The home stretch
<p>I never thought I’d be someone who actually thought it was a good idea to download GitHub as an App on my phone, but here we are. Charting territory I honestly felt like I didn’t belong in. To be honest, even working IT, there were moments where I just didn’t think I met the “mark” of someone who was “passionate” enough to do something like this. </p> <p>For the first time in my career, I do feel that I’ve found enough “passion” to warrant having access to my coding agent at all times. Now that I've entered the testing phase of the homeschool site build out, I've been using GitHub Copilot to help me resolve the issues that pop up as I'm walking through the workflows for the site. </p> <p>First, I've begun to use the issues on the repository to assign work to Copilot. The past few weeks
I never thought I’d be someone who actually thought it was a good idea to download GitHub as an App on my phone, but here we are. Charting territory I honestly felt like I didn’t belong in. To be honest, even working IT, there were moments where I just didn’t think I met the “mark” of someone who was “passionate” enough to do something like this.
For the first time in my career, I do feel that I’ve found enough “passion” to warrant having access to my coding agent at all times. Now that I've entered the testing phase of the homeschool site build out, I've been using GitHub Copilot to help me resolve the issues that pop up as I'm walking through the workflows for the site.
First, I've begun to use the issues on the repository to assign work to Copilot. The past few weeks have been a whirlwind of things on both the professional and personal side and this has been a really nice way to still stay plugged in without having to spend hours in front of the screens troubleshooting the website issues.
Also, my use of a public repo is coming in handy because I've been able to allow Copilot to help me make sure the dependencies for the React framework I'm using for the frontend stay up to date. Really nice. But I'm ready to use instructions to see if I can get Copilot to do this without my repo needing to be public. I decided to start with my backend because Python is easier for me to navigate
The first instruction I've created does these things:
-
Makes sure that my lambdas all stay on a supported runtime version
-
Pins package versions so that I can stay on versions that are not vulnerable and not automatically update latest just in case it's a compromised package
-
Makes sure I use the latest version of the github-actions for my python builds.
-
Runs a pip-audit to see if a package has any known CVEs
To get help writing this set of instructions, I used Copilot to assess my rough draft as an application security engineer and to make appropriate edits to the file to make it align with best practices. Some suggestions it included then was to ensure that I had something in place to protect against typosquating, address CORS, guard against injection attempts, and ensure there's a limit to input to my site.
These instructions are going to load into Copilot and essentially guide Copilot to help me ensure that the recommendations it makes will always align to these standards for my repository. Currently only have this enabled on my backend repo, so my frontend repo will be next.
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
versionupdateapplication
Perplexity, Smoothing, and What Words Mean
By the end of this post, you'll know how to evaluate a language model using 'perplexity', why unseen n-grams break everything and how smoothing patches the holes, and how interpolation lets you mix n-gram orders instead of betting on one. You'll also understand why word meaning is harder to pin down than it looks, what kinds of relationships exist between words, and how a 1951 insight from philosopher Ludwig Wittgenstein laid the intellectual groundwork for word embeddings. Two halves, one thread: the first half shows you the limits of n-gram language models. The second half shows you why those limits forced NLP to rethink how words are represented, which is where the deep learning side of NLP starts. Where We Left Off Last post, we built n-gram language models: chain rule, Markov assumpti
I Built 25 Cloudflare Workers APIs — Here's What I Learned
Over the past few months, I built and deployed 25 APIs on Cloudflare Workers . All running on the free tier. Total monthly hosting cost: $0 . Here's what I learned about building, deploying, and monetizing utility APIs at scale. The Stack Every API follows the same pattern: worker-api/ ├── src/ │ └── index.js # Single entry point ├── wrangler.toml # Cloudflare config └── package.json No frameworks. No bundlers. Just vanilla JavaScript on Cloudflare Workers. The 25 APIs Here's a sampling of what I built: API Purpose Complexity Readability Score Text analysis (Flesch-Kincaid, SMOG, ARI) Medium QR Code Generator Generate QR codes from text/URL Low Password Generator Cryptographically secure passwords Low Markdown to HTML CommonMark-compliant conversion Medium Color Converter HEX/RGB/HSL conve
490+ Calculator Tools with Zero Hosting Cost — Architecture Deep Dive
I built a site with 490+ free calculator tools — BMI, loan payments, unit conversions, tax calculators — all statically generated with Next.js. Monthly hosting cost: $0 . Here's the architecture behind keisan-tools . The Problem Japanese users search for specific calculators constantly. "BMI計算" (BMI calculator), "住宅ローン計算" (mortgage calculator), "年齢計算" (age calculator). Each query has moderate search volume with surprisingly low competition. Instead of building each tool by hand, I built a system that generates tools from configuration . Architecture Overview keisan-tools/ ├── data/ │ └── tools.json # 490+ tool definitions ├── app/ │ └── [category]/ │ └── [slug]/ │ └── page.tsx # Dynamic route template ├── lib/ │ ├── calculator.ts # Calculation engine │ └── tools.ts # Tool loader + search └
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
490+ Calculator Tools with Zero Hosting Cost — Architecture Deep Dive
I built a site with 490+ free calculator tools — BMI, loan payments, unit conversions, tax calculators — all statically generated with Next.js. Monthly hosting cost: $0 . Here's the architecture behind keisan-tools . The Problem Japanese users search for specific calculators constantly. "BMI計算" (BMI calculator), "住宅ローン計算" (mortgage calculator), "年齢計算" (age calculator). Each query has moderate search volume with surprisingly low competition. Instead of building each tool by hand, I built a system that generates tools from configuration . Architecture Overview keisan-tools/ ├── data/ │ └── tools.json # 490+ tool definitions ├── app/ │ └── [category]/ │ └── [slug]/ │ └── page.tsx # Dynamic route template ├── lib/ │ ├── calculator.ts # Calculation engine │ └── tools.ts # Tool loader + search └
I Built a Complete E-commerce REST API with Laravel 12 — Here's What's Inside
Building an e-commerce backend from scratch takes months. Authentication, payments, inventory, shipping, admin panel, tests… the list never ends. I decided to build it once, properly, and package it for other developers. The result is LaraCommerce API — a complete, production-ready e-commerce backend built with Laravel 12. WHAT I BUILT A fully-featured REST API covering every e-commerce use case, with an admin panel, full Stripe integration, and 100% test coverage. THE NUMBERS API Endpoints: 325+ across 25+ modules Database: 88 Eloquent models, 72 migrations Admin Panel: Filament v3 with 46 screens Tests: 2,452 tests / 100% coverage Docker: 12 services, one-command setup Languages: 3 (EN, FR, AR) Currencies: 7 (USD, EUR, GBP, MAD, CAD, CHF, AED) TECH STACK Framework: Laravel 12 (PHP 8.5+)
DeepSource vs Qodana: Code Quality Platforms Compared (2026)
Quick Verdict DeepSource and Qodana are both code quality platforms that bring static analysis into CI/CD pipelines - but they come from fundamentally different philosophies, serve different audiences, and are not really competing for the same buyer in most cases. DeepSource is a modern AI code review and static analysis platform. It combines 5,000+ analysis rules with an industry-leading sub-5% false positive rate, LLM-powered Autofix AI that generates working code fixes, and structured AI code review across five quality dimensions on every pull request. Its February 2026 pricing restructuring pushed the Team plan to $24/user/month, reflecting its repositioning as a premium AI-enabled platform rather than a static analysis commodity. Qodana is JetBrains' code quality platform built on the
Stop Building AI Into Your Product. Start Building Products With AI.
Everyone is chasing the wrong thing. I watch company after company pour millions into embedding generative AI into their applications. RAG pipelines. Vector databases. Fine-tuned models. Prompt engineering teams. Guardrail frameworks. Data privacy reviews that take longer than the product development itself. And after 18 months and a seven-figure budget, they have... a chatbot. A slightly smarter chatbot. That hallucinates 12% of the time and requires a legal review before every deployment. Meanwhile, we just shipped a complete, custom-built automation platform for a customer in 6 days. Not a chatbot. Not a wrapper around GPT. A real application. With real business logic. That does exactly what the customer asked for. No hallucination. No prompt injection risk. No data leaving the building
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!