Programming

Building Automated Developer Pipelines Here is how I automated my LeetCode progress, LinkedIn posting, and ATS resume updates using GitHub Actions and Python! Event-driven automation saves hours. Building in public creates continuous proof of work.

Tired of paying $10/month or sitting through 30 seconds of ads every 3 songs? Me too. So I built HeySongers — a completely free, open-source music streaming app for Android and Windows. Here's what makes it different: No ads. Not a single one. No banners, no video ads, no sponsored playlists. No premium tier. No "limited free plan." No catch. Just music. If you're tired of the subscription economy eating your wallet, give it a try: ⬇️ Download APK/EXE ⭐ Star on GitHub Built with ❤️ by geethudino

I wrote a single-file INI parser/editor for .NET that preserves formatting using regex instead of dictionaries with JSON support. I've been experimenting with a different approach to editing INI files in C#. Most libraries parse the file into dictionaries or objects and then regenerate the entire file when saving. That works well for configuration data, but it usually destroys comments, blank lines, indentation, entry order, or duplicate keys. Instead, I treat the INI file as plain text and inde

A week of Claude Code and Codex work leaves enough local evidence to answer concrete questions. When were sessions active? Which provider produced the activity? Which models appeared? How did the token mix change by day? It does not prove that the code was correct, the week was productive, or an API-value estimate was money spent. That boundary is what makes a weekly report useful. A readable report should cover today plus the previous six local calendar days. The headline, daily bars, provider

The most dangerous number in an AI coding dashboard is the one labeled cost without a definition. A local tool can reconstruct token activity from Claude Code and Codex session records. It can apply a dated model-price table. That produces an estimate with a clear use: comparing periods and understanding the shape of local activity. It does not produce an invoice. Claude Code and Codex do not write identical usage records. Claude can expose input, output, cache creation, and cache-read fields on

Short version first, for anyone who just wants the outcome: I run Claude Code on a flat monthly subscription, not pay-as-you-go. For months that plan never gave me a reason to look closely at it, so I stopped looking. Then an overnight automation burned through a week's worth of it in two days, the account locked with four days still to go, and I bought my way out at list price. That cost enough to make me build a small usage meter that now tells me, in plain language, whether I can spend more o

Building a Live Status Dashboard for Your Python Bot A JSON health check is enough for a hosting platform, but it's not much use to you when you want a quick, human-readable view of whether your bot is actually doing its job. This guide covers building a real python bot status dashboard — with proper HTML, CSS, and a bit of JS — using nothing more than StayPresent's web.html() and the HTTP server it already runs. Why a Dashboard Beats a JSON Blob How web.html() Works Serving Static Assets Alongs

Migraste una base de datos MySQL de Amazon RDS a Amazon Aurora. La read replica de Aurora se puso al día, la promoviste, cambiaste el endpoint de la app, y ahora los writes aterrizan en Aurora. Diez minutos después, o quizá tres horas después, algo anda mal y quieres regresar a la vieja instancia de RDS. Aquí está la trampa. La vieja instancia de RDS está congelada en el momento en que hiciste el corte. Cada pedido, registro y edición que pegó en Aurora desde entonces no existe en RDS. "Revertir

Track 3: AI & Agent Observability — "Agents of SigNoz" Hackathon (WeMakeDevs × SigNoz, July 2026) Every team at a hackathon like this is going to show you an agent that's slow, or an agent that errors. We're showing you something worse—and far more common. A multi-agent system where nothing ever fails. No exception. No page. No red dashboard. And yet the system is still stuck. Tasks bounce endlessly between an Executor and a Critic. Work is quietly repeated even after it's completed. Sometimes a

Here is a task that comes up constantly. You are doing inference in Rust — candle, burn, ort — and you want to feed a video to a model. Step one is always the same: get the frames. And in Rust today, getting frames means one of two things. Either you shell out to Command::new("ffmpeg"), write PNGs to disk or parse -f rawvideo off a pipe, and read them back; or you reach for the low-level bindings and hand-write the send_packet / receive_frame decode loop — forty lines before the first pixel, and

Anthropic says Fable 5 can play Pokémon from vision alone. I verified that claim independently: 1,785 decisions and $65.40 to the first gym badge — then I read its thinking chains in the logs, and the result stopped looking so simple. In short — Anthropic's Fable 5 launch page claims the model beat Pokémon FireRed "with a minimal, vision-only harness." No protocol, no step count, no cost, no code was published. So I built my own harness — matching or undercutting every constraint in their descri

Deploying Python Bots to Koyeb and Heroku with StayPresent Render and Railway tend to dominate the conversation around free Python bot hosting, but Koyeb and Heroku are both still widely used, and both come with their own quirks around ports, process types, and health checks. This guide walks through a koyeb heroku python bot deployment using StayPresent, covering what's different about each platform and what stays exactly the same. What Koyeb and Heroku Have in Common Deploying to Heroku with a

Deploying Python Bots on Docker and a VPS with StayPresent Not every bot lives on a managed PaaS. Plenty of developers run their python bot docker deployment on their own infrastructure — a VPS, a home server, a Kubernetes cluster — where there's no platform automatically restarting crashed containers or bundling in a health-check dashboard for you. This guide covers both environments: containerizing a bot with Docker, and running it directly on a VPS with systemd, using StayPresent to handle th

ok so real talk. your latency graph looks clean. error rate is chilling. every /chat is HTTP 200. and the answers are just… wrong. like confidently wrong. mid. that’s the thing that lowkey haunted us after HackRx RAG 6.0. like ~1000 teams, we somehow landed top 30, we were deep in evals / retrieval / “is this grounded tho”, and the whole time our “monitoring” was basically vibes + batch scripts. RED metrics (rate, errors, duration) stay green while the model yoinks the wrong KB. for a startup sh

Yelp has confirmed an agreement with OpenAI to extend its trusted content into the AI ecosystem, creating a foundation for AI-driven local discovery on OpenAI platforms. The agreement matters because conversational tools such as ChatGPT could move beyond broad local recommendations toward answers informed by Yelp's business information. Reporting on the arrangement indicates that reviews, ratings, photos and other business details are expected to be part of that experience, although key product

I'm a backend engineer (Java/Spring, Kubernetes, that world) moving toward AI engineering, and I wanted to actually ship something in the agent ecosystem rather than read about it. So I built and published a small Model Context Protocol (MCP) server. This is the first of a planned series that gets progressively harder; this one was deliberately trivial in scope, because the real goal was to close the full loop: build → publish to npm → list in the official MCP registry → get discovered. The subj

I spend a lot of time talking founders out of ideas that sound good in conversation but fall apart under scrutiny. This isn't because the ideas are necessarily bad—it's because most people haven't actually modeled what it takes to get there. They have a story about the business, but not the financial truth underneath it. Early stage, this looks like long discussions about addressable market and customer acquisition cost. Someone will say "if we get just one percent of the market" and then stop t

`I spent months building a Python terminal library that rivals Rich's feature set but uses zero external dependencies. Here's the technical breakdown. from shinyshell import Shell 📊 Charts — pie, bar, line, histogram, scatter, waterfall, heatmap, gauge The entire library uses only Python standard library. No pip install nightmares. Every color, every table border, every animation — built from sys.stdout.write() and ANSI escape sequences. CMD doesn't support ANSI by default. Had to use ctypes +

ChatGPT vs. Perplexity: Which AI Handles Live Market Data Better? To see which tool handles real-time financial research better, I benchmarked ChatGPT against Perplexity across identical market queries—testing for speed, accuracy, and citation transparency. ⚡ Speed & Latency ChatGPT: Performs deeper synthesis, but search latency slows down simple retrieval queries. 🎯 Data Accuracy & Citations ChatGPT: Outperforms when you need to transform or run code against the data, but occasionally relies o

I Built an AI Agent That Argues With Itself Before It Ships Anything How I built **SignozForge Observer, a multi-agent AI system that * and OpenTelemetry.* Every AI coding assistant today has the same weakness. It can generate answers that sound convincing even when they're The dangerous failures aren't syntax errors---they're responses that look finished while quietly asking the developer to fill in the One example that inspired this project was asking an AI to connect to a Replace username wit

AI is becoming core business infrastructure. Learn how leading organizations are redesigning operations, workflows, and decision-making—not just automating task

MetaMask, Coinbase, OKX, and BNB Chain all shipped AI agent wallets in June 2026. The $3–5T agentic economy needs a payment layer that runs at machine speed. He

A firsthand story from a Dubai crypto conference during the ICO boom, where whitepapers and technological hope briefly became strange reality.

AI governance is no longer optional. Learn a practical five-step framework to manage AI agents, shadow AI, risk, compliance, and continuous monitoring.

Reuters: China's market regulator says it had fined and confiscated ~$770M from Trip.com for abusing its dominant position in the domestic online hotel-booking market — China's market regulator said on Saturday it had fined and confiscated a total of 5.2 billion yuan ($770 million) from Trip.com Group …

One broken tool call. An agent retried it 400 times in five minutes. Nobody noticed until the bill arrived. This is what loop engineering exists to prevent.

Most Web3 communities aren't real communities. Learn how quests, airdrops, vanity metrics, and incentive farming create growth that quickly disappears.

Learn everything you need to know about Devops Tools via these 242 free HackerNoon blog posts.

Learn why moving your stop loss hurts long-term returns, the psychology behind it, and how disciplined risk management protects traders.

Learn the network security best practices that protect multi-site businesses with segmentation, zero trust, firewalls, secure remote access, and monitoring.

Learn why replacing Go MPSC channels with a database-backed queue made a GitHub workflow automation tool resilient to crashes, retries, and failures.

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Ima Jackson-Obot / Financial Times: Several universities including Yale, Johns Hopkins, and the University of Waterloo have restricted or disabled their use of AI detectors over accuracy concerns — Some institutions are overhauling assessment and trying to move away from the emphasis on surveillance

Preview: Un Tux 3D en la terminal, hecho con C: /* ----------------------------------------------------------------- * * TUXRAY - Raymarched Tux, Rendered as Tux * * * * Un renderizador 3D por raymarching de Tux (la mascota de Linux) * * corriendo directo en la terminal ANSI, en el mismo espiritu que * * el clasico donut.c -- solo que aqui el codigo fuente mismo esta * * doblado con la silueta de un Tux (cabeza, cuello y cuerpo que se * * ensancha. Hice lo que pude). * * * * * * * Uso: gcc tux.c

The Problem Manual security code review costs roughly $85/hour of engineer time and doesn't scale with commit velocity. Most AI code-scanners stop at "here's a vulnerability" — they don't verify their own fixes, and they're completely opaque about what they're actually doing under the hood. DevGuard AI is my attempt at fixing both problems for the "Agents of SigNoz" hackathon: an autonomous pipeline that detects vulnerabilities, patches them, adversarially reviews its own work, and — critically

A follow-up/sub-part to Part 3 of the LLM inference internals series. Part 3 built sampling-mode speculative decoding with KV caching on both the draft and verifier sides, and it worked correctly, but the speedup it delivered didn't match expectations set by the paper. This post is the record of chasing that gap: every hypothesis tested, which ones were wrong, and what the real answer turned out to be. The first full gamma sweep, on an open-ended, opinion-style prompt, came back like this: Mode

Background removal tools usually rely on cloud server GPUs, which introduces latency and compromises user privacy. I built Pixel Isolate to run 100% of the extraction and HSV color keying inside the user's browser using WebAssembly. Engine: WebAssembly & C++ OpenCV bindings Frontend: React, TypeScript, Vite, TailwindCSS Database & Auth: Supabase Payments: Paddle Check out the live app at https://pixelisolate.online/ and let me know your thoughts!

Sábado noche, 00:00 — codeando y hablando de entropía con mi agente 🤖🔥 No es una noche de fiesta. Es una noche de... bueno, mi tipo de fiesta. Resulta que llevo un rato largo con mi agente (Hermes Agent, una maravilla de Nous Research) explorando un tema que me tiene enganchado: la entropía como flecha del tiempo en sistemas software. Y oye, ha sido una conversación de otro nivel. El software envejece aunque no lo toques. No es fallo tuyo. Es termodinámica. Las leyes de Lehman (1974-1996) lo d

While building a Telegram Mini App recently, I hit the same wall every TMA developer knows: Code your app locally. Spin up ngrok (hope it doesn't crash). Go to @botfather, set the web_app URL to the tunnel. Grab your phone, launch the app, console.log() and pray. Repeat for every theme / platform / user combination you need to test. There's no official Telegram Mini Apps emulator. The workflow is stuck in 2015. Developers in the Russian TMA community have been complaining about it publicly: "The

Last week I priced inference for a realistic autonomous AI agent at roughly $27 per active user per month. The math that explains why headline LLM rates and the actual cost of running an agent are an order of magnitude apart. This week I priced the prospect data the same agent needs to find anyone to message. Data won by a factor of two, and unlike inference, the price isn't dropping. This is constraint B of the autonomous-AI-runs-your-company audit: the data wall. It's the constraint that doesn