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If you’ve been thinking about upgrading your gaming monitor, LG’s 27-inch 27GX704A-B pairs a glossy WOLED panel with a fast refresh rate, and it’s currently on sale for $379.99 (about $70 off) at Amazon and directly from LG, marking a new low price. It originally launched at $799.99, but has been available for under $500 since […]

Hi HN, I’m Alex Southmayd, the founder of Bloomy ( https://bloomylearning.com ) – an AI-powered mastery-learning platform for K-12 students. Bloomy provides students with an AI tutor alongside adaptive curriculum (right now Math, English Language Arts, and Writing). How it works: we diagnose students’ skill gaps, place them on personalized learning paths, and give them standards-aligned lessons and a Socratic AI tutor that scaffolds their learning without just giving away the answer. The goal is

I worked on this project ( https://github.com/workofart/harness-training ) for the past few months to reframe "Agent-driven Self-improving Harness" to "Harness Training". The idea is simple, the harness is trained once with a frozen task LLM against a given task environment. Then you can then swap out the task LLM to any model and evaluate the "frozen trained harness" with any task LLM on any new task environment. Since this was a general problem, I took the chance to create a general PyTorch-like training framework. Right now, you can train with any OpenAI-compatible API for interfacing with the task LLM and train against Terminal-Bench or SWE-Bench tasks, but you can easily extend it to support any task environments. criterion = StrictPareto() optimizer = GreedyMonotonic() trainer = Trainer( config_path="config/train_harness.yaml", estimator=AgenticEstimator( backend=CodexAgentBackend(...) ), criterion=criterion, optimizer=optimizer, ) for loss in trainer.epochs(30): # Records the baseline-vs-candidate verdict loss.backward() # Optimizer either fast-forwards the candidate change (git commit) as a new baseline or rejects it (preserved as git ref) optimizer.step() I wrote a blog post ( https://www.henrypan.com/blog/2026-07-18-harness-training ) on this journey, including (but not limited to): results from using this harness training framework to improve general capabilities across many task LLMs to beat Terminal Bench 2.0 (Terminus Harness) and also transfer learnings towards better task-solving abilities in unseen task environments (e.g. harness trained on SWE-Bench tasks solving Terminal Bench tasks). how this framework is built learnings on what was missing in my initial version of the project (hint: determinism) Any feedback is appreciated. Thanks! submitted by /u/Megadragon9 [link] [留言]

A video from Gamers Nexus explains how, after connecting a new LG UltraGear monitor to a PC running Windows 11 for the first time, Windows Update is silently installing LG driver updates and the LG Monitor App Installer, without so much as a permission pop-up or notification. The app doesn't appear to include special controls […]

Adobe's experimental camera app has taken an unexpected turn. After Project Indigo was launched last year to provide a "more natural (SLR-like) look" for iPhone photography, the Indigo camera app is now being updated with a suite of generative AI tools. And the change doesn't rely upon Adobe's own Firefly AI models. Adobe describes the […]

Celebrating $100 million contributed by the community to the people who build and sustain open source every day. The post $100 million for open source: A milestone built by the community appeared first on The GitHub Blog .

Many will say Spa is the best racetrack on earth, but not for these F1 cars.

If you've ever cat -ed a Terraform or OpenTofu state file, you already know the uncomfortable truth: it's a plaintext JSON dump of everything your infrastructure knows, including secrets. Database passwords, generated private keys, API tokens injected through providers — they all land in state, in the clear. OpenTofu is the one place where you can fix this at the source, because native state and plan encryption is a first-class OpenTofu feature that upstream Terraform does not have. Here's how it actually works and how I roll it out on existing projects without breaking them. Why plaintext state is a real risk State is not a cache you can regenerate. It's the authoritative map between your HCL and the real resources, and OpenTofu has to store the values of attributes to compute diffs. That includes sensitive ones. Marking an output sensitive = true only hides it from the CLI output — it's still written verbatim to state. On real infra I've seen state end up in three places it shouldn't: an S3 bucket without SSE and with overly broad read IAM, a CI artifact that got uploaded to a build cache, and a developer laptop with terraform.tfstate committed to a feature branch by accident. Backend encryption (like S3 SSE) helps for one of those. It does nothing for the other two, because the moment state leaves the backend it's plaintext again. OpenTofu's encryption operates at the data layer, before the bytes ever hit the backend or a local file. The state is encrypted at rest everywhere: in the backend, in local copies, in CI artifacts. That's the property I want. Anatomy of the encryption block Encryption lives in a terraform { encryption { ... } } block. It has three moving parts: a key provider (where the encryption key comes from), a method (the actual cipher), and targets ( state and/or plan ) that bind a method to what you want encrypted. terraform { encryption { key_provider "pbkdf2" "passphrase" { passphrase = var . tofu_encryption_passphrase } method "aes_gcm" "defa

I had a website blocker, a Pomodoro timer, a tab suspender, and a time tracker installed at the same time — four separate extensions, four separate settings pages, none of them talking to each other. Starting a focus session meant manually turning on the blocker, then starting the timer, and neither knew the other existed. So I built TabInsights , which does all four and actually connects them. What it does Website blocker — block by domain, category, or schedule, with an optional typed "unblock challenge" for the days willpower isn't enough. Pomodoro focus timer — one click starts a 15/25/45-minute sprint, which also auto-blocks distracting categories for the duration and unblocks them automatically when it ends. This is the part that actually solves my original problem — the timer and the blocker are the same feature, not two extensions coincidentally running at once. Memory saver — auto-suspends tabs you haven't touched in a configurable window (15–60 min), freeing roughly 50MB of RAM each via chrome.tabs.discard() . Suspended tabs stay in your tab bar and reload exactly where you left off with one click. Automatic time tracking — logs time per domain with no manual start/stop, and shows a daily focus score. A few implementation notes Manifest V3 removed persistent background pages, which meant every "ongoing" feature — sprint timers, the daily summary, auto-suspend checks, license re-validation — had to be rebuilt on chrome.alarms instead of a long-lived timer. The gotcha: Chrome clamps alarm intervals to a minimum of 1 minute in packaged (published) extensions, so anything needing finer granularity has to accept that floor rather than fight it. The blocker uses declarativeNetRequest — you hand Chrome a set of match rules and it enforces them at the browser level. The extension never actually reads the blocked request; it can't, by design, which is also the honest answer any time someone asks whether a blocker "sees" their browsing. The bigger architectural deci

8 min read · telecom provisioning platform, 30M+ subscribers Most companies avoid migrating their monolith for one reason: they imagine it as a single, terrifying event — months of a feature freeze, a weekend cutover, and a rollback plan that's really just hope. That fear is reasonable. A big-bang rewrite of a live system serving 30M+ subscribers really would be terrifying. So we didn't do that. We used a pattern that lets you migrate a monolith one slice at a time, while the system keeps running and the product team keeps shipping features — the same pattern Martin Fowler named Strangler Fig , after the vine that grows around a host tree, gradually taking over, until eventually the original tree is no longer needed. Why the "just rewrite it" instinct is usually wrong The instinct to rewrite a legacy monolith from scratch is understandable — the old code is scary, undocumented, and nobody wants to touch it. But a full rewrite has a well-known failure pattern: it takes far longer than estimated, the business can't freeze feature development for that long, and by the time the rewrite is "done," the old system has changed underneath it and the rewrite is already out of date. The alternative isn't "don't migrate." It's: migrate in slices small enough that each one is boring , and never require the business to stop shipping while you do it. The pattern: a facade, and one slice at a time Strangler Fig works by putting a routing layer — a facade or API gateway — in front of the monolith. At first, 100% of traffic passes through to the old system untouched. Then, one capability at a time: Build the new version of that one capability as an independent service. Update the facade to route just that capability's traffic to the new service. Run both in parallel long enough to trust the new one (see "shadow traffic" below). Retire that piece of the old monolith. Repeat for the next capability. At every point in this process, the system is fully functional. There's no "half-migrat

На прошлой неделе DeepSeek отказался писать мне регулярку. Не «не смог», а вежливо объяснил, что регулярка тут плохая идея, и предложил цикл. Тогда я поймал себя на том, что уже год сравниваю модели «на глаз»: одна затупила — скопировал промпт в соседнюю вкладку, посмотрел, сделал выводы из ничего. Надоело. Я устроил маленький честный замер: три типовые задачи из моих рабочих будней, буквально один и тот же промпт, три модели. Про сетап — один абзац, чтобы дальше не отвлекаться. Хотелось убрать переменную «у одной вкладки отвалился VPN, у другой слетела сессия», поэтому гонял всё в одном окне через агрегатор; ссылка будет внизу, здесь она не важна. Важно одно: модель переключается прямо над полем ввода, так что все три участника получали идентичный текст без копипасты между сервисами. Участники: GPT (флагман, GPT-5-класс), Claude, DeepSeek. Задача 1. Регулярка: split CSV-строки, не ломая кавычки Промпт: «Напиши JS-регулярку, чтобы разбить строку CSV по запятым, но запятые внутри двойных кавычек разделителями не считать». Классическая ловушка: наивный split(',') рвёт поле "Иванов, Иван" пополам. GPT сразу выдал вариант с lookahead: const parts = row . split ( /, (?=(?:[^ " ] *" [^ " ] *" ) * [^ " ] *$ ) / ); Работает. Правда, на моём проверочном кейсе с экранированной кавычкой "" внутри поля — уже нет. Но я такого и не просил, засчитываю. Claude дал ту же регулярку, но с примечанием: lookahead на каждой запятой пробегает хвост строки, на длинных строках это квадратично, для продакшена берите csv-parse . Занудно, но по делу — я однажды именно такую конструкцию ловил на таймауте. DeepSeek — это тот самый отказ, с которого всё началось. Регулярку писать не стал, предложил цикл с флагом «мы внутри кавычек» — мол, так надёжнее. Формально это даже честнее, но задание было «напиши регулярку», и выдал он её только после уточнения. Итог раунда: GPT и Claude ровно, Claude на полбалла впереди за предупреждение. Задача 2. Рефакторинг: функция скидок с вложенными if Скормил всем

A team lead asks the question in a planning meeting and the room splits three ways: do we build this agent in Copilot Studio, write the orchestration ourselves and host it, or hand our container to Foundry and let it run our code? All three are official Microsoft build paths in 2026, all three end up in the same tenant-wide agent inventory, and the wrong pick costs you a rebuild once the project outgrows it. The answer is not "the most powerful one." It is the one whose service model matches who is building the agent, who owns the runtime, and how much pro-code control over orchestration and protocols you actually need. This article is the decision framework for that choice, grounded in Microsoft Learn and current as of mid-2026. Two of these three paths are public preview, so this is a guide to architectural fit and direction, not a production-reliability scorecard. TL;DR Three build paths, picked by service model, not power. Copilot Studio: low-code managed SaaS for makers. GA. Foundry Hosted agents: managed PaaS runtime for your own container. Public preview. Microsoft 365 Agents SDK: pro-code, self-hosted, widest channel reach. Agent Framework orchestrator in public preview. Monday move: before picking a platform, write down four things for this agent - who builds it (maker or pro-dev), who must own the compute, what channels it has to reach, and whether you need custom protocols or background/async behavior. Those four answers pick the path more reliably than a feature checklist. The three paths in one paragraph each Microsoft's own Cloud Adoption Framework frames the build options as three service tiers, which is the cleanest mental model to start from. The CAF positions them as Copilot Studio (SaaS, no/low-code), Microsoft Foundry (PaaS, pro-code or low-code), and GPUs and Containers (IaaS, code-first frameworks for maximum flexibility). The first two are managed by Microsoft. The third is where the self-hosted SDK path lives when you own the compute end to e

DISCLAIMER: This post was written entirely by me! I used AI for a little research, spelling, grammar, and comprehension checks. This post was originally shared on Hackernoon Entertain me for a moment, let's appreciate where we are today by understanding where we've been… or at least, where I've been. I remember when I first learned to code. I was bad, like really bad. But I was so curious! It all started by writing some VBA in an MS Access Database to create an IT Inventory app in the late 90s. Then I learned JavaScript, ASP (without the .Net), then C#, .Net ( I still have my .Net for Dummies book, see below) , jQuery, Python, Java, Angular, ReactJS, and Python again (yeah, had to relearn that one for some reason), and I'm sure there are others in there I forgot about. Learning to write code was rewarding! There were those days I'd spend hours on a bug, only to realize I didn't initialize the variable or forgot a semicolon. I learned .Net over a weekend thanks to the above book. I never became an artist of the craft, like some of my colleagues have (you know who you are: Josh, Kevin, and many others), but I knew how to build anything. I loved that ability: I could build anything. Pure joy! If you haven't had the joy of learning to code, do your best to learn it, because you can't prompt your way to being a Senior Engineer . As I progressed in my career, I became an architect and senior lead. I started off by leading a single engineering team, and now I support large programs and teams. All the while, I never let go of hands-on-code. I still love coding for work and my myriad of side projects. Then, a few years ago, this GenAI thing showed up. Put me in that group of: oh-no-there-goes-my-joy. Joy, yes, not my job. I love my job because I get to do what I love. I loved the dramatic rollercoasters: architecting a perfect solution, realizing it's wrong, getting to write every line of code, chasing impossible bugs, panicking with deadlines, late nights chasing hot fixes,

Adobe's Project Indigo can now remove all kinds of backgrounds from photos you snap using the app

Every time you run a JavaScript program, a lot happens behind the scenes. Variables are allocated memory, execution contexts are created, functions are pushed onto the call stack, and the engine starts executing your code. But before we dive into all of that, let's first understand what JavaScript actually is and why it was created. An Introduction to JavaScript What is JavaScript? JavaScript is a programming language that was originally created in 1995 by Brendan Eich in just 10 days while he was working at Netscape. JavaScript is a high-level programming language primarily used to make web pages interactive. Today, it is also used to build servers, mobile applications, desktop software, and much more. Why was JavaScript created? JavaScript was created to make web pages alive . But what does "alive" mean? it means adding interactivity (e.g., animations, clickable buttons, popup menus, etc.) to the static web pages. Today, JavaScript isn't limited to browsers. With runtimes like Node.js, it can also be used to build backend applications and APIs, which allow you to add more functionality to a website. Did you know? When JavaScript was created, it initially had another name: “LiveScript”. Where can JavaScript be used? In your browser — every interactive website uses it (Facebook, YouTube, Gmail). On servers — through Node.js, you can build backend APIs. In mobile apps — using frameworks like React Native. In desktop apps — VS Code itself is built using JavaScript (Electron). In smart devices, games, robots, and much more. Now that we know what JavaScript is, another question comes to mind: How does JavaScript execute my code? Before answering that, let's first understand Who executes my code? . The answer is: The JavaScript Engine The JavaScript Engine We already know what JavaScript is, but what exactly is this engine ? The "Engine" A JavaScript engine is a piece of software responsible for executing JavaScript code. Every environment that runs JavaScript, whether i