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One word broke into a private repository this month. Not a zero-day. Not stolen credentials. Not...

Originally published on 2022-07-04. Published on July 3rd, 2022 We have a ll been there.. We all have that long list of unfinished side projects that we hope to complete some day.. If you're anything like me, that 'some day' is always eluding you and never getting here.. Ripping up the bandaid Today I have decided to finally go ahead and finish one of my long list of unfinished tutorial projects. I recently read a long Twitter thread that gave me a lot of food for thought. To paraphrase my understanding of one tweet, 'success is a combination of all the small wins'. Therefore, by finishing and publishing one unfinished project today, I set myself up to finishing another one tomorrow. Small, consistent gains I just need to make it a habit of finishing what I have started so that they do not get out of hands. After all, my Github account has about 126 repositories, but my portfolio website only has a dozen completed and published projects. Almost Done I have finished up the tutorial project. My next step is to rebuild the project from scratch without the handholding of the tutorial. One of the mental blocks that prevented me from finishing up the project in the first place seems insignificant now. Next time that mental block tries me, I will be better prepared! The project we're talking about! Published Link: https://blockchain.tioye.dev

I Built Flowstate Because We Somehow Made Productivity More Complicated Than The Actual Work Live: https://flowstate.chromitedev.xyz/ GitHub: https://github.com/ChromiteDev/flowstate We have a strange problem. Humans built some of the most advanced technology in history. We created: Computers that fit in our pockets Instant communication across the planet Machines that explore space Software that can do incredible things And somehow... We still struggle with: "What should I actually focus on today?" That is the problem I wanted to solve. So I built Flowstate . The productivity paradox We have never had more productivity tools. Seriously. Think about it. There are apps for: Tasks Notes Calendars Habits Goals Projects Time tracking Team management There is probably an app to help you organize the apps that organize your life. At some point we stopped being productive and started managing productivity. The funniest part? Sometimes creating the perfect productivity system becomes the biggest productivity project. You spend two hours making a beautiful workspace... Then realize: You have done absolutely nothing. A masterpiece of organization. Zero progress. The moment I realized something was wrong I noticed a pattern. People were not struggling because they were lazy. They were struggling because their attention was constantly being divided. Every day we fight: Notifications Endless information Too many choices Too many responsibilities Too many things competing for our attention The internet gave us unlimited access to information. But our attention? That is still limited. The question behind Flowstate I kept coming back to one question: "What actually deserves my attention right now?" Not: "What are all the possible things I could do?" Not: "How can I create the most complicated workflow imaginable?" Not: "Should I reorganize my folders for the fifth time?" (We have all been there.) The goal was simple: Create a tool that helps people find clarity. Introducing Flowsta

Every form I ever shipped before React 19 needed the same three pieces of state, and I wired them up...

TL;DR Modern web applications change constantly. Components are re-rendered, generated...

We've all had the same experience. You ask your coding agent to integrate with a new platform. It confidently writes code. It references endpoints that don't exist anymore. It misses required headers. It mixes API versions. It hallucinates authentication flows. None of this is surprising. Large language models don't "know" an API. They know about an API from their training data. Even when you hand them documentation, they're still trying to reconstruct a mental model from hundreds or thousands of pages of text. The problem isn't writing code. It's building context. Understanding an API is still mostly manual Every integration starts the same way. Read the authentication docs. Figure out the important entities. Learn the object relationships. Understand the common workflows. Find the endpoints that matter. Jump between documentation tabs for an hour. Only then do you actually start building. Ironically, AI made writing code dramatically faster while leaving this entire process mostly unchanged. Documentation wasn't designed for AI Most documentation is optimized for humans. OpenAPI specifications are optimized for machines. Neither tells the complete story on its own. The spec explains what exists. The documentation explains why it exists. Neither builds a coherent mental model. I wanted a better starting point That's why I built Scout. Scout takes an OpenAPI specification and the accompanying documentation, then synthesizes them into a grounded understanding of the platform. Instead of asking: "Can Claude figure this out?" The workflow becomes: import the API crawl the documentation build an understanding ask questions against grounded context generate integration code expose the same understanding to coding agents through MCP Everything runs locally. No hosted backend. No accounts. No telemetry. The interesting part isn't the AI The AI chat isn't the product. The generated code isn't the product. The MCP server isn't even the product. The product is the context tho

When an application starts using more than one language model provider, the hard part is rarely the first API call. The hard part is everything that follows: separate credentials, different request shapes, provider-specific errors, billing dashboards, and model migrations scattered across the codebase. A useful way to reduce that surface area is to keep one OpenAI-compatible client contract and move provider choice into configuration. This guide shows the smallest working setup with Routara , plus the production checks I recommend before sending real traffic. 1. Keep the SDK, change the endpoint If your project already uses the OpenAI Python SDK, the client initialization is the only part that needs to change: import os from openai import OpenAI client = OpenAI ( api_key = os . environ [ " ROUTARA_API_KEY " ], base_url = " https://api.routara.ai/v1 " , ) response = client . chat . completions . create ( model = " deepseek-chat " , messages = [ { " role " : " user " , " content " : " Explain idempotency in two sentences. " } ], ) print ( response . choices [ 0 ]. message . content ) Store the key in an environment variable. Do not put it in browser code, a public repository, screenshots, or support messages. The same pattern works in Node.js: import OpenAI from " openai " ; const client = new OpenAI ({ apiKey : process . env . ROUTARA_API_KEY , baseURL : " https://api.routara.ai/v1 " , }); const result = await client . chat . completions . create ({ model : " deepseek-chat " , messages : [{ role : " user " , content : " Return one short test sentence. " }], }); console . log ( result . choices [ 0 ]. message . content ); 2. Treat model IDs as configuration Do not spread model names throughout the application. Put them in environment variables or a typed configuration object: model_id = os . environ . get ( " ROUTARA_MODEL " , " deepseek-chat " ) That makes model evaluation and rollback much safer. Routara's live model catalog is the source of truth for current availa

JWT authentication has more failure modes than most developers realise. Correct signature verification is necessary but far from sufficient. This checklist is what I run through before every production JWT deployment. 1. Secret Is Generated With a CSPRNG Not a password. Not a UUID. Not a timestamp. A cryptographically secure pseudorandom number generator output. In Node.js: crypto.randomBytes(32).toString('hex') In Python: secrets.token_hex(32) In the browser: jwtsecretgenerator.com/tools/jwt-secret-generator A 256-bit CSPRNG secret takes 10^59 years to brute force at current GPU speeds. 2. Algorithm Is Explicitly Specified in verify() // Wrong jwt . verify ( token , secret ); // Right jwt . verify ( token , secret , { algorithms : [ ' HS256 ' ] }); 3. exp Claim Is Present and Validated Short-lived tokens (15 minutes) limit the damage from leaks. Verify your library is actually checking exp — some require explicit configuration. 4. iss and aud Claims Are Validated Validates the token was issued by your service and intended for your API. Prevents token reuse across services. 5. Tokens Are in httpOnly Cookies, Not localStorage localStorage is readable by any script on the page. httpOnly cookies are invisible to JavaScript. 6. HTTPS Is Enforced JWT in a query parameter over HTTP is visible in every proxy, CDN, and server log on the path. Use the Authorization: Bearer header over HTTPS only. 7. Refresh Tokens Are Server-Side Revocable Short access tokens + server-side refresh tokens = the ability to end sessions immediately. Long-lived access tokens without refresh logic cannot be revoked. 8. The jti Claim Is Used If You Need Immediate Revocation Store revoked jti values in Redis with TTL matching token expiry. Check on every request. Adds one Redis lookup per request — worth it for high-security endpoints. 9. Different Secrets for Each Environment Dev secret leaks should not compromise production. Keep them separate. 10. Secret Is Not in Source Code or Version Control

A few months ago I got obsessed with a question: can you run a real LLM entirely inside a browser tab, with zero backend, zero GPU, and zero install? The answer is yes. Here's what I built. ghost is a single HTML file that downloads a quantized language model into your browser's cache on first visit, then runs inference locally in WebAssembly forever after. Fully offline after that first download. No API key. No npm. No build step. Open the file, pick a model, chat. How it works The inference engine is wllama — a WebAssembly binding for llama.cpp. It runs GGUF quantized models directly in the browser using WASM SIMD. I pin it to a specific version so the JS and WASM files always match (learned this the hard way after a fun debugging session involving mismatched memory imports). Models are downloaded from HuggingFace on first load and cached via the browser's Cache API. On every subsequent visit they load instantly from cache, no network needed. Features Three models: Qwen2.5 1.5B (smart), Qwen2 0.5B (fast), TinyLlama (lightweight) Markdown rendering from scratch — no library, just regex transforms RAG: drag a .txt or .pdf onto the chat window. It chunks the text, embeds each chunk using wllama's embedding API, stores vectors in memory, and retrieves the top-3 relevant chunks on each message. Fully local, fully offline Voice input via the Web Speech API — mic button auto-sends on silence Multi-turn conversation memory capped at 10 turns PWA installable — works on mobile home screen too The hard parts Getting wllama to load from a cached model was genuinely tricky. Blob URLs created in the main thread aren't accessible from wllama's internal Web Worker. IndexedDB chunk reconstruction hit a 2GB ArrayBuffer limit on Windows Chrome. The final solution was using wllama's built-in loadModelFromHF with useCache: true which handles everything internally. The embeddings API requires toggling a flag (embeddings: true) that conflicts with normal chat completion — so I toggle it

Introduction Hello from Japan! 🇯🇵 I am a professional truck driver teaching myself Python and web development while working toward a career transition into web engineering. This article records what I learned after approximately 122 hours of programming study , starting on May 12, 2026. Recently, I added a ripple animation effect to the answer buttons in my self-developed application: 🚛 DPT — Driver Personality Test https://qiita.com/tosane932/items/220d0f7d36bd79b2aa81 At first, I thought it would be a small visual improvement. However, while implementing it, I realized that the blinking light on my toilet control panel and a JavaScript flag named isProcessing were performing exactly the same role. This article explains that connection. It Started as Protection Against Repeated Clicks In DPT, clicking an answer button moves the user to the next question. In the original version, the next question appeared immediately after the button was clicked. However, this created a problem. If a user repeatedly clicked the button, the application continued advancing through the questions at the same speed. In an extreme case, someone could finish all 50 questions in only a few seconds. That would reduce the reliability of the personality test and could also create invalid answer records. To prevent this, I introduced a processing-state flag . let isProcessing = false ; testContainer . addEventListener ( " click " , ( event ) => { if ( isProcessing ) return ; const button = event . target . closest ( " .option-btn " ); if ( ! button ) return ; isProcessing = true ; createRipple ({ currentTarget : button , clientX : event . clientX , clientY : event . clientY }); const qIdx = Number ( button . dataset . qIndex ); const oIdx = Number ( button . dataset . oIndex ); setTimeout (() => { if ( oIdx === - 1 ) { handleAnswer ( qIdx , - 1 , " No answer " , 0 ); } else { const option = shuffledQuestions [ qIdx ]. shuffledOptions [ oIdx ]; handleAnswer ( qIdx , oIdx , option . text , optio

Did you know that Kiro doesn't just have a Spec-Driven Development (SDD) flow, but also a bug fix workflow that helps you resolve one issue at a time? That's one of seven features worth knowing about. If you're completely new to Kiro, it's an agentic harness for the CLI, web, IDE, iOS, and more. It helps teams and individuals do their best work while coding. I've been using it since it launched in July last year, and I keep finding features I didn't know were there. (Full disclosure: I'm a Developer Advocate at AWS, and Kiro is a part of AWS. I use it every day, and I'll be forthcoming about the parts that are still preview or experimental.) Heads up: Kiro ships fast. I've flagged the version-sensitive bits of these features inline. Check the docs if something looks different in your build. 1. Stop approving every single command After talking to a lot of people about Kiro, one of the main pieces of feedback I get is on approving commands. When Kiro asks permission to run a shell command, the default reaction is to hit yes and move on. Then it asks again for the next git command. And the next one. Press Tab instead in the CLI. This allows you to edit it and put the exact permissions you'd like. For example you can be pickier on the trust tiers: git pull --rebase # this exact command git pull * # git pull with any arguments git * # anything git * # the entire shell tool Whatever you pick persists for the session and gets stored as a regex in your agent's allowedCommands . There's also /tools trust-all , which trusts everything. It's the documented replacement for the old /acceptall , and the security docs are blunt about it: don't use it in production or with sensitive data, and you're responsible for whatever it does. One version note: on CLI v3 this moves to a permissions.yaml file, so the agent JSON advice above is v2. More on v3 in a minute. Full details: tool permissions 2. The # menu is bigger than #file in the IDE Type # in the IDE chat and you get a list of co

You’ve probably seen that little prompt that says “Sign in with Face ID” or “Use a passkey” instead...

The same parser, four times Over the last year I kept running into the same shape of problem: A docs site generator that wanted Markdown chapters turned into navigation JSON. A RAG ingestion script where each Markdown file needed to become a list of text chunks plus its frontmatter metadata. An n8n flow that took Markdown emails and extracted only the tasklists. A static-site backend that accepted user Markdown and needed to validate structure before persisting. Each one is small on its own. But every time I reached for a different library — remark here, gray-matter there, marked once, a hand-rolled regex once too many — and every time one of them broke on the same edge cases: Nested GFM tasklists where the checked state was silently lost YAML frontmatter that included quoted booleans (parsed as strings, not booleans) Tables whose headers contained spaces (regex parsers treated them as one key) Code blocks containing Markdown — re-parsed as Markdown instead of fenced code So I built one endpoint that does it once, properly. What it returns POST /v1/parse takes a Markdown body ( text/markdown ) or a JSON envelope ( application/json ) and returns one stable JSON shape: { "success" : true , "data" : { "title" : "Project Alpha" , "frontmatter" : { "title" : "Project Alpha" , "status" : "shipping" }, "headings" : [ { "level" : 1 , "text" : "Project Alpha" , "id" : "project-alpha" } ], "sections" : [ { "heading" : { ... }, "children" : [ ... ], "content" : [ ... ] } ], "lists" : [ { "ordered" : false , "items" : [ "ship MVP" , "write README" ] } ], "tasklists" : [ { "items" : [ { "text" : "ship MVP" , "checked" : true } ] } ], "tables" : [ { "headers" : [ "Module" , "Status" ], "rows" : [{ "Module" : "API" , "Status" : "Done" }] } ], "codeBlocks" :[ { "lang" : "js" , "value" : "..." } ], "links" : [ { "text" : "..." , "url" : "https://..." } ], "paragraphs" :[ "..." ], "ast" : null } } The sections tree is the part I care most about. It's not just a flat list of headings

Every service I've worked on eventually grows the same scar tissue: a retry loop copy-pasted into six files, a circuit breaker bolted onto the payment client after an outage, a timeout wrapper someone wrote at 3 a.m. Each one slightly different. None of them talking to each other. And when things go wrong, nobody can answer the only question that matters during an incident: what is the resilience layer actually doing right now? Java solved this years ago with resilience4j . .NET has Polly . Node.js... has pieces. The gap I evaluated what the ecosystem offers before writing a single line: opossum is the best-known circuit breaker, mature and well maintained. But it's only a circuit breaker — retry is rudimentary, there's no bulkhead, no composition. Metrics need a plugin. cockatiel is the closest thing to Polly: retry, breaker, timeout, bulkhead, composition. I genuinely like its design. But observability is where it stops — no native metrics, no pipeline-wide correlation — and maintenance has slowed. The Sindre micro-libs ( p-retry , p-timeout , p-limit ) are excellent at exactly one thing each. But resilience is a system : a retry that doesn't know the circuit is open will happily sleep through backoff to hammer a dead dependency. Isolated pieces can't coordinate. And there was one thing nobody documented properly, which became the reason I finally started typing: Ordering is the whole game Take four policies: retry, circuit breaker, timeout, fallback. The same four, nested in two different orders, produce two very different systems: retry ( circuitBreaker ( timeout ( fn ) ) ) // A circuitBreaker ( retry ( timeout ( fn ) ) ) // B In A , every attempt flows through the breaker, so the breaker sees the dependency's true failure rate — and when the circuit opens mid-retry, the retry finds out immediately. In B , the breaker sees one outcome per retry cycle : three real failures against the dependency count as a single failure. The circuit opens far later than the depe

இன்னும் ஜோஷ்! 🔥 முதல் நாள் St. Joseph's Institute of Technology, சென்னையில ஜோர்தான் இருந்தது. இரண்டாம் நாள் வந்ததும் என்னன்னா, க்ரவுட் இன்னும் அமைதியா, ஆனா உள்ள ஆர்வம் இன்னும் ஜாஸ்தியா இருந்துச்சு. எல்லாரும் "இன்னிக்கி ரொம்ப tech-ஆ போகணும்" னு மனசுல வெச்சிட்டு உட்கார்ந்திருந்தாங்க. இண்டு "தமிழன் நினைச்சா முடியாதது இல்ல" ங்கிற வார்த்தை என் மனசுல ஓடிக்கிட்டே இருந்தது — ஒரு சின்ன அறையில கூட, கம்ப்யூட்டர் screen-ல open source code-ஐ பார்த்துக்கிட்டே இருந்தா, அது எவ்ளோ பெரிய புரட்சின்னு தெரியும். FOSS-ன்னு சொல்ற ஒவ்வொரு லைனும், நம்ம மொழியில நம்ம கம்யூனிட்டியால எழுதப்படுற ஒவ்வொரு code-உம் ஒரு சிறிய வெற்றி தான். Session 1: வேகமா App கட்டணுமா? Meet Framework இருக்கே! 🚀 முதல் session-ல Meet Framework அறிமுகமானது — Python + JS ரெண்டையும் சேர்த்து ஒரே கூரையின் கீழ கொண்டு வர்ற ஒரு full-stack framework. இது என்ன பண்ணுது தெரியுமா? "Setup fatigue" ங்கிற பெரிய பிரச்சனையை ஒரே அடியில தீர்க்குது. Backend, frontend, database எல்லாத்தையும் தனித்தனியா தேடி, ஒட்டி, configure பண்ணி — இதெல்லாம் இல்லாம, ஒரே framework-ல எல்லாமே ready-ஆ இருக்கும். Speaker live-ஆ ஒரு app-ஐ கட்டி காமிச்சாங்க — routing, models, ஒரு simple UI எல்லாம் நிமிஷங்களில ready! அது பார்க்கும்போதே ஒரு எனர்ஜி கிடைச்சது. "Idea இருந்தா போதும், tool நம்ம கூட இருக்கு" ங்கிற நம்பிக்கை தான் FOSS-ன்ற அழகே. சின்ன Motivation: ஒரு framework கத்துக்கிறதும், ஒரு புது மொழி கத்துக்கிறதும் ஒண்ணுதான். ஆரம்பத்துல கஷ்டமா தான் தெரியும், ஆனா ஒரு அடி எடுத்து வெச்சா, மொத்த பாதையும் தெளிவா தெரியும். "தொடங்குறது தான் பாதி வெற்றி!" Session 2: NPM vs NixOS — ஒரு "Love-Hate" Relationship 😅 இரண்டாவது session ரொம்ப relatable-ஆ இருந்தது — NixOS-ல npm use பண்றது! அறையில இருந்த பலருக்கும் இது தெரிஞ்ச பிரச்சனை தான், எல்லாரும் தலையாட்டிட்டே இருந்தாங்க. பிரச்சனை என்னன்னா — Nix ரொம்ப strict-ஆ இருக்கும், file system-ஐ read-only-ஆ வெச்சிருக்கும். அதனால npm சாதாரணமா install பண்ற மாதிரி இங்க straight-ஆ வேலை செய்யாது. Speaker மூணு வழிகள் சொன்னாங்க: Local user prefix வெச்சு — npm-ஐ ஒரு writable இடத்துல install பண்ண வைக்கிறது. node2nix use பண்ணி — npm dependencies-ஐ

How to design, ship, and operate an AI agent that is reliable, efficient, performant, scalable, and secure enough to serve real companies — from a 5-person startup to a 50,000-person enterprise. This guide distills hard-won lessons from production agents (Claude Code, OpenHands, SWE-agent, GoClaw, Hermes, nanobot, PicoClaw, ZeroClaw, Multica, Paperclip) and grounds them in current engineering guidance from Anthropic and OpenAI plus the security and compliance standards you'll actually be audited against (OWASP Top 10 for Agentic Applications, NIST AI RMF, the EU AI Act, and 2025–2026 prompt-injection research). It focuses on the parts most articles skip: the enterprise tax — governance, security, compliance, integration, cost control, and the operating model — that separates a demo from a system a CISO will sign off on. 📖 How to use this guide Read Parts 0–2 to decide whether and what to build. Most failed agent projects die here. Read Parts 3–7 for the architecture and reliability engineering. Read Parts 8–10 for the enterprise gates: security, compliance, multi-tenancy, observability, cost. Read Parts 11–15 for delivery, scale & rollout: deployment topologies (SaaS/self-hosted/hybrid), how to adopt from pilot to org-wide, how to handle thousands of concurrent requests, the operating model, and a 30/60/90 plan. Every part ends with an ✅ Actionable checklist . Skim those for a design review. 📋 Table of Contents 🧮 Part 0 — The Core Equation 🧭 Part 1 — Decide Before You Build: Workflow vs Agent, Build vs Buy 🏛️ Part 2 — The Enterprise Tax: What Actually Changes 🏗️ Part 3 — Reference Architecture: The Layered Stack 🔄 Part 4 — The Reliable Kernel: The Agent Loop 🛠️ Part 5 — Tools & Enterprise Integration 🧠 Part 6 — Context & Memory: The Cost Center 🛟 Part 7 — Reliability Engineering 🔐 Part 8 — Security, Compliance & Governance 🧱 Part 9 — Multi-Tenancy & Isolation 📊 Part 10 — Observability, Evals & Cost Governance 🚀 Part 11 — Deployment & Delivery Models 📈 Part 12 — The

Free Online PDF Converter – Word, HTML, Image & More Tools Free online PDF converter with complete privacy protection. Merge, split, compress, sign, and convert PDFs directly in your browser — plus a free age calculator and scientific calculator. No uploads, no data storage. onepagepdfconverter.com I built One Page PDF Converter (onepagepdfconverter.com) — a set of 20+ PDF tools (merge, split, compress, sign, convert to/from Word/Excel/PowerPoint/images, etc.) plus a couple of everyday calculators, all running entirely client-side in the browser. There's no backend processing your files: no upload, no storage, no server round-trip. Everything happens locally via JS, so your documents never leave your device. That's the whole pitch — it's the same reason I built it, since most PDF tools online quietly funnel your files through a server you have no visibility into. All 20+ tools are free with no sign-up. Today I'm adding a small premium option: batch processing, for ₹9.99 per batch (~$0.10 USD), one-time — no subscription. Run a tool across multiple files in one go instead of one at a time. Everything else on the site stays free and unlimited. Tech-wise it's a single HTML file backed by client-side JS libraries — no framework, no build step. It's a PWA, so it installs and works offline once loaded. Would love feedback — especially on the privacy angle, the batch pricing, or tools you think are missing. Free Online PDF Converter – Word, HTML, Image & More Tools Free online PDF converter with complete privacy protection. Merge, split, compress, sign, and convert PDFs directly in your browser — plus a free age calculator and scientific calculator. No uploads, no data storage. onepagepdfconverter.com

An ecosystem whose job is to full-fill our dream of opening and collecting pokemon cards, which we all had in our childhood. Instead of just clicking a button to reveal static images, I wanted to recreate the whole experience of getting , collecting and showing off your pokemon cards , i even added live card auctions and card shows and PSA card grading simulator to give the full-on experience which pokemon has to offer. 🔗 Live Sandbox: https://pokemontcgsim.vercel.app 💻 GitHub Repo: https://github.com/sohamSanat/PokemonTcgSimulator ** Screen shots of different segments of the web app -> ** 1)Main page (pack opening) 2)Binder section where you sort your cards in the personal collections and see your cards’ portfolio 3)Card grading simulation where you can get your cards’ price increased based on the condition of your card 4)Card show where there are different vendors with their own specialty in cards 5)live cards auction of thousands of cards **Tech fluff for people who cares ;D -> Architecture & Major Engineering Achievements : -** 1)Era-Calibrated Pack Engine & Rarity Probability Mathematics: Pack generation creates historically accurate card pools from over 25 years of card sets, from 1999 Base Set to 2025 Mega Evolution. It recreates slot weights, ensures holos, and handles complex probability mechanics for Secret Illustration Rares and many other cards 2)gemini powered NPC Negotiation Engine: During the virtual card convention, users negotiate with 9 different NPC vendors through natural language interactions. The backend NLP pipeline analyzes the user’s language, tokenizes the negotiation and makes offers. 3)Simulated PSA Grading Laboratory & Restoration Studio: The multi-step card authentication system considers card centering, surface, corners, and edges, and provides realistic grade distribution (PSA 1-10) with dynamic slab encasement and grade multipliers. It also has an interactive pre-grading restoration studio where you can clean surfaces and press corne

Disclosure: I work on the team behind SynapEditor. 🧩 TL;DR: Moving from CKEditor 5 to SynapEditor is a one-to-one swap in three steps: installation, toolbar config, and content/event APIs. The main reason to consider it is Office document fidelity (Word, PowerPoint, Excel import/export). Full runnable example at the end. Switching rich text editors sounds like a big job, but most of the work is a straightforward, one-to-one swap. This guide walks through moving an existing CKEditor 5 integration over to SynapEditor: loading the library, wiring up the toolbar and content APIs, and a complete working example you can copy and run. ⚖️ Which is better: CKEditor or SynapEditor? Both CKEditor and SynapEditor are mature, capable editors. If you already have CKEditor running, it clearly does a lot right. So the question isn't really "which is better" in the abstract, it's which one fits where your product is heading. Two things tend to drive the decision: 📜 Licensing and support. CKEditor 4 reached end of life in 2023, and security fixes now sit behind a paid Extended Support agreement. If you're revisiting the integration anyway, it's a natural moment to reconsider the editor itself. 📄 Office documents. This is where SynapEditor differs most. It imports a broad range of office formats: MS Word (.doc, .docx), PowerPoint (.ppt, .pptx), Excel (.xls, .xlsx, ODT, and HTML, and exports back to Word (.docx) with formatting preserved. If your users upload real documents and expect the layout to survive, that's worth weighing. CKEditor 5 SynapEditor Core editing ✅ ✅ CKEditor 4 still supported Paid ESM only n/a Word / PPT / Excel import-export Limited ✅ Native With that out of the way, let's migrate. 📋 What you'll need [ ] An existing CKEditor 5 integration [ ] A SynapEditor license and API key (free at Get Started ) [ ] About 15 minutes for a basic swap ⚙️ 1. Installation CKEditor 5 loads from a single script. SynapEditor loads from a script and a stylesheet: the UI is styled by tha

Every developer has done it — copy-pasted a JWT, a private key, or a JSON blob with sensitive data into some random website and held their breath. Wondering if it was being logged, tracked, or worse. Every developer has done it — copy-pasted a JWT, a private key, or a JSON blob with sensitive data into some random website and held their breath. Wondering if it was being logged, tracked, or worse. I built KRUMB.DEV because I wanted tools that didn't make me feel dirty after using them. What Is It? 46 developer tools, all in one place. No signup. No uploads. No tracking. Open source. The terminal-inspired interface isn't just aesthetic — it's a constraint. Every tool fits in a single column, zero sidebar, zero popups. Just you and the tool. What's Inside Formatters — JSON, SQL (17 dialects), HTML, JavaScript, CSS Encoders — Base64, URL, JWT decoder, YAML↔JSON, JSON↔CSV Generators — Passwords, UUIDs (v1/v3/v4/v5), hashes (MD5/SHA/HMAC), QR codes, Lorem Ipsum, color palettes, CSS gradients/shadows/grids, meta tags, robots.txt, .gitignore Testing & Debugging — Regex tester, diff checker, webhook tester, cURL→code, HTTP status reference, cron expression builder Converters — Unix timestamps, hex↔RGB, binary, SVG→JSX, JSON→TypeScript, HTML playground, markdown editor Network — DNS lookup, SSL checker, IP lookup, QR code decoder, IBAN validator Why I Built It This Way Most "free" dev tools follow the same pattern: create an account, hit a rate limit, and wonder if your data is being stored somewhere. KRUMB.DEV flips that: Everything runs in your browser — JSON, JWT, source code, passwords never touch a network request Zero accounts — open the page, use the tool, leave. No signup wall between you and the output Clean interface — ⌘K opens a command palette to jump to any tool in seconds Open source — MIT license, deploy your own if you want The Tech Next.js, TypeScript, and Tailwind. Static-first, client-side execution for all core tools. Server routes exist only for DNS/SSL l