标签：#buildinpublic

Marc Lou read a tweet, slept on it, and woke up still annoyed. The tweet, from Pieter Levels, was about all the fake revenue screenshots on X. By the next evening Lou had built a thing to fix it. By the day after that, the thing had made $20,378. That is the part everyone retweets. I want to walk you through it, and then I want to show you the line in his own year-end letter that complicates the whole legend. The setup Lou got fired by Tai Lopez in November 2021, was broke and depressed, and moved to Bali. He started shipping tiny products in public, copying the playbook of, yes, Pieter Levels. His breakout was ShipFast , a Next.js starter kit that did $40,000 in its first month in September 2023. By December 2025 he was running 15 startups generating about $84,900 a month, with cumulative revenue past $2.26 million, per his verified TrustMRR data. The reason I trust his numbers more than most is that he verifies them through Stripe on his own product, TrustMRR , which brings me to the 24-hour story. The moment something worked, absurdly fast TrustMRR exists to kill fake MRR screenshots. You connect a read-only Stripe key, and it shows your verified revenue on a public page nobody can edit. Lou built it in a day on top of his own boilerplate, which is the cheat code here. He was not starting from zero, he was starting from ShipFast. "TrustMRR is 24 hours old and was built in 24 hours." @marc_louvion on X He monetized it with sidebar ad slots. He listed them at $299 a month, then raised the price each time one sold, all the way to $1,499. In his newsletter he wrote that within three days every slot was gone and the side project had made $20,378. He called it the third fastest-growing thing he has ever built. Five days in, he posted the run-rate dream out loud. "20/20 spots filled! TrustMRR went from $0 to $18,380 MRR in 5 days. That's $220,000 ARR if I'm allowed to dream a little" @marc_louvion on X It kept going. By December 2025 TrustMRR was his single biggest inco

I'll Be Honest: The Internet Already Has Translators I know. Language translation isn't a new idea. There are already huge translation platforms out there. So when I started working on a translator for my tools website, I wasn't thinking: "I'm going to reinvent translation." Not at all. My thought was much simpler: "Can I make quick translation feel less distracting?" My Frustration Was Actually Pretty Simple Sometimes I just need to translate text. That's it. I don't want to: Create an account Open five different menus Break a long text into tiny pieces Jump between multiple tools I want to paste the text... Choose a language... And get the translation. So I Built My Own Version 👉 https://allinonetools.net/language-translator/ The tool currently supports 200+ languages and language variations . You can: Detect the source language Select the target language Translate long text Upload text Use voice input Listen to the result Copy or share the translation And I wanted to keep the text experience simple without forcing users into tiny input limits. Just: Enter → Choose Language → Translate 200+ Languages Sounded Simple Until I Saw the List English. Hindi. Gujarati. Spanish. Arabic. These are the languages most people immediately think about. But then I started going through the full language list. Abkhaz. Acholi. Afar. Alur. Aymara. Baluchi. And many more. Honestly... I hadn't even heard of some of them before building this. That was probably my biggest learning moment. I Realized How Small My Own View of the Internet Was As a developer, it's easy to build around the languages we personally know. For me, seeing English, Hindi, and Gujarati feels normal. But the internet is much bigger than my own experience. Someone somewhere may be trying to understand a sentence in a language I've never even heard spoken. That changed how I looked at this tool. The Hard Part Wasn't Adding a Dropdown A dropdown with 200+ options looks impressive. But that's not the real problem. The

Enhancing CI/CD and E2E Testing with Sentry Integration in tvview TL;DR: I integrated Sentry for error tracking and improved End-to-End (E2E) testing in the tvview project, enhancing the CI/CD pipeline. This resulted in a score increase from 85 to 95+. The Problem The tvview project lacked comprehensive error tracking and E2E testing, making it difficult to identify and resolve issues in production. The existing CI/CD pipeline needed improvement to ensure smoother deployments and better code quality. What I Tried First Initially, I focused on setting up E2E tests using Vitest, but encountered issues with the test configuration. I also attempted to integrate Sentry, but faced challenges with the DSN (Data Source Name) configuration. The Implementation Step 1: Configuring Sentry To integrate Sentry, I created separate configuration files for the client, edge runtime, and server: // sentry.client.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sent " , // Additional configuration options }); // sentry.edge.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sent " , // Additional configuration options }); // sentry.server.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sentry.io " , // Additional configuration options }); Step 2: Enhancing CI/CD Pipeline I updated the .github/workflows/ci-e2e.yml file to include Sentry configuration and E2E testing: name : 📺 CI + E2E — TVView on : push : branches : [ main ] workflow_dispatch : {} schedule : - cron : " 35 6 * * *" jobs : build-and-test : runs-on : ubuntu-latest steps : - name : Checkout code uses : actions/checkout@v2 - name : Install dependencies run : npm install - name : Generate Prisma client env : DATABASE_URL : " postgre

Enhancing CraveView's CI/CD Pipeline with Sentry and E2E Tests TL;DR: I upgraded CraveView's CI/CD pipeline by integrating Sentry for error tracking and implementing End-to-End (E2E) tests, boosting the score from 85 to 95+. This technical deep-dive explores the architecture decisions, code changes, and lessons learned. The Problem The initial problem wasn't a single error message but a series of inefficiencies in the CI/CD pipeline. The existing setup lacked comprehensive error tracking and test coverage, leading to potential issues in production. Specifically, the pipeline didn't have: Robust Error Tracking : No integrated system for capturing and analyzing errors. End-to-End Tests : Limited test coverage, which could lead to undetected issues in production. What I Tried First Initially, I focused on enhancing the test suite. I explored various testing frameworks but decided to implement E2E tests using Vitest, given its compatibility with the existing tech stack. The first approach involved setting up a basic E2E test framework. However, I encountered issues with the test environment configuration, particularly with database connectivity. The tests required a realistic database setup, which wasn't properly simulated. The Implementation Step 1: Configuring Sentry To integrate Sentry, I created configuration files for client, edge, and server initialization: sentry.client.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sentry.io/4511629 " , // Additional config options }); sentry.edge.config.ts and sentry.server.config.ts follow a similar structure, adjusted for their respective environments. Step 2: Implementing E2E Tests I added a new test file e2e-production.test.ts in src/__tests__ : import { test , expect } from ' @playwright/test ' ; test ( ' should render the homepage ' , async ({ page }) => { await page . goto ( ' https://craveview.vercel.app ' ); await expe

Upgrading CI Workflows: From Node 20 to Node 22 and Actions v5/v6 TL;DR: I upgraded the CI workflows for the content-automation repository from Node 20 to Node 22 and Actions v5/v6, addressing compatibility issues and improving performance. Key changes included updating upload-artifact from v5 to v7 and implementing retry with backoff. The Problem The CI workflows for the content-automation repository were using Node 20 internally, despite the configuration specifying Node 20. This discrepancy caused compatibility issues with newer versions of the GitHub Actions. Specifically, the upload-artifact action was still on version 5, which was internally targeting Node 20. What I Tried First Initially, I attempted to update the upload-artifact action to version 7, which supports Node 22. However, this change alone did not resolve the issue, as other actions like checkout and setup-python were still on older versions. The Implementation To address the compatibility issues, I updated the following actions: upload-artifact from v5 to v7 checkout to v5 setup-python to v6 Here are the specific code changes: // .github/workflows/main.yml steps: - name: Checkout code uses: actions/checkout@v5 - name: Setup Python uses: actions/setup-python@v6 - name: Upload artifact uses: actions/upload-artifact@v7 Additionally, I implemented a retry mechanism with backoff for the CI workflows: // .github/workflows/main.yml steps : - name : Retry with backoff run : | for i in {1..3}; do if ./script.sh; then break else echo "Retry $i failed, backing off..." sleep $((i * 2)) fi done Key Takeaway The key takeaway from this experience is the importance of keeping CI workflows up-to-date with the latest versions of GitHub Actions. This not only ensures compatibility but also improves performance and reliability. What's Next Next, I plan to monitor the CI workflows for any issues and continue to optimize the retry mechanism for better performance. I will also explore other ways to improve the reliabili

The best developer tools share one quality: you forget you are using them. Think about it. Your IDE fades into the background when you are in flow. Your terminal becomes muscle memory. These tools are invisible because they match your mental model so perfectly that there is zero friction between thought and action. This is exactly what online developer utilities should aspire to. The Problem with Fragmented DevTools Most developers have a bookmarks folder full of single-purpose websites: One for JSON formatting One for timestamp conversion One for Base64 encoding One for URL decoding Every switch between these tabs is a context loss. Every tool has a slightly different UI, different copy-paste format, different quirks. The Invisible Toolkit Opennomos Json (opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y) consolidates timestamp conversion, JSON formatting, and Base64 encoding into a single workspace. No install. No accounts. No pricing tiers. Just open a tab and work. This is the north star for developer tools: make them so simple that the user never has to think about the tool itself — they only think about their actual task. The Trend Is Clear We have seen this pattern across the dev ecosystem: GitHub Codespaces made local IDE setup invisible Vercel made deployment invisible Replit made runtime environment invisible The next frontier is utility tools. The sooner they become invisible, the better. Try it: opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y Part of the Nomos Build-in-Public series.

Finding a co-founder is one of the hardest parts of building a startup, and most platforms weren't built for it. LinkedIn is a professional directory, not a matching network. Reddit threads are noisy and unstructured. Cold outreach is a gamble. FoundrGeeks is built specifically for this problem . It's an AI-powered co-founder and team matching platform that connects builders based on what they're building, what skills they bring, and what gaps they need to fill, not just their job title or who they already know. The problem with finding a co-founder most builders looking for a co-founder face the same wall: the people they need aren't in their network, and the platforms that exist weren't designed for this specific search. You're not just looking for someone with the right skills. You need someone at the same stage, with the same intensity, who fills exactly the gaps you have right now. And you need to know that before spending three hours on discovery calls. That's the gap FoundrGeeks fills. How FoundrGeeks works When you create a profile, you describe what you're building, what you bring to the table, and what you need. You set your stage, idea, MVP, or funded, and your weekly availability. From there, the AI takes over. It surfaces people whose strengths complement your gaps, scores each match as Strong, Good, or Potential, and generates a plain-English explanation of why each person fits what you're building right now. Three features stand out at launch: Complementary matching: the engine looks for people who fill your gaps, not mirror your background Scored matches with explanations, every match tells you exactly why, before you reach out Stage-aware feeds, as you move from idea to MVP to funded, your matches reshuffle automatically You also control your visibility, go public and let talent find you, or stay private and let the AI work quietly on your behalf. Why we built this This platform exists because of a project that never got finished. I had an idea I wa

Instead of writing long build logs or recording traditional vlogs, my co-founder and I wanted to try something different. We're documenting our startup journey by turning it into an AI-generated anime series. Not for fiction. For real startup moments. Episode 2 follows our cold outreach journey: Finding an ICP Testing different niches Sending DMs Getting ignored Learning what works (and what doesn't) We're treating this as an experiment to see whether AI-generated storytelling can make the process of building a startup more engaging than the usual "build in public" content. The goal isn't perfect animation. It's authentic documentation—with AI as the creative medium. We're still figuring it out, improving every episode, and learning as we go. Would love to hear what fellow builders and developers think about this approach. Could AI-powered anime become a new way to document products, startups, and open-source projects? Feedback is always welcome. 🚀

A year and a half ago, I wasn't trying to build an AI company. I was building a small social platform called spritex-social — nothing fancy, just a side project a handful of friends were testing with me. No grand plan, no investors, no roadmap beyond "let's see if people like this." At some point, users started asking the same basic questions over and over: how do I change my profile, where's this setting, how does that feature work. Instead of writing endless documentation, I thought — why not just let AI answer this? So I wired up Google's Gemini API through Google AI Studio, built a small Retrieval-Augmented Generation (RAG) system, and gave it context about the platform. It was supposed to be a support chatbot. Nothing more. That's not how it went. I found myself spending more time improving the chatbot than improving the actual social app. Every small upgrade made me ask another question: could it remember conversations? Could it use tools? Could it search the web? Could it do things instead of just answering questions? The more I asked, the less interested I became in the social platform I was supposed to be building. Eventually I had to admit it to myself: I wasn't building spritex-social anymore. I was building something else entirely. So I stopped. Not because the project failed — because my attention had already moved somewhere else, and I finally stopped pretending otherwise. That "somewhere else" became RexiO — a Bangla-first AI platform I've been building solo ever since: my own orchestration layer, an intent classifier, 30+ tools, model routing across providers, and eventually our own fine-tuned models trained from scratch on borrowed Colab GPUs. RexiO went public on July 10, 2026. This chatbot pivot is just one chapter of a much longer story — one that actually starts on a Nokia button phone, ২ টাকা data packs, and a ৳20 freelance job that became my first line of code in production. I wrote the whole thing down, unfiltered — the rewrites, the 12-hour

How I Built 7 Apify Actors and Started Earning Passive Income from Web Scraping A few weeks ago I had zero Apify actors. Now I have seven, all published on the Apify Store, monetized with pay-per-event pricing, and slowly building a passive income stream. Here's exactly how I did it — the strategy, the tech stack, the mistakes, and what I'd do differently. The Strategy: Zero Competition Most new Apify developers go after hot categories. LinkedIn scrapers, Amazon product extractors, Twitter data. Makes sense — those have demand. But they also have dozens of established actors with hundreds of reviews. I took the opposite approach. Find niches with zero existing actors. This means lower total addressable market, but 100% of whatever traffic exists goes to you. No competing on price, no fighting for reviews, no SEO war against actors with years of history. How I found the niches: Browsed Apify Store categories sorted by actor count Searched for common developer pain points with no existing Apify solution Checked search volume for "[keyword] API" and "[keyword] scraper" Verified zero results on Apify Store for each candidate The winners: domain intelligence, screenshot comparison, Swedish company registry, IP geolocation, QR code generation, and link metadata extraction. The Tech Stack Every actor uses the same foundation. Apify Python SDK v3.4 handles input/output, storage, proxy, and deployment. Playwright for JavaScript-heavy sites and screenshots. aiohttp for lightweight API scraping (way faster than a full browser). Pillow for image processing. Deployment is one command: apify push The Actors {{domain-intel}} WHOIS, DNS, SSL, and tech stack in one API call. Uses socket + ssl + python-whois for data collection, no external API dependency. $0.005 per run. {{screenshot-api}} Full-page screenshots via Playwright. Handles lazy-loading, infinite scroll, and viewport sizing. $0.003 per run. {{metadata-extractor}} Open Graph, Twitter Cards, JSON-LD, and meta tags from any

Every developer has been there: you need to format a JSON blob, decode some Base64, or convert a timestamp. You open your terminal, look for the right npm package, or — worse — write a quick script. I used to do this too. Then I discovered a better pattern. The Problem with Local CLI Tools Local tools have real drawbacks: Installation overhead : npm install -g some-tool for a one-time task Version rot : tool stops working after OS update No sharing : you format JSON but cant send the result to a colleague Environment drift : works on your machine, not on staging Online Tools as a Pattern Opennomos Json (reachable via opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y) represents a shift: developer tools as a platform , not as utilities you install. What makes this different: Zero install — browser tab, done Cross-device — phone, laptop, any OS Shareable results — formatted output has a URL you can send to teammates Timestamp converter built in — ms, seconds, ISO 8601, bidirectional Base64 codec — no need for a separate site The Bigger Trend We are seeing the same pattern across the dev ecosystem: GitHub Codespaces (IDE in browser), Replit (runtime in browser), Vercel (deployment in browser). The next frontier is utility tools in browser . Why run jq locally when a well-designed online tool does it faster and gives you a share link? Try It Head to opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y — the JSON tools are free, fast, and part of a broader contributor rewards system that makes open-source tooling sustainable. Built as part of the Nomos Build-in-Public series.

It Started With a Simple Misunderstanding I was spelling something over a phone call. I said: "B" The other person heard: "D" So I repeated it. Still wrong. Then I remembered something I'd heard before: "B as in Bravo." Instantly... There was no confusion. That's When I Realized Some letters sound almost identical. Especially over: Phone calls Weak connections Noisy environments Different accents And repeating the same letter five times doesn't always help. Why I Built This Tool So I built something simple: 👉 https://allinonetools.net/phonetic-alphabet-converter/ A tool that instantly converts normal text into the NATO phonetic alphabet. For example: CHAT Becomes: Charlie Hotel Alpha Tango No signup. No setup. Just: Paste → Convert → Read What I Learned Before building this, I thought the phonetic alphabet was mostly for pilots or the military. Turns out it's useful for anyone who needs to spell things clearly. Like: Email addresses Usernames License keys Customer support Phone conversations The Small Problem It Solves Have you ever said: "M" And someone replied: "N?" Or: "P?" 😅 That's exactly the kind of confusion this avoids. Why It Works So Well Instead of similar-sounding letters... You use unique words. Like: A → Alpha B → Bravo C → Charlie D → Delta It's much harder to misunderstand. What Surprised Me I expected only developers or IT people to use it. But it also makes sense for: Customer support Call centers Students Remote workers Anyone spelling things over the phone What I Focused On I wanted the tool to be: Fast Simple Easy to copy Beginner-friendly Because if you're already on a call... You don't want extra steps. The Real Insight Good communication isn't always about saying more. Sometimes it's about making sure the first attempt is understood. Simple Rule I Follow Now If people keep repeating themselves... 👉 There's probably a simpler way to communicate. Final Thought The NATO phonetic alphabet has been around for decades. But after using it once... Yo

This is the story of a for-fun project, Luka Fit Index that started with me typing "ai for fun?...

The 2026 World Cup has $3.89 billion bet on it across Polymarket. That's not retail money — that's whales. I built WhaleTrack to track exactly what those big wallets are doing. Here's the stack: Backend: Node.js server fetching live data via Bullpen CLI Frontend: Vanilla JS, real-time updates Data: Polymarket CLOB API via Bullpen Analytics: Google Analytics for traffic tracking The hardest part wasn't the code — it was getting users. Pure SEO and content distribution (Reddit, Twitter, IH). The site is live at whaletrack.app — would love feedback from devs on the UX and performance. Happy to open source parts of it if there's interest.

A note before we start: this is about the machine, not the money. I'm not going to show you returns, positions, or a single "this strategy made X%." Partly because that's a regulatory minefield, and partly because the returns aren't the interesting part — the engineering is. If you came for a get-rich screenshot, this isn't that. If you came to see how one person ships production infrastructure with an AI, pull up a chair. The thing I built Over the last few months I built, with an AI coding agent as my pair-programmer, a fleet of ~35 automated trading bots. They run across five equity markets plus crypto. Each one is a long-running service. They share a single database, post to a live dashboard, fire alerts to my phone, and — the part that took the longest — they're built to survive restarts, reconcile against reality, and refuse to do anything stupid. I'm one person. I am not a team. The "team" is me plus an AI in a terminal, working the way you'd work with a very fast, very literal junior engineer who never gets tired and occasionally needs to be talked out of a bad idea. Here's how it's put together, and the handful of lessons that cost me the most to learn. The architecture, in one breath One Postgres database is the brain — every trade, signal, and piece of state lives there. Around it sit ~35 containerized bots, each isolated (its own tables, its own config, its own identity), orchestrated with Docker Compose. A Streamlit dashboard reads the database and renders the whole fleet — open positions, P&L curves, health. A notification layer pushes Telegram alerts on every meaningful event. Schema changes go through migrations so a new bot is never born with a stale database shape. Each bot is the same skeleton wearing a different hat: a signal module (the strategy logic), a trader that turns signals into orders, a storage layer that persists everything, a runner loop on a schedule. Strategies are swappable. The infra underneath them is identical. That sameness is

SokoFlow Build Log — Month 1 of 4 Last semester I set out on a new strategic plan to level up my software development skills through deliberate, project-based learning. That work produced one of the most ambitious things I've built so far: Sim-Pesa , a local-first transactional appliance that lets developers working in the M-Pesa ecosystem test and simulate STK Push workflows entirely on their own machines, without depending on the Daraja sandbox. I documented that build in 16 weekly posts, which you can find here . This semester, the focus shifts — from fintech foundations to cloud-native integration and real-world systems. The flagship project is SokoFlow , a conversational ERP for small Kenyan shopkeepers to track inventory and record sales entirely through WhatsApp chat. No app to download, no training session required — just natural language. Where Sim-Pesa lived in a controlled, predictable transactional world, SokoFlow steps into the mess of cloud-native reality: third-party API failures, webhook signature verification, the statelessness of HTTP, and container orchestration. The target audience shifts too — Kenyan SMEs operating on infrastructure that is often unreliable by design, not by exception. It's an ambitious project, but the goal was always to learn as much as possible from it. With the plan in place, I got to work. 1. The Vision of a Headless ERP The first real question I had to answer before writing a line of code: what does "headless" actually mean? Headless architecture decouples the frontend — the "head," or user interface — from the backend, the "body" that holds the data and business logic. A conventional ERP bundles both: backend plus a dashboard or UI on top. A headless ERP, by contrast, is just the engine. The brain. There's no built-in screen. So how do users interact with a system that has no interface of its own? SokoFlow doesn't actually care. It could be: WhatsApp SMS A web app A mobile app A voice assistant In this case, the "frontend

Hi 👋 I'm Jonas. CS bachelor, Entrepreneurship master. By day I'm at nono . On the side I'm building SportsFlow solo — and I'm going to write about every hard part of it out in the open. This is the intro. What I'm building SportsFlow replaces the thing every amateur handball coach still uses: a clipboard and a pen. The idea is simple. Live-track every shot, assist and save during the game on a phone or tablet, and get real season analytics out the other end — shooting percentages, heatmaps, goalkeeper saves, momentum, lineup impact. Handball first, then volleyball, basketball, ice hockey. I sat on enough benches to know the problem is real: the data is right there in the game , and it evaporates the second the whistle blows. Nobody should need a spreadsheet and a good memory to coach with numbers. Why I write about both code and product I build at the seam between engineering and product — that's the CS + Entrepreneurship combo. So I won't only post architecture. I'll also post the decisions about what's worth building at all : where I drew scope lines, what I deliberately didn't build, how billing shapes the data model. The recurring theme in everything here is one idea: Make the correct thing structural. Idempotency in the schema, not the network. Tenancy in the procedure, not the query. Types from one zod definition, not two. Discipline the system enforces, not discipline you have to remember — because when you're solo, the stuff you have to remember eventually fails. What you'll get if you follow along Biweekly build-in-public deep-dives, including the honest costs and not just the wins: Offline-first capture — sports halls have zero WiFi, so the whole tracking pipeline works offline and reconciles later without double-counting goals. One analytics codebase, three runtimes — the same shooting-percentage code runs in the web app, the native app, and offline during live tracking, so the numbers can never disagree. Shipping four apps solo from one monorepo — web, n

I use my SSH manager every day. I also use a separate monitoring tool every day. For a long time I just accepted that these were two different things. Then one day I was SSH'd into a server that was behaving weird. I wanted to check if it was CPU or memory, but I had to open a different app, find the server in there, and wait for the dashboard to load. It took maybe 15 seconds. Not a huge deal. But it broke my flow every single time. I already had an SSH connection open to that server. Why was I opening a second thing just to see what was happening to it? That's what pushed me to build server monitoring directly into Termique, the SSH manager I've been working on. The interesting part: reusing the existing SSH connection SSH connections aren't just for terminals. The protocol supports multiple channels over a single TCP connection. You can have a terminal session running in one channel while sending short exec commands through another channel on the same connection. That's how the monitoring feature works. When you open the metrics panel for a server, Termique creates a separate exec channel on the existing SSH connection and polls /proc/stat for CPU, /proc/meminfo for RAM, and /proc/loadavg for system load. Short-lived commands, called on an interval, over the connection you already have open. No second SSH handshake. No separate auth. Just another channel on the same pipe. The tradeoff: you do need an agent I want to be upfront about this. The monitoring feature requires a small agent installed on each server. It's not agentless. I considered going agentless, relying entirely on /proc reads through exec channels. That works fine on most Linux servers. But the agent makes it easier to handle edge cases properly and opens the door for future features like alerts and longer history retention. Without it, I'd be fighting a lot of fragile shell parsing. If you're managing Linux servers, it's a one-command install. Non-Linux systems aren't supported yet. That's a real l

I got laid off in March 2026. The day HR handed me the 30-day notice, I had a small panic attack, then opened my laptop and started building things. Here's the deal: I had 30 days before severance ran out, and I wanted to see how much I could ship with AI tools before the money (and motivation) ran dry. I gave myself a single rule — every project gets a 7-day deadline, otherwise I kill it. I built 6 things. One has real users. One broke in production. Two I never opened again. This is what happened, in the order I built them. 1. AI Buddy (Chrome sidebar) — shipped, 15 users A Chrome extension that puts an AI assistant in a sidebar. Select text on any page, hit a keyboard shortcut, it goes to the AI, reply shows up without you leaving the page. Works with GPT-4, Claude, Gemini, DeepSeek. No login, no credit card. Time: 11 days (April 1–11). Status: Live on Chrome Web Store. 15 real users as of June 28, 2026. Rating 4.2. What I used AI for: 90% of the code (500 lines of JavaScript, written in Cursor). The README, the Chrome Web Store description, the marketing tweets — all AI-drafted, then I rewrote the parts that sounded like AI. What went wrong: The first version had a Stripe integration. AI wrote 90% of the webhook signature verification. I had to rewrite it from scratch. Also the model-picker UI went through 5 revisions because AI kept proposing what looked right but didn't work. → Chrome Web Store 2. Weekly report generator — personal use only Every Friday at 4pm, a script grabs my git commits, Slack messages, and Linear ticket changes, throws them at GPT-4, and asks for a "manager-readable" weekly report. I review, tweak, send. Time: 2 days. ~200 lines of Python. Status: Running for 11 weeks. Has 1 user. Me. Cost is $0.12/week. What I used AI for: The prompt. It's surprisingly tricky to get GPT-4 to write a weekly report that doesn't sound like a robot. The single most useful line: "if you don't have data, write 'no progress this week' — don't make things up." T