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If you've been writing Python for more than a year, you know the ritual. A new project. A fresh terminal. And then: pyenv install 3.12.3 pyenv local 3.12.3 python -m venv .venv source .venv/bin/activate pip install pip --upgrade pip install -r requirements.txt Six commands before you've written a single line of code. And that's if nothing breaks. Enter uv a single binary that replaces pip , virtualenv , pip-tools , pyenv , and pipx . Written in Rust. 10–100x faster than pip. And honestly, one of the most pleasant tools I've used in the Python ecosystem in years. Let's dig into it. What Even Is uv ? uv is a Python package and project manager built by Astral , the same team behind ruff , the linter that everyone switched to and never looked back. The goal is simple: be the Cargo for Python . One tool, one lockfile, no friction. It's a standalone binary with zero Python dependencies, which means it works even before Python is installed. Installing uv # macOS / Linux curl -LsSf https://astral.sh/uv/install.sh | sh # Windows (PowerShell) powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" # Or via pip if you prefer pip install uv Verify: uv --version # uv 0.9.x The Speed Claim Is It Real? Yes. Embarrassingly so. Here's a timed comparison on Apple Silicon (Python 3.14): Operation pip / venv uv Create virtual env ~2 seconds 35 milliseconds Install FastAPI + deps (cold) ~12s ~1.2s Install with warm cache ~8s ~0.1s The warm cache case is where uv really shines it uses a global cache and hard-links packages into environments instead of copying them. If you've installed requests in any previous project, your next project gets it nearly instantly. Starting a New Project This is where uv feels like a completely different world: uv init my-api cd my-api That single command gives you: my-api/ ├── .git/ ├── .venv/ ← already created ├── .python-version ├── pyproject.toml ├── README.md └── main.py No separate python -m venv , no git init , no template c

AirDrop only works between Apple devices. Most alternatives require an app install, a cloud account, or route files through a third-party server. I wanted something simpler: Open a URL → discover nearby devices → send files. So I built LocalDrop — a peer-to-peer file transfer app that works entirely in the browser over local Wi-Fi. GitHub: https://github.com/akshaykdadheech/localdrop Live Demo: https://localdrop-4fddd39fb6ad.herokuapp.com How It Works Devices connected to the same Wi-Fi network automatically discover each other through a lightweight signaling server. Once discovered, WebRTC establishes a direct peer-to-peer connection: Browser A ──► Signaling Server ◄── Browser B └──────── WebRTC P2P ────────┘ The signaling server only helps devices find each other. File transfers happen directly between browsers via WebRTC and are DTLS encrypted, so the server never sees your files. Interesting Challenges Backpressure Handling WebRTC DataChannels on Chromium have a ~16 MB buffer limit. Sending data too aggressively can crash the tab. I solved this using: bufferedAmountLowThreshold Flow control based on drain events Cross-Platform Compatibility Different browsers expose different capabilities. Android Chrome supports the File System Access API iOS Safari does not This required separate file-receiving flows for each platform. Large File Transfers Keeping multi-gigabyte files in memory isn't practical. On Chrome, showSaveFilePicker() is triggered after the transfer completes, allowing transfer progress to remain visible throughout the process without buffering everything in RAM. Tech Stack Svelte 5 + Vite TypeScript WebRTC DataChannel Node.js + ws Docker Self-Hosting git clone https://github.com/akshaykdadheech/localdrop cd localdrop docker compose up -d Then open: http://your-ip:3001 from any device connected to the same Wi-Fi network. I'd love feedback from anyone who's worked with WebRTC DataChannels, especially on mobile browsers. If you find the project useful, a

I applied for a role at a mid-sized SaaS company about two years into my career. Strong company, interesting problem, good pay. I sent my application, got a recruiter callback, and then nothing for two weeks. When the feedback finally came: "We went with candidates with a stronger portfolio presence." I had 23 GitHub repositories. I had a portfolio site. I had projects. What I didn't have — and what I didn't understand for another six months — was a portfolio that told a story. I had code. Not evidence of thinking, decision-making, or the ability to ship something real. I've since built, rebuilt, and advised on a lot of developer portfolios. I've seen what gets people calls and what gets them ghosted. This isn't a guide about which framework to use or how to pick colors. It's about what actually moves the needle — the things I wish someone had told me in year one. Lesson 1: Two Great Projects Beat Twenty Mediocre Ones The instinct is to fill the portfolio. More projects = more evidence of experience. This is wrong. A hiring manager or engineering lead looking at your portfolio has about three minutes. They're going to look at your two or three most prominent projects, click one or two live demo links, and form an opinion. If they see twenty repositories and most of them are "Todo App v2," "Weather App," "Netflix Clone," "Portfolio v1 through v6" — they've already categorized you as someone who builds tutorials, not someone who builds things. The better approach: three to five projects, each with: A real problem it solves (not "I wanted to learn React") A live deployment that actually works A README that explains why you made the decisions you made Enough complexity to have generated at least one interesting engineering problem Projects that tend to work: tools you built because you were frustrated with an existing tool, apps solving problems you personally had, projects where you integrated with a real API or real data source, anything with a live user base (even 10

Last year I helped a startup integrate the OpenAI API into their product. It was a chat feature — users could ask questions about their data and get natural language answers. The integration took about a day. Three days after launch, the founder messaged me: "Hey, something's wrong. Our AWS bill just showed an unexpected charge." It was $340. For three days. They had 60 users. The issue wasn't a bug — it was that production API usage looks nothing like a tutorial. The tutorial shows you openai.chat.completions.create() and returns a response. The tutorial doesn't show you what happens when users send 500-token messages, when they open 15 browser tabs each maintaining their own chat context, or when one user fires requests 30 times per minute because they think it's broken. This guide covers what the tutorials skip: rate limiting, token counting, cost guards, streaming, error handling with retries, and model selection. These aren't optional additions — they're what separates a demo from a production feature. Why Production Is Different Here's the gap between tutorial code and production code, stated plainly: Concern Tutorial Code Production Code Cost control Not mentioned Token counting, spending limits, model selection by task Rate limiting Not mentioned Per-user and per-IP limits to prevent abuse Error handling try/catch that logs to console Typed errors, retries with backoff, user-facing messages Response delivery Wait for full completion, return at once Streaming via SSE — response appears as it generates Context management Each request is independent Conversation history managed, truncated at token limit Secrets management API key hardcoded or in .env (no rotation) Rotation strategy, usage monitoring, per-feature keys Let's build a production-grade Express API that addresses all of this. We'll go layer by layer. The Architecture ┌─────────────────────────────────────────────────────────┐ │ CLIENT (Browser / Mobile) │ │ POST /api/chat { messages: [...] } │ │ GET

The first time I deployed a Next.js app to production, it took me three days. Not because the app was complicated — it was a straightforward portfolio site. It took three days because I had no idea what I was doing with AWS, I'd never written a GitHub Actions workflow, and every tutorial I found either skipped the hard parts or assumed I already knew them. By the time I was done, I had a deployment pipeline I was genuinely proud of: push to main, GitHub Actions runs the build, tests pass, the app deploys to an EC2 instance behind CloudFront. Zero manual steps. Zero downtime deploys. Total cost: about $5/month. This guide is the one I wish had existed. We're going to deploy a Next.js app to AWS from scratch — EC2 for compute, CloudFront for CDN, GitHub Actions for CI/CD — with every step explained so you understand what you're building, not just copying commands. Why AWS Instead of Vercel? This is a fair question. Vercel is genuinely excellent for Next.js, and for most projects it's the right call. You push, it deploys. Done. AWS makes sense when: You need to control the infrastructure (compliance, data residency, custom VPC configuration) You're running other services (databases, queues, lambdas) in AWS and want everything in the same network You want to learn infrastructure skills that transfer to enterprise environments Your app has specific performance requirements that benefit from custom CloudFront configuration You're a freelancer or consultant who wants to bill separately for infrastructure If none of those apply to you, use Vercel. This guide is for when they do. The Architecture Here's what we're building: ┌────────────────────────────────────────────────────────┐ │ GITHUB ACTIONS CI/CD │ │ │ │ Push to main → Build → Test → Deploy to EC2 │ └──────────────────────┬─────────────────────────────────┘ │ SSH deploy ▼ ┌────────────────────────────────────────────────────────┐ │ AWS EC2 INSTANCE │ │ │ │ Ubuntu 22.04 LTS │ │ Node.js 20 + PM2 (process manager) │ │ N

The LedgerSync pipeline was inserting 1.5 million rows into PostgreSQL using pandas.to_sql() . It took four minutes per run. I switched to psycopg2's COPY command and it dropped to 18 seconds. Same data, same schema, same machine. That is not an optimization tip. It is the difference between a pipeline that fits in an Airflow schedule and one that does not. This article is about patterns like that: the ones that matter when you are building pipelines that run on a schedule, not when you are writing ad-hoc queries. Loading Data: to_sql vs execute_values vs COPY There are three ways to write rows from Python into PostgreSQL, and the performance gap between them is significant. pandas to_sql issues one INSERT statement per row by default, or a multi-row INSERT with method="multi" . It is the easiest to write and the slowest for any serious volume. psycopg2 execute_values batches many rows into a single multi-row INSERT VALUES statement. About 5x faster than to_sql for medium-sized loads. psycopg2 COPY streams rows directly to PostgreSQL using its native bulk-load protocol. No statement parsing, no row-by-row overhead. For LedgerSync at 1.5M rows, this was the one that mattered. import psycopg2 import io import pandas as pd conn = psycopg2 . connect ( " host=localhost dbname=proj_db user=proj_user password=proj_pass " ) def bulk_copy ( df : pd . DataFrame , table : str , columns : list [ str ]): buf = io . StringIO () df [ columns ]. to_csv ( buf , index = False , header = False ) buf . seek ( 0 ) with conn . cursor () as cur : cur . copy_from ( buf , table , sep = " , " , columns = columns ) conn . commit () print ( f " Loaded { len ( df ) } rows into { table } " ) Use COPY for initial loads and large backfills. For incremental daily writes of a few thousand rows, execute_values is fine and gives you more control over conflict handling: from psycopg2.extras import execute_values def bulk_insert ( rows : list [ dict ], table : str ): if not rows : return columns = list

The first gameplay trailer shows off bloody melee combat.

At its big State of Play show on Tuesday, Sony shared new look Marvel's Wolverine, the next big title from Insomniac Games that's launching exclusively on PS5 on September 15th. Dressed in the iconic yellow Wolverine outfit, Logan slunk around and stabbed his way through a bunch of enemies, using his blades to take them […]

There's a specific feeling you get after your third production caching incident. It's not panic....

While the majority of Summer Game Fest takes place over the weekend, Sony is getting a jump on things with its next State of Play showcase, which airs on June 2nd at 5PM ET. It’s coming at a pivotal time for the PlayStation brand: The PS5 keeps getting more expensive and Sony’s big bet on […]

Lawsuit: Ring cameras scan guests and passersby and use AI to identify faces.

Manage fleets of local and cloud agents from one surface Discussion | Link

Intel couldn't catch a break. Layoffs. Shakedowns. Crashing CPUs torpedoing its reputation, sending desktop gamers fleeing to AMD. Apple and Qualcomm pushing Intel out of multiple flagship laptops. A gaming graphics card going MIA. But its Panther Lake laptop chip, the first on its all-important 18A process, turned out excellent - and a handheld version […]

Neanderthals had some wild stuff in their toolkits.

Peek inside any folder without opening it Discussion | Link

A federal IT staffer filed a complaint about DOGE, then went public. Shortly after Elon Musk boosted a post calling his claims false, his brake lines were cut. Now he’s suing for defamation.

Uber's cutback has occurred after the company had reportedly encouraged staff to use AI as much as possible.

The National Center for Atmospheric Research won't be losing its supercomputer.

Last week I had a single tool: a JSON-to-TypeScript converter. Today I have 10. I spent a day building a full suite of developer tools - all free, all client-side (nothing leaves your browser), and all open source on GitHub. The Suite DevForge is a collection of tools I built because I kept opening 15 different tabs every day: 1. JSON ? TypeScript Paste JSON, get TypeScript interfaces or types. Supports nested objects, arrays, custom root names. 2. CSV ? JSON Bidirectional conversion with header detection and delimiter support (comma, semicolon, tab). 3. Regex Tester Live regex testing with flags (g, i, m, s, u, y), match highlighting, and replace functionality. 4. Base64 Encoder/Decoder Encode text to Base64 and decode Base64 back to text. Unicode-safe. 5. JWT Decoder Decode JWT tokens into header, payload, and signature components. Read-only - no verification, no security risk. 6. SQL Formatter Uppercase keywords, indentation control. Handles SELECT, JOIN, INSERT, CREATE - the common stuff. 7. Diff Checker Side-by-side text comparison with LCS-based diff algorithm. Color-coded additions and removals. 8. UUID Generator Generate UUID v4 (or v1) in bulk. Copy individual or all at once. 9. Timestamp Converter Convert Unix timestamps to readable dates and back. Shows seconds, milliseconds, ISO 8601, UTC, and relative time. 10. Color Converter Convert between HEX, RGB, HSL, and CMYK. Color picker, presets, named color support. The Tech Stack Zero frameworks. Zero dependencies. Zero backend. Every tool is a single HTML file with embedded CSS and JavaScript. They all run entirely in the browser - no data is sent to any server. Hosted on GitHub Pages. Free forever. Why I Built This I'm a solo developer based in Nepal. Building things that help other developers is how I learn, grow, and (hopefully) earn. The suite is free, but there's a Pro tier ( one-time) that unlocks: File exports Batch operations Custom naming conventions Priority support I also do freelance development

Migrar 100 usuários manualmente no console da AWS é lento, suscetível a erros e impossível de auditar com precisão. Neste artigo você vai ver como automatizar esse processo usando AWS CLI e Shell Script direto no AWS CloudShell — sem instalar nada localmente. O resultado final: usuários criados, alocados nos grupos corretos e com MFA obrigatório, tudo em minutos. O que é o IAM? O AWS Identity and Access Management (IAM) é o serviço que controla quem pode acessar os recursos da sua conta AWS e o que cada pessoa ou serviço pode fazer. Com o IAM você gerencia: Conceito Descrição Usuário Identidade individual com credenciais próprias Grupo Conjunto de usuários que compartilham as mesmas permissões Política Documento JSON que define o que é permitido ou negado Role Identidade temporária assumida por serviços ou usuários A boa prática é nunca conceder permissões diretamente a um usuário — sempre use grupos. Visão geral da solução O fluxo é simples: Criar os grupos IAM no console Montar um arquivo CSV com os dados dos usuários Rodar um shell script no CloudShell que lê o CSV e cria tudo automaticamente Aplicar a política de MFA obrigatório nos grupos Passo 1 — Criar os Grupos IAM Antes de importar os usuários, os grupos precisam existir. No AWS Console , acesse IAM → User groups → Create group e crie um grupo para cada perfil do seu ambiente. Neste exemplo usaremos: RedesAdmin — administradores de rede LinuxAdmin — administradores de servidores Linux DBA — administradores de banco de dados Estagiarios — acesso limitado para estagiários Nomes de grupos suportam até 128 caracteres (letras, números e + = , . @ _ - ), são únicos por conta e não diferenciam maiúsculas de minúsculas. Passo 2 — Montar o arquivo CSV Crie uma planilha com os dados dos usuários e salve como CSV separado por vírgula (UTF-8) . O arquivo deve ter exatamente três colunas: Username , Group e Password . Username , Group , Password joao . silva , LinuxAdmin , Senha @2024 ! maria . souza , DBA , Senha @2024