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Bytedance released Bernini for video editing. The architecture splits the pipeline into a semantic planner that understands intent, then a renderer that executes. The planner draws a semantic sketch before any pixels are committed. This is the exact structure I want from coding agents. A planning stage that understands the codebase, the constraints, the dependencies. Then an execution stage that respects that plan. Right now most coding agents skip the planning layer or treat it as advice the agent can ignore mid flight. Bernini gets around the ignore problem by making the plan structural. The semantic sketch is a contract. The renderer's job is to match it. Coding agents need the same contract: a structured task graph where each step has inputs, outputs, and exit conditions. The hardware angle is interesting too. Gemma 4 at 2GB vram means the planner can run locally while the renderer stays in the cloud. Local intent understanding plus remote execution. Latency drops. Privacy improves. I have been using verdent partly for this reason: the plan comes before execution. Coding agents should pay attention to how video generation solved the same problem. submitted by /u/SherbertDazzling3661 [link] [留言]

I’m trying to set up SSL for a newly added domain on a shared cPanel hosting account. The domain is already added inside cPanel and shows under the SSL/TLS section, but the certificate status says the installed certificate does not cover the new domain. The existing certificate appears to be old/expired and was not issued by AutoSSL. When I go to the SSL/TLS Wizard, it says: “There are no SSL/TLS products available at this time. SSL/TLS providers can be enabled by the server administrator.” I’m not seeing a clear “Run AutoSSL” button anywhere in cPanel. Is this something I can fix myself from cPanel, or does the hosting provider need to enable AutoSSL / SSL providers on the server side? I’m mainly trying to get SSL working for the main domain and www version of the domain. submitted by /u/PersonalityLife6196 [link] [留言]

submitted by /u/magenta_placenta [link] [留言]

This is too cool to gate-keep, I’ve decided to open-source Munder Difflin. Munder Difflin a local multi-agent harness that allows you to run the office with as many agents as you want. To put simply it completes ambitious tasks autonomously(almost) by running a cluster of your own claude code agents performing various activities in a controlled environment with inter agent connectivity and one of the top benchmarked memory layer. You can choose to only talk to Michael the god orchestrator which will automatically distribute the asks among other agents. It has gives your agents one of the top bench marked memory layer, gives you full orchestration access with a GOD agent(Michael), schedule repeated instructions that your agents will perform, integrate with slack and much more. (Link in comments) submitted by /u/chaitanyagiri [link] [留言]

I've spent years trying to organize reusable code. I've tried: ● snippet managers ● internal libraries ● private repositories ● documentation systems Everything works initially. Then projects evolve, code diverges, and suddenly there are multiple versions of the same utility everywhere. At this point I'm wondering if perfect organization simply isn't realistic. How are experienced developers handling this? submitted by /u/Unhappy-Pepper- [link] [留言]

Starting this year, I’ve been receiving fake inquiries through the form on my website—about one or two a month. The name, company, and email address are correct, but the message wasn’t sent by the people listed. Either they don’t respond to my reply, or they write back saying they never contacted me. This is frustrating because it reflects poorly on my business. What can I do about it? submitted by /u/Weekly-Month-9323 [link] [留言]

One thing I kept putting off when I started with Ansible was properly securing my secrets. Passwords, API keys, tokens — they were just sitting in plain text inside my vars files. I knew it was wrong but it worked and I kept moving forward. Then I started thinking about what happens when this goes into GitLab. Anyone with access to the repo can see every password. That's when I properly sat down and learned Ansible Vault — and honestly I wish I'd done it from day one. In this blog I'll walk through how I use Ansible Vault from the command line and then how I handle it properly inside AWX so scheduled jobs and workflow templates can still run without someone manually typing a password every time. What is Ansible Vault? Ansible Vault is a built-in feature that lets you encrypt sensitive data — passwords, keys, tokens — so they can be safely stored in your playbooks and pushed to GitLab without exposing anything. The beauty of it is that it works right inside your existing YAML files. Your playbook structure doesn't change — Vault just encrypts the values that need protecting. What I Was Doing Before — The Wrong Way This is what my vars file looked like before Vault: # vars/main.yml ubuntu_sudo_password : MyPassword123 db_password : SuperSecret456 api_token : abcd1234efgh5678 Pushed straight to GitLab. Anyone with repo access could read every single one of those. Not good. Encrypting a Single Value with Ansible Vault The first thing I learned was how to encrypt just a single variable value — not the entire file. This is cleaner because the rest of the vars file stays readable. ansible-vault encrypt_string 'MyPassword123' --name 'ubuntu_sudo_password' It asks you to create a vault password — this is the master password you'll use to decrypt later. Enter it twice: New Vault password: Confirm New Vault password: The output looks like this: ubuntu_sudo_password : !vault | $ANSIBLE_VAULT;1.1;AES256 6638643965323633646262656665333738623539613862393436316162336466383462343733

For years I saw "open source contributions" on job descriptions and just... nodded along. Typed it into Google once, got overwhelmed, closed the tab. It always seemed like something other people did. People who actually knew what they were doing. People who weren't me. Then I started looking into it properly. And honestly? It still seemed big. Like I'd need to understand an entire codebase, find a complex bug, write some genius fix that the maintainers would applaud. Turns out that's not it at all. I found some resources that changed how I saw it completely. The bar to start is embarrassingly low, and that's intentional. The open source community built it that way on purpose. So I did it. Was it a few lines of code? Yes. Did I do it directly in the browser like a person who has no idea what they're doing? Also yes. Do I care? Absolutely not. Where to actually start: goodfirstissue.dev — filters repos by good first issue label up-for-grabs.net — same idea, different interface Docs you already use — if you read something and think "that's oddly worded," you're already there GitHub search — label:"good first issue" is:open and filter by language Here's the thing though, this isn't just about open source. Everything seems big and intimidating at first. So you start small. One tiny contribution. Not because it's impressive but because it's real, and it's yours, and it builds something. Confidence mostly. Then you do a slightly bigger thing. Then a bigger thing after that. You don't level up by waiting until you're ready. You level up by starting small and not stopping. My first contribution exists now. That's enough for today.

My Journey Towards AI and Software Development Hello everyone, My name is Kunal Tiwari, and I am a student who is passionate about technology, artificial intelligence, and software development. Technology has always fascinated me because it allows people to transform ideas into real-world solutions. Over time, I developed a strong interest in understanding how software is built and how AI can help solve everyday problems. I started exploring programming and software development with curiosity and a desire to learn. Although I am still at the beginning of my journey, I believe that consistent learning and practical projects are the best ways to grow as a developer. My current interests include: Artificial Intelligence (AI) Android App Development Software Engineering Problem Solving Building useful applications Through this blog, I plan to share my learning experiences, projects, challenges, and lessons that I discover along the way. My goal is not only to improve my technical skills but also to document my progress and connect with other learners and developers. I know the journey ahead will require patience, dedication, and continuous learning. However, I am excited about the opportunities that technology offers and look forward to building meaningful projects in the future. Thank you for reading my first post. I hope to share valuable insights and experiences as I continue my journey towards AI and software development. Best regards, Kunal Tiwari

A meta tag audit is a pile of binary checks. Title present, yes or no. Title in range, yes or no. Description present. One H1. og:image set. Canonical present. Run them all and you get a few dozen booleans. The problem is that a wall of green and red checkmarks does not motivate anyone. People glance at it, feel vaguely bad, and close the tab. A single number does motivate. "You are at 62" is a thing a person will act on. But a number only works if it is honest, and a number is only honest if it is explainable. So we set one hard constraint before writing any scoring code: every point a page loses has to trace back to a named check with a specific fix. No mystery deductions. If you are at 62 and not 100, the tool can point at the exact items that cost you the other 38. That constraint shaped every decision that followed, and it is the reason the rubric looks the way it does. This is the write-up of how we got from a pile of booleans to a number we are willing to defend. Choosing the dimensions and the weights The first decision was how to group the checks. We landed on five dimensions, each with a fixed weight, and the overall score is their weighted average: Basic meta, 30 percent. Title tag and meta description. Headings, 20 percent. H1 count and heading-level hierarchy. Open Graph, 20 percent. og:title, og:description, og:image, og:url. Twitter Card, 15 percent. twitter:card, twitter:title, twitter:description, twitter:image. Technical, 15 percent. Canonical, html lang, viewport, robots. The weights are the opinionated part, and they encode what we actually believe about how pages get found now. Basic meta gets 30 percent, the largest slice, because the title and description are the strings an AI engine quotes when it summarizes or cites a page. They are the highest-value characters on the whole page, so a gap there should cost the most. Technical gets the smallest slice at 15 percent, but for a subtler reason than "it matters least." Technical failures are rarer

We run an app where you describe a task and an AI agent does it. The first step after you hit submit is a planning call: POST /api/web/tasks/plan, which turns your free text into a structured plan the agents can pick up. One submit should mean one plan. While testing locally I noticed two plan requests going out per submit. Same payload, fired back to back. The agents handled it fine because the second plan just overwrote the first, but it bothered me. A doubled write is a doubled write, and the next one might not be idempotent. First wrong guess: a double-click My first assumption was the obvious one. The user double-clicks, or the button is not disabled during the request, so two clicks sneak through. I added the disabled state, watched the network tab, and got two requests from a single click. So it was not the button. The thing I had stopped seeing The submit logic lived in an effect. When the form phase flipped to submitting, the effect ran and fired the plan call. There was a second effect too: when the user changed the tier or output format mid-flow, a matching effect re-planned, because a different tier means a different plan. Neither effect had any guard against running twice. And in development, React StrictMode mounts every component, unmounts it, and mounts it again, on purpose, to surface effects that are not safe to re-run. My plan effect was exactly the kind of effect StrictMode is built to expose. The double mount fired it twice. The detail that made it click: I built the app for production and watched the network tab there. Exactly one request. The double was a development-only artifact of StrictMode doing its job. The bug was never in production traffic, but the fact that StrictMode could double it meant my effect was not safe, and an unsafe effect is a latent bug waiting for a real remount. The fix: ref guards set before the await, not reset in cleanup The instinct is to reach for a boolean. The catch is where you reset it. If you reset the guard

Hey Folks! 👋 Good Day... This blog is a summary of the concepts covered during the last two classes at my institute. One of the reasons I enjoy writing these blogs is that they serve as my personal knowledge journal. Whenever I need a quick refresher on a concept, I can simply revisit my blog instead of searching through notes or recordings. It helps me reinforce what I've learned while also documenting my learning journey. Over the past two days, we explored several important Java concepts, including constructors, the this keyword, inheritance, constructor chaining. In this blog, I'll share what I learned in the simplest way possible, using real-world analogies, practical examples, and the thought process that helped me understand these concepts more clearly. If you're a beginner learning Java, I hope this walkthrough makes these topics a little easier to grasp and a lot more memorable. What Is a Constructor? According to Oracle Java Documentation: A constructor is a special method that is used to initialize objects. The constructor is called when an object of a class is created. In simple terms: Imagine you order a new smartphone. Before the phone reaches your hands, the factory installs the operating system, configures the hardware, and prepares everything for use. A constructor does exactly the same thing for an object. Before you use an object, Java uses the constructor to prepare it. My First Confusing Example I wrote the following code: public class SuperMarket { String name = "python" ; int price ; public SuperMarket ( String name , int price ) { System . out . println ( "Are you constructor?" ); name = name ; price = price ; } public static void main ( String [] args ) { SuperMarket product1 = new SuperMarket ( "abc" , 20 ); System . out . println ( product1 . name ); } } I expected the output to be: abc But Java printed: python And honestly... I was completely confused. After all, I passed "abc" into the constructor. Why was Java ignoring it? The Hotel Roo

I don’t know if the adoption of 7.0 is just too low for this to become widespread or if I’m just a weirdo and most people don’t use their arrow keys to navigate the editor but this used to work perfectly and is now completely broken. Before 7.0 if you had a set of nested blocks, let’s say a group block with columns and then heading, paragraphs, images etc in the individual columns, you could use your arrow keys to move through the layers. For example if I was in the heading tag at the top of a column and clicked the up arrow it would make the column block the active selected block. Press it again and you’re on the columns container. One more time and you’re at your parent group. Now if I’m in that exact same scenario and click up from the heading block it will jump me to the lowest nested child block of the next highest root level block or if I’m already in the highest root level block it will take me to the page title. There is absolutely no way to use the keyboard to navigate between layout block layers anymore and it’s infuriating. This functionality is so engrained into my brain that it’s muscle memory at this point and I keep flying all over the page when I just want to adjust my column gaps or something. Forcing me to point and click around to the breadcrumbs or expanding the document overview sidebar is such a pain and takes so many steps. I have to imagine this is also absolutely horrible for accessibility, not being able to even get to certain blocks without a mouse. I just have no clue why they would change something that was so logical and just worked exactly as expected since the inception of the block editor. Was this just a mistake or did someone intentionally do something this stupid? I truly can’t see any value to how the keyboard navigation works now and see no point in why someone would choose for it to behave this way over the old way. Is there something I’m missing? Am I just a stubborn old developer who hates change? I feel like this is not unre

i'm having a hard time deciding which approach i should implement. i'm developing a Laravel api which is consumed by Vue & Nuxt and i didn't noticed that i actually implemented two approaches of the returned response: [1] return ArticleResource::collection($articles); this returns a JSON like this: { "id": 1, "title": "My Article" } [2] return response()->json([ 'data' => new ArticleResource($article), 'success' => true, 'message' => 'OK', ]); JSON: { "data": { // output of ArticleResource transformed $article }, "success": true, "message": "OK" } considering that the API and frontend are private repositories. does wrapping all of the response inside 'data' makes sense or should i just stick on [1] for less nesting? what do you guys think what do you usually do with your years of experience? submitted by /u/Totoro-Caelum [link] [留言]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/allenaa3 [link] [留言]

Yesterday, I read a blog by Shubham on how Generative UI is changing entire frontend space. The frontend has always been something you build ahead of time and ship. The agent just works inside it. For 30 years that was the deal. What's actually shifting: the interfaces shipping in 2026 are drawn partly by the agent itself, in real time, from what the user actually asked for. He breaks down the different approaches (A2UI, MCP Apps, AG-UI) via CopilotKit and where each one actually falls apart depending on how deep in the app you go.. along with the token tax - how typical tool description with its JSON schema runs around 400 tokens. 25 components are 10,000 tokens on every turn, you pay that tax per request. worth a read especially the declarative generative UI pattern where you hand the agent a catalog and let it assemble layouts you didn't pre-build. blog link: https://x.com/Saboo_Shubham_/status/2062220865643982875 repo: https://github.com/CopilotKit/copilotkit do you think it's just marketing hype or actually the future of UI? submitted by /u/allenaa3 [link] [留言]

Apple's App Store generated $1.4 trillion in sales, up from $1.3 trillion last year, with $149 billion in sales for digital goods.

Agentic AI in software development: what's actually production-ready in 2025 There's a lot of noise about AI agents right now. This post is an attempt to be precise: what is an agent architecturally, what can it actually do in a dev workflow today, and where does it still break. **What makes something an "agent" vs. a standard LLM call **A standard LLM call is stateless. You send a prompt, you get a response. No memory of previous turns (unless you manage it yourself), no external actions, no loop. An agent is a system built around an LLM that adds: Persistent memory across steps in a task Tool use - structured access to external systems (file I/O, shell execution, HTTP calls, database queries) A planning + evaluation loop - the agent generates a plan, executes a step, checks whether it succeeded, and decides next action Without all three, you don't have an agent. You have a capable model with maybe some extra context. What's actually production-ready today High confidence (use in production): Unit test generation for existing, well-documented code Boilerplate scaffolding (new modules, new endpoints, CRUD patterns) Documentation generation tied to code diffs Code migration tasks (framework upgrades, Python 2→3, ORMs) PR description generation from diffs Bug triage: given an issue, find likely affected files * Works but needs oversight: * Multi-file refactoring Dependency updates with breaking changes Writing integration tests (more surface area for wrong assumptions) Not there yet: Novel architecture decisions Debugging in unfamiliar/undocumented codebases Tasks with genuinely ambiguous requirements Long autonomous chains (>10 steps) without human checkpoints The failure modes to build around Ambiguous task specification Agents optimize for completing the task as specified. If the spec is loose, they'll complete the wrong task confidently. Be more precise with agents than you'd be with a junior engineer - there's no informal Slack thread to resolve ambiguity. Error

I am a full time trader and part time developer based in Karachi, Pakistan. A year ago I sat down to research how to properly compare brokers on the Pakistan Stock Exchange. Three hours later I had 11 browser tabs open, two of which had broken links, one had data from 2019, and none of them had everything I needed in one place. So I built PSX Pulse. What PSX Pulse Is PSX Pulse is a free stock market education platform for Pakistani retail investors. Everything a beginner needs to start investing in Pakistan's stock market — in one place. What is live right now: 35 verified SECP-licensed brokers with full contact details Complete mutual funds directory across 15 AMCs DCA calculator with realistic return scenarios 30-day beginner learning path Islamic investing guide PSX sector guide covering 12 sectors IPO tracker 100-term searchable glossary Weekly market recap every Friday All free. No login required. Live at: https://psxpulse.xwen.com.pk/ The Stack React + Tailwind CSS for the frontend. Vercel for hosting — free tier handles everything comfortably. No backend for most features — localStorage and static data keeps it fast and simple. Newsletter handled via a serverless Vercel function writing to a private GitHub CSV. What I Learned Building This Solo 1. The information gap in emerging markets is enormous Pakistani investors are not underserved because nobody cares. They are underserved because nobody with the technical skills to build tools also has the market knowledge to know what those tools should do. Being both a trader and a developer turned out to be the actual unfair advantage. 2. Free tools beat content for SEO My DCA calculator and broker directory pages get more consistent Google clicks than any article I have written. Tools solve a specific search intent that AI overviews do not replace — people still need to interact with a calculator, not just read about one. 3. Building in public is uncomfortable but worth it Sharing what you are building before it i

As a developer who frequently switches between competitive FPS titles like CS2 and Valorant, re-tuning mouse sensitivity is always a hassle. I wanted a fast, ad-free tool to translate my aim perfectly across titles, so I built a clean Game Sens Converter . The Approach I built this using 100% Vanilla JS. It’s a simple utility, so there was absolutely no need for a backend or heavy frameworks. It loads instantly and calculates right in the browser. Here is a quick look at the core logic handling the sensitivity conversion multipliers: function convertSensitivity ( gameFrom , gameTo , currentSens ) { // Standardized multipliers relative to CS2 / Source engine const multipliers = { ' cs2 ' : 1 , ' valorant ' : 3.181818 , ' overwatch ' : 0.3 , ' apex ' : 1 }; if ( ! multipliers [ gameFrom ] || ! multipliers [ gameTo ]) return null ; // Convert to base (CS2), then to the target game const baseSens = currentSens * multipliers [ gameFrom ]; const convertedSens = baseSens / multipliers [ gameTo ]; return convertedSens . toFixed ( 3 ); } Try it out You can use the live tool for free here: Game Sens Converter Let me know what your main game is or if you'd add any other FPS titles to the list in the comments!