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Here's a thing that surprises people the first time: an email sent through the API does not carry the signature the user set up in Gmail or Outlook. Provider signatures live in the provider's compose UI, and a programmatic send bypasses that entirely, so a message your app sends goes out with no signature at all unless you add one. The Nylas Signatures API is how you add it: store an HTML signature once, then attach it to a send by ID, and the signature gets appended to the message for you. This post covers signatures from two angles: the HTTP API your backend calls, and the nylas CLI for creating and testing one from the terminal. I work on the CLI, so the terminal commands below are the ones I reach for when I'm setting a signature up. Nylas signatures are separate from provider signatures The first thing to get straight is that these are not the user's existing signature. Nylas doesn't sync the signature configured in Gmail, Outlook, or any other provider, and that provider signature is never applied to mail sent through the API. If a message your app sends needs a sign-off, you create that signature with this API and attach it explicitly; there's no inheriting it from the connected account. That separation is deliberate, because a programmatic send is a different context from a person typing in their webmail. It does mean the responsibility is yours: a user who connects their mailbox expecting their familiar signature to appear on app-sent mail won't get it automatically. Stored signatures are grant-scoped, living at /v3/grants/{grant_id}/signatures , so each connected account has its own set, and they're HTML, so a branded sign-off with a logo and links works the same as a plain one. Create a signature Creating a signature is a POST /v3/grants/{grant_id}/signatures with a name and an HTML body . The name is for you, a label to find it by later; the body is the markup that gets appended to outbound mail. The response returns the signature with its ID, which is w

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Introduction I'm Ahmer. I'm 19, I'm doing a 4-year Software Engineering degree at a fairly...

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Most people lose hours pretending to work or study. I was one of them. I kept setting Pomodoro timers and ending up scrolling Twitter during breaks. That's not rest. That's just a different kind of distraction. So I built Mognota — a free wellness companion for screen workers. The Problem You don't have a discipline problem. You have a system problem. Your brain has a hard biological limit. Sustained attention peaks at 20-45 minutes before quality drops sharply. Long unfocused hours are not work — they are the feeling of work. The solution isn't more willpower. It's intentional recovery. What I Built Mognota pairs a Pomodoro timer with 50+ guided wellness activities: 👁️ 20-20-20 eye break reminders 🫁 Guided breathing & 7 pranayama techniques 🧘 Desk yoga, HIIT, Tai Chi, stretches 🎵 Binaural beats & ambient soundscapes 🧠 Meditation & NSDR protocols 📓 Gratitude journal, mood tracker, brain dump 🎮 Sudoku, sliding puzzle, fractal explorer The Technical Part This is what dev.to might find interesting: Pure HTML, CSS, vanilla JS — zero frameworks Everything in a single HTML file All 8 notification sounds synthesized with Web Audio API localStorage only — nothing leaves your device Works offline once loaded 109+ languages supported No npm. No build step. No dependencies. Just open and use. Try It 👉 https://mognota.com/ Completely free. No account. No ads. Forever. Would love brutal honest feedback from this community.

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Over the past weeks, I’ve been sharing a series of posts that gravitate around one question: How do...

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I build Voxis, an open-source Windows app that translates whatever your system is playing — a video, a game, the other side of a call — and plays the translation back as spoken voice, a few seconds behind the speaker. No subtitles, no virtual audio cable, no bot joining your meeting. The "no virtual cable" part is the bit worth writing about. Almost every system-audio tool on Windows tells you to install VB-CABLE or VoiceMeeter, or to drop a bot into your call. Voxis doesn't, for incoming audio. This post is how that capture engine works, and the sharp edges I hit building it in Python. I'll be specific about what's hard and honest about what's not mine to fix. The goal Read the exact audio the user is hearing — the post-mix system output — at 16 kHz mono, and do it without installing anything. Then stream it to a translation model and play the result back, all while the original keeps playing underneath. Three constraints fall out of that: Driverless. If it needs a reboot and a driver, it's not zero-setup. No self-feedback. The app plays translated audio into the same system mix it's capturing . Naively, it would capture its own voice and translate the translation. That has to be impossible by construction, not patched with an echo gate. Realtime-safe. Capture can't stall. If the downstream VAD or garbage collector hiccups, the WASAPI ring buffer must not overflow. WASAPI process-loopback: capturing the mix, minus yourself Windows 10 version 2004 added the ApplicationLoopback API — a way to activate an IAudioClient in loopback mode scoped to a process tree, either including only that tree or excluding it. Excluding our own process tree is exactly what constraint #2 needs: the captured mix is everything the user hears, with Voxis's own output removed. You don't get this client from the normal IMMDeviceEnumerator path. You activate it by name through ActivateAudioInterfaceAsync , passing the loopback parameters in a PROPVARIANT carrying a BLOB : params = AUDIOCLIENT_

About a year ago, I turned my gaming PC into a local AI Lab. And yes, the most important word in that sentence is LOCAL . Let me tell you the story of how I sacrificed my gaming hours to build several tools, and now I'm going to tell you about this one that I use every single day. The Problem: Token bankruptcy Day to day, all of us developers who work with Artificial Intelligence share the same headache: tokens and rate limits . We're all victims of the high prices that come with constantly running inference with AI agents like Claude Code, Codex, or Gemini CLI (yeah, I love working from the terminal, I LOVE CLIs). While I was building AI systems (agent orchestration, LLM fine-tuning ), I was burning through way too many tokens. I tried tweaking the prompts and cleaning up the junk in my context, but the real devourer of my quota showed up when I had to learn a new tool. I was implementing solutions in QGIS (QGIS is a free, open-source Geographic Information System (GIS) software that allows users to create, edit, visualize, analyze, and publish geospatial data on maps) for a project and I didn't know the interface 100%. Like any dev facing something new, I leaned on AI agents: I'd take a screenshot, send it over, and ask for explanations. Here's an important fact that hurt my wallet: A screenshot on my MacBook (Full HD resolution of 1920x1080) burns about 258 tokens per tile on models like Claude. That adds up to roughly 1,548 tokens per image (sounds like a lot, and yeah my friend, it is way too much when we're talking about context). Now imagine sending dozens of these images a month trying to understand a complex interface as a 2x dev (99x, I'd say, in this new AI era). I was eating through my hourly Claude allowance just doing visual queries, leaving me with no quota left to generate the actual code I really needed for my development. The Epiphany (and the Hardware) One day, during a forced break thanks to a Claude rate limit , I looked over at my Gaming PC. I

Claude Code is a genuinely powerful CLI coding agent. Its context window handling and multi-file reasoning set a high bar in 2026. But it comes with real constraints - it requires an Anthropic API key, charges per token, locks you into Claude models only, and its source code is closed. For developers running local-first workflows, working in air-gapped environments, or simply preferring auditable tooling, those limitations are dealbreakers. The good news: the open-source ecosystem has matured significantly. Nine production-ready alternatives now cover every major workflow pattern - from terminal-first pair programming to fully autonomous task execution. Why Open Source Matters for AI Coding Agents AI coding agents operate at a high level of system trust. They write files, run commands, and modify your repository. That makes transparency genuinely important - not just philosophically. Open-source licensing lets you read the code, audit its behavior, self-host without sending data to a third party, and customize it for your team's needs. Beyond trust, the practical advantages are real. Open-source agents are model-agnostic by design. They connect to whichever LLM you prefer - Claude, GPT, Gemini, DeepSeek, or a local model via Ollama - letting you optimize for cost and capability on a per-task basis rather than being locked to one pricing tier. OpenCode - The Closest Open-Source Drop-In for Claude Code OpenCode has emerged as the de facto open-source answer to Claude Code in 2026, crossing 161,000 GitHub stars under an MIT license. It connects to over 75 LLM providers via Models.dev - including local Ollama models - and lets you switch providers mid-session. Internally it uses a dual-agent architecture: a Plan agent handles task decomposition while a Build agent executes changes. LSP integration brings symbol resolution into the terminal. Multi-session support lets you run parallel agents on the same project simultaneously. OpenAI Codex CLI - Auditable and Sandbox-Fir

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Sometimes, the best ideas come when you least expect them — like at 2 AM, scrolling through social media with nothing exciting to do. That’s exactly how IGram, my latest side project, came to life. No grand plans, no investors, no pressure — just a spark of curiosity and a desire to build something fun. In this post, I’ll share the story of how IGram started, what it is, the challenges I faced, and what I learned along the way. If you’ve ever wondered what it’s like to build a messaging app from scratch or are just curious about side projects, this one’s for you. HOW IT BEGAN _**A few days ago, stuck in an endless social media scroll loop, I suddenly thought, “Why not build my own messaging app?” Not to compete with the giants like Telegram or WhatsApp, and certainly not because I had a startup idea or funding. Simply because I wanted to see how far I could take it. That spontaneous idea turned into IGram, a project born purely out of boredom and a hunger to learn. What Is IGram? IGram is a modern messaging app inspired by platforms like Telegram and Discord. But it’s not a clone. Instead, it’s designed to feel fast, smooth, and enjoyable—an experience I wanted to craft from the ground up. It’s my personal challenge and learning experiment, built solo and fueled by the excitement of creating something new. Features You’ll Find in IGram Even though it started as a simple idea, IGram has grown to include a solid set of features: One-to-one messaging Group conversations Channel support Message reactions, editing, and deletion Reply and message forwarding Search functionality Dark and light themes Responsive design and mobile-friendly layout User profiles and modern UI animations Every feature is designed to keep the app feeling smooth and responsive, because the user experience matters just as much as the functionality. The Biggest Challenge: User Experience Surprisingly, writing the code wasn’t the toughest part—it was designing how everything flows and feels. Modern

Introduction Most developers today are learning how to “use AI APIs.” But that’s not enough anymore. The real shift happening in software engineering is this: We are moving from building APIs → to building AI-powered systems. And that requires a completely different mindset. The Problem with Most AI Tutorials Most tutorials show this: Call OpenAI API Get response Print output That’s it. But in production systems, this approach fails because it ignores: Context management State handling Reliability Tool integration System design In real applications, AI is not a function call — it is an orchestrated system. What an AI System Actually Looks Like A production AI system usually includes: 1. Input Layer Validation Preprocessing Safety checks 2. Reasoning Layer (LLM) Prompt engineering Context injection Model selection 3. Tool Layer APIs Databases Search engines Internal services 4. Memory Layer Conversation history Vector DB / embeddings User context 5. Output Layer Formatting Validation Response filtering Simple Example: From API Call → AI Agent Thinking Instead of this: response = client . chat . completions . create (...) We design something like this: class AIAgent : def __init__ ( self , llm , tools ): self . llm = llm self . tools = tools def run ( self , user_input : str ): context = self . build_context ( user_input ) response = self . llm . chat . completions . create ( model = " gpt-4o-mini " , messages = context , temperature = 0.2 ) return self . post_process ( response ) Now AI becomes: ✔ structured ✔ extendable ✔ production-ready Key Shift in Thinking Old mindset: “How do I call the model?” New mindset: “How do I design the system around the model?” That’s the difference between: ❌ AI script ✅ AI product system Why Tools Matter More Than Prompts Modern AI systems are not just text generators. They are tool-using systems . Examples: Search APIs (RAG systems) Databases (SQL, NoSQL) External APIs Internal business logic This turns AI from “chatbot” into “agent

If you've ever watched a hacker movie and seen someone plug in a USB and own a machine in seconds — that's not Hollywood magic. That's a Rubber Ducky. And I built one for under ₹150. Here's exactly how I did it, what it taught me, and why every security student should build one. What Even Is a Rubber Ducky? A Rubber Ducky is a USB device that pretends to be a keyboard. The moment you plug it in, the operating system trusts it completely — because keyboards don't need driver approvals or admin permissions. Once trusted, it starts "typing" pre-programmed commands at superhuman speed. We're talking 1000 keystrokes per second. By the time you blink, it's already opened PowerShell, run a script, and closed the window. The original Hak5 Rubber Ducky costs around $80. I built mine for ₹150. What I Used DigiSpark ATtiny85 — ₹120–150 on Amazon India Arduino IDE — free A Windows test machine (my own laptop) 15 minutes That's it. No soldering. No special skills. Just a tiny microcontroller the size of a thumb. Setting It Up Step 1 — Install Arduino IDE Download from arduino.cc and install normally. Step 2 — Add DigiSpark Board Support Go to File → Preferences and paste this into Additional Board Manager URLs: http://digistump.com/package_digistump_index.json Then go to Tools → Board → Board Manager, search Digistump and install. Step 3 — Install Drivers DigiSpark needs Micronucleus drivers on Windows. Download from the official Digistump GitHub and run the installer. Step 4 — Write Your First Payload This opens Notepad and types a message — my first ever "attack": cpp#include "DigiKeyboard.h" void setup() { DigiKeyboard.delay(2000); DigiKeyboard.sendKeyStroke(KEY_R, MOD_GUI_LEFT); // Win+R DigiKeyboard.delay(500); DigiKeyboard.print("notepad"); DigiKeyboard.sendKeyStroke(KEY_ENTER); DigiKeyboard.delay(1000); DigiKeyboard.print("Hello. Your keyboard is now mine."); } void loop() {} Upload it, plug in the DigiSpark, and watch it type on its own. That moment hits different when y

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