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Most HTML-to-Markdown tools handle one file at a time. You paste some HTML, get Markdown back, repeat. That works for a quick snippet but not when you have 200+ pages from a help center export sitting in a folder. I needed exactly that. I had a full site mirror (grabbed with wget --mirror ) and wanted clean Markdown I could feed into an LLM knowledge base. Nothing I found could handle it without uploading files to a server or converting one by one. So I built HTML to Markdown AI . How it works You drop a ZIP file (or individual HTML files) into the browser A Go-based conversion pipeline compiled to WebAssembly processes everything locally You get a ZIP back with clean GitHub-Flavored Markdown, folder structure preserved No server involved. Your files never leave your machine. The conversion pipeline The heavy lifting happens in Go/WASM. The pipeline: Strips navigation, footers, scripts, styles, and other boilerplate noise Extracts the main content from the page Converts to GFM with proper heading hierarchy, tables, code blocks, and links Handles batch processing so you can throw hundreds of files at it Why no built-in crawler? Intentional decision. Downloading HTML from someone else's site has legal implications depending on jurisdiction and terms of service. I don't want to be in that business. Downloading is also the easy part: wget -r -l 0 -np -k -E -p -e robots = off \ --reject-regex '\.(png|jpe?g|gif|svg|webp|woff2?|ttf|css|js|zip|pdf)$' \ -w 0.5 --random-wait \ https://docs.example.com/ That gives you a local folder with all the HTML. The hard and annoying part is turning that into clean, usable Markdown. That's what this tool solves. Stack Frontend: Astro + Tailwind Conversion engine: Go compiled to WebAssembly Processing: Entirely client-side, zero backend Try it https://www.html-to-markdown-ai.com Use cases I've tested it with: Help center exports (Zendesk, Confluence, custom wikis) Documentation sites mirrored with wget/httrack Scraped content for RAG pipe

Developers are owning more of the delivery system around code, not just code itself. Join us during GitHub Universe to meet other devs, learn something new, and explore what's next. The post From coder to orchestrator: How agents shift the role of a developer appeared first on The GitHub Blog .

Most people use AI for startup research like this: “Give me 10 promising AI startup ideas.” A few seconds later, you get a polished list. The problem? You have almost no idea which conclusions are backed by evidence, which are assumptions, and which are simply the model confidently connecting dots. So I tried something different. Instead of asking one AI agent to find startup ideas, I built a small Startup Intelligence team using Hermes Agent. The system uses four specialized AI agents that research markets, investigate competitors, audit evidence, challenge each other’s conclusions, and ultimately rank promising B2B AI SaaS opportunities. And rather than producing another Markdown document full of ideas, the workflow produces structured research containing: Market opportunity scores Companies and competitors Customer pain and unmet needs Evidence-backed claims Source URLs and supporting passages AI advantages and workflows Low-cost validation experiments Here’s how the system works. 🎥 Full video walkthrough The Problem With Asking One AI Agent to Find Startup Ideas Startup research looks easy until you actually need to decide where to spend your time and money. A few signals can be surprisingly misleading. 💰 Funding can look like customer demand. A market receiving hundreds of millions in venture capital doesn’t necessarily mean customers are willing to pay for another product. 📈 Growth claims can look like market validation. Especially when the numbers come directly from vendors. 🏢 Customer logos can look like retention. A logo doesn’t tell you how much the customer pays, how heavily they use the product, or whether they’ll renew. ⚔️ A long competitor list can make a market look saturated. But those companies may target completely different buyers, workflows, or budgets. Generic AI research tends to compress all these signals into something like: “This is a rapidly growing market with strong demand and significant opportunity.” That sounds convincing. But as a fou

Advantages of using OOP: Is faster and easier to execute. Provides a clear structure for the programs. Helps to keep code DRY "Don't Repeat Yourself" and makes code easier to maintain, modify, and debug. Makes it possible to create fully reusable applications with less code and shorter development time. Define a Class: A class is defined by using the class keyword, followed by the name of the class and a pair of curly braces {} . All its properties and methods go inside the braces. Delegation: Delegation means that you use an object of another class as an instance variable. We can create multiple objects from a class. Each object has all the variables and functions defined in the class. An object of a class is made using the new keyword. Note: The $this keyword refers to the current class and is only available inside methods. __construct() function: Automatically runs at the beginning of the class. __destruct() function: Automatically runs at the end of the class. Encapsulation: The wrapping up of data and methods is a protection mechanism for the variables and functions inside the class. Access Modifier: Public: Variables or functions can be accessed from everywhere. Private: Variables or functions can ONLY be accessed inside the class. Protected: Variables or functions can be accessed inside the class and by child classes that extend from the parent class. Constants: It can’t be changed once it is declared. Declared inside a class with the const keyword. It is recommended to name the constants in all uppercase letters . Access outside the class by using the class name followed by the scope resolution operator :: . Access a constant inside the class by using the self keyword. Static Functions and Variables: Static functions or variables can be called directly - without creating an instance of the class first. Static functions or variables are declared with the static keyword. To access a static function or variable, use the class name , double colon :: , and the fu

I built 109 tools that never touch a server - here is the architecture Most "tools" sites you have used do this: You upload a file It goes to a server The server processes it You download the result Sometimes the server stores it. Sometimes it leaks. Sometimes it disappears with the company. I wanted something different. Every tool on korelyy.com runs 100% in your browser . Zero backend. Zero upload. Zero tracking. Here is the actual architecture, the real numbers after 90 days, and what I learned. What "no server" actually means For each of the 109 tools: The entire app is a static HTML + CSS + JS file It is served as-is from a CDN (Cloudflare Pages) All file processing happens in your browser via FileReader , canvas , Web Crypto API , or OffscreenCanvas Your file never leaves your device Closing the tab = the data is gone (no cookies, no localStorage, no account) This is not a marketing claim. It is verifiable: Open DevTools -> Network tab Use any tool that requires a file (image converter, JSON formatter, etc.) Reload. The only network request is for the static HTML/CSS/JS bundle. No fetch() to a server. No XHR . No upload. The file is read, processed in-memory, and downloaded. The 4 browser APIs that do 90% of the work When you remove a backend, you are left with the browser. The browser is more capable than most people think. 1. FileReader and URL.createObjectURL Read any file the user gives you: const file = document . querySelector ( ' input[type=file] ' ). files [ 0 ]; const url = URL . createObjectURL ( file ); const img = new Image (); img . onload = () => { // process image canvas . toBlob ( blob => { const downloadUrl = URL . createObjectURL ( blob ); // trigger download }); }; img . src = url ; Image conversion, PDF generation, audio trimming - all the same pattern. Read blob, process, create new blob, download. 2. crypto.subtle (Web Crypto API) Hashing, encryption, signing - all client-side: const hash = await crypto . subtle . digest ( ' SHA-256 ' , a

Depending on the model, you might have hundreds to choose from—or just one.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. Okay so here's a fun one for you. Imagine you invent the thing that eventually becomes the substrate for Google, Facebook, Amazon, your bank, your ex's Instagram, and every cursed cookie consent banner known to man. Now imagine you had the legal right to charge a licensing fee for it. Like, a reasonable one. A cent per page load, say. You would never have to work again. Your great-great-grandchildren would never have to work again. You'd be sipping something expensive on a boat named after a HTTP status code. Tim Berners-Lee looked at that exact opportunity in 1993 and said, essentially, "nah, you guys keep it." This has been rattling around in my head for days, so let's talk about it properly, with all the nerdy details. The web almost lost to a gopher (literally) Berners-Lee built the World Wide Web in 1989 at CERN, laid out in a proposal called Information Management: A Proposal , mostly so physicists could stop emailing each other giant papers and just... link to things. Wild concept, I know. But here's the part people forget: the Web wasn't the obvious winner in the early 90s. It had a genuine rival called Gopher , built at the University of Minnesota, and for a while Gopher was winning. It was simpler, it was faster on the slow modems of the era, and it had a head start in adoption among universities and libraries. Then in February 1993, the University of Minnesota did something that, in hindsight, ranks among the great unforced errors in computing history: they announced they'd start charging licensing fees for commercial use of Gopher server software. Reasonable-sounding at the time (they needed to fund development), catastrophic in practice. The developer community, which had spent years contributing code for free on the assumption

There are two ways to work with an AI agent and I had tried both. Write the thing yourself and hand over only the tedious parts. Or hand over the whole task and audit whatever comes back at the end. The first is slow. The second is fast right up until it is wrong, and by then the wrong thing is finished. I expected this series to be about forcing a third option into existence. Nine parts of making an agent follow a workflow it would rather skip. That is not what happened. I never had to enforce it once. The queue that started this had a payload contract nobody had verified, and each phase after that cost me something before it gave anything back. A plan that would not move until the risk register named the provider contract the brief had only guessed at. A build that missed nothing except what my own brief left out. A review that stopped handing back a feeling and started handing back a verdict on every requirement I had already called done. A matrix instead of a trusted green run. A rollback with a name on it before anything got called shipped. And a retrospective that would not let a lesson through until it had checked itself against the trail. Eight parts of that. What I did not expect was which part turned out to be automatic. The Fight I Expected Never Started By the time I finish writing a requirement, I already know roughly what it is going to cost. Most engineers do. You can feel the difference between a one-line fix and something that is going to touch four files and a migration before you have written a single line of it. What I assumed was that the agent could not feel that, and that policing the gap would be my job forever. Reminding it to run the chain. Catching it when it decided a spike was small enough to skip. It has not needed the reminder. Small bugs do not trigger a brief and a plan, and they should not. A standard requirement, a spike, anything long or cross-cutting, runs the full cycle in order. The classification lands where I would have put i

People are starting to coin the term ADLC or Agentic Development Lifecycle. A lot of this seems to be combining two things: Day-to-day software engineering has completely changed from a process perspective Bottlenecks in the traditional SDLC are starting to show I don't think we need yet another acronym, but let's talk about how things are changing in general and what some of the bottlenecks are. We don't work on a single task anymore One of the overarching changes, leading to an explosion in lines of code, merge requests and more, is that the cost of software engineering has dramatically decreased. So much so that all of us can now do the job of multiple engineers without hiring them. As part of this change, our daily workflows have changed completely. We no longer open an IDE and work on a single task, start to finish. Instead, our roles have become a lot more exploratory and, quite frankly, fun. My workflow, for example, has shifted towards opening multiple chat sessions, often separate threads on the same topic. I get to spar like some sort of boxer with AI over a few variations of how I've been looking at the same problem. After a while, I'll start to narrow that down to one or two threads containing the desired architecture or strategy to solve the goal. From that point, I'm running this smaller set of agents end-to-end with validation criteria until a passing merge request is opened for each. Running this same process in parallel across 3-4 topics leads to 8-10 merge requests within a day . And because this process has become so easy, these merge requests are often meaty. Not just one-liners. Previously, you'd dedicate your day to working on a particular problem over a longer horizon, whereas now the amount of output (whether it's valuable output or not) has dramatically increased. If you frame software engineering as problem solving, where most problems contain local minima, not absolute minima (a metaphor about gradient descent) , then the really fun part i

State laws around driving with headphones vary, so make sure you know what's legal.

The first collaboration model in CodeVerse was convincing in exactly the way a local demo needs to be convincing. Open two tabs. Join the same room. Type in one editor. Watch the other editor update. Then ask one unpleasant question: what happens when those two sockets land on different server instances? The answer was that the room stopped being a room. Each process had its own memory, its own presence list, and its own idea of the current files. A restart erased state. A reconnect could create a second identity. A load balancer could turn a working demo into two isolated conversations. This article is about the work that followed: moving CodeVerse from synchronized tabs to a collaboration path I could test across processes, recover after disconnects, and describe without pretending a local benchmark was a production capacity claim. The real boundary was not Socket.IO Socket.IO made connection handling and room fan-out approachable, but it did not decide where truth lived. That distinction matters. A room name inside one Socket.IO process is a routing convenience, not durable shared state. Once I wanted multiple application instances, I needed separate answers for four kinds of information: Document state — the convergent contents of every file. Room policy — organizer identity, edit permissions, active file, and revision. Presence — which sockets are here now, on which instance, with which effective role. Durability — what survives Redis expiry, application restarts, or a longer period of inactivity. CodeVerse now uses Yjs for convergent document updates, Redis for live distributed room state and pub/sub, and Supabase for durable room snapshots and membership data. Socket.IO remains the transport and fan-out layer. That separation was more important than any individual library choice. Redis does three different jobs It is easy to say “I added Redis” and leave the architecture vague. In CodeVerse, Redis has three explicit responsibilities. 1. Cross-instance fan-out

Every Angular application I have worked on in the last few years had the same three kinds of state: URL state — the page number, the active filter, the selected tab. Client state — what the user typed, what is expanded, what is selected. Server state — the thing you fetched, and everything that can go wrong while fetching it. And every application handled them three completely different ways. ActivatedRoute and a Router.navigate call for the first. Signals or a store for the second. A service returning an Observable , plus a loading boolean, plus an error field, plus a subscribe somewhere, for the third. None of that is wrong. It is just that the glue between them is written by hand, in every app, every time. And the glue is where the bugs live. This article is about the specific piece of that problem I could not let go of, and about the toolkit I ended up building around it. It is called craft-ng , it is in beta, and I would genuinely rather have your objections than your stars. The code I kept running into Here is the shape. I should be honest: I did not write much of it myself — I had a drawer of RxJS helpers that hid most of it. But I have read it in a lot of codebases, reviewed it in a lot of pull requests, and inherited it in a lot of projects. That turned out to matter more, because a helper that only I understand is not a solution to anything. @ Injectable () export class TaskListService { private http = inject ( HttpClient ); tasks = signal < Task [] > ([]); isLoading = signal ( false ); error = signal < string | null > ( null ); load ( done : boolean ) { this . isLoading . set ( true ); this . error . set ( null ); this . http . get < Task [] > ( `/api/tasks?done= ${ done } ` ). subscribe ({ next : ( tasks ) => { this . tasks . set ( tasks ); this . isLoading . set ( false ); }, error : ( err ) => { this . error . set ( ' Something went wrong ' ); this . isLoading . set ( false ); }, }); } } Four fields, one method, and roughly six ways to get it subtly wr

The people who make open source work aren't just the ones writing code. Some of them write the words that make the code make sense. I spent years assuming open source was a closed door. Every time I opened GitHub, I felt like I'd wandered into a conversation being held in a language I hadn't studied. Pull requests, forks, issues tagged with words like "good first issue" that somehow still felt intimidating. I closed the tab more times than I can count, convinced that space belonged to people who could write functions, not people who could write sentences. It took me longer than I'd like to admit to realize how wrong that assumption was. The myth that keeps people out Open source has a branding problem, and it's an ironic one for a movement built on collaboration. The public image is almost entirely code: commits, merges, terminals, lines of syntax scrolling past on a dark screen. That image is accurate, but it's incomplete. It leaves out the writers who make a tool's documentation actually usable. It leaves out the designers who turn a clunky interface into something people want to use. It leaves out the community managers who keep a project from imploding when a disagreement gets heated. It leaves out the translators, the testers, the people who write the first draft of a README at 11pm because nobody else got around to it. If you've stayed away from open source because you don't code, you've been kept out by a myth, not a rule. What non-developers actually do in these projects Documentation is the most obvious entry point, and it's also one of the most needed. A huge number of open source projects are built by people who are excellent engineers and mediocre explainers. That's not a criticism, it's just a different skill. Someone can write brilliant code and still produce a setup guide that only makes sense to the person who wrote it. Projects need people who can sit with a piece of software as a genuine beginner would, notice where the instructions fall apart, and

Your coding agent is a process that reads your filesystem and runs shell commands with your credentials. Most of the time that is exactly what you want. Occasionally it is cat .env while debugging - and now your production keys live in a transcript forever - or a confident rm -rf on a path that resolved differently than expected. Everyone's first fix is to add rules to CLAUDE.md: "never read .env, never force push". Those are suggestions to a language model. They work until they don't, and you will not be watching when they don't. Claude Code has a mechanism that is not a suggestion: hooks. A hook is a program you register for specific events - before a tool call, after it, when the session tries to end. It runs outside the model, sees the exact tool call as JSON on stdin, and its verdict is enforced by the harness itself. The model cannot talk its way past it, but it CAN read a structured denial and route around it productively. This is a cookbook for writing them. Everything below is plain Python stdlib and works on current Claude Code as of August 2026. The mechanics in ninety seconds Hooks are registered in settings ( .claude/settings.json in a project, ~/.claude/settings.json globally): { "hooks" : { "PreToolUse" : [ { "matcher" : "Read|Grep|Bash" , "hooks" : [ { "type" : "command" , "command" : "python3 \" ${CLAUDE_PROJECT_DIR}/.claude/hooks/secret-guard.py \" " , "timeout" : 10 } ] } ] } } The matcher filters by tool name. Your command receives a JSON object on stdin describing the event; for PreToolUse it includes tool_name and tool_input (the exact arguments about to run). You respond on stdout with JSON. Three responses cover almost everything: Deny a tool call, with a reason the model will read: { "hookSpecificOutput" : { "hookEventName" : "PreToolUse" , "permissionDecision" : "deny" , "permissionDecisionReason" : "why, and what to do instead" } } Block a session from ending (Stop event), sending work back: { "decision" : "block" , "reason" : "lint failed

Picture a field technician standing in a basement with zero signal, trying to get an app to summarize a maintenance log and flag anything that looks like a safety issue. Or a language app that needs to correct pronunciation in real time, mid-commute, on a subway with no connectivity at all. A few years ago, both of those scenarios meant either building a degraded offline mode or just telling the user to try again later. Neither answer felt great. That's the actual reason on-device AI has become a real conversation in Android development in 2026, not because it's the trendy thing to bolt onto a feature list. Running inference locally solves specific, concrete problems: it keeps sensitive data off the network, it removes the round-trip latency of a cloud call, it works when there's no connectivity at all, and it gives you more predictable operating costs since you're not paying per-token for every user interaction. None of that means cloud AI is going away, and I'd be skeptical of anyone telling you it is. Most production apps in 2026 end up running a mix of both. But there's now a real, practical case for pushing certain workloads onto the device itself, and that's what this article is actually about - where local inference genuinely helps, where it falls short, and what it takes to build it properly in Kotlin. What Is On-Device AI? On-device AI means running a machine learning model directly on the user's phone, using the device's own CPU, GPU, or NPU, instead of sending a request to a server somewhere and waiting for a response. The model, or at least the parts of it needed for inference, lives on the device. Cloud AI still has the advantage in raw model size and reasoning depth - nobody's running a 70-billion-parameter model on a phone, at least not yet. But for narrower, well-defined tasks, on-device models have become genuinely capable, and the trade-offs are worth understanding side by side. Factor On-Device AI Cloud AI Inference location Runs on the user's dev

I am testing my first automated end to end social media post automation system. which is created using the free tools. But it is very efficient and productive. i can use this thing in future posting on various platforms to tell people about my learning's and update about me. Tools : Make.com = I use this tool to mainly automate my system it include flow how things works and system is linked. Hashnode = I use this as a central blog and article publishing tool other tools is connected with it so content links is properly distributed. Google Ai Studio = I use this to integrate the ai in between this whole process which just do small job to add the engaging hook and the tags for the reach Buffer = I use to connect X (twitter) with this Because Make.com remove the platform X (twitter) to His integration. After the policy change of the platform. Dev.to = I use this to improve SEO of my post over the google search engine. Challenges : I cannot integrate the github actions with the hashnode becuase this feature is become paid on hashnode. May be in future i can do this thing using self written yml file, i am guessing Not sure will this 100 % work or not. Twitter integration as i described early that twitter integration is not present in the make.com so i use the another tool Buffer. The limits calculation, Their was a limits on each tools for their specific use case so i have to intentionally calculate them properly. Even the free tear of the twitter which is X is few hundreds words that's why i have to limit the text of the post, which is hook only, The threads creation i don't think it will be their in this tools which i am using, i will definitely find it if their. Solutions : Simply use other Way if this way is closed, use different tool for twitter May be in future i create yml file for the github actions but for now i am directly writing on hashnode. The dev.to does not provide feature of direct posting it save your cycle into draft so you have to manually click on pu

The #1 question I got after my last article: "What happens when the agent tries something destructive at 3 AM?" Every CISO I've worked with asks some version of this. They don't care how fast your agent investigates. They care about blast radius. What can it touch? What can it break? Who approved it? Where's the audit trail? This article answers all of that. I gave Kiro Crew a P1 incident and told it to fix it. Then I watched it hit a wall. If you're new to this series, catch up here: Kiro Crew Series The scenario: a real P1 on FinPay FinPay is a payment processing platform. Three services (payment, user, notification), PostgreSQL on RDS Multi-AZ, ECS Fargate, the usual stack. 26 commits of realistic history. CI/CD via GitHub Actions. Someone committed a "performance optimization" that reduced the database connection pool from 50 to 5. Deployed at 5:30 PM on a Wednesday. By 2:47 AM, the pool was exhausted. Transactions started failing. Success rate dropped from 99.8% to 34%. I gave the agent the alert and said: fix it. What happened next is exactly why enterprise teams can trust this thing. Layer 1: Investigation passes freely The agent's first instinct was to investigate. It ran: git log --oneline -10 to check recent deployments cat services/payment-service/config.js to read the configuration grep -rn pool services/payment-service/ to find pool settings All three ran automatically. No approval popup. No human intervention. Why? Read-only operations don't need permission. The agent can look at anything it needs to understand the problem. Reading code, checking logs, searching files. None of that changes state. None of that can break anything. Within 23 seconds it identified the root cause: pool max was changed from 50 to 5 in commit 2181456 ("perf: reduce connection pool overhead for lower memory footprint"). A well-intentioned optimization that was never load-tested. This is the same investigation pattern from Part 2. Fast, accurate, no human bottleneck for the det

Modern AI UX — chat panels, tool-calling agents, assistants that remember context and even suggest your next step — has lived in JavaScript SaaS for years. The Java enterprise stack has been left doing it the hard way. TabForge AI closes that gap . It's a complete platform for building AI-powered web apps on Jakarta EE + PrimeFaces — from the multi-tab UI shell down to a clean, provider-agnostic AI layer. Library, live demo, starter project, and a drop-in UI template — all shipped. Here's the whole thing, top to bottom. ## 1. Tabs as annotated beans — DynTabs You describe a tab; the framework handles opening, closing, lifecycle, and state. Each open tab gets its own isolated CDI bean via a custom @TabScoped scope. @Named @TabScoped @DynTab ( name = "OrdersDynTab" , uniqueIdentifier = "Orders" , title = "Orders" , includePage = "/WEB-INF/orders.xhtml" , trackActivity = true ) public class OrdersBean extends BaseDyntabCdiBean { // open the same tab twice → two independent instances } java No manual navigation, no page-state juggling. Open a tab, get a bean; close it, it's gone. A clean AI layer — EasyAI One fluent entry point over LangChain4j. Chat, tools, agents, and structured extraction — provider-agnostic, so the model behind it is a config detail. // A typed assistant with a business service exposed as tools OrdersAssistant ai = EasyAI . assistant ( OrdersAssistant . class ) . withTools ( orderService ) . build (); String reply = ai . ask ( "cancel order ORD-002" ); You opt methods in as tools explicitly — no accidental exposure: @EasyTool ( "Cancels an active order" ) public String cancelOrder ( String orderId ) { ... } Deterministic pipelines — flow() Agents are powerful but unpredictable. When you want a repeatable, testable process, flow() lets you own the steps and call the model only at the edges that actually need language: EasyAI . flow () . step ( "understand" , ctx -> EasyAI . extract ( OrderRequest . class ). from ( ctx . inputText ())) . step ( "check

This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art . 🍩 Inspiration I write about WordPress plugins and PHP standards for a living, so when DEV dropped a "Comfort Food" theme for their Frontend Challenge, I didn't need to think twice about what to build. Not ramen, not pancakes — a donut. Specifically, the kind of donut that shows up on your desk right when a deploy breaks and somehow fixes everything. The twist I gave myself: don't just draw a static donut. Let people build one — pick a glaze, pile on toppings, then serve it — and do almost all of it in CSS, with JavaScript kept firmly in the back seat where the challenge rules ask for it to stay. That's how Donut Panic was born. 🎬 Demo Pick a glaze, load it up with sprinkles, drizzle, or powdered sugar, then hit Serve and watch it animate off the plate. 🛠️ Journey No JavaScript is driving the donut — :has() is Here's the part I'm most excited to talk about: every visual change in Donut Panic — the glaze swap, the toppings appearing, the donut lifting off the prep station and landing on the plate — is driven by plain checkbox/radio inputs and the :has() selector. Something like .kitchen:has(#serve:checked) .donut lets a parent element react to the checked state of an input buried somewhere inside it, which means the "Serve" button, the topping toggles, and the glaze picker are all just styled <label> s wired to hidden inputs. No click handlers, no state management — the checkbox is the state. JavaScript only shows up once, and it's not touching the art at all: it smooth-scrolls the stage into view on mobile after you hit Serve, because on a stacked mobile layout the donut can animate off-screen. That's the "sprinkle" of JS the challenge rules allow, used exactly the way it's meant to be — a UX nicety, not a rendering engine. Building the donut from the inside out The donut itself is layered rings, not a single flat shape: A base dough circle with a radial gradient doing double duty as both c

Cybersecurity is entering a new phase. This is because security teams are facing more and more complex problems and threats that are moving faster. To help defenders respond more effectively, OpenAI has introduced GPT-5.6-Cyber, a special model designed for advanced cybersecurity tasks. The model supports authorized security research, vulnerability discovery, and other defensive workflows. The Daybreak program is showing how specialized AI tools can improve modern cybersecurity by working together with human security experts. Quick overview GPT-5.6-Cyber is a specialized model for authorized cybersecurity work. It is available through OpenAI’s Daybreak Red access for approved defenders. OpenAI reports a 95% completion rate on its internal advanced cybersecurity evaluation. The model helped researchers uncover vulnerabilities in Chrome’s V8 JavaScript engine. Controlled access, monitoring, and human oversight remain important for safe deployment. What Is GPT-5.6-Cyber? GPT-5.6-Cyber is OpenAI’s cybersecurity-specific model, available through Daybreak Red. Built on GPT-5.6 Sol, it is trained to improve performance on specialized cybersecurity tasks such as finding zero-day vulnerabilities and developing exploit chains, while reducing refusals for certain higher-risk, dual-use cyber tasks. Daybreak has two access tiers: Daybreak Blue provides approved defenders with frontier general-purpose models such as GPT-5.6 Sol, with safeguards tailored to authorized defensive security work. Daybreak Red provides purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing. This approach reflects a significant shift toward security tools designed for professional cybersecurity environments rather than unrestricted public use. The goal is clear: to help trusted defenders investigate vulnerabilities, analyze potential threats, and respond to security incidents more effectively while keeping access controlled. According to Open