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a local, Claude-powered brain-assistant for solopreneurs I kept losing good ideas — not because I forgot to write them down, but because nothing ever went back and connected them. A task on Monday, an idea on Wednesday that was secretly the same problem, a question on Friday that contradicted something I'd decided two weeks earlier. All of it just... sat there. So I built Dot Connector : a small local app where every capture is a "dot," and every few captures, Claude reviews the stream against a standing memory and surfaces three things — non-obvious connections between notes, contradictions with what you said before, and open loops you haven't closed yet. The architecture is deliberately simple. It's Express + vanilla JS, no build step, no account system. Every capture gets a cheap, fast Claude call for auto-tagging. Every 3rd capture triggers a deeper "sweep" — a second Claude call that looks at recent captures alongside standing memory and open loops, and returns structured updates: new memory facts, dot-connects, contradictions, and open-loop resolutions. Why local-first. no server of my own, notes never leave your machine except the direct calls to Anthropic's API, bring-your-own API key so there's no subscription — you pay Anthropic directly, typically well under $1/month for personal use.] Its just a steal one-time $29 download All info and download get it here: https://dot-connector.eu

Part: 4 of 18 About this series This series documents the engineering evolution of a production-ready algorithmic trading platform in Python. It focuses on architecture, state management, execution, real-time data processing, persistence, and the engineering decisions that transformed a simple trading bot into a production-ready platform. In Part 3: Building a Production-Ready Position Manager for Algorithmic Trading , I described the component responsible for maintaining persistent position state throughout the entire trade lifecycle. Read Part 3 here: https://dev.to/pydevtop/building-a-production-ready-position-manager-for-algorithmic-trading-55n4 The Position Manager could remember every open position. The next challenge was deciding what should happen to those positions as market conditions continuously changed. Project Website This article is part of the engineering story behind the Bybit Signal Trading Platform . If you'd like to learn more about the project, see additional screenshots, features and technical details, visit: https://py-dev.top/application-software/bybit-signal-trading-bot The Problem Was Never Stop Loss If someone had asked me during the first weeks of development where the Stop Loss logic should live, I wouldn't have hesitated. Inside the Trade Execution Engine. Where else? The engine already received TradingView webhooks. It validated incoming requests. It calculated Take Profit. It opened positions. Adding one more calculation felt completely natural. The implementation looked something like this. signal = receive_signal () validate ( signal ) entry = execute_order ( signal ) stop_loss = calculate_stop_loss ( entry ) take_profit = calculate_take_profit ( entry ) Simple. Readable. Everything related to opening a trade existed in one place. At that moment there was absolutely no reason to introduce another component. There was only one trading pair. Only one open position. No persistence. No restart recovery. No Break Even. No Trailing Stop.

A practical introduction to WebGPU, WGSL, render pipelines, compute shaders, and the future of high-performance graphics on the web. Your browser can stream 4K video, run a complete code editor, render complex 3D scenes, and host multiplayer games. But for years, web developers accessed the GPU through an API based on an older generation of graphics programming. WebGPU changes that contract. WebGPU is not simply a faster version of WebGL. It is a new approach to graphics and parallel computation on the web—one built around explicit pipelines, modern GPU architecture, compute shaders, predictable resource management, and a shader language designed specifically for the browser. This article expands on the progression presented in the uploaded High Performance Graphics: Introduction to WebGPU material: why WebGPU matters, how it differs from WebGL, how WGSL works, how the rendering pipeline is constructed, and how compute shaders extend the GPU beyond graphics. WebGPU is not WebGL 3.0 This is the first mental model to correct. WebGPU does not build on WebGL. WebGL exposes a browser-friendly version of the OpenGL ES programming model. WebGPU instead uses concepts associated with modern GPU APIs and provides a portable abstraction over the graphics capabilities available on the user’s system. WebGPU and WGSL are W3C standards for accessing GPU acceleration from web applications. The API supports both graphics rendering and general-purpose parallel computation. ( W3C ) WebGL │ └── OpenGL ES-style state machine WebGPU │ ├── Explicit pipelines ├── Explicit resource bindings ├── Command encoding ├── Compute shaders └── Modern GPU execution model The difference is architectural, not cosmetic. In WebGL, you frequently change global rendering state and then issue a draw call. In WebGPU, you describe the pipeline and resources more explicitly, record commands, and submit those commands to the GPU. WebGL mental model Change state ↓ Change more state ↓ Bind resources ↓ Draw WebGPU

A few months ago, a founder posted about the SaaS he'd just shipped — built entirely with an AI coding assistant, not a line of it typed by hand. He was proud of it, and he had every right to be. Within days of launch, someone found the API key sitting in plain sight in the client-side code. It got used to bypass the paywall, spam the backend, and write garbage into the database. The founder spent the next stretch rotating every key, moving secrets into environment variables, and locking down the API endpoints that should have been locked down before anyone ever saw the site. Nothing about that story is about the AI being bad at its job. The AI did exactly what it was asked: build a working product, fast. Nobody asked it to think about what happens when a stranger opens dev tools. In the replies, someone made a simple point: AI is a great research aid, but shipping a large application still means understanding the code — copying and pasting isn't programming. The founder didn't push back. He agreed: he'd learned it the hard way. The same story, over and over Swap the platform and the same shape of story repeats. Here's the WordPress version — three separate, ordinary launches, three separate silent failures. A site goes live and Google never finds it. Somewhere in Settings → Reading, "Discourage search engines from indexing this site" got left checked — a setting every staging environment needs and every production site must not have. Nobody notices until weeks later, when someone asks why the brand-new site isn't showing up in search at all. A debug log sits in a predictable place, readable by anyone. wp-content/debug.log collects whatever errors WordPress throws — database credentials, API keys, fragments of user data — in plain text, at a URL automated scanners check within hours of a new site going live. Turning debug mode off doesn't delete the file it already wrote. The admin username is still admin . It's the default nobody bothered to change, and it happens

A tutorial series, DEV.to blog series — from your first HTML element to production-grade User Interfaces, all in pure Python. Modern Python web frameworks force developers into a split workflow: business logic lives in Python files with full IDE support, while presentation logic is exiled to template files that offer none of it. Template languages like Jinja2 introduce their own syntax for conditionals, loops, and variable access — syntax that your linter cannot check, your type checker cannot verify, and your debugger cannot step through. Every context variable passed across that boundary is a potential KeyError waiting to surface at runtime. Probo eliminates this divide entirely by making HTML a native Python construct — written, validated, and refactored with the same tools you already use for the rest of your codebase. PART 1: Introduction to Probo — Write HTML Entirely in Python What is Probo? Probo is a Python-first, declarative UI rendering framework . Instead of writing HTML in .html files or using template languages like Jinja2, you write everything in pure Python. No template files. No string concatenation. No f-strings full of angle brackets. Just Python functions and classes that are your HTML. The Two Flavors of Every Tag Every HTML tag in Probo comes in two forms: Flavor Example Returns Use Case Function (lowercase) div() , h1() , p() Rendered HTML Quick rendering, lightweight Class (uppercase) DIV() , H1() , P() SSDOM tree node Tree manipulation, streaming from probo import div , DIV # Function: returns a string immediately # return_list=True html_string = div ( " Hello World " ,) # → "<div>Hello World</div>" # Class: returns a tree node, call .render() to get the string node = DIV ( " Hello World " , Id = " main-title " ) # Because it's a Node, you can manipulate it dynamically node . add ( div ( " Subtitle added later! " )) html_string = node . render () # → '<div id="main-title">Hello World<div>Subtitle added later!</div></div>' Note: by adding ret

This button passes every automated accessibility scan we've thrown at it: <button class= "btn-primary" type= "button" > Check availability </button> And it breaks every AI agent that tries to book a room through it. The markup is clean: a real <button> , a proper accessible name from its text content, an explicit type . Nothing to flag. The failure isn't in the button, it's in what happens after the click. And no static scanner ever clicks. What a scanner actually sees Static accessibility scanners evaluate the DOM at a point in time. Usually the initial render: HTML parsed, framework hydrated, nothing interacted with. They check that state against WCAG rules, missing alt text, contrast ratios, label associations, heading order. That's genuinely useful. It's also a photograph of a lobby, when the task happens in the hallways. Here's what never appears in the initial DOM of a typical booking flow: The date picker that mounts when the check-in field receives focus The error message injected after a failed form submit The room-selection modal that opens on "Check availability" The loading state between "Book now" and the confirmation A scanner reports zero issues on all of these, for the simple reason that at scan time, none of them exist. What an agent actually traverses An AI agent completing a booking doesn't evaluate a snapshot. It walks the flow: reads the accessibility tree, decides on an action, performs it, waits for the interface to respond, reads the tree again. Every state transition is a place where the tree can lie to it. Let's look at three patterns we keep finding in real audits. All three pass static scans. All three stop an agent. 1. The modal that exists on screen but not in the tree { isOpen && ( < div className = "modal-overlay" > < div className = "modal" > < h2 > Select your room </ h2 > < RoomList rooms = { available } /> </ div > </ div > )} Visually: a modal. In the accessibility tree: a div soup appended somewhere in the body, with no role="di

Users are creating accounts on your new Saas. Yay (and not just family and friends or bots). Yay! Now comes the next step from every marketing handbook: capturing newsletter subscriptions. This article builds on Add Sign Up to Rails 8’ Authentication . Add a simple checkbox to let users opt in to product updates during signup. Store their preference using Rails Vault and manage the subscription with Rails Courrier . First, add Rails Vault and Rails Courrier to your Gemfile: gem "rails_vault" gem "rails_courrier" Rails Vault adds simple and easy settings, preferences and so on to any ActiveRecord model (I recently pushed 1.0.0). Courrier is API-powered email delivery for Ruby apps with support for Mailgun, Postmark, Resend and more. Rails Courrier is the Rails “wrapper” for Courrier. These two gems work really nicely together for this feature. Run bundle install and generate the Rails Vault migration: rails generate rails_vault:install rails db:migrate It creates a new file app/models/user/subscriptions.rb : class User::Subscriptions < Vault vault_attribute :product_emails_subscribed_at , :datetime # Add more subscription types as needed: # vault_attribute :marketing_emails_subscribed_at, :datetime # vault_attribute :weekly_digest_subscribed_at, :datetime end And updates your User model to use this vault: # app/models/user.rb class User < ApplicationRecord + vault :subscriptions has_secure_password has_many :sessions , dependent: :destroy end This keeps subscription data organized without cluttering your User table. More subscription types can be added later without database migrations. Now the plumbing is done, add a checkbox to your signup form in app/views/signups/new.html.erb : <%= form . check_box :product_emails %> <%= form . label :product_emails , "Subscribe to product updates" %> Update the Signup model to accept this parameter: # app/models/signup.rb class Signup include ActiveModel :: Model include ActiveModel :: Attributes attribute :email_address , :stri

This is the next big thing! I've been working on Moonlight AI for almost 6 months now and today I have big news. Automated Applications . That's right, Moonlight AI will have automated applications for the 2.0 version! What is Moonlight AI? Moonlight AI at first, was a project that was aimed to compete with Upwork. That definitely didn't work out even at the development phase so I had to pivot to another project. A month after the conception of the original version, I met a potential co-founder for a new initiative. Moonlight has been repurposed to be a capabilities mapping engine based on worker experience. We used a resume parser and a local LLM to map the capabiities based on skills and job experience alongside Github integration to use as proof of work. The project ultimately didn't work out, the potential cofounder was MIA so my only recourse was to rewrite Moonlight AI to be another thing, which ended up being a glorified job board which is how it works now. The Development Process Moonlight AI was vibe-coded in a night. Modifications were done the same way with different LLM models. LLMs today are what I call an automated entry-level developer since juniors is the wrong term because juniors at least have from 1-3 years of experience in the professional landscape and are more than just coding monkeys, where as entry-levels are just that, developers with 0 years of experience. Today, Moonlight AI should be done the correct way: reading the code and writing it too. Why let an LLM to do my job as well as think for me? DO YOU WANT ME TO GET ALZHEIMER? BECAUSE THAT'S HOW YOU GET ALZHEIMER! Anyways, exercising the mind is very important, that's what make us humans. People today have an obsession with automating their lives completely and end up like the humans in WALL-E. End of rant. The Future (Conclusion) I don't know the future, but I envision Moonlight AI to be a tool to make unemployed people lives a little less unbearable. I know how frustrating is to use a jo

What happens when you stop writing controllers, services, repositories and mappers - and let PostgreSQL be the backend. MIT-licensed, and yes, I built it. Full disclosure right away: I built the thing I'm about to show you. It's called NpgsqlRest , it's MIT-licensed, there is no paid tier, no telemetry, no "book a demo" button. I'm just a guy who spent two years deleting layers from his stack and now wants to show someone the hole where the backend used to be. The standard pattern The standard data access pattern for modern business applications looks like this: UI → Fetch → Controller → Service → Repository → ORM → SQL → Database Seven arrows. And if you look closely at what most of those layers actually do - they take data from one side and pass it to the other side, slightly renamed. The Controller maps the request to a DTO. The Service passes it to the Repository. The Repository asks the ORM nicely. The ORM generates SQL that you then inspect in a log because you don't trust it (correctly). We built entire careers on maintaining this pipeline. I know because I did, for decades. But once you realize database-aware tests are trivial to wire up, you can drop the Repository. Once you get good at SQL, you can drop the ORM. And then you look at the Controller and realize it's just boring glue code that ships bytes between HTTP and the database. Glue code can be automated. So: UI → Database That's it. That's the architecture. Show me or it didn't happen Fine. This is a file called users.sql . Not a function, not a stored procedure - a plain SQL file sitting in your repo: /* HTTP GET /users/ @authorize admin, user @cached @cache_expires_in 30sec @timeout 5min @param $1 department_id text */ select id , name , email , role from users where $ 1 is null or department_id = $ 1 ; You run npgsqlrest (a single native executable, no runtime to install) pointed at your PostgreSQL, and: $ curl -s 'localhost:8080/users/?department_id=1' | jq [ { "id": 1, "name": "Alice", "email":

Vercel published an OpenAI Agents SDK with FastAPI template on July 17, 2026. A template can remove setup work, but successful generation is not the production boundary that usually breaks. Task ownership is. Primary source: Vercel template, “OpenAI Agents SDK with FastAPI” . Before adopting any agent starter, I would add one vertical test: Alice must be able to create and cancel her task; Bob must not be able to read, stream, or cancel it—even if he guesses the task ID. State the cross-layer contract UI -> POST /tasks -> ownership row -> worker UI <- GET /tasks/:id <- authorization <- state UI <- event stream <- authorization <- events UI -> POST /tasks/:id/cancel -> authorization -> cancellation Use explicit states: queued -> running -> succeeded -> failed queued|running -> cancelling -> cancelled The database, API response, stream, and UI must agree on the same task and owner. Minimal schema create table tasks ( id text primary key , owner_id text not null , state text not null check ( state in ( 'queued' , 'running' , 'succeeded' , 'failed' , 'cancelling' , 'cancelled' )), created_at text not null , updated_at text not null , revision integer not null default 0 ); create table task_events ( task_id text not null , revision integer not null , kind text not null , payload text not null , primary key ( task_id , revision ) ); Do not derive ownership from a browser-supplied field. Resolve the authenticated principal on the server and store it when creating the task. FastAPI authorization seam from fastapi import Depends , FastAPI , HTTPException app = FastAPI () def current_user (): # Replace with verified session/JWT middleware. return { " id " : " alice " } def load_owned_task ( task_id : str , user = Depends ( current_user )): task = db_get_task ( task_id ) # application function if task is None or task [ " owner_id " ] != user [ " id " ]: # Avoid revealing whether another user's task exists. raise HTTPException ( status_code = 404 , detail = " task not found " )

The label that gets the sequence backwards "AI-first" has become a branding exercise....

There are beginner mistakes. Then there are "I spent three days doing the wrong thing" mistakes. This is one of mine. When I first joined a company as a trainee, I was excited... and completely unprepared for real-world software development. Back in college, our web development classes mostly covered HTML and CSS. We did touch a bit of programming, but if I'm being honest, many of our projects involved copying code, tweaking a few lines, and hoping everything still worked. I graduated knowing how to make things look nice , but not necessarily how professional development teams solved problems. Reality hit pretty quickly. Our First Task A few days into training, my friend and I were assigned our first real task by our senior team lead. He gave us an Excel file. It had two sheets , each with roughly 1,000 rows of data. Every row had around 7 to 10 columns . Then he said something like: "Import all of this into the database." Simple enough... right? Well... Neither of us had ever imported data into a database before. Asking the Wrong Person Instead of asking our team lead for clarification, we asked another trainee who happened to be our former classmate. His advice? "Just make an INSERT INTO script." Perfect. Say no more. Without questioning it, my friend and I started generating SQL INSERT statements. One row. After another. After another. And another. By the end, we had spent almost three days creating what felt like an endless wall of SQL. At the time, we were actually proud of ourselves. "Look at us. Future software engineers." Looking back... We were basically expensive copy-paste machines. The Reality Check Our senior team lead eventually came over to check our progress. He looked at our SQL file for a few seconds. Then smiled and said: "You tricked me, huh? That's not what I meant." My friend and I just stared at him. Confused. We thought, "But... the data is going into the database. Mission accomplished, right?" Wrong. What he actually wanted was a CakePHP scr

Retail investors hold digital coins on standard mobile apps. They also use basic physical hardware devices. These devices protect small amounts of money perfectly well. Things change entirely when an account holds fifty million dollars. A single hardware device creates a huge physical weakness. A home invader can force an investor to hand over the pin code. This makes the underlying computer math completely useless. Wealthy investors skip this physical risk entirely. They divide control across different global regions. They ensure no single person can approve a money transfer alone. When you ask where do rich people store their crypto, the answer is never a single app. The answer is a shared digital network. This guide explains exactly how ultra-high net worth crypto management works. We break down shared vaults. We look at the exact differences between multiple signatures and mathematical key splitting. We also cover the severe technical failures that retail investors ignore completely. Table of Contents The Core Strategy for Whale Wallet Management Why Standard Hardware Wallets Fail at Scale How Asset Transfers Actually Work for Whales The Multisig Boardroom Approach The Multi Party Computation Breakthrough The Fragmented Lens Analogy The Firmware Desync Edge Case Inside the Air Gapped Fortress The Rise of Institutional Crypto Custodians Segregated Accounts Versus Omnibus Pools Constructing a Family Office Security Standard Handling Succession and Inheritance Planning Physical Threats and Bunker Security Network Fees and Trading Execution for Whales The Institutional Blockchain Storage Comparison The Three Point Technical Audit for Large Vaults The Bottom Line The Core Strategy for Whale Wallet Management Rich people store their crypto by using qualified institutional custodians, multi-signature (multisig) wallets, and Multi-Party Computation (MPC) systems. Instead of leaving large funds on regular exchanges, wealthy investors split their cryptographic private key

Most LLM failures are easy to describe and surprisingly hard to reproduce. A user reports that the model returned an empty answer. A tool call disappeared halfway through a stream. One provider rejected a request that worked everywhere else. Then I open the logs and find something like this: LLM request failed: 400 Bad Request Technically true. Operationally useless. The missing piece is usually the exact request shape: model, parameters, message roles, tool definitions, timeout behavior, and the raw provider response. I wanted something smaller than a full observability platform, so I built a request recorder around fetch . It stores enough information to inspect or replay a failed call without logging the API key. What the recorder captures For each request, I want: a unique request ID timestamp and duration URL and model sanitized request body HTTP status raw response body network or timeout errors I deliberately do not record the Authorization header. Prompt content is also redacted by default. Full payload capture must be enabled explicitly because storing production prompts can create a much worse problem than the bug being investigated. The recorder This example runs on Node.js 18 or newer and has no external dependencies. Create recorded-fetch.mjs : import { randomUUID } from " node:crypto " ; import { mkdir , writeFile } from " node:fs/promises " ; import path from " node:path " ; function sanitize ( value , captureContent ) { if ( Array . isArray ( value )) { return value . map (( item ) => sanitize ( item , captureContent )); } if ( ! value || typeof value !== " object " ) { return value ; } const result = {}; for ( const [ key , child ] of Object . entries ( value )) { const normalizedKey = key . toLowerCase (); if ( normalizedKey . includes ( " api_key " ) || normalizedKey . includes ( " apikey " ) || normalizedKey . includes ( " authorization " ) ) { result [ key ] = " [REDACTED] " ; continue ; } if ( ! captureContent && ( normalizedKey === " content "

HTML Forms and the <form> Tag HTML forms are used to collect information from users through a webpage. They are commonly found in login pages, registration forms, contact forms, search bars, and online shopping websites. The <form> tag acts as the main container that groups different form elements together. When a user submits the form, the browser collects the entered data and prepares it to be sent to a server. <form> <label for= "name" > Full Name </label> <input type= "text" id= "name" name= "fullname" > <button type= "submit" > Submit </button> </form> Understanding the action and method Attributes The action attribute specifies where the form data should be sent after submission. This destination is usually called an endpoint . The method attribute defines how the data is sent. The GET method sends data through the URL, while the POST method sends data inside the request body, making it suitable for sensitive information. <form action= "/submit" method= "post" > <input type= "text" name= "username" > <button type= "submit" > Submit </button> </form> Understanding Common Form Attributes The <form> tag supports several attributes that control its behavior. Attributes such as autocomplete , target , enctype , novalidate , and accept-charset improve the user experience and define how the browser handles form data before and after submission. <form action= "/submit" method= "post" autocomplete= "on" target= "_self" > </form> How an HTML Form Works When a user enters information and clicks the Submit button, the browser collects all form data and sends it to the location specified by the action attribute using the HTTP method defined in method . The server processes the request and returns a response to the browser. <form action= "/login" method= "post" > <input type= "email" name= "email" > <input type= "password" name= "password" > <button type= "submit" > Login </button> </form> Why HTML Forms Are Important HTML forms make websites interactive by allowing users t

In 1995, Netscape released SSL. The web didn't really take off commercially until then. Before SSL, you couldn't trust a website with your credit card. After SSL, e-commerce exploded. AI agents are at the same inflection point in 2026. Here's why. The problem Agents are starting to call each other autonomously. Each hop is a trust decision. But agents have no way to verify each other. Today, when Agent A calls Agent B: Is Agent B who it claims to be? No way to verify Has Agent B been audited for security? No standard Has Agent B's key been compromised? No revocation mechanism This is exactly where the web was in 1994. No SSL, no trust, no commerce. The analogy Web (1995) Agents (2026) HTTP (transport) A2A + MCP (transport) No HTTPS = can't trust No ATC = can't trust SSL certificate ATC Trust Card Certificate Authority MarketNow Sentinel CA Revocation list (CRL) /api/atc?action=verify What I built ATC (Agent Trust Card) — SSL certificates for AI agents. How it works Agent registers with MarketNow CA CA signs the agent's identity with Ed25519 Agent presents its ATC to other agents Other agents verify the signature with the CA public key If compromised, the CA revokes the ATC Real cryptography (not a mock) Ed25519 signatures (RFC 8032) CA private key in Vercel env var (never exposed) CA public key committed to public GitHub repo Every ATC persisted as signed JSON in _data/atc/ Anyone can verify signatures offline using crypto.verify Sentinel integration The ATC's trust score comes from Sentinel — the 8-layer security audit pipeline: L1.5: metadata checks L1.6: Semgrep + secrets + OSV L1.7: binary/malware detection L1.8: malware family signatures (Emotet, Cobalt Strike, etc.) The positioning MarketNow is not competing with A2A or MCP. It's the trust layer that sits on top: ATC (Trust Layer) <- MarketNow A2A / MCP (Transport Layer) <- Google / Anthropic HTTP / WebSocket (Network) <- Standard Every agent with an A2A card can have an ATC Trust Card. Every MCP skill can hav

Chrome ships a real LLM inside the browser now — Gemini Nano, exposed through a handful of built-in...

As a developer, I find myself doing the same conversions and lookups over and over. Here are 5 free, no-signup tools that live in my bookmarks: 1. BitwiseCalc — Bitwise Operations Calculator https://bitwisecalc.com When you're debugging bit flags, network masks, or color channels, mental math gets old fast. BitwiseCalc handles AND, OR, XOR, NOT, left and right shifts on binary, decimal, and hex numbers. It supports 32-bit and 64-bit precision and keeps a calculation history so you don't lose track of your operations. 2. BinTranslate — Binary ↔ Text Converter https://bintranslate.com Need to decode a binary string into readable text? Or convert text to binary? BinTranslate supports five conversion modes: binary to text, text to binary, binary to English, binary to ASCII, and words to binary. Everything runs client-side — no data ever hits a server. 3. Epoch Converter — Timestamp Tool https://www.epochconverter.com/ The classic. Convert Unix timestamps to human-readable dates and back. Supports milliseconds, microseconds, and nanoseconds. 4. JWT.io — JWT Debugger https://jwt.io/ Decode, verify, and debug JSON Web Tokens right in the browser. Supports HS256, RS256, ES256, and more. Great for debugging auth flows. 5. RegExr — Regex Playground https://regexr.com/ Learn, build, and test regular expressions with a cheatsheet, reference, and real-time highlighting. All of these are free, no sign-up, and run in the browser. Got any tools you keep coming back to? Drop them in the comments. P.S. I built BitwiseCalc and BinTranslate myself — feedback welcome.

Hey DEV, I'm Tobore. Let's actually connect. I've been on here for a while now, mostly writing and...

Most people open /etc/hosts , change one line, refresh the browser, and hope. That works until it does not. Then you spend twenty minutes on Permission denied , a forgotten DNS flush, or a commented line from last week that is still active. This is a simple workflow that keeps hosts edits boring. What the hosts file does When your machine resolves a name like myapp.test , it can use a local override before public DNS. Common cases: Point myapp.test to 127.0.0.1 for local work Point a real domain at a staging IP before DNS cutover Temporarily block a host with 0.0.0.0 Give services readable names instead of raw IPs The idea is simple. The mess comes from how people edit and apply it. A workflow that holds up 1. Do not treat /etc/hosts as your only copy Keep a file you own: ~/dev/hosts/personal.hosts Or one file per project / client. Edit that. Apply it on purpose. 2. Edit the copy, then copy it into place macOS / Linux: code ~/dev/hosts/personal.hosts sudo cp /etc/hosts "/etc/hosts.bak. $( date +%Y%m%d-%H%M%S ) " sudo cp ~/dev/hosts/personal.hosts /etc/hosts Windows: edit your copy, back up the live file, then replace: C :\ Windows \ System32 \ drivers \ etc \ hosts You need admin rights for the live file. That is normal. 3. Flush DNS every time you apply Make this part of the apply step, not a later panic search. macOS sudo dscacheutil -flushcache ; sudo killall -HUP mDNSResponder Windows (Admin) ipconfig /flushdns Linux (systemd-resolved) sudo resolvectl flush-caches 4. Verify in the terminal before the browser ping -c 1 myapp.test # Linux: getent hosts myapp.test Right IP in the terminal, wrong page in the browser? Stop rewriting hosts. Look at browser DNS, HTTPS, redirects, or HSTS. 5. Avoid two active lines for the same hostname This breaks people constantly: 10.0.0.5 www.client.com 127.0.0.1 www.client.com Pick one. Comment the other, or better, keep separate profile files and swap the whole file. Example: local frontend + API 127.0.0.1 shop.test 127.0.0.1 api.