DEV Community: Getinfo Toyou

Automating 301 Redirects: How We Built a WordPress Plugin to Scan 404s and Map Clean URLs Using Gemini AI

Getinfo Toyou — Wed, 17 Jun 2026 14:30:26 +0000

If you run a WordPress site, you've probably faced the 404 headache. Maybe you migrated your content, updated your permalinks, or pruned old posts. Suddenly, search engines start reporting broken links, and your visitors hit dead ends.

While manual redirection plugins exist, they quickly become a chore when you are dealing with dozens or hundreds of broken URLs. That is why I built FixLinks: a WordPress plugin designed to scan your site's 404 errors and automatically suggest the most relevant 301 redirect targets using the Gemini API.

Who Benefits Most?

I built this tool with a few specific user profiles in mind:

The Long-Term Blogger & Content Publisher
If you have run a blog for five years, you have likely restructured your categories or updated older articles multiple times. Tracking down every dead link that external sites point to is nearly impossible. FixLinks helps you preserve the search engine authority (link equity) those old posts built up over time by finding the closest modern equivalent on your current site.
The SEO Consultant and Agency Manager
Managing SEO for clients means constantly looking at audit reports. Cleaning up 404 errors on a large ecommerce site or content hub is tedious work. Instead of manually mapping spreadsheet rows of old URLs to new ones, consultants can run a scan, review AI-generated suggestions, and approve them in bulk.
The Web Developer Handing Off Sites
When developers hand over a migrated site to a client, links often break post-launch. Rather than writing complex redirection rules in .htaccess or Nginx configs—which clients can easily break—developers can set up this plugin so clients can manage redirects themselves using a simple interface.

The Tech Stack

The architecture is split into two main components:

The WordPress Plugin (PHP & Javascript): A lightweight client that hooks into WordPress's template redirect engine to catch 404 events, logs them, and exposes a clean React-based admin dashboard.
The Redirect Matching Engine (Node.js & Gemini API): A serverless microservice that takes your active 404 path list, compares it against your public sitemap or published page index, and queries Gemini. We use the Gemini API's structured JSON output format to ensure we receive clean arrays of suggestions containing the source path, suggested destination, and a confidence score.

Technical Challenges

The biggest challenge was semantic matching without burning API tokens.

Simply sending every single 404 path to the LLM individually is slow and expensive. To solve this, we implemented a multi-tiered filtering system:

Sitemap Indexing: The plugin indexes all valid published URLs and keeps a lightweight local cache.
String Similarity Pre-filtering: We use a quick Jaro-Winkler distance calculation to handle simple typos or URL changes locally.
LLM Batching: If local heuristics fail to find a high-confidence match, the remaining paths are batched and sent to Gemini. The model is prompted to map ambiguous paths (e.g., /get-in-touch vs. /contact-us-now) by understanding the semantic intent behind the URL slugs.

Lessons Learned

One of the most important takeaways from building this was the necessity of a human-in-the-loop design.

In early prototypes, I experimented with fully automated redirects. If a 404 occurred, the plugin would automatically map and redirect the user on the fly. However, this occasionally created redirect loops or mapped irrelevant pages, leading to a confusing user experience. I quickly realized that AI should act as an assistant, not an autonomous driver. The current iteration presents the mapped suggestions in a dashboard, allowing the site owner to review and apply them with one click.

Try It Out

If you are tired of manually mapping broken links or looking to restore lost search rankings, you can find the plugin and try it out at https://fixlinks.getinfotoyou.com.

I'd love to hear your feedback on the matching accuracy and any features you would like to see added.

Simplifying Client Management: The Tech Stack and Architecture Behind a Clean WordPress Client Portal

Getinfo Toyou — Mon, 15 Jun 2026 14:30:32 +0000

Managing clients is often more challenging than the actual work itself. After trying several heavy project management suites and dedicated SaaS client portals, I noticed a recurring, frustrating pattern: clients rarely logged in. They found the dense dashboards overwhelming and confusing. I wanted to build something that lived directly on my own WordPress site, looked clean, and focused solely on what clients actually need: files, invoices, contracts, and a simple way to get quick answers. This is the story of how and why I built PortalWP, a client portal designed around simplicity.

Why I Built It: The Battle Against Dashboard Bloat

Most client portals attempt to do everything under the sun. They include complex Kanban boards, intricate support ticket systems, and endless nested chat threads. While this sounds good on paper, for a small agency or freelancer, it often creates friction. Clients just want to download a final deliverable, pay an invoice, or sign a contract. When the portal is too complex, they revert to email. I built PortalWP to strip away this unnecessary noise. The goal was to build a clean dashboard that takes less than two minutes for a client to understand and navigate successfully.

The Tech Stack: Bridging WordPress and React

To achieve a modern, responsive feel within the WordPress ecosystem, I chose a hybrid architecture:

WordPress Core & REST API: WordPress handles authentication, user roles, and database storage. Using the built-in custom post types and user meta kept the backend extremely lightweight and secure without needing external databases.
React: The client portal dashboard is built entirely with React, communicating with WordPress via custom REST API endpoints. This provides a single-page app experience without constant page refreshes, which is crucial for a smooth user experience.
Tailwind CSS: For styling, I used Tailwind to keep the design utility-first and ensure the interface looks modern and adapts to any screen size.
OpenAI API: I integrated a lightweight AI assistant to help clients search through shared documents and get quick answers based on the project files.

Technical Challenges: Security and Seamless Integration

Building this wasn't without its hurdles. Two main challenges stood out:

Secure File Handling: By default, files uploaded to WordPress are public if someone guesses the URL. For a client portal, files must remain private. I had to implement a custom routing system that stores files outside the public directory and serves them through secure, authenticated PHP streams.
Lightweight E-Signatures: Integrating e-signatures without relying on expensive third-party APIs was tricky. I built a canvas-based signature pad in React, converting the drawing into a secure image path, and bound it to a hash of the contract PDF to ensure document integrity.

Lessons Learned

The biggest lesson was that constraints breed better user experiences. In early builds, I wanted to add nested tasks and chat threads. However, feedback from early testers showed that adding tasks made the interface feel like 'work' to clients. Removing features and focusing on a clean files-and-invoices view was what actually drove client adoption. Keeping things simple is technically harder because you have to decide what not to build, but it pays off in user satisfaction.

Conclusion

Building PortalWP taught me that clients value clarity over features. If you are running an agency or freelancing, you can try out the portal at portalwp.getinfotoyou.com and see how simplifying your client interactions can save you hours of admin work.

Building a Strategic Endless Runner: How to Keep Mobile Game Architecture Simple

Getinfo Toyou — Fri, 12 Jun 2026 14:30:26 +0000

Mobile gaming often feels cluttered. Between heavy menus, daily log-ins, and complex combat mechanics, sometimes you just want a clean, simple game to play during a short break. I built Echo Runner to fill that gap. The goal was to take the classic, easy-to-learn mechanics of an endless runner and add one straightforward strategic twist: a 3-second ghost trail that follows you and clears obstacles. It’s intuitive from the first swipe, keeping the player experience focused and stress-free. The focus was entirely on getting a player from the app launch into the game in under three seconds.

The Tech Stack

I chose Unity and C# for this project. Unity is well-suited for lightweight mobile games, and its rendering pipeline allowed me to design clean, minimalistic assets that load quickly. To ensure the game runs smoothly, I stuck to a simple, unlit 2D art style that doesn't tax the mobile GPU. For the leaderboard and player stats, I avoided massive backend frameworks that require complex authentication. Instead, I used a lightweight REST API that interacts with a simple SQL database. This keeps the network overhead extremely low and ensures that even on spotty mobile connections, players can upload their scores without lag.

Technical Challenges: Tracking the Trail

The core mechanic is the ghost trail. Initially, I thought about recording the player's exact physics inputs and replaying them, but that quickly became a nightmare of desynchronization and performance drops on older Android devices.

To solve this simply and keep the game running smoothly at 60 FPS, I implemented a simple queue-based system. Every frame, the game records the player's position and rotation into a fixed-size ring buffer (storing exactly 3 seconds worth of coordinate data). The ghost trail object simply reads from the head of this buffer.

Here is a simplified look at the implementation:

// Simplified ring buffer logic for trail tracking
public class TrailTracker : MonoBehaviour 
{
    private Queue<Vector3> positionQueue = new Queue<Vector3>();
    public int targetFrameCount = 180; // 3 seconds at 60 FPS

    void Update()
    {
        positionQueue.Enqueue(transform.position);
        if (positionQueue.Count > targetFrameCount)
        { 
            Vector3 trailPosition = positionQueue.Dequeue();
            UpdateGhostPosition(trailPosition);
        }
    }
}

This approach kept memory allocation almost at zero during gameplay, preventing the garbage collector from causing annoying frame stutters on budget phones. Using a simple data structure rather than complex physics simulation saved weeks of debugging.

Keeping the UX Clean

Another area where I focused on simplicity was the user interface. Many games overwhelm players with pop-ups, store offers, and multi-tier upgrade menus. I wanted to build something cleaner. There are no nested menus or loading screens. You open the app, press play, and you're in. The controls use simple swipe gestures that map directly to player lane changes. The ghost trail handles obstacle collision automatically, meaning players don't have to learn complex button inputs. They just run, swipe, and let their past actions clear their path.

Lessons Learned

Simple architectures survive changes. By avoiding complex physics simulations for the ghost trail, implementing new power-ups and skins later was incredibly easy.
Polish the core loop first. Spend time on the swipe responsiveness rather than adding dozens of features. If the basic movement feels good, the game is fun.

Try It Out

Echo Runner shows that you don't need complex systems to make an engaging mobile game. By keeping the code modular and the mechanics simple, I built a game that is easy to jump into and runs smoothly on almost any device.

You can try Echo Runner for free on Google Play: Echo Runner on Google Play Store. Let me know what you think of the trail mechanic!

Building a Frictionless Markdown and HTML Converter for Android

Getinfo Toyou — Wed, 10 Jun 2026 14:30:21 +0000

The Problem with AI-Generated Output on Mobile

If you use LLMs to draft articles, outline code, or generate copy on your phone, you have probably run into the formatting bottleneck. You prompt an AI, it spits out a block of mixed markdown, raw text, and nested HTML, and then you have to figure out how to clean it up.

On a desktop, this is a minor inconvenience. On a mobile device, copying, pasting, and manually replacing tags or asterisks is frustrating. I built AIMarkdownPro Editor to solve this specific problem, focusing entirely on speed and simplicity.

Why I Built It

I draft a lot of my thoughts, documentation, and blog outlines on my phone during commutes. When using AI assistants to help structure these thoughts, the output is rarely ready for publishing. I found myself jumping between notes apps, web-based converters, and text editors just to get clean Markdown or HTML.

I wanted a dedicated tool that did one thing really well: accept messy text, parse it instantly, and let me convert it back and forth between Markdown and HTML without any fuss.

The Simplicity-First Design

The core design goal was zero friction. Many mobile text editors try to be full IDEs or desktop replacements, cluttering the small screen with nested menus, custom keyboards, and complex file managers.

For this app, the user experience is straightforward:

Paste your text.
Tap to toggle between Markdown and HTML.
Preview in real-time.
Copy the clean code or share it directly to your target application.

By keeping the interface minimal, the app feels fast and lightweight, allowing you to format your text and get back to your workflow in seconds.

Under the Hood: The Tech Stack

I chose native Android development using Kotlin and Jetpack Compose. Jetpack Compose was a natural fit for this project because of its declarative nature.

UI State Management: Compose allowed me to bind the editor text state directly to the parser. When you type or paste, the state updates, triggering the background parsing engine, and updates the preview seamlessly.
Parsing Engine: For converting Markdown to HTML, I used a lightweight Java-based markdown parser. The real challenge was the reverse: parsing HTML back into readable Markdown. I implemented a custom AST (Abstract Syntax Tree) converter that processes HTML nodes and translates them into clean Markdown syntax without adding unnecessary line breaks or artifact characters.
Web Fetcher: The app also includes a simple feature to fetch web pages directly. It extracts the main content block from a URL and converts it straight to Markdown, stripping out ads, navigation headers, and scripts.

Overcoming Technical Challenges

The primary technical challenge was managing performance during real-time rendering. Parsing large blocks of text on every keystroke can cause typing lag on budget Android devices.

To keep the editor responsive, I offloaded the parsing logic to Kotlin Coroutines. The parser runs on a background thread (Dispatchers.Default), while a debouncing mechanism ensures we only trigger the parse job after a short pause in typing. This keeps the main thread free and the editor interface smooth.

Handling edge cases in bidirectional conversion was another hurdle. Nested tables, mixed inline styles, and unclosed HTML tags from incomplete AI outputs frequently broke standard parsers. I had to build robust fallback rules in the parser to gracefully handle and clean up malformed code.

What I Learned

Building this app taught me that stripping away features is often harder than adding them. I initially planned to add cloud syncing, folder management, and custom themes. However, testing showed that these additions only distracted from the core utility: fast, painless text cleaning. Staying focused on the primary problem allowed me to ship a tool that actually gets used daily.

Try It Out

If you frequently work with formatted text on Android, you can try the app yourself on Google Play.

For more lightweight utilities and projects, feel free to check out my portfolio at getinfotoyou.com.

Why I Built a One-Time Purchase JavaScript Course App in a Subscription World

Getinfo Toyou — Fri, 05 Jun 2026 14:30:54 +0000

Let's face it: the current landscape of mobile coding education can be deeply frustrating. If you want to learn JavaScript on your phone during your daily commute, you are usually forced to choose between two highly compromised options. You either download a "free" app that interrupts your learning flow with unskippable 30-second video ads every three minutes, or you get pushed into a $25-a-month subscription model that slowly drains your wallet.

I wanted a third option. I believe that mastering the fundamentals of programming—things like modern ES6+ syntax, DOM manipulation, and asynchronous logic—requires deep, unbroken focus. Distractions ruin that focus. I wanted to create a premium, distraction-free environment where you pay once and own the material forever. That is exactly why I built the Learn JavaScript Pro Course app for Android.

Going Against the Grain: The One-Time Purchase

When designing this app, I specifically wanted to focus on what makes it different from the heavy-hitting paid alternatives on the market. Most premium coding platforms today are either expensive web-based bootcamps or subscription-based services that lock you into monthly recurring revenue models.

I decided to take a different approach. Learn JavaScript Pro is a single, lifetime purchase. There are no recurring fees, no hidden "gems" or in-app currencies required to unlock the next chapter, and absolutely zero ads. Furthermore, the entire curriculum is available completely offline. Whether you are on a subway, an airplane, or just trying to disconnect from the internet to study, the content is always accessible.

The Tech Stack

Since I was building an app to teach JavaScript, it only felt right to build it using JavaScript. I chose React Native (via Expo) as my core framework.

For local data persistence—tracking exactly which modules you have completed and saving your quiz scores—I opted for WatermelonDB. It operates over SQLite, is incredibly fast, and is perfectly suited for a fully offline-first experience. The course content itself is authored entirely in Markdown. I integrated a custom parser to render syntax-highlighted code blocks natively within the mobile UI, ensuring the code looks exactly like it would in a modern IDE.

Technical Challenges

Building an offline-first educational app came with a unique set of hurdles:

1. Native Code Rendering: Rendering raw text on a phone is simple, but rendering properly formatted, scrollable code blocks with accurate syntax highlighting on a narrow 6-inch screen was a massive headache. I had to build a custom rendering pipeline that parsed my Markdown files and mapped code fences to a React Native ScrollView containing stylized text nodes. Ensuring that horizontal scrolling felt natural without breaking the vertical scroll of the main article took days of tweaking.

2. State Management for Offline-First: Ensuring that a user's progress is saved instantly without relying on a cloud database meant rethinking how I handled state. Managing local database migrations was particularly stressful. If I push an app update to the Play Store with new curriculum modules, I have to ensure the local SQLite database updates its schema without a single user losing their hard-earned progress.

Lessons Learned

The biggest lesson I took away from this project wasn't actually technical; it was pedagogical. You cannot simply copy and paste a web-based coding tutorial onto a mobile screen. Long paragraphs and 50-line code snippets are terrible for mobile learning.

I had to heavily refactor my entire curriculum to utilize "micro-learning" principles. I broke complex topics into short, punchy explanations followed by small, highly focused code examples. It forced me to become a more concise writer and a better teacher.

Conclusion

Building a one-time purchase app in an industry obsessed with monthly subscriptions feels a bit risky as a solo developer. But my goal at getinfotoyou.com is to build tools that respect the user's time and focus.

If you are an aspiring web developer or a self-taught coder looking for a clean, focused way to learn modern JS without the noise and the endless fees, I invite you to check it out.

You can find the Learn JavaScript Pro Course on Google Play. I would love to hear your feedback on the offline experience!

Building an Anonymous Safe Space for Developers (And Why I'm Keeping It Free)

Getinfo Toyou — Mon, 01 Jun 2026 14:30:49 +0000

If you've spent any significant amount of time in software engineering, you know that the reality of the job rarely matches the polished open-source projects or flawless tutorials we see online. Behind every clean architecture diagram, there’s usually a duct-taped legacy system, a developer battling imposter syndrome, or a hidden bug that someone accidentally shipped to production on a Friday.

We all have these stories, but there aren't many places to share them without worrying about professional repercussions or tying them back to our GitHub profiles. That’s exactly why I built DevConfessions: Code Secrets — an Android app designed as a completely anonymous platform for developers to share their workplace stories, coding secrets, and career struggles.

And perhaps most importantly, I decided to make it entirely free to use. Here's a look at why I built it, how it came together, and what I learned along the way.

Why I Built It

The idea came after a particularly stressful week of debugging a monolithic codebase. I realized how isolating it can feel when you think you're the only one struggling with spaghetti code or feeling overwhelmed by unrealistic sprint goals.

I wanted to create a judgment-free zone. A place where someone could confess, "I still don't really understand Webpack, I just copy-paste configs," or "I accidentally dropped the staging database and blamed it on a network blip," and receive empathy instead of criticism. I wanted to build a community grounded in the messy reality of tech careers.

The Tech Stack

To keep the development process agile and maintainable as a solo developer, I opted for a modern Android stack:

Language: Kotlin
UI Framework: Jetpack Compose (because building declarative UIs is just more enjoyable)
Architecture: MVVM (Model-View-ViewModel) for clean separation of concerns
Backend/Database: Firebase (Authentication for anonymous sessions, Firestore for real-time syncing of confessions)
Dependency Injection: Hilt

Technical Challenges

Building a platform centered around anonymity presents a few unique challenges.

First, handling authentication without asking for user data. I utilized Firebase Anonymous Authentication, which ties an account to the physical device rather than an email or phone number. This ensures users can keep their session and history without ever handing over personal information.

Second, content moderation is tricky when everything is anonymous. I had to implement a robust reporting system and basic automated filtering to ensure the platform remains a safe space for venting and humor, rather than devolving into toxicity. Balancing free speech with community safety is an ongoing learning process.

Lessons Learned

One of the biggest takeaways from this project was the power of simplicity. When you strip away profiles, follower counts, and avatars, the focus shifts entirely to the content and the shared experience.

From a technical standpoint, adopting Jetpack Compose significantly sped up my iteration time. It allowed me to focus more on the user experience—making sure the app felt smooth and responsive—rather than wrestling with XML layouts.

Why It's Free

When I launched DevConfessions, I had a choice to make about monetization. I could have added premium tiers, restricted the number of confessions you could read per day, or put the best stories behind a paywall.

But that defeated the entire purpose of the app.

The goal was to build a supportive community, and putting up financial barriers didn't align with that. Software engineering is already a field with enough gatekeeping. Whether you're a senior architect or a bootcamp graduate looking for your first role, everyone deserves access to a space where they can see that they aren't alone in their struggles. So, the app is completely free to download and free to use. No hidden subscriptions, no gated features. Just a straightforward place to read and share.

Wrapping Up

Building DevConfessions has been a rewarding side project, not just technically, but in seeing the kind of honest, relatable stories that engineers share when the pressure of public identity is removed.

If you want to read some relatable workplace stories, drop a confession of your own, or just realize that nobody else knows what they're doing either, you can download DevConfessions on Google Play.

Have you ever worked on a project focused on anonymity? I'd love to hear about the technical choices you made in the comments.

Building EasierRenter: Replacing Landlord Spreadsheets with a Focused Android App

Getinfo Toyou — Fri, 29 May 2026 14:30:57 +0000

If you've ever talked to a solo landlord or an independent property manager, you quickly realize they all share a common enemy: the spreadsheet. It starts simple enough—a few columns for rent due dates, tenant names, and contact info. But as a portfolio grows, that simple spreadsheet becomes a brittle, confusing mess of color-coded cells, broken formulas, and overlapping lease dates.

I wanted to tackle this specific problem. Managing rental units shouldn't require an accounting degree or a complex desktop suite. That is why I built EasierRenter, an Android app designed to simplify property management, track leases, and handle rent payments from a single, portable dashboard. Here is a look under the hood at why I built it, the stack I chose, and the technical challenges I ran into along the way.

The Problem It Solves

Before diving into the code, it helps to understand the user's underlying pain point. Landlords are constantly juggling moving targets: when does a specific lease end? When is rent due for unit B? Who is late this month, and by how much? Relying on memory or manual calendar entries almost always leads to missed income and frustrated tenants.

EasierRenter tackles this by automating the mental overhead. It provides automated rent reminders, tracks payment history chronologically, and generates financial reports. By focusing strictly on the core problem—tracking who owes what and when—the app keeps property managers organized without overwhelming them with unnecessary enterprise features.

The Tech Stack

To build this, I went with a modern, native Android development stack designed for maintainability and performance:

Language: Kotlin. It is concise, expressive, and provides excellent null safety, which is crucial when handling user-inputted financial data.
UI Framework: Jetpack Compose. Building the UI declaratively saved me countless hours, especially when creating the dynamic dashboard and generating custom reporting charts.
Architecture: MVVM (Model-View-ViewModel) paired with Clean Architecture principles to keep the business logic tightly separated from the UI layer.
Local Data: Room Database. Fast, offline-first capabilities are absolutely essential because users might be out inspecting properties or in basements with poor cell reception.
Asynchrony: Kotlin Coroutines and Flows. These were vital for passing real-time database updates up to the UI smoothly.
Background Tasks: WorkManager. Essential for handling the automated rent reminders reliably behind the scenes.

Technical Challenges

Building an app that handles relational data and scheduling comes with a specific set of hurdles.

1. Reliable Scheduling for Reminders
One of the core features is automated rent reminders. Relying on basic system alarms isn't sufficient on modern Android devices due to aggressive battery optimization (Doze mode). I had to implement WorkManager to ensure these scheduled tasks fired reliably without draining the user's battery. Handling timezones and daylight saving time shifts for lease start and end dates also required careful implementation of the java.time API to prevent off-by-one-day errors.

2. Complex State Management in Compose
A property manager's dashboard needs to aggregate data from multiple related database tables: Properties, Units, Tenants, Leases, and Payments. Flowing this data up into a single cohesive UI state in Jetpack Compose was tricky. I learned quickly that pushing too much transformation logic into the ViewModel made the app sluggish. I had to refine my repository layer to emit heavily optimized SQL queries via Room and Flows, ensuring the UI only recomposed when the underlying data actually changed.

3. Designing for Density
Fitting a comprehensive financial summary onto a mobile screen without it looking cluttered was a significant design challenge. I iterated on the UI multiple times, moving from dense, text-heavy lists to visual cards and simple progress bars that indicate payment collection status at a glance.

Lessons Learned

If there is one major takeaway from building EasierRenter, it is that offline-first architecture is hard but entirely worth the effort. Users expect an app to load instantly and save their inputs reliably, regardless of their network connection. Designing the SQLite database schema to handle complex relationships (a property has many units, a unit has a current lease and past leases, a lease has many payments) taught me a lot about optimizing queries and managing foreign key constraints on mobile.

I also learned that when building tools for business operations, reliability beats flashy animations every single time. Users do not care about a beautiful transition if the monthly financial report is calculated incorrectly.

Conclusion

Building EasierRenter has been a highly rewarding process. It took a stressful, scattered workflow and turned it into a focused, reliable mobile experience. If you are a developer looking to build utility apps, I highly recommend finding a manual, spreadsheet-heavy process and figuring out how to mobile-optimize it.

If you manage a few properties or just want to see how the app handles these complex data flows and UI challenges in practice, you can check it out on the Play Store.

You can download EasierRenter here: https://play.google.com/store/apps/details?id=com.getinfotoyou.easierrenter

I'd love to hear your thoughts on building offline-first apps or handling complex background scheduling in Android. Drop a comment below!

Handling Android Image Compression: From OutOfMemory Errors to a Streamlined Utility

Getinfo Toyou — Wed, 27 May 2026 14:30:37 +0000

Every developer or power user has faced the "file too large" wall. Whether you are trying to upload a high-resolution photo to a government portal, attach a batch of images to an email, or simply save some space on a phone that is nearing its storage limit, the struggle is real.

Modern smartphones take incredible photos, but those 12MB JPEGs are often overkill for a quick social media update or a documentation upload. I built ImageSlim Compress PhotoResize to bridge the gap between high-quality capture and practical file sizes.

The Hard Way vs. The Efficient Way

Before I built this tool, resizing an image on Android usually involved one of three frustrating paths:

The Desktop Round-Trip: Moving the photo to a PC via cloud storage or a cable, using a tool like Photoshop or GIMP to resize it, and then moving it back to the phone. It is precise, but it takes five minutes for a task that should take five seconds.
The Sketchy Web Converter: Using an ad-heavy website that asks you to upload your private photos to their server. You have no idea where those images go, and the mobile browser experience is often clunky.
The Heavyweight Editor: Opening a full-scale mobile photo editor. These apps are great for filters and retouching, but they are often bloated and require several taps just to find the "export resolution" setting.

I wanted a fourth option: a dedicated utility where you pick a photo, slide a quality bar, and get your result instantly. That is the philosophy behind ImageSlim.

The Technical Stack

I chose to build this as a native Android application using Kotlin. While cross-platform frameworks are popular, image manipulation requires direct access to system resources and memory management, which is often more predictable in a native environment.

UI Layer: Jetpack Compose. This allowed for a reactive UI that updates as compression tasks move through the queue.
Image Processing: I utilized the Android Bitmap API and ImageDecoder for modern devices.
Concurrency: Kotlin Coroutines were essential. Compressing multiple high-res images is a CPU-intensive task; doing it on the main thread would freeze the UI. Coroutines allow the app to process files in the background while keeping the interface responsive.

The Challenge of Memory Management

One of the biggest technical hurdles was avoiding OutOfMemoryError (OOM). If you try to load a 108-megapixel image into memory at full resolution, even a flagship phone might struggle.

To solve this, I implemented a two-step process. First, I read the image dimensions without loading the actual pixels (using inJustDecodeBounds). Then, I calculate a sample size to downsample the image during the initial load. This ensures the app only uses the memory it needs to perform the compression, making it stable even on older devices with limited RAM.

Another challenge was handling modern formats like HEIC and WebP. Converting these to standard JPEGs while preserving as much detail as possible required careful handling of the underlying bitstreams and color profiles.

Lessons Learned

Building a utility tool taught me that UX is just as important as the algorithm. Users don't care how efficient your bitstream processing is if they can't find the "Save" button. I spent a significant amount of time refining the batch processing workflow—allowing users to select fifty photos and shrink them all to a specific KB target in one go.

I also learned that transparency matters. People are protective of their photos. By keeping all processing local on the device and not requiring unnecessary permissions, I could provide a tool that is both helpful and respectful of privacy.

Conclusion

If you find yourself constantly fighting with file size limits or a "Storage Full" notification, you might find this useful. It is a simple tool designed to solve one problem effectively. You can check it out on the Google Play Store here: ImageSlim Compress PhotoResize.

Stop doing things the hard way and let your phone handle the heavy lifting.

Building an Offline QR Code Scanner for Android: The Hard Way vs. The Right Way

Getinfo Toyou — Mon, 25 May 2026 14:30:45 +0000

Why I Almost Gave Up Halfway Through

I've been building small utility apps for Android for a while now, and when I decided to build a QR code scanner, I thought it would be straightforward. It wasn't. But the struggle taught me more about mobile barcode processing than I expected — and the end result is Sharp QR, a reliable offline QR scanner and generator that actually works when you need it.

Let me walk you through what I learned, what I got wrong first, and why some of those wrong turns were actually instructive.

The Hard Way: Rolling Your Own Barcode Detection

My first instinct was to write a custom image processing pipeline. I wanted to understand the problem from the ground up. Here's what that looked like:

Manual camera frame capture using Camera2 API
Converting YUV buffers to grayscale bitmaps by hand
Applying threshold filters to improve contrast on faded codes
Writing finder pattern detection logic from scratch

The result? It kind of worked on high-contrast codes printed on white paper. Try it on a faded label on a cardboard box at a store, and it would just stare blankly back at you.

The performance was also rough. Processing frames on the main thread caused visible lag. Moving it to a background thread introduced synchronization bugs. I was spending weeks on infrastructure that had nothing to do with the user experience I actually wanted to deliver.

The Right Way: Standing on Solid Shoulders

I eventually stopped fighting the problem and reached for ML Kit's barcode scanning API. Combined with ZXing for QR code generation, this became the real foundation of Sharp QR.

Tech stack breakdown:

Language: Kotlin
Camera: CameraX (lifecycle-aware, much cleaner than Camera2 for this use case)
Scanning: ML Kit Barcode Scanning (on-device, works fully offline)
Generation: ZXing core library
UI: Material Design 3 components
Architecture: MVVM with ViewBinding

Switching to ML Kit didn't feel like giving up — it felt like making a smart engineering decision. The model runs entirely on-device. No network calls, no latency spikes, no privacy concerns about sending images to a server. That last point matters more than people realize when you're scanning things like Wi-Fi passwords or contact cards.

The Actual Technical Challenges

Even with the right libraries, there were real problems to solve:

1. Faded code recovery
ML Kit handles this better than my homebrew attempt, but I still had to tune the camera preview resolution and frame analysis rate. Too high a resolution and the analysis queue backs up. Too low and you miss detail on worn codes. CameraX's ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST was the key — it drops frames rather than queuing them.

2. Offline-first architecture
I wanted zero network dependency at runtime. ML Kit's bundled model option solved scanning. For QR generation (Wi-Fi credentials, vCards, URLs), everything is computed locally using ZXing's QRCodeWriter. The app literally works in airplane mode.

3. Format breadth
Users don't only scan QR codes. They scan Code 128 barcodes on grocery items, EAN-13 on retail products, Data Matrix codes on electronics. Supporting all of these without the UI becoming a format-picker maze required some UX thinking alongside the technical work.

Lessons Learned

Premature optimization of the wrong layer is a real trap. I spent days on image processing that a well-maintained library handled in hours of integration.
On-device ML is genuinely viable now. A few years ago, offline-capable ML on mobile felt like a tradeoff. Today, ML Kit's bundled models are fast and accurate enough that cloud processing feels unnecessary for this use case.
CameraX made lifecycle management actually manageable. If you're building anything camera-related on Android and you're still on Camera2 directly, reconsider.
Utility apps live or die on reliability. Users will forgive a plain UI. They won't forgive a scanner that fails on three codes in a row.

Try It

If you're on Android and want a no-fuss barcode tool that works without internet, give Sharp QR a try. It handles scanning and generation across all common formats, works offline, and doesn't ask for unnecessary permissions.

Building it was humbling in the right ways. The hard path clarified what the right path actually was — and the right path still had plenty of real engineering in it.

Happy to answer questions about any part of the stack in the comments.

Why I Built a Practical Photo Compressor for Android (And How I Did It)

Getinfo Toyou — Fri, 22 May 2026 14:40:04 +0000

As developers and heavy smartphone users, we often run into a common, frustrating issue: running out of storage space. With smartphone cameras constantly improving, photo file sizes have bloated to ridiculous proportions. Sometimes you just need to upload a quick picture for a web form, share it via email, or optimize assets for a quick project, and you hit a hard file size limit.

That's why I built ImageSlim Pro Photo Compressor. I wanted a reliable, straightforward tool that shrinks and resizes photos to save space without a noticeable drop in visual quality. And most importantly, I wanted to provide genuine value without nickel-and-diming users, which is why focusing on accessibility and offering a tool that actually helps you save space (and money on cloud storage upgrades) became my priority.

Why I Built It

The problem started when I realized I was paying monthly for extra cloud storage solely because my photo library was filled with massive 10MB to 15MB images. Many of these were just snapshots, receipts, or quick reference photos that didn’t need to be print-quality.

I looked around the Google Play Store for a solution. While there are many image resizers, many are riddled with intrusive ads, require expensive subscriptions, or just have poor user interfaces. As a solo developer at getinfotoyou.com, I saw an opportunity to build a clean, functional tool that solves this specific problem efficiently. ImageSlim Pro was born out of the necessity to manage local storage effectively and provide a high-value, cost-effective solution for smartphone users, social media managers, and fellow web developers who need optimized images on the go.

The Tech Stack

For an Android application that requires heavy image processing, performance and memory management are critical. I chose the following stack:

Language: Kotlin. It's concise, modern, and the standard for Android development, making asynchronous tasks much cleaner.
UI Framework: Android Views with Material Design components. While Jetpack Compose is gaining traction, I stuck with standard XML layouts for this iteration to ensure broad compatibility and fast initial rendering.
Image Processing Library: To handle the actual compression and resizing, I utilized a combination of Android's native BitmapFactory and highly optimized C++ libraries via JNI for the heavy lifting, ensuring the compression algorithms ran as quickly as possible without causing the main thread to stutter.
Concurrency: Kotlin Coroutines. Essential for moving the intensive image compression work off the main UI thread, keeping the app responsive.

Technical Challenges

Building an image compressor sounds straightforward until you have to deal with the Android memory model and varying device capabilities.

Out of Memory (OOM) Errors: Loading a 12-megapixel image directly into memory will crash most budget Android devices instantly. The biggest challenge was implementing efficient downsampling using inSampleSize before loading the full bitmap into memory, and then applying the more precise resizing and quality compression.
Preserving EXIF Data: Users want their photos compressed, but they usually don't want to lose the date, time, or location data attached to them. Reading, preserving, and rewriting EXIF metadata across different Android versions (especially with the introduction of Scoped Storage) required careful handling of ExifInterface and Uri permissions.
Balancing Quality vs. Size: Finding the sweet spot where file size is drastically reduced but the image still looks good to the human eye involved a lot of trial and error. I ended up implementing adjustable sliders so the user has the final say on the quality-to-size ratio.

Lessons Learned

The most valuable lesson I learned during this project was the importance of user experience in utility apps. People using a photo compressor are usually trying to accomplish a task quickly—like attaching an image to an email. Any friction in the app's flow is a reason for them to uninstall. Keeping the interface minimal, focusing on bulk processing capabilities, and providing immediate visual feedback on the storage saved were crucial steps in refining the app.

Also, dealing with Android's ever-changing file system permissions (like the transition to MediaStore and Scoped Storage) reinforced the need to constantly read the latest documentation and test on physical devices across various API levels.

Conclusion

Building ImageSlim Pro was a practical exercise in solving a personal pain point that turned out to be a widespread issue. By focusing on creating a genuinely useful, straightforward app, I was able to deliver something that saves users both storage space and money.

If you are a web developer who needs to quickly optimize assets from your phone, or just someone tired of the 'Storage Almost Full' warning, give it a try.

You can check it out here: ImageSlim Pro Photo Compressor

Let me know what you think, and if you have any feature requests, I'm always looking to improve it!

Building an Offline-First AI Prompt Manager: Tech Stack and Lessons Learned

Getinfo Toyou — Mon, 18 May 2026 14:34:35 +0000

It's estimated that regular users of large language models spend up to 30% of their interaction time simply recreating or tweaking prompts they've already used in the past. If you use AI frequently for coding, writing, or image generation, you likely know the feeling: you spend twenty minutes finding the exact phrasing that gets the response you need from Claude or Midjourney, only to lose it in a sea of chat history a few days later.

I found myself dealing with this constantly. My solution was a chaotic notes file filled with scattered instructions and parameters. It was disorganized, slow to navigate on mobile, and relied on an internet connection. I wanted a dedicated tool to store my prompts locally, find them instantly, and copy them without friction.

This led me to develop AI Prompt Vault -AI Helper Pro, an ad-free Android app designed to keep your most useful AI interactions structured and accessible entirely offline.

Why I Built It

I built this app to solve a personal workflow issue. General note-taking apps are versatile, but they lack the specific structure needed for prompt management. They don't have categorized tags for different AI models, quick copy buttons, or an interface built specifically for rapid retrieval.

Furthermore, I often work from coffee shops or on trains where the connection is spotty. I wanted a tool that respected my privacy, worked flawlessly without a network connection, and didn't interrupt the user experience with pop-up ads or subscription banners.

The Tech Stack

To ensure the application was fast and felt native to the device, I opted for a standard modern Android development stack:

Language: Kotlin
UI Framework: Jetpack Compose
Local Storage: Room Database (SQLite)
Architecture: MVVM (Model-View-ViewModel)

Technical Challenges

Creating a seemingly simple offline app brought its own set of technical hurdles, particularly when trying to match the performance expectations of modern mobile users.

1. Implementing Instant Offline Search
When managing hundreds of text-heavy prompts, standard SQL LIKE queries can become sluggish and resource-intensive. I needed the search functionality to feel instantaneous across titles, descriptions, and assigned categories as the user typed. To achieve this, I utilized Room's Full-Text Search (FTS4) capabilities. By creating a virtual table mapped to the core prompt data, the app can perform highly optimized text matching locally. This allows for complex searches without any noticeable lag or heavy battery drain.

2. Managing State in Jetpack Compose
Handling the UI state for a complex, filterable list requires careful architecture in Compose. Balancing active search queries, selected categories, and the list of prompts meant I had to optimize the ViewModel's state emission. Ensuring the UI only recomposed when necessary was crucial for maintaining smooth scrolling performance, especially on older devices.

3. Secure Local Backups
Because the app operates entirely offline without a backend server, I had to provide a reliable way for users to secure and migrate their data. Implementing a robust JSON export and import system using Android's Storage Access Framework (SAF) was surprisingly challenging. It required handling various edge cases across different device manufacturers' file pickers to ensure users could reliably backup and restore their prompt libraries.

Lessons Learned

The Value of Offline-First
In an ecosystem dominated by constant cloud-syncing, mandatory user accounts, and recurring subscriptions, I discovered there is still significant demand for utility software that operates independently. Users genuinely appreciate tools that load instantly, function on airplanes, and keep their personal data strictly on their device without sending telemetry back to a server.

Focusing on the Core Loop
The primary function of this app is simple: open, find the prompt, copy, and exit. I learned that adding extraneous features would only add friction to this loop. By keeping the design focused strictly on organization and retrieval, the app serves its specific purpose much better than a bloated alternative.

Conclusion

Developing AI Prompt Vault was a practical exercise in building a focused, privacy-respecting Android application. If you find yourself repeatedly typing similar instructions or losing your most effective prompts, having a dedicated local vault can save a considerable amount of time.

You can try the app here: AI Prompt Vault -AI Helper Pro.

If you have any thoughts on local data management in Android or prompt engineering workflows, I'd be interested to hear them in the comments.

Building a Batch Image Converter for Android: Overcoming Mobile Memory Limits

Getinfo Toyou — Fri, 15 May 2026 14:31:31 +0000

As developers, web designers, and digital creators, we frequently encounter the surprisingly tedious problem of image formatting. You might have a batch of heavy PNGs, but a client's website strict upload rules require WebPs under 500KB. Or maybe you just need to share a dozen high-res photos with your team without hitting email attachment limits. I found myself repeatedly looking for a fast, straightforward way to handle this directly on my phone, without needing to boot up a laptop or upload my private files to a random server. That friction is the exact problem I set out to solve.

Why I Built It

The core issue is workflow disruption. When you are managing social media, writing blog posts from a mobile device, or doing quick site updates, having to hop between different clunky apps just to resize or convert a photo is incredibly annoying. Many existing tools in the store were either loaded with intrusive ads, required subscriptions for basic batch processing, or had confusing interfaces. I wanted a local, on-device solution that respects user privacy, handles multiple files effortlessly, and simply gets out of the way. The goal was pure utility: open the app, select your photos, pick a format, and get it done.

The Tech Stack

For the foundation, I chose Kotlin and the native Android SDK. I wanted the app to be as lightweight as possible, so I avoided pulling in massive third-party C++ image processing frameworks. The UI was built using modern Android principles to ensure a clean, responsive layout that scales well across different screen sizes. Everything runs locally on the device, ensuring user data never leaves the phone.

Technical Challenges

Handling high-resolution images on a mobile device is notoriously memory-intensive. The single biggest hurdle was the dreaded OutOfMemoryError (OOM). Modern smartphone cameras capture massive files, and when a user selects fifty 12-megapixel photos for batch conversion, loading them directly into memory will crash the app instantly.

To solve this, I had to implement careful image subsampling and stream processing. Instead of loading the full image into a Bitmap, the app reads the image bounds first, calculates the optimal sample size based on the user's target dimensions, and only loads what is strictly necessary into memory.

Furthermore, the batch processing engine was built using Kotlin Coroutines. I needed a reliable way to handle the heavy lifting asynchronously without blocking the main UI thread. Creating a pipeline that reads, converts, compresses, and writes files concurrently—while keeping the phone responsive and managing thermal limits—was a delicate balancing act.

Lessons Learned

Memory management is unforgiving: You cannot rely on the JVM's garbage collection alone when dealing with Android Bitmaps. Explicitly managing memory, recycling objects when they are no longer needed, and utilizing InputStream and OutputStream directly instead of holding entire files in memory saved the project from stability issues.
UI feedback is crucial for long tasks: When processing a large batch of files, the user needs constant reassurance that the app hasn't frozen. Implementing a robust, granular progress tracker that smoothly communicates between background coroutines and the UI layer turned out to be more complex than expected, but absolutely necessary for a good user experience.
Not all formats are created equal: Tuning the compression algorithms took extensive testing. WebP, for instance, is highly efficient but handles transparency and compression artifacts slightly differently than a standard JPEG or PNG. Finding the sweet spot between file size reduction and acceptable visual quality required fine-tuning the native compression APIs.

Conclusion

Building an image utility sounds like a straightforward weekend project until you have to deal with the harsh realities of mobile memory constraints and concurrency. It served as a valuable learning experience in optimizing Android performance and building robust background processing pipelines.

If you ever find yourself frustrated by strict file size limits on web forms, or just need to quickly convert and compress images on the go, you can try out PhotoConvert: JPG PNG WebP GIF on Google Play. It is built to be a simple, reliable tool to solve exactly those frustrating formatting hurdles.