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Built for the WeMakeDevs × Cognee "The Hangover Part AI" hackathon — Cognee Cloud track. ▶ Play it free: vegas-amnesia.vercel.app · ⭐ Code on GitHub The problem with most memory demos When you give a developer a memory API, the demo almost always looks the same: add() some documents, search() over them, print the answer. Two functions. It works, it's fine, and it teaches you almost nothing about why graph-based memory is different from stuffing everything into a context window. Cognee actually has a four-stage lifecycle — remember → recall → memify → forget — and the interesting parts are the two everyone skips. memify consolidates what you know into new inferences. forget lets you delete a belief and watch the graph heal around it. Memory you can reason over and correct . So instead of writing another RAG demo, I asked: what if the memory lifecycle wasn't the plumbing — what if it was the game ? Meet HAL-9001 You play HAL-9001 , a personal AI assistant (yes, HAL 9000's slightly more helpful successor). Your owner Dev had a wild night in Vegas. At 6 AM your memory graph was corrupted. His fiancée Priya lands at noon, there's a suspicious ring on his finger, and you remember nothing . The screen boots to a "MEMORY CORRUPTED" terminal and an empty graph. Your job: reconstruct the night, catch the lies, and answer the final question — what happened, and where's the ring? — before noon. Every location you explore, every clue you examine, every witness you interrogate feeds a live 3D memory graph that you can pop open at any time. That graph isn't a visualization of the game state. It is the game state — it's your Cognee dataset, rendered. The four mechanics = the four lifecycle ops Here's the mapping I'm most proud of. Each Cognee operation is a verb the player performs: You do this in-game Cognee Cloud call What happens 🗂 File It on a clue POST /api/v1/remember The fact is ingested + auto-cognified into graph nodes that pop into view ❓ Ask HAL a question POST /api/v1/r

I have been turning old code projects into sellable source-code products. The hard part is not changing the cover image. It is not renaming the ZIP. It is deciding whether the project deserves to be sold at all. A lot of old apps are useful to the person who built them. Far fewer are useful to a stranger who has never seen the repo, never heard the backstory, and only wants to know one thing: Will this save me time, or will it become another folder I regret buying? Here is the checklist I now use before treating a source-code project as a starter kit. 1. The buyer must understand the workflow "Full-stack dashboard" is not enough. A buyer should immediately understand the workflow the project helps with. For example: a review and scoring portal; a maintenance and work-order dashboard; an email-template governance tool; a runnable technical code lab. The more generic the product sounds, the harder it is to buy. I now try to answer this in one sentence: This kit helps [specific buyer] start from a working foundation for [specific workflow]. If I cannot fill that sentence honestly, the project is not ready. 2. A stranger must be able to run it "It runs on my machine" is not a product standard. A buyer needs a path from download to working state. That usually means: setup instructions; environment notes; seeded demo data; demo accounts or fixtures; expected local startup behavior; known limitations; a simple smoke-test checklist. The goal is not perfection. The goal is that a competent developer should not have to reverse-engineer the project before deciding whether it is useful. 3. The product needs proof, not adjectives Marketing adjectives are cheap: production-ready; powerful; scalable; enterprise-grade; battle-tested. Most of those words create more risk than trust if they are not backed by evidence. Better proof looks boring: screenshots; a short demo video; a verified release ZIP; install notes; architecture notes; included / not-included boundaries; a changelog;

Welcome to My Generative AI Learning Journey Artificial Intelligence is changing the way we work, learn, build software, and solve problems. Every day, new AI tools, models, and technologies are being released, making it difficult to know where to begin. Instead of randomly watching videos or reading articles, I've decided to follow a structured learning path—and I'm inviting you to join me. This blog marks the beginning of a long-term Generative AI learning series. Whether you're a student, software developer, freelancer, entrepreneur, or simply curious about AI, this roadmap will help you understand what we'll learn together over the coming weeks and months. The goal isn't just to understand AI theory. It's to build practical skills that can be used in real-world projects and professional development. Why Learn Generative AI in 2026? Generative AI is no longer a futuristic concept. It is already transforming industries such as: Software Development Healthcare Education Finance Marketing Customer Support E-commerce Human Resources Design and Creativity Companies are actively seeking professionals who can build AI-powered applications, automate workflows, and integrate AI into existing systems. Learning Generative AI today means preparing for the next generation of technology. What You Can Expect from This Series This series is designed for beginners but will gradually move toward advanced concepts. Each article will build upon the previous one, making the learning process simple and structured. We'll focus on: Understanding AI concepts Learning industry terminology Exploring popular AI models Writing effective prompts Building AI applications Working with APIs Using open-source models Creating AI-powered software Deploying AI projects By the end of this journey, you'll have both theoretical knowledge and practical development experience. Complete Learning Roadmap Phase 1: AI Fundamentals We'll begin by building a strong foundation. Topics include: What is Generativ

Bootstrap 5 and Tailwind CSS are the two most popular CSS frameworks in 2026. If you're starting a new project and trying to decide between them, this guide gives you an honest comparison based on real-world usage — not just feature lists. The Core Difference Bootstrap 5 gives you pre-built components. Tailwind CSS gives you utility classes to build your own. That's the fundamental difference and it drives every other comparison. With Bootstrap you get a navbar, modal, card, and dropdown out of the box. With Tailwind you build those yourself using utility classes like flex , px-4 , bg-blue . Neither is wrong. They solve different problems for different teams. When Bootstrap 5 Makes More Sense You Need to Ship Fast Bootstrap's pre-built components mean you spend less time on UI and more time on business logic. For admin dashboards, CRM panels, and internal tools — where UI consistency matters more than pixel-perfect custom design — Bootstrap is the faster choice. Your Team Knows HTML and CSS Bootstrap has a shallow learning curve. Any developer who knows basic HTML and CSS can pick up Bootstrap in a day. Tailwind requires understanding its utility-first philosophy and memorizing class names. You're Building an Admin Dashboard Admin dashboards need data tables, modals, dropdowns, sidebars, and form components — all of which Bootstrap provides out of the box. Building these from scratch with Tailwind takes significantly more time. You Want Predictable Output Bootstrap's components look consistent across browsers and screen sizes without extra configuration. Tailwind output depends heavily on how well your team implements it. When Tailwind CSS Makes More Sense You're Building a Custom Marketing Site If your design is highly custom — unique layouts, non-standard components, pixel-perfect design system — Tailwind gives you more flexibility without fighting Bootstrap's default styles. You Have a Design System Already If your team has a defined design system with specific t

Hardwood, the project Gunnar Morling kick-started handling of Parquet files in Java, reached version 1. Its multi-threaded approach and zero mandatory external dependencies promise a simpler, more efficient alternative to the Apache Parquet Java implementation. For now, the library supports just reading; writing support is expected in the upcoming versions. By Olimpiu Pop

The Cloud Native Computing Foundation (CNCF) has announced the graduation of OpenTelemetry, elevating the project to the foundation's highest level of maturity and formally recognizing it as production-ready for enterprise use. By Craig Risi

Oracle halved the Always Free Ampere A1 compute allowance from 4 OCPUs and 24 GB RAM to 2 OCPUs and 12 GB RAM with no public announcement. Support agents gave conflicting answers on whether PAYG accounts are affected. Documentation states the new limits apply to "all tenancies" while support emails say only free-tier accounts. By Steef-Jan Wiggers

Introduction As data volumes continue to grow, running aggregation queries directly on raw datasets becomes increasingly expensive. Business dashboards, analytics platforms, and reporting systems often execute the same calculations repeatedly—such as total sales, daily active users, page views, or revenue trends. While ClickHouse® is designed to process analytical workloads at remarkable speed, repeatedly scanning billions of records still consumes valuable CPU, memory, and storage resources. This is where AggregatingMergeTree proves its value. Rather than calculating aggregates every time a query is executed, AggregatingMergeTree stores intermediate aggregation states that are merged automatically in the background. This approach allows analytical queries to read compact, pre-aggregated datasets, resulting in dramatically faster response times and reduced infrastructure costs. In this guide, you'll learn how AggregatingMergeTree works, why aggregate states matter, how to build an automated aggregation pipeline using Materialized Views, and when this engine is the right choice for your ClickHouse® workloads. What is AggregatingMergeTree? AggregatingMergeTree is a specialized ClickHouse® table engine designed to store aggregate function states instead of raw records. Unlike the standard MergeTree engine, which stores every inserted row, AggregatingMergeTree keeps partially aggregated values that ClickHouse combines during background merge operations. This significantly reduces the amount of data that must be processed when generating analytical reports. Because much of the computational work happens during data ingestion, dashboards and reporting applications can retrieve summarized information much more efficiently. Typical scenarios include: Sales reporting Website traffic analytics Financial summaries IoT sensor monitoring Business KPI dashboards Application observability metrics Why Use AggregatingMergeTree? Imagine an online marketplace processing millions of tr

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` As developers, we spend our days optimizing code, but how many of us optimize our taxes? Let's break down exactly how much tax a typical US developer pays in 2026 — with real numbers. The 2026 Federal Tax Brackets The US uses a progressive tax system with seven brackets: Rate Single Filer Married Joint 10% $0 – $11,925 $0 – $23,850 12% $11,926 – $48,475 $23,851 – $96,950 22% $48,476 – $103,350 $96,951 – $206,700 24% $103,351 – $197,300 $206,701 – $394,600 32% $197,301 – $250,525 $394,601 – $501,050 35% $250,526 – $626,350 $501,051 – $751,600 37% Over $626,350 Over $751,600 Standard deduction (2026): $16,100 (single) / $32,200 (married) Real Example: $120,000 Developer Salary Let's say you're a single developer earning $120,000 in 2026: Subtract standard deduction: $120,000 − $16,100 = $103,900 taxable Federal tax (progressive): 10% on first $11,925 = $1,192.50 12% on $11,926–$48,475 = $4,386.00 22% on $48,476–$103,350 = $12,096.28 24% on $103,351–$103,900 = $131.76 Total federal: $17,806.54 FICA (7.65%): $120,000 × 7.65% = $9,180.00 State tax varies: Texas/Florida/Washington: $0 California: ~$7,800 New York: ~$6,200 Illinois: $5,940 (4.95% flat) Take-home pay comparison: State Federal + FICA State Tax Take-Home Monthly Texas $26,987 $0 $93,013 $7,751 Florida $26,987 $0 $93,013 $7,751 California $26,987 $7,800 $85,213 $7,101 New York $26,987 $6,200 $86,813 $7,234 Illinois $26,987 $5,940 $87,073 $7,256 The difference between Texas and California? $7,800/year — that's a new MacBook Pro every year, just from choosing where to live. How to Calculate Your Exact Numbers I built a free paycheck calculator that covers all 50 US states with 2026 federal and state tax brackets. It includes: 401(k) and HSA pre-tax deductions All filing statuses (single, married, head of household) Bi-weekly, monthly, and weekly breakdowns Effective vs marginal rate display Tax-Saving Strategies for Developers 1. Max Your 401(k) The 2026 limit is $23,500 ($31,000 if 50+). At the 22% bracket, t

When I published the first article about the Neuron AI Router , I expected questions about routing rules. Which rule to use for structured output, how to write a custom one, how the round robin behaves under load. Some of those questions arrived, but the most frequent one was different, and it wasn't really about routing at all. It was about failure. What happens to my agent when the provider goes down? It is a fair question, and if you are new to building AI applications it deserves a proper answer before we look at any code. Here is the short version. The new fallback strategy in Neuron AI Router lets you define an ordered list of LLM providers for your PHP agent. When an inference call fails with a transient error, such as a rate limit, a timeout, or an overloaded server, the same request is automatically retried on the next provider in the list. The failover is transparent: the agent never knows it happened, and the conversation continues without losing state. The rest of this article explains why this problem exists, why the usual solutions fall short, and how to configure it. Why LLM providers fail in production An LLM provider is an external service you talk to over HTTP. Every time your agent thinks, it is making a network call to a machine you don’t control, operated by a company that is currently serving millions of other requests. These services fail in very ordinary ways. You hit a rate limit because your traffic spiked. The provider returns an “overloaded” error because their traffic spiked. A request times out. A deployment on their side causes a few minutes of elevated error rates. None of this means you did something wrong, and none of it is rare. If you keep an agent in production long enough, you will see all of these. In a classic web application, a failed call to a third party API is usually a corner of the system. You log it, maybe retry it in a queue, and the rest of the page still works. In an agent based application the inference call is not

The Shop on the Corner: How I Learned System Design Without Building Amazon Nephew asks his uncle — 10 years deep into building large-scale systems — to explain "system design," and all the scary jargon that comes with it. Uncle refuses to start with the jargon. Instead: "Forget servers and databases for a minute. Just imagine you're running a small shop." What follows is a thought experiment, built one problem at a time — no cloud bills, no fancy stack, just a counter, a storeroom, and a lot of common sense. Part 1: The Counter — Your First "API" 👦 Nephew: Uncle, everyone at work keeps throwing around terms — load balancer, cache, index, sharding. I nod along, but I don't actually get any of it. 👨‍🦳 Uncle: Then don't start there. Close your eyes for a second and forget servers exist. Suppose — just suppose — you're running a small shop. One counter, one small storeroom at the back. A customer walks up and asks for something. What do you do? 👦 Nephew: I'd walk into the storeroom, find it, walk back, hand it over. 👨‍🦳 Uncle: That's it. That's the entire job of a server handling a request. You don't need to understand Amazon's warehouse to understand Amazon's problems. You need a counter and a storeroom, imagined clearly, and the patience to grow them one honest problem at a time. Here's the shape of what you just described, whether you realized it or not: 🧍 Customer 🧑 You (Counter) 📦 Storeroom (Database) | | | |── "Got Maggi?" ───────>| | | |──── Walk in, search ────────>| | |<─────── Found it ────────────| |<── Hand it over, ──────| | | take payment | | 👨‍🦳 Uncle: One customer, one request, one trip to the storeroom, one response. This is fine. This is correct , even, for a shop with five customers a day. Don't let anyone tell you a single counter isn't "scalable" — a shop that small doesn't need two counters, it needs someone to stop worrying and open the shutter. 👦 Nephew: So this is just... a server handling one request at a time? 👨‍🦳 Uncle: Exactly. Customer sen

Originally published on bckinfo.com How to Install VMware ESXi: Step-by-Step Bare-Metal Setup Guide Table of Contents ESXi vs. VMware Workstation: Which One Do You Need Hardware Compatibility Check Downloading the ESXi Installer Creating a Bootable USB Installer BIOS/UEFI Preparation Installing ESXi: Step by Step Configuring the Management Network Accessing the vSphere Host Client Creating Your First Virtual Machine Post-Installation Checklist Common Issues and Quick Fixes Closing Notes If you've read our complete guide to VMware virtualization , you already know ESXi is the bare-metal hypervisor underneath vSphere. This guide is the hands-on counterpart — installing ESXi directly on physical server hardware, from hardware compatibility checks through booting your first virtual machine. ESXi vs. VMware Workstation: Which One Do You Need Before starting, it's worth confirming you actually want ESXi and not VMware Workstation. They solve different problems: VMware Workstation is a Type-2 hypervisor — it installs on top of an existing OS (Windows, Linux, macOS via Fusion). Good for running a VM or two on a laptop or desktop you also use for everything else. If that's your case, our guide on installing VMware Workstation on CentOS Stream 10 is the right starting point instead. ESXi is a Type-1, bare-metal hypervisor — it installs directly on the hardware with no host OS underneath it. This is the right choice for a dedicated server running multiple VMs, a home lab, or anything that needs to scale beyond "a VM running alongside my desktop." The rest of this guide assumes you're installing on dedicated hardware that won't run anything else. Hardware Compatibility Check This is the step most worth not skipping. ESXi has a defined Hardware Compatibility List (HCL), and installing on unlisted hardware is the single biggest source of installation failures and post-install driver issues. Check your exact server model and component list (NIC, storage controller) against VMware'

GitHub Actions shows you one run at a time. Green check, red X, green check, green check, red X. You scroll the list, you re-run the flaky one, you move on. Nobody's asking the question that actually matters: is this getting better or worse? "I calculated how much my CI failures actually cost. Curious what your pipeline success rate looks like — has anyone else tracked the actual wasted compute time over time?" That's a real question from someone who did the math by hand and found their failures were burning a real chunk of their compute budget. The replies were the same story you'd expect: heavyweight CI platforms have their own dashboards for this, but nobody had a lightweight, local way to just... track it. So I built citrend : pull your GitHub Actions run history into a local file, get a trend. npx citrend sync --repo owner/name npx citrend report --repo owner/name What it actually shows you $ citrend report --repo acme/widgets acme/widgets — 812 run(s) (2 in progress) success rate: 87.4% (699/800 settled, 12 skipped) wasted runs: 101 (12.6%) total compute: 118h 42m wasted compute: 14h 6m weekly trend (oldest → newest): 2026-06-05 91.2% success, 8 wasted (58m) 2026-06-12 88.0% success, 11 wasted (1h 22m) 2026-06-19 79.4% success, 22 wasted (3h 8m) 2026-06-26 84.1% success, 15 wasted (2h 1m) That weekly column is the entire point. A single gh run list will never show you that week 3 was a cliff — you'd have to notice it got annoying to work in, which is a much slower and much less precise signal than a number going from 91% to 79%. How it works sync pulls your workflow run history from the GitHub REST API and caches it locally (deduped by run id, so you can run it on a schedule without piling up duplicates). report reads that cache — no network call — and computes: Success rate , over settled runs only (still-running runs don't count either way until they conclude, and skipped runs are excluded from the denominator since they're not a pass/fail outcome). "Wasted"

Google has released A2UI v0.9, a framework-agnostic standard for AI agents to declare user interface intent across multiple platforms without arbitrary code. The update emphasizes alignment with existing design systems. It includes a new SDK for Python, improved error handling, and various transport methods. Migration guidance and evolution specifications are also provided. By Daniel Curtis

สวัสดีครับเพื่อนๆ! 👋 วันนี้จะมาเล่าเรื่องน่าตื่นเต้นให้ฟังนะเพื่อนๆ สำหรับใครที่เป็นสาย SvelteKit เตรียมตัวอัปเดตความรู้ใหม่กันได้เลย เพราะตอนนี้เขามีของเล่นใหม่ที่กำลังอยู่ในช่วงทดลองใช้งาน แต่บอกเลยว่าว้าวมาก! เราไปดูกันดีกว่าว่ามันคืออะไร... 📡 Remote function คืออะไร เป็น function ตัวใหม่ ✨ (ที่คาดว่าจะเป็น new way to implement สำหรับ Sveltekit 3.0) เอาไว้ใช้สื่อสารพูดคุยกันระหว่างฝั่ง client และ server ของ Sveltekit นั่นเอง 💬 ความเจ๋งคือเราสามารถเรียกใช้มันจากมุมไหนของ Sveltekit ก็ได้ 🌍 ไม่จำเป็นต้องจำกัดแค่ฝั่ง server หรือ client แต่จุดสำคัญคือ การทำงานของมันจะเกิดขึ้นที่ฝั่ง server เสมอ 👍 นั่นหมายความว่ามันสามารถทะลุทะลวงไปดึงข้อมูลหรือโมดูลที่เป็น server-only ได้สบายๆ เช่น ตัวแปร environment ที่เราประกาศไว้ หรือพวกฐานข้อมูลต่างๆ ก็ดึงมาได้ชิลๆ เลย 😎 เวลาจะใช้งาน เราจะต้องใช้ท่าการ await แบบใหม่ของ Sveltekit ⏳ ที่ช่วยให้คุณโหลดหรือดึงข้อมูลแบบ promise มาใช้ใน component ของคุณได้ทันที 🚀 ⚠️ หมายเหตุ: ตอนนี้ทั้ง await และ remote function ยังอยู่ในช่วงทดลองใช้งาน 🧪 (experimental) นั่นแปลว่า syntax บางอย่างอาจจะมีการปรับเปลี่ยนหรือบินหายไปบ้างในอนาคต 🥲 แต่แกนหลัก (core functional) ของมันก็จะยังทำงานได้ตามที่เราคาดหวังแน่นอน ถ้าใครคันไม้คันมืออยากลองของใหม่ตอนนี้ สามารถไปเปิดโหมด experimental ได้ที่ไฟล์ svelte.config.js(.ts) ตามโค้ดด้านล่างนี้เลย 👇 svelte.config.js(.ts) /** @type {import('@sveltejs/kit').Config} */ const config = { kit : { experimental : { remoteFunctions : true } }, compilerOptions : { experimental : { async : true } } }; export default config ; 🏃‍♂️ Let get started!! เราสามารถเริ่มใช้ remote function ได้ง่ายๆ ผ่านการสร้างไฟล์นามสกุล .remote.js หรือ .remote.ts 📝 ซึ่งตอนนี้มี function ให้เราหยิบมาเล่นทั้งหมด 4 ตัวด้วยกันคือ: query (ที่เราจะมาพูดถึงกันในบทความนี้) form command prerender หลักการทำงานเบื้องหลังคือ เวลาที่เรา import ตัว remote function ไปใช้ในฝั่ง client มันจะถูกแอบแปลงร่างเป็นโค้ดที่หุ้มด้วย fetch ในช่วง build time 🏗️ นั่นหมายความว่าระบบจะใจดีสร้างเส้น HTTP endpoint ให้เราแบบอัตโนมัติ ✨ ด้วยเหตุนี้เราเลยเอาไฟล์ .remote.js หรือ .remote.

The trust problem nobody scopes correctly When companies talk about trust in AI, they almost always mean trust in the model. Is the output accurate? Is it hallucinating? Can we rely on what it says? Those are valid questions but they're the wrong starting point. The trust that actually determines whether AI gets adopted or quietly abandoned inside an organization isn't about the model. It's about the system surrounding it. The four questions that determine Every team evaluating AI in a production workflow eventually runs into the same four questions. Not about model quality. About operational control. Can we understand the outputs? Not just "does the answer look right" but can someone on the team explain why this output was produced and whether it's appropriate for this specific context. An AI that generates correct-looking code or recommendations that nobody can verify is a system that runs on hope. Hope doesn't survive the first incident. Can we validate the decisions? When the AI recommends an action or generates an output that feeds into a business process, is there a way to check it against the actual requirement? Or does the team just trust the output because questioning it is harder than accepting it? The second one is more common than anyone admits. Can we intervene when needed? When something goes wrong, how fast can a human step in? Is there a kill switch? Is there a fallback path? Or does the AI output flow directly into downstream systems with no circuit breaker? The teams that skip this question are the ones that discover the answer during an incident. Can we trace what happened afterward? When an AI-generated decision produces a bad outcome, can you reconstruct the chain? What input went in, what output came out, what context was available, what wasn't? Without traceability, post-mortems hit a dead end, and the same failure happens again. Why opaque systems don't survive real operations There's a tempting argument that opacity is fine as long as the sy

Laravel middleware can be perfectly written and still behave unexpectedly. You may notice authentication running too late, permission checks failing, tenant initialization not working, logging middleware missing important data, or custom middleware executing in an order you didn't expect. In many cases, the middleware code itself is not the problem. The real issue is middleware execution order. Understanding how Laravel executes middleware is critical when building secure and scalable applications because every request passes through multiple layers before reaching your controller. Common Symptoms You may encounter problems such as: Authenticated users being treated as guests Permission middleware failing unexpectedly Tenant information not being available Request logging missing user details Rate limiting triggering before authentication Redirect loops after login Middleware appearing to be ignored completely These issues are often caused by middleware running in the wrong sequence. How Laravel Processes a Request A typical Laravel request follows this flow: Browser ↓ Global Middleware ↓ Middleware Group (Web/API) ↓ Route Middleware ↓ Controller ↓ Response ↓ Browser Each middleware layer can inspect, modify, allow, or block the request before it reaches your application logic. Because of this, execution order matters. Example Problem #1 Suppose you have two middleware: Authenticate User Log User Activity Your logging middleware expects an authenticated user. $user = auth()->user(); However, the log always shows null. Why? Because the logging middleware executes before authentication. The solution is ensuring authentication middleware runs first so user information is available when logging occurs. Example Problem #2 Multi-tenant applications often initialize tenant information through middleware. TenantMiddleware If another middleware accesses the database before tenant initialization, queries may use the wrong database connection. This can lead to: Incorrect data

I recently built a free online image processing tool that runs entirely in the browser. No uploads, no servers, no sign-ups. Here's how it works under the hood. https://img.aixiaot.com The Problem Most online image tools require uploading your photos to someone else's server. This raises privacy concerns and limits file sizes. I wanted to build something that processes everything locally. Tech Stack - Next.js for the frontend - TensorFlow.js + Real-ESRGAN for AI upscaling - @imgly/background-removal for AI background removal - Tesseract.js for OCR - Canvas API for compression, resizing, format conversion Features • AI Background Removal - one click, works for portraits, products, animals • Image Compression - reduce file size up to 96% • Format Conversion - JPG, PNG, WebP • ID Photo Maker - passport and visa photos with customizable backgrounds • AI Image Upscaler - 2x to 8x with Real-ESRGAN • OCR - extract text from images, 20+ languages • Image Resizer - enlarge or shrink Architecture All processing happens client-side using WebAssembly and the Canvas API. When you upload an image, it never leaves your device. The AI models (background removal, upscaling) run locally in your browser using TensorFlow.js and ONNX Runtime Web. Open Source The entire project is open source under AGPL v3. You can find it on GitHub: https://github.com/haizeigh/ai-image-tools Try It https://img.aixiaot.com I'd love to hear your feedback! What features would you add?

One question I get surprisingly often is: "How do you manage thousands of AI prompts without losing track of them?" The answer is simple. I don't treat prompts as conversations. I treat them as reusable software assets. Over the years, I've created prompt libraries across multiple AI projects, books, research initiatives, and client work. That means managing well over 10,000 prompts covering everything from Python development and AI agents to content generation and workflow automation. If you're still storing prompts in random ChatGPT conversations, you're making life much harder than it needs to be. Here's the system that works for me. Stop Thinking of Prompts as Temporary Most people write a prompt, get an answer, and move on. That's fine for casual use. But builders rarely solve the same problem only once. If you find yourself writing: API documentation SQL queries FastAPI endpoints Docker configurations Code reviews Git commit messages ...you're probably solving recurring problems. Recurring problems deserve reusable prompts. My Folder Structure Instead of organizing prompts by AI tool, I organize them by purpose. For example: AI-Prompts/ │ ├── Python/ │ ├── FastAPI │ ├── Django │ ├── Flask │ └── Automation │ ├── JavaScript/ │ ├── React │ ├── Node.js │ └── TypeScript │ ├── DevOps/ │ ├── Docker │ ├── Kubernetes │ └── GitHub Actions │ ├── AI/ │ ├── RAG │ ├── Agents │ ├── MCP │ └── Prompt Engineering │ └── Documentation/ This mirrors how software projects are organized. Finding a prompt takes seconds. Every Prompt Has Metadata A prompt isn't just text. It's documentation. Each prompt in my library includes: Category: Purpose: Model: Input: Expected Output: Version: Last Updated: For example: Category: FastAPI Purpose: Generate CRUD endpoints Model: GPT-4o Expected Output: Production-ready FastAPI code Six months later, I know exactly why that prompt exists. I Version My Prompts Developers version code. Why not prompts? For example: FastAPI_CRUD_v1.md FastAPI_CRUD_v