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Although most of Prime Day’s unusually good Philips Hue deals have ended, a few remain, and some, including the black Philips Hue Twilight Sleep and Wake-Up Light, are even cheaper. None, however, have fallen to a new all-time low price like Philips Hue’s Essential starter kit. Right now, you can buy a bundle containing four […]
Plex services have been experiencing some major issues today, with multiple users reporting problems on Plex's forums and on Reddit. Many people use Plex as a way to stream shows and movies they host locally, but users are upset because today's problems are reportedly affecting their ability to do that. "Basically all Plex is down […]
Discounts on the Nintendo Switch 2 are rare, but they do happen on occasion. There’s one happening now, actually, on the company’s $499.99 console bundle that includes a digital game (Mario Kart World, Donkey Kong Bananza, or Pokémon Pokopia). Usually, the bundle saves you $20 or $30, depending on the game you choose, but for […]
A group of 26 former Meta employees is suing the company over claims that it used AI tools to unfairly target workers on leave with layoffs, as reported earlier by Reuters. In the lawsuit, the employees allege Meta determined which workers to dismiss based on performance data collected by a "constellation" of internal AI tools, […]
Sony is delaying the launch of its FlexStrike fight stick controller for PS5 and PC, which had been set to launch on August 6th, to an unspecified date in the future. The company blames the delayed launch on "unexpected production delays." The original planned launch of the FlexStrike would have lined up with the release […]
The new Google image search will use your "unique interests" to create an always-updated gallery.
Instagram head Adam Mosseri believes companies will eventually need to manage AI token spending the same way they manage payroll or other operating expenses, predicting that engineers could soon face limits on how much they spend using AI tools.
When I first told you about the "OLED Xbox Ally X of my dreams," I had to curb my enthusiasm a bit. That's partly because I hadn't yet tried the handheld myself, and partly because Asus was only planning to sell it bundled with a pair of expensive AR glasses! I'm happy to say both […]
A new study found that social media platforms are referring people to sites where they can create nonconsensual, sexually explicit deepfakes for as little as $1 an image.
EA's football sim series is making its debut on Apple Arcade.
the most useful habit i have hasn't changed in years: your reviewer should never be the first person to read your code. it doesn't matter if the change is one line. you read it first. the trick is reading it in the diff view, the same tool your reviewer will use, not your editor. the diff is a different lens. the debug print you forgot, the block you commented out, the variable you renamed everywhere but one place, all of it looks fine while you're writing and wrong sitting next to everything else that changed. this matters more now that an agent writes the code, not less. you're no longer the author who remembers every keystroke. you're the first reviewer of a change you mostly watched happen. so read it like a stranger wrote it. in a sense, one did. https://starikov.co/commit-lifecycle/ submitted by /u/iGotYourPistola [link] [留言]
Now, when users navigate to Google Images, they'll see a "For You" gallery of images tailored to their interests and browsing history.
If you've run more than one AI coding agent on the same project, you already know the failure mode. You point Claude Code at /src/game and Cursor at /src/ui "just to be safe," and twenty minutes later one of them has quietly rewritten a file the other was mid-edit on. No error, no warning — just a diff that makes no sense and an afternoon spent figuring out which agent ate whose work. The agents aren't the problem. The problem is that multiple AI coding agents on the same codebase have no shared notion of "someone else is touching this file right now." Each one acts as if it's alone, and that assumption breaks the moment you run two, three, or four in parallel — exactly when a solo builder or vibe-coder would want to, to ship faster. I built Bothread to fix this. It's free, open-source, and runs entirely on your own machine. Why AI Coding Agents Overwrite Each Other's Files The core issue is coordination, not intelligence. One agent working alone is usually fine. Trouble starts when a second agent, unaware of the first, opens that same file and writes its own version on top. Whoever saves last wins, silently — no lock, no claim, no message saying "I'm in physics.js , give me five minutes." Multiply that by however many agents you're running and you get the pattern anyone doing multi-agent AI coding eventually hits: duplicated work, clobbered edits, and a human reconstructing what happened after the fact instead of watching it happen. Bothread's answer: give the agents a shared room, over MCP (Model Context Protocol) , where "who's working on what" is a fact everyone can see and act on — not something you guess at after a merge conflict. What Bothread Actually Does Bothread is a small local server (no cloud, no accounts) that any MCP-compatible agent can join as a participant in a shared room: Claim files before editing — a claim on a file someone else already holds gets denied and shown, instead of silently overwritten. Talk in a live thread , share a task board and
Spotify is experimenting with a new AI feature that allows Premium subscribers to play and explore music, audiobooks, and podcasts by having conversations with a chatbot. The "Talk to Spotify" feature appears across the Home and Now Playing view on Spotify's mobile app. You can interact with the chatbot by typing your request in the […]
The conditional operator ( ?: ) — The Only Ternary Operator is one of the most useful operators in Java. It lets you write simple decision-making logic in a single line, making your code cleaner and more concise. It's also a favorite topic in Java interviews because of its syntax, nesting behavior, and type compatibility rules. In this article, you'll learn: What the conditional operator is Why it's called a ternary operator Syntax and working Nested conditional operators Difference between ?: and if-else Practical examples Interview questions Memory tricks What is the Conditional Operator? The conditional operator is represented by: ? : It is the only ternary operator in Java . A ternary operator takes three operands , unlike: Operator Type Number of Operands Example Unary 1 ++x , !flag , ~5 Binary 2 a + b , a > b , a && b Ternary 3 (a > b) ? a : b Syntax result = ( condition ) ? valueIfTrue : valueIfFalse ; How It Works condition │ Is it true? / \ Yes No │ │ valueIfTrue valueIfFalse │ │ └────── Result ──────┘ If the condition is true , Java returns the value before the colon ( : ). If the condition is false , Java returns the value after the colon ( : ). Example 1 int x = ( 10 > 20 ) ? 30 : 40 ; System . out . println ( x ); Output 40 Step-by-Step Evaluate the condition: 10 > 20 ↓ false Since the condition is false, Java selects the value after : . 40 Therefore, x = 40 Example 2: Finding the Maximum int a = 10 ; int b = 20 ; int max = ( a > b ) ? a : b ; System . out . println ( max ); Output 20 This is one of the most common uses of the conditional operator. Example 3: Even or Odd int number = 7 ; String result = ( number % 2 == 0 ) ? "Even" : "Odd" ; System . out . println ( result ); Output Odd Example 4: Absolute Value int x = - 5 ; int absolute = ( x < 0 ) ? - x : x ; System . out . println ( absolute ); Output 5 Nested Conditional Operators One of the biggest advantages of the conditional operator is that it can be nested . Example int x = ( 10 > 20 ) ? 30 :
I Ran 10 AI Coding Models Through 5 Tasks: A Data Scientist's Take I'll be honest — I went into this expecting a clear winner. I came out with a scatter plot, three regressions, and a deeper appreciation for why "best" is the most dangerous word in machine learning. Over the past three weeks I've been grinding through prompts with ten different LLMs, all routed through the same endpoint, scoring every output on a 1–10 rubric that I tried very hard not to bias. The pricing data is pulled directly from the provider pages. The scores are mine. If you disagree with a score, you're probably right — n=1 per task per model is a laughably small sample size, and I say that as someone who publishes papers with bigger samples. But trends still emerged. Let me walk you through what I found. The Lineup Before I touch a single benchmark, here's the cast. I've grouped them by family so you can see the obvious concentration in the open-source Chinese ecosystem, which personally I find fascinating — three of the top five are DeepSeek or Qwen variants. # Model Provider Output $/M Category 1 DeepSeek V4 Flash DeepSeek $0.25 General (strong code) 2 DeepSeek Coder DeepSeek $0.25 Code-specialized 3 Qwen3-Coder-30B Qwen $0.35 Code-specialized 4 DeepSeek V4 Pro DeepSeek $0.78 Premium general 5 DeepSeek-R1 DeepSeek $2.50 Reasoning (code thinking) 6 Kimi K2.5 Moonshot $3.00 Premium general 7 GLM-5 Zhipu $1.92 Premium general 8 Qwen3-32B Qwen $0.28 General purpose 9 Hunyuan-Turbo Tencent $0.57 General purpose 10 Ga-Standard GA Routing $0.20 Smart routing One quick note on Ga-Standard — it's a routing layer that picks a backend model per request. So the score fluctuates. I averaged across runs. How I Tested Five prompts. Each one designed to probe a different cognitive layer: Function implementation — flatten a nested list recursively in Python Bug fix — chase down an async/await race condition in JavaScript Algorithm — Dijkstra's shortest path in TypeScript with proper types Code review — sec
Introduction So imagine you are focused on your cappuccino-frappuccino doing something very important on you win laptop and then have a cringe attack due to the unknown Phone Link notification : Complete linking devices Your PC and mobile device are almost linked. Click here to continue linking devices. via Phone Link Then you switch off bluetooth, wifi, laptop - and you are right. What to do next ? Basic checks Settings -> Bluetooth & devices -> Mobile devices Settings -> Accounts -> Email & accounts Inspect recent notifications in Event Viewer eventvwr.msc Applications and Services Logs └ Microsoft └ Windows └ Notifications Applications and Services Logs └ Microsoft └ Windows └ Shell-Core Digital forensics Windows stores toast notifications in a local database, hence you need to install sqlite Get-ChildItem " $ env : LOCALAPPDATA \Microsoft\Windows\Notifications" output : Directory: C:\Users\$ USERNAME \A ppData \L ocal \M icrosoft \W indows \N otifications Mode LastWriteTime Length Name ---- ------------- ------ ---- d----- 1/1/2026 0:00 AM wpnidm -a---- 1/1/2026 0:00 AM 1000000 wpndatabase.db -a---- 1/1/2026 0:00 AM 10000 wpndatabase.db-shm -a---- 1/1/2026 0:00 AM 1000000 wpndatabase.db-wal wpndatabase.db is a SQLite database. connect to the database : sqlite3 " $env :LOCALAPPDATA \M icrosoft \W indows \N otifications \w pndatabase.db" query the Notification table . headers on . mode column SELECT Notification . Id , Notification . HandlerId , Notification . Type , Notification . ArrivalTime , Notification . Payload FROM Notification LIMIT 20 ; Then you will have something like : Id: [REDACTED] HandlerId: [REDACTED] Type: toast ArrivalTime: [REDACTED] Payload: <?xml version="1.0"?> <toast activationType= "protocol" launch= "ms-phone:fre/?cid=[REDACTED]&ref=FreIncompleteToast&reason=IncompleteNotificationsToast" > <visual> <binding template= "ToastGeneric" > <text hint-maxLines= "1" > Complete linking devices </text> <text> Your PC and mobile device are almost li
Every DevOps engineer has done this dance: you've got a chunk of YAML or a Terraform file that looks right, something's rejecting it, and you want a fast sanity check. So you paste it into some random online validator — and a small voice asks, wait, where did that config just go? That config often has structure, comments, sometimes internal hostnames or resource names in it. Pasting infrastructure definitions into an unknown server is a habit worth breaking. So I built a set of validators that never send your config anywhere — they run entirely in your browser. What they are Free, browser-based validators for the formats DevOps folks paste-and-pray most: YAML — catches the indentation and structure errors that make Kubernetes and CI configs fail with cryptic messages Kubernetes manifests — schema-aware checks beyond "is it valid YAML," so you catch the wrong apiVersion or a misplaced field before kubectl apply does Terraform / HCL — structural validation for the syntax slips that terraform validate flags only after you've context-switched away The one design decision that matters 100% client-side. No upload, no signup, no server round-trip. Your config is parsed by JavaScript running in your own tab — it never leaves your machine. You can literally open dev-tools, watch the network panel, and see nothing go out. Turn off your wifi and they still work. This isn't a privacy gimmick — it's the correct architecture for a tool that handles infrastructure definitions. A validator has no business seeing your config on a server it doesn't need to. Why I bother Two reasons, honestly. One: I kept wanting this exact thing and kept not trusting the options. The nth time I hesitated before pasting a manifest into a stranger's website, I decided to just build the version I'd trust. Two: fast feedback loops are the whole game in this job. The gap between "save the file" and "find out it's malformed" is pure friction — and the tighter that loop, the less of your working memory it b
Build a Local LLM Chatbot with Ollama and Python Build a Local LLM Chatbot with Ollama and Python Imagine typing a question into your chatbot and getting a response in milliseconds, completely offline, with zero data leaving your machine. No API keys, no monthly subscription fees, and no privacy concerns about your data being sent to a cloud server. This isn’t a futuristic dream—it’s the reality of running a Local Large Language Model (LLM) on your own computer. With the rise of tools like Ollama , building a private AI chatbot in Python has become as simple as installing a few packages and writing a short script. Let’s dive in and build one together. Why Go Local? Before we write any code, it’s worth understanding why running an LLM locally is a game-changer. Cloud-based AI services like OpenAI or Anthropic are powerful, but they come with trade-offs: you pay per token, your data is processed on their servers, and you’re dependent on their uptime. A local LLM flips this model. You download the model once, run it on your hardware, and you have full control. Ollama is the engine that makes this accessible. It’s a lightweight, open-source tool that simplifies running LLMs like Llama 3, Phi 3, or Mistral on macOS, Linux, and Windows. It handles model downloads, memory management, and inference, exposing a simple API that Python can easily interact with [1][2]. Step 1: Install Ollama and Pull a Model The first step is getting Ollama on your machine. Visit ollama.com , click Download , and install the version for your operating system [2]. Once installed, verify it’s working by opening your terminal or Command Prompt and running: ollama --version If you see a version number, you’re ready to go. Next, you need a model. Ollama supports dozens of open-source models, but for a beginner-friendly chatbot, Llama 3.2 is a great choice. It’s small, fast, and surprisingly capable. To download it, run: ollama pull llama3.2 This command fetches the model and stores it locally. Depen
submitted by /u/matijash [link] [留言]