标签：#Build

A lesson in dependency wars, version pinning, and the reality of building in public. Every founder dreams of a perfect launch. You hit "Deploy," the logo appears, and the users start flowing in. For Lawyie, my intelligent legal infrastructure for Africa, the launch started exactly that way. But then, the screen went blank. "Error running app." No red lines in the code. No obvious bugs in my logic. Just a silent failure at the very moment the world was starting to look. As the lead architect at Sunverse AI, I had to move from "Creator" to "Digital Detective." I pulled the logs from the Streamlit Cloud and found a cryptic traceback: TypeError: GZipResponder.__init__() missing 1 required keyword-only argument: 'thread_minimum_size' This wasn't an AI hallucination. This wasn't a database leak. This was an Infrastructure War. It turns out I had fallen victim to an industry-wide conflict. A core library called Starlette had recently updated to version 0.37.0+, changing its grammar for handling GZip compression. Meanwhile, the server environment hadn't caught up. In my requirements.txt , I hadn't specified a version. I just said "install it." Because I didn't "lock the door," the latest (and broken) version walked right in and crashed my entire engine. In a "Unicorn" startup, you don't just wait for things to get better. You force stability. I applied Version Pinning to my requirements. By hard-coding the stable version of the library, I overrode the server's defaults and restored the infrastructure: # The Pinned Shield streamlit>=1.35.0 starlette==0.36.3 # The specific fix for the GZip error supabase groq fpdf2 Building Lawyie from Abuja, Nigeria, taught me three things today: The Latest isn't always the Best: In production, stability beats "newness." Always pin your critical dependencies. Logs are your best friend: When the screen goes blank, don't panic. Read the trace. The answer is always in the bytes. Transparency builds Trust: When my community on Dev.to pointed out

I'm a self-taught developer. No CS degree, no funding, no team. Just me, a laptop, and a problem I kept watching people struggle with. The Problem Every freelancer and small agency I know deals with the same mess: client details scattered across WhatsApp chats, email threads, Google Drive folders, and random Notion pages. Nothing lives in one place. When a client asks "wait, didn't we already send you the logo files?" you're digging through three different apps trying to remember. I didn't just hear about this problem — I lived it. So four months ago, I started building Kray. What Kray Actually Does Kray gives freelancers and agencies one organized workspace per client — projects, links, and notes, all in a single place instead of scattered across five different tools. The part I'm most proud of: when you share a project with a client, they can open the link and see everything instantly — no sign-up, no account creation, no friction. Just a clean, simple view of what they need to see. The Stack Since I was building this entirely solo with zero budget, I leaned on tools that let me move fast without infrastructure headaches: React 19 + Vite + TypeScript (strict mode — no shortcuts) Tailwind v4 for styling Supabase for auth, database, and storage Deployed on Vercel No backend servers to manage. No DevOps to worry about. Just me shipping features. What I Learned Building Solo You will hit bugs that eat entire days. I spent hours debugging a sitemap indexing issue that turned out to be one missing header. That's the job — most of building isn't writing new features, it's fixing the thing that should've worked but didn't. Deploy discipline matters more than you think. I once tested a feature locally, assumed it was live, and spent 20 minutes confused about why production wasn't behaving — because I'd forgotten to push. Lesson learned: always verify what's actually deployed before debugging further. Marketing is its own skill, and it's humbling. I've spent the last severa

Recently, I’ve been spending more time building small projects on my own. One thing I’ve learned is that it’s usually better to keep things simple and ship early instead of trying to make everything perfect. A small project can still teach you a lot about coding, deployment, design, and how people actually use what you build. I’m planning to share some of my development notes and experiments here from time to time. Looking forward to learning from everyone on DEV.

Most AI image generators look like a prompt box with a Generate button. That is also how my first version started. But once real users entered the workflow, the difficult problems appeared somewhere else: browser refreshes, external task IDs, reference images, partial failures, credit refunds, private assets, and public artwork moderation. While building Magggic , I learned that an AI image generator is less like a form submission and more like a small distributed job system. This article covers the decisions that made that workflow more reliable. The code samples below are intentionally simplified. The important part is the shape of the workflow, not a specific database or image provider. The prompt box is only the beginning A synchronous prototype is easy to imagine: const images = await provider . generate ( prompt ); return images ; That version works until the request takes a minute, the provider times out, one of four requested images fails, or the user refreshes the page. The production workflow I needed looked more like this: Prompt + references ↓ Create a local queued task ↓ Charge credits with an idempotency key ↓ Submit work to the image provider ↓ Persist every completed output immediately ↓ Finalize the task and refund failed outputs ↓ Keep the result private until the user publishes it The provider request is only one step. The local task is the source of truth for what the user sees. 1. Persist the task before calling the provider The first important decision was to create a generation record before making the external API request. A generation stores the information needed to reconstruct the job: type Generation = { id : string ; userId : string ; idempotencyKey : string ; prompt : string ; referenceImages : string []; model : string ; ratio : string ; resolution : string ; count : number ; cost : number ; status : " queued " | " generating " | " completed " | " failed " ; outputs : string []; providerRequestIds : string []; failureReason : string |

Hey everyone! 👋 I'm a developer who got tired of visiting 10 different websites to do simple tasks like formatting JSON, compressing images, or generating QR codes. So I built DevToolBox — a single place with 15+ free online tools, all running in your browser with no sign-up required. 👉 https://toolbox-site.asia Why I Built This Every time I needed a quick tool, I'd end up on a site full of ads, popups, or "create an account to continue" walls. I wanted something clean, fast, and respectful of users' time and privacy. The idea was simple: one website, all the tools you need, zero friction. What's Inside Here are some of the tools available: Developer Tools: JSON Formatter & Validator — Format, validate, minify JSON with syntax highlighting Base64 Encoder/Decoder — Encode and decode Base64 strings instantly UUID Generator — Generate v4 UUIDs in bulk 🔧 Unix Timestamp Converter — Convert between timestamps and human-readable dates 🔧 Regex Tester — Test regular expressions with real-time matching 🔧 Markdown Preview — Write Markdown and see the output live Hash Generator — MD5, SHA-1, SHA-256, SHA-512 🔧 Diff Checker — Compare two texts side by side Daily Tools: 🖼️ Image Compressor — Compress images right in your browser Image Format Converter — Convert between PNG, JPG, WebP Password Generator — Create strong, customizable passwords 📱 QR Code Generator — Generate QR codes with custom colors BMI Calculator — Calculate Body Mass Index 🎂 Age Calculator — Calculate exact age from birth date 📝 Word Counter — Count words, characters, sentences 📏 Unit Converter — Length, weight, temperature, and more How It's Built The whole site is a Vue 3 + TypeScript + Vite project with Tailwind CSS for styling. Everything runs client-side — no data is ever sent to a server, which means your data stays on your device. Key tech: Vue 3 with Composition API TypeScript for type safety Vite for blazing fast dev experience Tailwind CSS for styling Vue Router with history mode for clean URLs vue-i1

Bridging Design and Code to Empower Local Businesses As a full-stack developer specializing in JavaScript and React, one of the most exciting ventures I'm currently on is building ready-made websites and Next.js templates through Softchic. This isn't just about coding; it's about deeply understanding the needs of businesses, particularly within the vibrant and rapidly evolving Nigerian market, and translating those into high-performance, beautiful web solutions. Why Next.js? Performance, SEO, and Developer Experience My choice of Next.js as the primary framework for these templates was deliberate: Performance: Server-side rendering (SSR) and static site generation (SSG) capabilities are crucial. In areas where internet speeds might vary, a fast-loading website isn't just a nice-to-have; it's essential for user retention and conversion. SEO: For businesses looking to establish a strong online presence, robust SEO capabilities out-of-the-box mean our templates provide a solid foundation for discoverability. Developer Experience: Building with Next.js allows for efficient development, leveraging the power of React while simplifying routing, data fetching, and API routes. This means faster iteration and higher quality templates. The Nigerian Market: Unique Challenges, Immense Opportunity Crafting templates specifically for the Nigerian market presents a fascinating set of considerations: Design Aesthetics: Understanding local preferences in terms of color palettes, layouts, and user flows is critical. It's not just about what looks good globally, but what resonates locally. This is where my dual role as creative director for promotional materials comes into play – applying that eye for design directly to the templates. Mobile-First Mentality: A significant portion of internet users in Nigeria access the web via mobile devices. Every template is meticulously designed with a mobile-first approach to ensure optimal responsiveness and user experience on smaller screens. Aff

Hey everyone, it's your friendly neighborhood dev-dad here. Mid-thirties, full-time engineer by day, battling AI trading bots by night (weekends, really). Today, I want to share a subtle but potentially catastrophic bug I found in my bot. Seriously glad I caught this before deploying with real money. The symptom: Discord notifications for order fills just weren't arriving. The culprit: I forgot to load my .env variables consistently across multiple Python scripts. This is a super common pitfall when you're linking several Python scripts in a personal project, and it can be a real headache. What Happened: A "Silent Failure" Uncovered by a DRY_RUN Last weekend, I was running my usual DRY_RUN tests for my FX bot. My bot's logic is split into two main parts: planner.py , which strategizes trades, and executor.py , which actually sends orders to the exchange. The console logs looked perfectly normal. executor.py seemed to be doing its job: I saw messages like "[DRY_RUN] Order placed: ...". But the Discord notifications, which should have been firing, never appeared. At first, I thought it was a Discord outage or just a delay. But after 30 minutes, nothing. Something was definitely wrong. Thinking about what would have happened if this were real money sent shivers down my spine. "I thought I placed an order, but it never went through." "I thought I closed a position, but I was still holding it." Bugs in notification systems are terrifying because they create these silent failures. You think everything is okay, but it's not. This is precisely how real money gets lost. The Investigation: Unmasking the Culprit To narrow things down, I first tried calling notify.py (which handles all notifications) directly. It worked flawlessly; the Discord notification came through. This pointed to an issue within executor.py , which calls notify.py . I re-examined executor.py 's logs more carefully and immediately saw it: the webhook URL being passed to the notification function was None .

Hey everyone, it's your friendly neighborhood senior dev here. I'm 38, working as a full-time engineer during the week, and tinkering with AI-powered algorithmic trading bots on the weekends. Today, I want to share a story about a subtle but potentially catastrophic bug I found in my bot. Seriously, thank goodness I caught this before deploying with real capital. The TL;DR: My Discord notifications for order confirmations weren't firing, and the culprit was a forgotten .env load across multiple Python scripts. I think this is a pretty common pitfall when you're working on personal projects with several interconnected Python scripts. What Happened: A "Silent Failure" Uncovered by DRY_RUN Over the weekend, I was running my usual DRY_RUN tests for my forex bot. My bot's architecture splits responsibilities: planner.py handles strategy logic, and executor.py executes actual trades on the exchange. Looking at the console logs, executor.py seemed to be working perfectly. I saw logs like [DRY_RUN] Order placed: ... . But the Discord notifications, which are supposed to arrive after an order, simply weren't showing up. Initially, I thought it might be a Discord issue or just a delay. But after 30 minutes, still nothing. This felt wrong. The thought of this happening with real money sent shivers down my spine: "I thought I placed the order, but it never went through." "I was sure I closed that position, but it's still open." Bugs in notification systems are notorious for creating these kinds of silent failures, and they're genuinely scary. The Investigation: Aha! Found You... My first step was to isolate the problem. I directly invoked notify.py , the script responsible for sending notifications. It worked perfectly, sending a test message to Discord. This strongly suggested the issue was upstream, likely within executor.py , which calls notify.py . I took a closer look at executor.py 's logs. And there it was: the webhook URL, which should have been passed to the notificati

For a while, I spent more time reading about SEO than actually doing SEO. Keyword research, domain authority, backlinks, technical SEO, search intent—there was always another guide to read and another tool to try. Eventually, I decided to stop preparing and build a small website from beginning to end. The result is Get Password Generator , a free password generator that creates passwords entirely inside the browser. This is what I have learned so far. Step 1: Finding a keyword with Google Trends I started with Google Trends. Google Trends does not provide exact search volume, but it is useful for comparing keywords and checking whether people’s interest is stable, growing, or disappearing. Instead of looking for the “perfect” keyword, I wanted to find something that: solves a clear problem; can become a focused single-purpose tool; has relatively stable demand; does not require a large backend; can be shipped quickly. A password generator matched those requirements. People already understand what the tool should do, and there is no complicated onboarding process. They open the page, choose their settings, generate a password, and copy it. Step 2: Checking the actual Google results After looking at trends, I searched the keyword directly on Google and examined the first page. This step was more useful than looking at a single difficulty score. I checked: what kinds of pages were ranking; whether the results were tools, articles, or product pages; how quickly users could access the generator; whether the pages worked well on mobile; how clearly they explained privacy and security; whether there was room for a simpler experience. I was not trying to prove that the keyword was “easy.” Search results can change, and established websites are difficult to compete with. I only wanted to answer a practical question: Is there enough room here to build something useful and learn from the process? For me, the answer was yes. Step 3: Buying the domain I purchased: https://getpas

I'm building PetSignal — a browser-based AI that reads dog and cat body language from a photo and flags stress signals (whale eye, freezing, lip curl) before they escalate. It's a solo project, the core purchase is a $2.99 credit pack, and that one number dictates the entire growth strategy. Here's the math that rules everything: at a ~$3-10 one-time AOV, paid ads can never work. US pet-niche CPC runs $0.5-2; even at optimistic conversion rates you're paying $50+ to acquire a $3 customer. So the product lives or dies on organic search. That constraint turned out to be a gift — it forced me to treat SEO as an engineering discipline with real feedback loops instead of a checklist. Twenty-eight days of Search Console data later: 230 clicks, 15,953 impressions, and impressions in the second half up 105% over the first. Small numbers, real slope. These are the five things the data actually taught me. 1. Symptom pages beat product pages — but not the way I expected My content engine is ~35 "symptom pages": Dog Opening and Closing Mouth Repeatedly , Cat Whale Eye , Cat Breathing Fast . Each one answers a moment of owner anxiety that ends with a photo the owner has already taken — which is exactly what the product analyzes. The surprise: one page carries 54% of all clicks. Not the homepage, not the tool pages — a page about dogs opening and closing their mouths. Meanwhile my four "commercial" analyzer pages have CTRs of 6-9% (site average: 1.8%) but almost no impressions. The lesson: content pages find demand, commercial pages convert it, and internal links are the pipe between them. I spent a day rebalancing internal links after realizing my refund policy — sitemap priority 0.4 — carried roughly twice as many site-wide links as any commercial page, while the general-purpose analyzer had exactly zero editorial links pointing at it. 2. Every page is data, not HTML All 35 symptom pages live in one TypeScript file as structured objects: title, quickAnswer, sections, tables, re

I'm a solo dev with zero users right now, and I just spent an afternoon on a decision most people would've hardcoded in five minutes. Here's the setup. NotebookBloom is my Chrome extension for Google's NotebookLM. At launch I don't want to charge for much — I want people to actually use it, tell a friend, leave a review. So the plan is: only cloud sync (Google Drive backup) is Pro on day one. Everything else — flashcard export to Anki, citation export, bulk import — free. But "free on day one" implies "not free forever." Once there are enough users, I want to flip some of those to paid, one at a time, watching what happens. And that's where I hit a wall that only exists for extensions: there is no server runtime. My extension runs in the user's browser. So if I write "is this feature paid?" as a hardcoded if in my code, then flipping it later means: edit code → rebuild → upload to the Chrome Web Store → wait for review[你查到的审核时长，如 "usually under a day, sometimes 3"]. Think about that. A pricing change — arguably the most business-critical lever I have — would be stuck in a review queue. That's absurd. So I stopped and rebuilt it as a switch. One file, features.ts , with a single decision function: canUse(feature, isPro, gates) → isPro OR the feature isn't currently gated Four flippable keys: cloudSync, ankiExport, citationExport, bulkImport. The default (compiled into the extension) is: cloudSync = paid, the rest = free. That's my day-one tiering. The switch values live in my Cloudflare Worker's KV. To flip Anki export to paid, I change one KV value — no rebuild, no store review. Every user picks it up within a day. The part I'm quietly proud of: it costs zero extra requests. The extension already calls /status to check "does this Google account have a subscription?" (you can't trust the client to self-report that — that's how you get pirated). I just piggybacked the switch values onto that same response. The paywall config rides along on a request I was already maki

Two weeks ago I wrote here that I killed my SaaS subscription 7 days after launch and rebuilt it as a buy-once product. I ended that post with a promise written down before I could talk myself out of it: 3 real purchases in 14 days of relaunch, or I move on and leave UIPrompt in maintenance mode. Either way I would post the numbers. The 14 days are up. Here are the numbers. Purchases: 0. New organic signups during the window: 0. The last real signup was a free account three days before the relaunch even went live. They looked once and never came back. So by my own written bar, this is a move-on. UIPrompt goes to maintenance mode today. I want to be useful about why, because "it didn't sell" is a result, not a lesson. What I did in those 14 days Quite a lot. That turns out to be part of the problem. I shipped a real product. The buy-once model was clean: a free playground with no signup, one $39 price, and an AI Design System Pack export that survives a blind test (a fresh AI session got only the exported files and matched 34 of 34 specced properties, inventing zero colors). I bought a custom domain. I launched on Product Hunt with a video, posted a Show HN, cross-posted the pivot article, made a 20-second promo video in Remotion with licensed music, put it on YouTube and X, and submitted to Peerlist, Dev Hunt, Indie Hackers, SaaSHub, and a stack of directories. None of it produced a single sale. Not one. The lesson I did not want On the first launch I blamed pricing. I killed the subscription, and I was right that a burst-usage tool should not bill monthly. But here is the uncomfortable part: fixing the pricing changed nothing, because pricing was never the binding constraint. Demand was. Two different pricing models, same zero, should have told me the problem lived upstream of the checkout page the whole time. I was tuning the part of the funnel I could see and control (the offer) while the actual leak was at the top: not enough of the right people, with pain acute

Gubernator Weekly Update: CoreDNS Aqueducts, SRE Stack, Network Topology & Cluster Auto-Updates! Gubernator Weekly Update Banner Review Gubernator Weekly Update Banner What an intense week for Gubernator (gbnt)! If you're new here, Gubernator is the "Goldilocks" container orchestrator that bridges the gap between Docker Swarm's simplicity (native Compose support, simple node joining) and Nomad's scheduling flexibility (hardware targeting, labels, task-based management). Over the past 7 days, Gubernator evolved from a single-node engine into a production-ready cluster ecosystem. Here is a breakdown of everything shipped this week! 1. Ingress & Service Discovery ("The Aqueducts") One of our biggest milestones this week was shipping automated internal DNS resolution and edge ingress routing: CoreDNS Integration: Every node running Gubernator can now deploy CoreDNS. Containers across all hosts can resolve internal service IPs using dynamic domain names (.gbnt.test). As containers spin up or die, Gubernator's manager updates CoreDNS records in real-time. Caddy Ingress: Exposing web services is now effortless. Services deployed with routing labels are automatically proxied by Caddy, managing SSL and HTTP/HTTPS ingress dynamically. 2. SRE Observability Suite (gbnt monitor init) Observability shouldn't require writing 500 lines of YAML. With a single command, gbnt monitor init, Gubernator deploys a complete, production-grade observability stack: Prometheus & cAdvisor: Detailed container and host-level metrics collection (CPU, RAM, Network I/O). Loki & Promtail: Centralized log aggregation across all containers. Grafana: Pre-configured dashboards for instant visualization out of the box. Jaeger Tracing: Full OpenTelemetry distributed tracing support (OTLP gRPC :4317 & HTTP :4318). Interactive Network Topology (Weave Scope Integration) Understanding how containers talk to each other across a distributed cluster can be tough. We integrated Weave Scope directly into the Flutter

Introduction For the last few weeks, I’ve been running a small experiment. Instead of building another SaaS startup or chasing investors, I decided to build a simple website around AI tools and document everything publicly. No team. No marketing budget. No SEO agency. Just curiosity, consistency, and a lot of trial and error. I genuinely want to answer one question: Can a small AI website still grow organically in 2026? ⸻ Why I Started AI tools are everywhere now. Every day another directory, another “best AI tools” list, another comparison website appears. Most people say it’s already too late. Maybe they’re right. I wanted to find out myself instead of trusting opinions. So I bought a domain and started building. ⸻ My Rules To make the experiment interesting, I gave myself a few restrictions. No buying backlinks. No paid traffic. No huge content team. No publishing hundreds of AI-generated articles. Everything has to be something I would actually publish. Quality first. ⸻ The First Product Instead of only writing articles, I decided the website should also offer something genuinely useful. The first tool is a free AI Background Remover. Nothing revolutionary. But it solves a real problem in a few seconds, and that felt like a better starting point than another generic blog post. ⸻ What I’ve Learned So Far The biggest surprise wasn’t building the tool. It was realizing how much work happens after pressing “Publish.” Indexing. Technical SEO. Site structure. Internal linking. Performance. Small details matter far more than I expected. ⸻ The Goal I’m not trying to build the next unicorn. I simply want to see whether a small independent website can still earn organic traffic by creating useful content and useful tools. If it works, great. If it fails, I’ll document that too. Either way, I’ll share the results. ⸻ Try the Tool If you’re curious, you can try the first tool here: 👉 https://letomix.com/free-tools/background-remover/ I’d genuinely appreciate any feedback.

You correct someone once. Not perfectly, but they get it. Next time, they do not make the same mistake. That is not optimism. That is just how correction works, "with people". I worked with agents on that assumption for a long time before I even noticed I was doing it. The plan that never held Before I had a single written rule anywhere, I would open a new session and ask for a plan first. Resolve the edge cases before touching a line of code, I said. The agent would agree, in whatever way a chat window agrees, and go straight to implementation anyway. I corrected it. Same session, it adjusted. New session, next day, same repo, same everything except the chat history: straight to implementation again. Every single time! So I did what looked reasonable. I wrote the plan myself. I resolved the edge cases myself, the open questions, the gaps the agent skipped past on its way to code. ' Tedious ' is the polite word for it. I was doing the one task I brought the agent in to do, and calling it collaboration. The same recipe, again The second correction arrived the same way. Every repo had its own shape. A recipe, a standard, a way things were supposed to be built here and not there. I would explain it. Full session, good results, the agent following the standard like it understood the standard. New session. Same repo, sometimes the new repo. Explain it again. Word for word, close enough. It was not that the agent forgot how to code. It was that nothing from the last conversation traveled with it into this one. Nothing said in the chat survives it I kept treating this like a training problem. Say it clearer. Say it earlier. Say it with an example next time. None of that was wrong exactly. It was aimed at the wrong layer. The actual mistake was assuming correction compounds the way it does with a person. It does not. A person carries what you told them into the next conversation without being asked to. An agent starts the next session exactly where it started the first one.

Published on Feb 16th, 2023 My solar e nergy startup platform FasoLara has reached a new milestone recently and I decided to start a new blog series about it! The project management platform has been a long journey since I published my first commit to GitHub in October 2020. What started based on a simple idea quickly became a behemoth of a software engineering project for my beginner programmer skills. I have poured thousands of hours into research, tutorials and coding to figure out how to put something like this together. Since then, I have made multiple changes to the FasoLara repository. The platform is currently open source, but I am using a private fork to publish the 3 different components to the Vercel platform. I had a basic demo of the admin dashboard with 6 pages before I removed all the sample data, then upgraded everything to the app directory in NextJS 13 and connected the dashboard to the backend server featuring Apollo GraphQL server v4. Yesterday, February 15th, 2023, I added Next-Auth to handle authentication. Initial testing of the next-auth version seems to work with the appDir in Next.JS 13. It is far from the login experience that I want. It will take more effort to iron out the details because proper documentation is still rare Lots of testing needs to be done Although I have successfully connected the Cypress testing framework to the frontend app, I have yet to do the same on the admin app. I am managing a lot of complexity with lots of new packages. Every mistake under the sun I have lost count of how many times I made breaking changes to the code base trying to implement new features on the main branch only to hard reset the branch after tens of hours of work that I could have done on a new branch instead. I can say that I am moving fast and breaking things per facebook's motto! Mobile app on the backburner I have 3 sample pages that I made on the mobile application. I would have liked to have at least a fully functional landing page on th

Honestly I almost didn't build this because everyone said "just make a normal portfolio, resume + project cards, keep it simple." But that felt fake to me. Like I'd be hiding the actual messy part of learning to code and just showing the highlight reel. So instead I built whoisrehan.vercel.app — it's less of a portfolio and more of me walking you through everything, starting from the first time I opened a code editor with literally no idea what I was doing, all the way to now. Including the stuff that usually gets left out — the projects that didn't work, the times I wanted to quit, the small wins that felt huge at the time. It's not polished. It's just honest. If you've ever started something with zero plan and just pure curiosity, I think you'll get it. whoisrehan.vercel.app Curious which part actually hits you if you check it out : BuildInPublic #WebDevelopment #DeveloperJourney

I shipped GoGBA (Android + iOS) to both stores in late December 2025. Six months in: MAU peaked at 8.3k, currently steady around 7.4k. No paid advertising, ever. This is a write-up of what the six months actually involved. I'll be specific about the technical work, and equally specific about the mistake that cost me RetroAchievements hardcore certification — because that part is the most useful thing here for anyone building in this space. Why GBA only I grew up on a GBA — Super Robot Wars, Fire Emblem, Pokémon, Castlevania, Zelda. Later NDS/3DS/PSP/Vita/Switch arrived and the GBA did its job and retired. On PC the emulator I remember is VisualBoyAdvance. I've used GBA, NDS and PSP emulators on phones. I kept coming back to GBA, for four reasons that are all practical rather than nostalgic: Pixel art holds up. Personal taste, no defense offered. Battery. A GBA game survives a long-haul flight. Single screen. The remaining screen space is exactly where virtual buttons want to go. NDS dual-screen on a phone is always a compromise. ROM hacks. The GBA hack scene is the richest of any handheld. Point 3 is the one that made me build something: GBA is the only handheld whose form factor natively fits a phone. That's a product observation, not sentiment. What existing emulators get wrong (for me) I used the main ones on both platforms: Delta and Linkboy on iOS; Pizzaboy, Linkboy and Lemuroid on Android. Lemuroid is open source and a lot of shipped emulators are built on it. They're all good. Every one of them had small things that annoyed me. The only genuinely cross-platform one is Linkboy (formerly MyBoy), but its configuration surface is extremely deep — second only to RetroArch in complexity. That's the gap. Everyone was solving "can it run" and "can it be tuned perfectly." Nobody was solving "pick it up and play." The methodology was just dogfooding I'm a Flutter GDE and tech lead for a 40-person cross-platform team; GoGBA was a solo test of that experience. The only r

Two weeks ago I had never run an AI model on my own machine. Every project I had ever built phoned a...

I build products solo. Every single one of them sits on top of somebody else's API — Stripe for payments, OpenAI and Anthropic for AI features, Meta for ads, print-on-demand APIs, map APIs. My code is maybe half of what actually runs in production. The other half belongs to vendors, and it changes whenever they decide it changes. Twice this year the first notice I got about a breaking change was a production error. Not an email, not a warning. An error, and then me digging through the vendor's changelog trying to figure out what they changed and when. The information was public the whole time. It was sitting in a changelog page I never visit, because nobody visits changelog pages until something is on fire. So I'm building the thing I wanted to exist BreakWatch is simple: you tell it which APIs your product depends on, and it reads their public changelogs for you. It fetches each changelog page once a day Diffs it against yesterday's snapshot Classifies the real changes: breaking (endpoint removed, field deprecated, "migrate by September") vs. informational (new feature, docs clarification — stuff you can ignore) Alerts you only when something looks like it will break an existing integration Keeps everything in a searchable timeline, so six months later "what changed on their side right before this broke" takes ten seconds instead of an afternoon No SDK, no credentials, nothing installed in your codebase. It only reads public pages. What I tested this week I ran it against the real changelogs of the ten APIs I'm watching first: Stripe, Twilio, OpenAI, Anthropic, Shopify, GitHub, Slack, Cloudflare, Google Maps and Plaid. Some honest findings: 10/10 scrape cleanly now, but it took fixes. Stripe's changelog page alone is 3.3 MB. SendGrid's standalone changelog doesn't exist anymore (it merged into Twilio's). PayPal's developer site serves a JavaScript shell with an HTTP 404 to anything that isn't a full browser, so it's out until I add rendering. The thing I was most a