gaps in every scene

A native Mac cockpit for Claude Code Discussion | Link

The 2026 edition of Summer Game Fest just wrapped up, and it was surprisingly hectic. The nearly week-long event came at a challenging time for the games industry, and for the most part the big keynotes were used as a chance to show some strength through major announcements, while largely ignoring pesky details like hardware […]

Using it daily for work. submitted by /u/Medium_Potato3703 [link] [留言]

Whenever I heard about harmonics, I thought they were only related to large substations, transmission systems, and industrial facilities. I assumed harmonics were something utility engineers dealt with and not something connected to everyday devices. Phone chargers can create harmonics. Laptop chargers can create harmonics. LED lights can create harmonics. Even a UPS sitting under a desk can create harmonics. Today, modern power systems use many power electronic devices such as EV chargers, solar inverters, battery energy storage systems (BESS), UPS systems, data centers, and Variable Frequency Drives (VFDs). While these technologies bring many benefits, they can also introduce harmonic distortion. The more power electronic devices we connect to the grid, the more important harmonic analysis becomes. In this article, I will explain what harmonics are, what causes them, how they affect power quality, how they can be analyzed using PSCAD, and why they are becoming more important in modern power systems. Before we talk about harmonics, let's first understand electrical loads, because this is where harmonics usually begin. What Is an Electrical Load? An electrical load is any device that uses electrical energy to perform useful work. For example, think about a typical evening at home. You turn on a ceiling fan, LED light, laptop, air conditioner, and phone charger. All of these devices use electricity, so they are called electrical loads. Examples of electrical loads include motors, heaters, fans, computers, air conditioners, lighting systems, and EV chargers. However, not all electrical loads use electricity in the same way. Some draw current smoothly, while others draw current in short pulses. This small difference is actually where the story of harmonics begins. Linear vs Non-Linear Loads To understand harmonics, we first need to understand the difference between linear and non-linear loads. Although both types of loads consume electricity, they draw current from the

In Part 1 we installed Presidio and ran a basic detection on clean sample text. Real data is messier. Emails have signatures with phone numbers buried in HTML. Support tickets mix PII with technical jargon. Chat logs have informal name references that NER models struggle with. And sometimes the PII isn't in text at all. It's in screenshots and scanned documents. This part covers how Presidio's detection engine actually works under the hood, how to process different text types you'll encounter in production, and how to handle structured data and images. How the Analyzer Engine Works Presidio doesn't rely on a single detection method. It layers three approaches and combines their results. Named Entity Recognition (NER) The NER model (spaCy by default) processes the text and identifies entities based on the language model's training. It's good at catching names, locations, and organizations even when they don't follow a fixed pattern. "John Smith" is easy. "Dr. J. Martinez-Garcia" is harder but the NER model handles it because it understands context and word patterns. The tradeoff is that NER is probabilistic. It can miss unusual names or flag common words as entities. That's why Presidio doesn't stop here. Pattern Matching (Regex) For entities with predictable formats, Presidio uses regex recognizers. Credit card numbers, SSNs, email addresses, IP addresses, phone numbers all have known patterns. A Luhn-validated 16-digit number is almost certainly a credit card. A string matching \d{3}-\d{2}-\d{4} in the right context is probably an SSN. Pattern-based detections typically get higher confidence scores than NER detections because the pattern itself is strong evidence. Context Scoring Here's where it gets interesting. Presidio looks at the words surrounding a potential match to boost or lower confidence. If the text says "my SSN is 123-45-6789," the phrase "my SSN is" provides strong context that the number is actually a social security number and not some random ID. Th

There is a class of projects that teaches you more about a language than any tutorial ever could. Building a PDF engine from scratch in Go is one of them. It is not glamorous. It is not trendy. But it forces you to confront memory management, binary serialization, concurrency safety, interface design, and performance profiling all at once, in a domain where correctness is non-negotiable. This article walks through the lessons learned building GoPdfSuit (~500 Github ⭐), a production PDF engine written in Go that generates 1.5 million financial PDFs in roughly 45 minutes on a single node, achieves PDF/A-4 and PDF/UA-2 compliance, and exposes itself as a REST API, a Go library, and Python CGO bindings simultaneously. Note : While I have six years of overall experience including two years working specifically with Go, I rarely encountered these types of challenges in my day-to-day work, as my role focused primarily on implementing new features within an existing architecture. Working on gopdfsuit was an excellent learning experience; it allowed me to dive deep into performance optimization and taught me a great deal. Below are some of the key takeaways. Building GoPdfSuit from a blank editor to a production-grade PDF engine-one that ships PDF 2.0 , PDF/A-4 , PDF/UA-2 , PKCS#7 signing, merge/split, XFDF fill, secure redaction, and a public gopdflib API-forced a shift from “business logic” to “systems engineering.” When you chase ~2,000+ aggregate ops/s on a mixed financial workload (48 workers, PDF/A on) and sub ~10 ms PDF generation, you stop debating frameworks and start fighting the allocator, cache lines, and ISO 32000 semantics. These fifty lessons are drawn from the actual codebase ( internal/pdf , pkg/gopdflib , benchmark harnesses under sampledata/ , and documented optimization passes in guides/cursor/ ). They mix specification pain with Go runtime craft and production reality-not generic blog advice. Part 1: Structural Hurdles & PDF Specification Nightmares Deco

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

Most merchants know they have unused files. Far fewer realize they're accumulating AI-generated media they never intended to keep. There's a problem quietly growing inside thousands of Shopify stores right now. It's not abandoned carts. It's not slow page speeds. It's not even the 400 unused product images you already know you should deal with. It's something newer, and most merchants have no idea it's happening. The Rise of AI-Generated Commerce Content Over the past two years, AI image tools have gone from novelty to routine. Shopify Magic. Canva AI. Midjourney. ChatGPT image generation. Adobe Firefly. Background removers. Lifestyle photo generators. Product shot enhancers. Merchants are using these tools constantly — to mock up new products, test background options, generate seasonal variants, create ad creatives, experiment with lifestyle photography. The workflow feels clean: generate a few options, pick the best one, move on. Here's what's actually happening on the backend. Every time you use Shopify's native AI tools to generate, edit, or enhance an image, Shopify quietly deposits files into your media library. Not just the one you kept. All of them. The rejected generations. The experimental edits. The "let me try one more variant" files. The abandoned attempts from six months ago when you were testing a new product that never launched. Every. Single. One. Most merchants assume the files they don't choose disappear. They don't. The lifecycle looks something like this: ┌─────────────────────┐ │ AI Image Generation │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Rejected Variants │ │ • Drafts │ │ • Test Images │ │ • AI Edits │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Hidden Media Files │ │ Accumulate Over Time│ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ AI Clutter │ │ Invisible Technical │ │ Debt │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Reduced Media │ │ Governance │ │ • More Noise │ │ • Less Visibility │ │ • Hard

Notion's head of product said he was "astonished" at “the amount of people RT-ing this."

"Chat is dead" — at least, according to a senior OpenAI employee.

Welcome back to TechCrunch Mobility — your central hub for news and insights on the future of transportation.

Every online Fake Data Generator tool I used had the same quiet flaw: whatever you paste gets POSTed to someone's server. For a Fake Data Generator, that paste is often exactly the sensitive thing — a token, a config, an API response. So I built Fake Data Generator to not do that. Generate fake names, emails, UUIDs, addresses — custom columns, CSV/JSON export. 100% browser-side — and it runs entirely in your browser, so there's nothing to send and nothing to breach. You don't have to take my word for it: open DevTools → Network, use the tool, and watch the tab stay empty. One HTML file, View-Source-able. https://faker.platotools.com/ It's part of platotools.com — a set of single-purpose, client-side dev tools. Feedback and edge-case bug reports very welcome.

If you have an outdoor space that needs some extra lighting, either for fun or security reasons (or a mix of the two), the Govee outdoor lamp post light is a good pick that’s 23 percent off its regular price right now. Typically retailing for $260, the Govee outdoor lamp post light is available for […]

Somewhere along the way, computing became disposable. Devices became sealed. Systems became rented. Ownership became licensing. Repairability disappeared. Infrastructure moved away from the user and into distant cloud platforms. And I think we lost something important because of it. Lately, I’ve found myself becoming increasingly interested in hardware again. Not just software. Not just cloud systems. But actual computing devices. Servers. Home infrastructure. Repairable machines. Set-top systems. Local AI appliances. Sovereign computing. Because I believe the next era of computing will belong to people who own their infrastructure again. The Provider Box Realization One thing that kept sticking in my head was this: Almost every home in America already has a provider box. A Comcast box. An AT&T gateway. A router. A modem. A streaming box. People are already comfortable with the idea of a dedicated computing appliance sitting in their home quietly powering their digital life. That realization changed how I thought about computing infrastructure. What if those boxes worked for the user instead of the provider? What if they: hosted local AI, managed home storage, coordinated smart devices, powered media systems, handled automation, protected privacy, synchronized intelligently, and operated as sovereign infrastructure? That idea became part of the thinking behind KiwiHome. The Return Of Home Infrastructure For a long time, the industry moved toward centralization. Everything shifted toward: SaaS, subscriptions, streaming, cloud storage, cloud intelligence, and rented operational environments. Convenient? Absolutely. But also fragile. If: pricing changes, services disappear, companies shut down, APIs get revoked, or platforms change policies, entire workflows collapse overnight. I think people are starting to feel that tension. Especially creators. Especially businesses. Especially technical users. That’s why I believe we’re going to see a major resurgence in: home serv

I built something I desperately needed: daily AI audio lessons from real developer trends (plus a Dutch coach for inburgering B1). The hardest part wasn't the AI. It was figuring out how to score genuine rising skills vs. one-day noise. I ended up building a cross-day momentum signal that rewards skills accelerating over 3+ days and dampers spikes. But I'm stuck on the next problem: how do you personalize this without storing user data? (I'm privacy-first, so no subscriber DB — Telegram holds the member list.) If you've solved this, I'd love your take. And if you're learning Dutch + coding, I'd appreciate you trying it and telling me what's useless. What I'm curious about: Is the momentum signal actually working — am I surfacing real trends or just noise? Would the Dutch coach be useful for expat developers in NL, or is it too niche? Technical details (for those who care): • 7 sources: GitHub Trending, HN (Who-is-Hiring + front page), Stack Overflow tag deltas, dev.to, Reddit, Lobste.rs • Map-reduce skill extraction with deterministic attribution (corpus scan, not LLM tally) • Grounded briefs: reads actual source text via Jina + Exa, cited sources • Delivered via Telegram (audio + PDF), Spotify podcast, email • Privacy: PII redacted at ingestion, no subscriber data stored Live: https://yusuprozimemet.github.io/LearnX-Radar/ GitHub: https://github.com/Yusuprozimemet/LearnX-Radar (P.S. This is still beta — I'm looking for feedback, not users. If you try it, tell me what's useless, not what's good.)