There’s a new fleet of TVs using new mini and micro RBG display tech, and Samsung’s R95H model isn’t as impressive as it should be.

Plus: The Pentagon is training amateurs to become part of its hacker army, a Flock license plate reader error led to cops surrounding a car reviewer, and more.

More dog owners have begun cooking for their canine companions in recent years. When my own dog fell ill, I became part of this growing group.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

Cross-posted from the HTML to Image blog , where the original lives. Browsershot is the package most Laravel developers reach for when they need to turn HTML into an image. It wraps Puppeteer, drives real Chrome and produces pixel-accurate output. On your machine it works first time. Then you deploy, and the first render throws Failed to launch the browser process! . The problem is not Browsershot's code. The problem is what it demands from the machine it runs on. What Browsershot actually asks of your server Browsershot is a PHP package with a second runtime hiding inside it. To run it in production you need Node.js, the Puppeteer npm package, a Chrome or Chromium binary, the long tail of shared libraries Chrome links against ( libnss3 , libatk , libgbm and friends on a slim Debian image) and a font set wide enough to cover whatever your templates contain, emoji included. That is manageable on a full VPS you control. It falls apart in the places Laravel apps increasingly run: Laravel Vapor and serverless. The PHP Lambda runtime ships neither Node nor Chrome, and you cannot apt-get your way out of a Lambda. The Puppeteer on Lambda guide covers just how deep that particular hole goes. Shared and managed hosting. No root, no system packages, no browser binary. Browsershot is simply off the table. Slim Docker images. php:8.3-fpm-alpine carries none of Chrome's dependencies. Adding Chromium, its libraries and fonts costs a few hundred megabytes and a permanent maintenance line in your Dockerfile. CI pipelines , where every job downloads a browser before your test suite can touch a render. The dependency does not stay contained either. Even Spatie's newer packages inherit it: spatie/laravel-og-image renders through laravel-screenshot , which drives Browsershot underneath, so the Node and Chrome requirement follows the whole family wherever it goes. The usual workarounds The first workaround is the fat container: bake Chromium, the shared libraries and a font stack into y

In the previous post I argued that agents are great planners and DAGs are great executors . This one is the practical follow-up: when you actually sit down to wire several LLM calls together, what tool do you reach for? Because the moment one prompt's output feeds the next, you've built a workflow — whether you call it that or not. download transcript → summarize → translate (tool) (LLM) (LLM) That tiny pipeline is already the whole problem in miniature: a non-LLM step (fetch a YouTube transcript), then a model call, then another model call that depends on the first. Run it as one giant prompt and you lose visibility; split it into steps and you gain debuggability — at the cost of more calls and more state to manage. The naming trap Half the confusion is vocabulary. The same idea ships under a dozen labels: Name What it whispers Chain sequential, output → input Pipeline stages, data flowing through Flow branches and conditions Workflow general orchestration Agent workflow the model also decides The word sets expectations. "Chain" promises a straight line; "agent workflow" promises the thing might re-plan on you mid-run. Pick the label that matches how much autonomy you're actually handing over — calling a deterministic two-step pipeline an "agent" only invites disappointment. The real choice: library or orchestrator? There are two families of tools, and they solve different problems. LLM-native chaining libraries — LangChain , LlamaIndex Workflows , Azure Prompt Flow , or visual layers like Flowise . These understand LLM-specific concerns out of the box: prompt templating, passing context between steps, token budgets, streaming, retries on a flaky model. General orchestrators — Airflow , Prefect , AWS Step Functions , Azure Logic Apps . These treat each LLM call as just another task in a DAG, and give you the heavyweight reliability machinery: durable state, scheduling, checkpointing, audit trails, human approval. The rule of thumb that falls out of the last post: F

A container's writable layer feels like a filesystem, and that's exactly the trap. Write a database into it, remove the container, and the data is gone — no warning, no recovery. If you want anything to survive docker rm , it has to live outside the container, and Docker gives you three ways to do that: named volumes, bind mounts, and tmpfs. Knowing which one to reach for is most of the battle. Why the writable layer betrays you Every running container gets a thin read-write layer stacked on top of its image layers. It looks persistent because you can docker exec in and see your files. But that layer is bound to the container's lifecycle. docker run --name scratch alpine sh -c 'echo hello > /data.txt; cat /data.txt' # hello docker rm scratch # the layer — and /data.txt — no longer exists There's no "oops." The writable layer is discarded with the container. Persistence is not a default you get; it's a decision you make. That decision is a volume, a bind mount, or tmpfs. Named volumes: the default for state A named volume is storage that Docker creates and manages for you. You give it a name, Docker keeps the actual bytes under its own directory, and you never have to care where that is. docker volume create pgdata docker run -d --name db \ --mount type = volume,source = pgdata,target = /var/lib/postgresql/data \ postgres:16 The container writes to /var/lib/postgresql/data , but those bytes land in a Docker-managed location on the host. Remove and recreate the container against the same volume and the data is still there. docker rm -f db docker run -d --name db \ --mount type = volume,source = pgdata,target = /var/lib/postgresql/data \ postgres:16 # same data, new container Where do the bytes actually live? Under Docker's data root, typically /var/lib/docker/volumes/<name>/_data : docker volume inspect pgdata --format '{{ .Mountpoint }}' # /var/lib/docker/volumes/pgdata/_data The point is that you're not supposed to reach into that path directly — Docker owns it. You

Imagine you are sitting at a table in a restaurant. You have a menu in front of you with a list of delicious meals, and the kitchen is ready to cook them. However, there is a missing link. You are sitting in the dining room, and the chefs are tucked away in the back. You can't just walk into the kitchen and grab your food, and the chefs don't leave the stove to come take your order. To bridge the gap, you need a mediator. You need someone to take your order from the table, deliver it to the kitchen, and then bring the food back to you. That person is your waiter. In the digital world, an API (Application Programming Interface) is that waiter. The Plain English Definition An API stands for Application Programming Interface. Strip away the technical jargon, and an API is simply a software messenger that allows two different computer programs to talk to each other and share data. Think of it as a digital bridge. When you use an app on your phone, it doesn't contain all the data in the world inside its small download file. Instead, it uses an API to send a request over the internet to a massive server, which then sends the correct information back. The Restaurant Analogy: Side-by-Side To see how this works in real life, let’s map our restaurant experience directly to how technology works on your phone or computer: The Customer (You): This is the Client (your web browser, smartphone app, or computer). You are the one asking for information. The Menu : This is the API Documentation. It lists out exactly what items you are allowed to order and how you need to ask for them. The Waiter : This is the API. They take your request, run it over to the system, and bring you back the result. The Kitchen : This is the Server / Database. It holds all the raw data, processes the request, and prepares the final output. Where Do You See APIs in Real Life? You interact with dozens of APIs every single day without even realizing it. Here are a few common examples: "Log In with Google" or

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From strenuous hikes and serious summits to weekend rambles in the park, these boots help you make the most of your time outdoors.

The probe sent back the first pictures of the asteroid Kamo’oalewa. Next step: landing on the surface and collecting samples to send back to Earth.

The Story: Picture this: It is 10 PM. I was eating my dinner while adding one final touch to my social media app, Vlox. It should just say "Processing..." while generating a card. Simple, right? See the nightmare. 🔥 The Nightmare Scenario 📉 Suddenly, my "Download Card" button broke. htmlToImage started spitting out completely empty 0b images. The hunt was on. Failed Mission Log 🛰️ Attempt 1: Swap to html2canvas 🔄 Result: Error stating the element was not found in the cloned iframe. Verdict: The parent container was completely lost. Attempt 2: Use CoolAlertJS Toast 🍞 Result: It looked ugly and meant loading two different alert libraries for the same job? Not my game. Verdict: Total waste of bundle size. Attempt 3: Append to body + display: none 🙈 Result: The canvas process failed entirely. Verdict: Canvas snapshot engines completely ignore hidden elements. The "Aha!" Moment 💡 Why did the element vanish? Because the card generator lives entirely inside a Swal popup. When you click the download/confirm button, Swal instantly destroys that entire popup DOM tree. You cannot snapshot an element that no longer exists. 👻 The Ultimate Fix 🚀 Inside the new "Processing" popup, I appended the #card-generator-image-preview directly into the new alert container. The element stays alive in the active DOM. The snapshot succeeds perfectly. Clean code. Happy developer. Delicious dinner. Want to see the exact JavaScript code block that fixed it? Drop a comment below or check Vlox on Github .

Lately, I've been doing some deep personal reflection. I'm talking about myself, I hope no one misunderstands, on how pervasive the use of AI has become in my daily development workflow. Through a bit of self-analysis, I've discovered some interesting dynamics. Dependencies often arise from the desire to fill a void. But what kind of void does an experienced developer like me face? As a professional, I have the skills. Sure, AI helps me get things done faster, but the final product is always the translation of my vision; if I don't fully understand the solution, I discard it. I'm not looking for "magic," I'm looking for efficiency. Yet, I realize I've used AI to fill a specific void: the need for discussion. Software development is inherently solitary. The satisfaction of a successful "execution" after hours of discussions, refinements, and clashes over an architecture is an experience I miss today. The chat interface is always there, ready to respond. But there's a problem: it's a "yes-man." Even when I force it to be critical or provocative via the system's prompts, I know it's just reciting a script to please me. There's no conviction, no risk of error, none of the friction that arises when a colleague courageously defends their vision, perhaps one that conflicts with mine. We are part of a huge community, but debate often remains superficial. One might argue that posts and comments are enough, but anyone who has tried knows it doesn't work very well: a debate is truly alive only when there is no latency. In comments, the time between thinking, writing, and waiting for a response diminishes the energy of the exchange, turning it into a series of monologues rather than a dialogue. Why don't we try creating "virtual tables" where we can discuss projects, architectures, and technical choices with the natural rhythm of a conversation? Direct, real-time discussions, in person or remotely, where the exchange of ideas can spark sparks, without the filter (and delay) of

Markdown to HTML: The Fastest Way to Convert Markdown Online Markdown is one of the easiest ways to write documentation, blog posts, README files, and notes. The only problem is that many platforms require HTML instead of Markdown. Instead of installing software or using complicated editors, you can convert Markdown directly in your browser. I built MDConvertHub to make this simple. It lets you: Convert Markdown to HTML instantly Preview the output before copying Work completely in your browser No signup required Free to use I started building MDConvertHub because I wanted a collection of small Markdown tools in one place instead of visiting different websites for every task. The project now includes multiple Markdown utilities, and I'm continuously adding new tools based on real use cases. If you'd like to try it, I'd love your feedback. 👉 https://mdconverthub.com/markdown-to-html What Markdown tool do you use most often? Feedback and suggestions are always welcome. I'm building MDConvertHub one tool at a time.

Finding a co-founder is one of the hardest parts of building a startup, and most platforms weren't built for it. LinkedIn is a professional directory, not a matching network. Reddit threads are noisy and unstructured. Cold outreach is a gamble. FoundrGeeks is built specifically for this problem . It's an AI-powered co-founder and team matching platform that connects builders based on what they're building, what skills they bring, and what gaps they need to fill, not just their job title or who they already know. The problem with finding a co-founder most builders looking for a co-founder face the same wall: the people they need aren't in their network, and the platforms that exist weren't designed for this specific search. You're not just looking for someone with the right skills. You need someone at the same stage, with the same intensity, who fills exactly the gaps you have right now. And you need to know that before spending three hours on discovery calls. That's the gap FoundrGeeks fills. How FoundrGeeks works When you create a profile, you describe what you're building, what you bring to the table, and what you need. You set your stage, idea, MVP, or funded, and your weekly availability. From there, the AI takes over. It surfaces people whose strengths complement your gaps, scores each match as Strong, Good, or Potential, and generates a plain-English explanation of why each person fits what you're building right now. Three features stand out at launch: Complementary matching: the engine looks for people who fill your gaps, not mirror your background Scored matches with explanations, every match tells you exactly why, before you reach out Stage-aware feeds, as you move from idea to MVP to funded, your matches reshuffle automatically You also control your visibility, go public and let talent find you, or stay private and let the AI work quietly on your behalf. Why we built this This platform exists because of a project that never got finished. I had an idea I wa