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What happens when thousands of people decide they're hungry at the exact same time? The Quiet Before the Storm 10:00 PM. The numbers are gentle tonight. One hundred eighty-nine requests trickle in. Someone in Lagos is ordering late-night suya. A rider in Ibadan is wrapping up his last delivery. In Bangladesh, someone is just discovering us for the first time. By 11:00 PM , things get quiet. Just 8 requests. The platform takes a breath. 2:00 AM. A mystery. 151 requests spike out of nowhere. We check the logs. Nothing unusual. Just a group of night owls ordering food, maybe shift workers, maybe students pulling an all-nighter. The beauty of a platform is we're always on, always ready. 7:00 AM. Good morning, Nigeria. Fifty-five requests. People waking up, checking their wallets, planning their day. The coffee hasn't even brewed yet, but the platform is already humming. The Morning Rush 9:00 AM. 315 requests. The workday begins. Offices buzz with conversations about lunch plans. If someone searches "foodmat site" for the third time this week, they're getting closer to finding us. A corporate client logs in to set up their employee meal program for the first time. By 10:00 AM , the traffic settles to 50 requests. A calm before the real storm. 11:00 AM. 173 requests. The hunger is building. People are making decisions about what to eat, where to order, and which vendor to choose. Our World Cup campaign notifications ping. Someone shares their referral code. The viral loop begins. The Lunch Explosion 12:00 PM. 321 requests. It's happening. The platform comes alive. 1:00 PM. 339 requests. The peak is building. Our servers are handling it smoothly. This is where the magic happens when thousands of people decide they're hungry at the exact same time. 2:00 PM. 289 requests. Still going strong. Vendor dashboards refresh. Riders accept orders. Laundry bookings come in alongside food deliveries. If someone cancels an order with a reason, we take note. Every interaction teaches us

I created Agentis Lux for the purposes of entering H0 Hackathon (Vercel + AWS Databases). #H0Hackathon See Agentis Lux's Devpost.com entry . It started with a comment at a hackathon. A you.com employee said the thing out loud: the web has a second audience now. When you ask ChatGPT or Perplexity a question, a retrieval agent fetches a page and reads its HTML to answer you. Not the laid-out site with the buttons and the hero image. The markup underneath. These agents arrive by the million, and many of them rely on the raw or minimally rendered HTML rather than running your JavaScript, so they often see far less of your page than a person does. That comment sent me to build. My first answer to it was Hermes Clew , for the GitLab Duo Agent Platform Challenge. Hermes lived inside GitLab Duo Chat, no frontend, no database: a Python engine that scanned the HTML, JSX, and TSX files in a repo, scored them across six categories, and let an LLM reason over the findings. It proved the core idea. It also told developers how to fix things, lived inside one vendor's chat, and only worked on files in a repo. Agentis Lux is what happened when I took that idea to the open web and rebuilt it with a different stance. Any live URL, not just repo files. Its own product on a real cloud architecture, not a chat window. And no fix suggestions, on purpose, where Hermes used to hand them out. Same six-category bones, a new body, a sharper philosophy. It scans your site and shows you what that second audience experiences when it tries to read it. What it does You paste a URL to Agentis Lux . You get a report. The report is written from the agent's point of view. Not "this is broken." More like: "an agent landing on this page can't tell which element starts checkout, because it's a styled div and not a button." It reports findings. It does not suggest fixes, and that is on purpose. I know what the agent sees, not what you should change. That is the whole value: visibility, and you decide what

I am committed to understand how systems actually work. I'm working on a multi-node lab to follow the complete path of a request from Python APIs to Linux processes, through Docker containers, networking and observability. The idea is simple: build a system that observes another system to understand the abstraction layers behind modern infrastructure. This project is about learning by building, experimenting and understanding what happens under the hood. Link: [ https://github.com/daniloprandi/devops-network-automation-lab ] DevOps #Linux #Python #Docker #Networking #Observability #Infrastructure

I've been building with AI for a while now. I can get these tools to do what I want, but I want to go a level deeper, past "it works" into actually understanding why. So I'm sharpening the fundamentals and the applied side, and writing it down here as I go. Expect short, honest posts on what I'm learning and building.

Suno has ambitions to be more than just a toy to churn out AI slop, it also wants to be a streaming destination and to break new artists. Spark is their new incubator program for independent artists that provides grants, mentorship, and marketing support. To apply, artists need to be an unsigned singer, songwriter, or […]

Bash4LLM is a single-file Bash wrapper for interacting with LLMs from the terminal. I created it because I wanted something simple that worked without installing Python, Node, or any other runtime. It uses only Bash, curl, and jq. You can send prompts, start a small chat, process files line by line, stream output, and save session metadata in JSON format. I tried to make it safe and predictable: no use of the system /tmp, no use of eval. Groq is supported by default, and other providers can be a

"Mistakenly we thought that by just introducing artificial intelligence ... that would produce a high-quality product.”

http://i.stanford.edu/pub/cstr/reports/cs/tr/68/85/CS-TR-68-...

Why Semantic HTML is a Superpower for Your Website As I’ve been building my personal portfolio during the IYF Season 11 program, I realized that writing code is about more than just making things look "right"—it’s about making them accessible for everyone. The secret is Semantic HTML. What is Semantic HTML? Semantic HTML uses tags that provide meaning to the web page rather than just layout instructions. Tags like , , , , and tell the browser and screen readers exactly what content they are interacting with. If you use for everything, you are handing a screen reader a blank map. Semantic tags act like a GPS, allowing users with visual impairments to navigate your site efficiently. Before vs. After The difference is clear when comparing structure: Before (Non-Semantic): HTML My Portfolio Home | About | Contact Welcome to my site... After (Semantic): HTML My Portfolio Home About Contact Welcome to my site... Fixing Accessibility Issues During my accessibility audit (Task 2.3), I identified three key areas to improve: Missing alt Attributes: I had images without descriptions. I fixed this by adding context, such as alt="Portrait of Gladwell Muthoni, web development student", ensuring screen readers can describe images to visually impaired users. Lack of Semantic Structure: My initial gallery relied on generic containers. Switching to and tags organized my project list logically, helping assistive technology categorize the content. Heading Hierarchy: I was using tags for visual styling rather than logical structure. I corrected this to follow a strict to hierarchy, which helps screen readers outline the page properly. My portfolios URL https://github.com/gladwellmuthoni/iyf-s11-week-01-gladwellmuthoni.git

TL;DR — Claude Code sends its requests wherever one environment variable points. Aim that at a small local translator and it runs on the Kiro plan you already pay for. Full setup below, plus the two snags worth knowing about. Claude Code and Kiro have something funny in common: underneath, they're powered by the same Claude models. Same brain. They just grew up speaking different dialects, so out of the box they can't hold a conversation. I noticed this right as I was about to start a second subscription for Claude Code. My Kiro plan was already renewing every month, already serving the exact models Claude Code wanted to charge me for again. Paying twice to talk to the same thing felt absurd. So instead of buying a second seat, I hired an interpreter. One small program that sits between them, listens to Claude Code, and relays everything to Kiro in a dialect it understands. Here's how to set it up, and what I learned doing it. Why they can't just talk Claude Code is more open-minded than people assume. It doesn't hard-code where it sends requests. It reads one environment variable, ANTHROPIC_BASE_URL , and ships everything to that address. Normally that's the official endpoint, but it'll happily send its requests anywhere you tell it to. That's the opening. Point it somewhere local and the whole thing becomes possible. Meet the interpreter The catch is that Claude Code and Kiro phrase things differently. You can't just redirect one at the other and expect them to understand each other. You need a translator fluent in both. That's kiro-gateway-next : a tiny proxy that runs on your own machine. A request arrives phrased one way and leaves phrased another: Claude Code (phrases it for Anthropic) ──▶ kiro-gateway (rephrases it for Kiro) ──▶ your Kiro account Claude Code gets a reply in the format it expects. Kiro receives a request it recognizes. The interpreter does the rephrasing in the middle, and the conversation just flows. Setting it up, step by step Six steps. Abo

You submitted your extension, waited days for review, and got back a rejection with a violation called "Purple Potassium." Your extension looks fine to you, so what does it even mean? Here is what it is, why it happens, and how to catch it before you ever hit submit. What "Purple Potassium" actually means "Purple Potassium" is Google's internal tag for excessive or unused permissions . Your manifest requests access to something your code does not actually use, and the reviewer flags it. It is one of the most common reasons a Chrome extension gets rejected, and it is frustrating precisely because the extension works fine in testing. Review is checking something testing never does: whether every permission you ask for is justified by your code. The usual causes 1. API permissions you declared but never call. You added tabs , bookmarks , or cookies to your manifest at some point, but there is no chrome.bookmarks.* call anywhere in your code. 2. Host access that is too broad. You requested <all_urls> when your extension only touches one site: // Flagged "host_permissions" : [ "<all_urls>" ] // Better "host_permissions" : [ "https://*.example.com/*" ] Leftover permissions after removing a feature. You shipped a feature that needed downloads, later removed the feature, and forgot to remove the permission. The tabs misunderstanding. The tabs permission does not grant access to the tabs API. Basic methods like chrome.tabs.create() work without it. It only grants four sensitive Tab properties: url, pendingUrl, title, and favIconUrl. If you declare tabs but never read those, it counts as unused. How to fix it by hand List everything in permissions, optional_permissions, and host_permissions. For each one, search your code for the matching chrome. call. Remove any permission with no usage. Narrow and other broad patterns to the specific hosts you need. In your reviewer notes, write one plain sentence per sensitive permission explaining why you need it. Reviewers often lack con

The biggest challenge wasn't choosing a language model or designing prompts—it was managing context over time. Once an application grows beyond isolated conversations, memory becomes just as important as reasoning. An assistant that remembers previous architectural decisions, coding preferences, and project history can contribute much more effectively than one that starts from scratch every session. Runtime intelligence proved to be equally important. Not every request deserves the same computational resources. Routing tasks based on complexity, enforcing execution budgets, and maintaining an audit trail make AI systems more predictable and practical for real-world development. DevPilot AI brings these ideas together by combining Google Gemini for reasoning, Hindsight for persistent memory, and cascadeflow for runtime intelligence. While the project will continue to evolve, building it reinforced one idea above all else: the future of AI applications isn't just about generating better responses. It's about building systems that can remember, adapt, and make better decisions over time. If you're interested in the architecture or would like to explore the project further, you can find the source code here: GitHub: https://github.com/siddharthg-7/DevPilot-Ai- I'm always interested in feedback and discussions around persistent memory, runtime intelligence, and AI engineering. If you've explored similar ideas or approached these challenges differently, I'd love to hear your perspective.

For the last 3 months I recorded every session I had with Claude Code. Not screenshots, not memory. Every prompt I typed and everything it did, saved to a small database I own. I did it because I kept losing my own work. I would finish a week, someone would ask what I shipped, and I genuinely could not remember. The work was real. It just lived in terminal scrollback I would never scroll through again. So I set up a chain of small agents to remember it for me. Every night, while I sleep, one agent reads that day's raw sessions and writes a single clear note: what I built, the decisions I made, what is still open. Plain language, the way I would write it in a journal, not a wall of logs. Once a week, a second agent reads all seven daily notes and updates a profile of me: the projects I am moving, the skills I have actually used, the things I learned. After a few months this turned into a more honest picture of my work than my resume. Then a third agent reads all of that and drafts posts for LinkedIn and X about what I actually worked on that week. Building in public, without me having to remember or sit down and write. The part I like most: none of it runs on my machine. It is all scheduled cloud routines. My laptop can be off. I wake up and the notes, the profile, and the draft posts are already waiting. I have started open-sourcing this as Pulse. The capture and the nightly daily-note agent are out now. You point it at your own database and your own notes repo, and it writes your day for you, in plain English, in files you own. The weekly profile agent and the post-writer are the pieces I am extracting next. It is early and rough in places. The honest caveat: the writing is only as good as the model behind it, and a quiet day still makes a quiet note. But after 3 months, I no longer guess what I did. I just open the vault. The graph at the top is 3 months of my own notes, each day linked to the projects it touched. Repo: https://github.com/muhammademanaftab/pulse

A few months ago, if someone had asked me to build a mobile app, I would've had absolutely no idea where to start. Today, an app I built is on the Google Play Store. It's called Stulo, and it's currently in closed testing. The funny part? I'm not a software engineer. I'm just a college student who got tired of missing opportunities. Internships were on LinkedIn, hackathons were buried somewhere on Instagram, college events lived inside WhatsApp groups, and competitions were scattered across random websites. If the algorithm didn't like you that day, you simply never found them. That felt... ridiculous. So I asked myself, "Why isn't there one place where students can find everything?" That simple question eventually became Stulo. Today, students can discover internships, hackathons, competitions, campus events, connect with other students, and share updates through a campus feed—all in one app. The biggest lie I believed was that building the app would be the hard part. It wasn't. Understanding why it wasn't working was. I built the first version using Emergent because, honestly, I didn't know enough to start from scratch. It got me surprisingly far. As the project became more serious, I moved development to Google AI Studio (Antigravity). That's when I learned something every AI-generated YouTube thumbnail forgets to mention: AI doesn't build products. It generates code. There's a huge difference. AI happily writes hundreds of lines of code, but it doesn't explain why your images randomly stop rendering after ten minutes, why scrolling suddenly feels like you're using a phone from 2013, or why fixing one bug somehow creates three completely unrelated bugs. Most days followed the exact same routine: generate code, run the app, watch something break, Google the error, ask AI, read Stack Overflow, realize the problem was my own code, and repeat. Some bugs took ten minutes to fix, while others stole an entire weekend. Looking back, one of the biggest things I learned wa

Prediction markets just hit $3.6B in volume. I wanted to know what the biggest traders were betting on — in real time. So I built WhaleTrack. Here's how it works under the hood. The Problem Polymarket has a public leaderboard. But it only shows P&L totals — not what whales are currently betting on, not their recent activity, not their win rate. If you want to follow smart money, you're flying blind. I wanted something that answered: what are the top traders doing right now? The Stack Vanilla JS frontend (no framework, keeps it fast) Vercel serverless function as a backend proxy (avoids CORS issues) Polymarket's public data API — no auth required Step 1: Finding the Whales Polymarket exposes a leaderboard endpoint: https://data-api.polymarket.com/v1/leaderboard?limit=20 This returns traders ranked by P&L. I pull the top 10, grab their wallet addresses, and that's my whale list. Step 2: Fetching Live Activity For each whale wallet, I hit: https://data-api.polymarket.com/activity?user={address}&limit=20 This returns their recent trades — market name, size in USDC, timestamp. Refreshes every 60 seconds. Step 3: Calculating Win Rate (the tricky part) The key is the redeemable flag — redeemable: true means they won, currentValue: 0 + redeemable: false means they lost. Took a few wrong attempts with cashPnl (always negative, not useful). Step 4: The Whale Alert Banner Every 60 seconds I check for trades over $5,000 placed in the last 10 minutes. When it fires, a green banner slides down with the whale name, market, and amount. Auto-dismisses after 12 seconds. First time I saw it fire live with a $28K bet — genuinely exciting. Results 129+ users in the first few days Zero ad spend Traffic from Twitter, Reddit, Quora What's Next More whale wallets (suggestions welcome) Click-through to open the same market on Polymarket directly Email/push alerts for big trades Check it out: whaletrack.app All feedback welcome — especially if you spot a whale I'm missing.

C is over 50 years old. Yet it still powers: Linux Embedded Systems Compilers Databases Device Drivers Why? Because it offers something very few languages do: Speed + Control + Portability 1. Fast Execution C compiles directly into machine code. That means very little runtime overhead and excellent performance. 2. Portability Well-written C code can be recompiled on Windows, Linux, and macOS with minimal changes. 3. Direct Memory Access Pointers allow precise control over memory. That's one reason C is used for operating systems and embedded software. 4. Structured Programming Functions and modular design make large programs easier to organize. 5. Small Language Core The language itself is relatively small, making it easier to learn the fundamentals. 6. Standard Library Useful libraries exist for: Input/Output Strings Files Memory Mathematics 7. Hardware Interaction Few languages communicate with hardware as naturally as C. That's why C remains the dominant language for firmware and drivers. What About the Downsides? C also has limitations. Manual memory management No garbage collection No built-in OOP Limited runtime safety These make C harder to master but also teach concepts that many higher-level languages hide. Simple Example #include <stdio.h> int main () { printf ( "Hello, World! \n " ); return 0 ; } Even this tiny program demonstrates: Header files The main() function Standard library usage Program execution Final Thoughts Learning C isn't about writing every future application in C. It's about understanding how software works beneath modern frameworks. Once you understand C, many other programming languages become much easier to learn. Full beginner guide with diagrams and explanations: [Feature and Application C Programming](# 7 Features of C Programming Every Developer Should Know C is over 50 years old. Yet it still powers: Linux Embedded Systems Compilers Databases Device Drivers Why? Because it offers something very few languages do: Speed + Control +

Apple's newest on-device model carries about 20 billion parameters, and on any given request it fires maybe one to four billion of them. That gap — 20B stored, roughly 3B running — is the whole story of 2026. The model that now ships inside the latest iPhone is no longer a shrunken, lobotomized cousin of the cloud model. It's a different kind of object: large in flash, small in motion, and it never phones home. For three years the on-device pitch was mostly aspirational. Demos ran, latency was rough, quality trailed the API by a generation, and every serious AI feature still resolved to a per-token bill in someone's datacenter. In mid-2026 that stopped being true. Two releases — Apple's third-generation Foundation Models at WWDC on June 8, and Google's Gemma 4 family on April 2 — quietly moved the floor. Genuinely useful agents now run on hardware you already own, offline, for free. The economics nobody priced in Forget benchmarks for a second; the load-bearing fact here is accounting. When the model lives in the cloud, every inference is a metered event — input tokens, output tokens, a line item that scales linearly with usage and explodes the moment you wrap the model in an agent loop. Agentic workloads are the worst case for the token meter: a single "go do this task" can fan out into dozens of model calls as the agent plans, calls tools, retries, and re-reads its own output. The bill grows with your ambition. Move the model onto the device and the marginal cost of an inference is approximately $0 . No API key, no rate limit, no usage dashboard. You paid for the silicon once; every token after that is free in the only sense a product manager cares about — it doesn't show up on a monthly invoice that grows with your success. That single change rewrites which features are worth building. A background task that re-summarizes your inbox every five minutes is insane on a per-token plan and trivial on-device. So is an agent that quietly loops a hundred times to get one