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A free model quota is a budget, not a gift. You should treat it like one if you plan to build anything on top of it. I learned this the hard way when my prototype stopped responding in the middle of a demo. I had silently burned through the monthly allowance, and the provider cut me off without warning. This article shows how I built a small token budget alarm on a free server. It watches a free model's usage and warns me before the quota runs out. Most developers track their cloud spend religiously but ignore the token consumption of free models. The free tier feels like a gift, so we assume it will last forever. Then the provider cuts us off at the worst moment, and we scramble to find the cause. A token budget alarm removes that uncertainty by measuring your actual burn rate. It projects the exhaustion date and alerts you before you hit the wall. The design is deliberately small: a reverse proxy sits in front of the model endpoint. It records the token usage from every response and stores it in a local database. A background thread then computes the average consumption over a sliding window. It compares that rate against the remaining allowance and fires a webhook when the projection looks dangerous. You can run this entire stack on a free server, which is exactly what I did with MonkeyCode's free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The proxy itself is a tiny Flask application that forwards requests to the model. It extracts the usage field from each response and records the token count. If your provider does not return a usage object, you can estimate the token count with a simple heuristic. Dividing the character count by four is a rough but workable approximation. The important part is that every request is accounted for, because a single long prompt can consume more than a hundred small ones. from flask import Flask , request , Response import requests import sqlite3 import time app = Flask ( __name__

I work in a 200-person Class II medical device company with two QA/RA folks, three embedded-hardware teams, a small supplier-quality group, and engineers who want automation — not more paperwork. We needed an eQMS that unifies design control (DHF), CAPA, supplier records, and traceability into something engineers will actually use. I evaluated several vendors and want to share a short, practical comparison for teams with that shape. A quick regulatory framing before the list Standards that mattered to us: ISO 13485 and 21 CFR 820, plus MDR-adjacent traceability requirements for components when applicable. A note on risk and regulation posture: don’t treat Article 50 (AI oversight posture for medtech tools) like an apocalypse — it sits in the general enforcement stack. Plan for reviewability and traceability for any AI-assisted workflows, but don’t let fear of hypothetical fines block good tooling decisions today. What I judged vendors on Fit for medtech design control / DHF workflows Traceability across requirements, risk, design, and CAPA Integrations — especially with CRM/supplier systems and engineering tools Operational fit for a 200-person org (configurability, onboarding burden) Capacity for process automation (automated CAPAs, connected workflow) The list — what each vendor practically means for a team like mine 1) Greenlight Guru Why I put it first: Greenlight Guru explicitly lists medical-device as its industry. For a Class II shop that needs DHF, design-control-native workflows, and templates tuned to medtech terminology, that industry focus matters. Fit: Strong for device-focused workflows and teams that want a medtech-first product experience rather than a generic QMS. When to pick: You want a purpose-built medtech experience, quicker ramp for QA/engineering, and vendor docs/templates that speak design control and DHF. 2) Qualio Public positioning: Qualio serves general industries including medical-device and pharma. Fit: Good option if you want flexibil

Every open-source tool has the same brutal first 90 days: zero users, zero signal, no idea whether anything works. I have shipped several free CLI tools and browser tool sets. This is the exact funnel I use — no ads, no paid growth, no "build in public" theater. Just a repeating sequence of small, concrete actions. Step 1: Make the Tool Trivial to Try The first rule: npx must work. If a reader has to install, configure, and read a README before running the first command, the funnel is already broken. npx @wuchunjie/dotguard . That is the entire onboarding. Zero dependencies, no config, instant output. The first 10 seconds decide whether the reader comes back. Step 2: Publish One Article Per Angle Not one article. One per angle , spread over time: Tutorial — "Scan your .env files in 1 command" (the how) Comparison — "Why I stopped using X" (the why) Listicle — "5 tools for Y" (the discovery) Workflow — "My dev setup" (the context) Security/devops — "Your CI is missing this" (the fear) Each article targets a different search intent. A developer looking for "pre-commit secret scan" lands on article 4, not article 1. The funnel is wide because the angles are wide. Step 3: Cross-Link Everything Every article mentions every tool. The footer of a snippet article lists the scaffolder and the scanner. The GitHub repo links to the articles. The npm README links to the articles. The effect is compounding: a reader of article 3 meets four tools, not one. Your content becomes a network instead of a pile. Step 4: Make the GitHub Repo the Hub The repo README is the landing page that never goes stale: One-line description per tool Install/run commands (copy-paste ready) Links to every article A donation link, present but quiet GitHub is where developers actually trust. Stars and forks are the signal that converts "interesting article" into "let me try it". Step 5: Add the Quiet CTA One line at the end of every article: If this saved you time, a Ko-fi keeps the next tool coming. No

I built an OLX scraper for 24 countries — the boring version that actually ships OLX runs classifieds in about two dozen countries. Same brand, different domains, different anti-bot setups. Everyone scraping it does one country at a time. I got tired of forking. So I put 24 countries behind one input. country: "id" or country: "pl" or country: "br" — same schema out. It's live on Apify as primesieve/olx-global-scraper . One file. No browser. Here is the boring part that matters. What it does Input: { "country" : "id" , "keywords" : [ "iphone 13" ], "maxResults" : 50 , "maxPages" : 3 , "proxyConfiguration" : { "useApifyProxy" : true , "apifyProxyGroups" : [ "RESIDENTIAL" ] } } country — two-letter code ( id , pl , in , br , ua , pt , ro , bg , kz , uz , pk , za , ng , ke , eg , lb , ph , co , ar , pe , ec , gt , az , ma ). Default id . keywords — one or more search terms. Each runs sequentially. maxResults / maxPages — caps. Defaults 50 / 3, max 1000 / 30. proxyConfiguration — optional for Indonesia, required for the other 23. Output — same shape every country: { "listingId" : "123456789" , "title" : "iPhone 13 128GB mulus" , "price" : 6500000 , "priceText" : "Rp 6.500.000" , "currency" : "IDR" , "city" : "Jakarta Selatan" , "location" : "Tebet, Jakarta Selatan, DKI Jakarta" , "images" : [ "https://...jpg" ], "thumbnailUrl" : "https://...jpg" , "listingUrl" : "https://www.olx.co.id/item/123456789" , "country" : "id" } Title, price (numeric plus display text), currency, location, images, URL. No seller PII beyond what the listing page shows. No tricks. Try: https://apify.com/primesieve/olx-global-scraper The boring stack // no playwright, no puppeteer // apify + fetch + cheerio. That's it. The scraper is one file. Apify SDK for input, dataset, and pay-per-event. Native fetch for HTTP. cheerio for the HTML path. Undici ProxyAgent when a proxy is configured. Node 20, 512 MB, 600s timeout. I check the endpoint before I write the scraper. Indonesia answered with clean JSO

Cómo solucionar el error “Enable JavaScript and cookies to continue” Este mensaje aparece cuando Cloudflare (u otro proxy de seguridad similar) bloquea la solicitud porque detecta que el cliente no cumple con los requisitos mínimos de seguridad: JavaScript deshabilitado o cookies deshabilitadas/expiradas . 🔍 Causa técnica Cloudflare implementa mecanismos de protección como: JavaScript Challenge : El navegador debe ejecutar un script para demostrar que no es un bot. Cookie de verificación : Tras superar el desafío, Cloudflare emite una cookie ( __cf_bm o cf_clearance ) que valida la sesión. Si el cliente (navegador o cliente HTTP personalizado) no ejecuta JavaScript o no maneja cookies correctamente, la validación falla y se muestra este mensaje. ✅ Solución definitiva (por escenario) 🌐 Si eres un usuario final (navegador) Habilita JavaScript : Chrome: Configuración > Privacidad y seguridad > Sitios web no seguros > Habilitar JavaScript . Firefox: Preferencias > Privacidad y seguridad > Permisos > Habilitar JavaScript . Habilita cookies de terceros (si usas extensiones como uBlock Origin o Privacy Badger): Añade el dominio a la lista blanca. Desactiva temporalmente los bloqueadores para probar. Borra cookies y caché del dominio afectado. Reinicia el navegador y vuelve a cargar la página. 🧪 Si eres desarrollador (automatización / scraping / cliente HTTP) ❌ No uses requests o curl sin soporte JS/cookies → fallarán siempre . ✅ Opción recomendada: Usa un navegador headless con soporte JS y cookies # Ejemplo con Playwright (recomendado) from playwright.sync_api import sync_playwright with sync_playwright () as p : browser = p . chromium . launch ( headless = True ) context = browser . new_context () page = context . new_page () # Navega a la URL (Cloudflare se resolverá automáticamente) page . goto ( " https://ejemplo.com " , wait_until = " networkidle " ) # Si aún falla, fuerza espera tras el desafío try : page . wait_for_selector ( " #challenge-error-text " , timeout = 5

Introduction Hello,I'm currently a data science student and this is my understanding of git workflow, from creating folders on my local computer to adding files, pushing and having them on my github repository. Working directory This is the active folder created in the local machine which will have all the files related to the project. We can create a folder on terminal by following the steps, -Launch your terminal -cd desktop :this is to ensure that we are in the desktop folder -mkdir data :this is to create a new folder on desktop -touch school.py :this is to create a python file inside the data folder -git status :this tells us the repository we are working in Staging This allows us to prepare to save the files that we have created. We can save a specific file or all files at once. For example;assuming we have three different files eg.schools.py ,books.py ,teachers.py -git add . :this saves all the files in the folder -git add schools.py :this saves only the schools files Commit This allows us to save the files from the staging step. .git commit -m "creating schools files" .git commit :saves the files to git hub .-m :this is a message that explains the change that happens in the folder ." " :this briefly explains the change that happened Push This allows us to move our work from our local computer to git hub. git push origin main ;origin points us to our online git hub while main is the name of the branch where we are making the changes

An AI assistant will write you a query in ten seconds, the query will run, and the number that comes back will look completely reasonable. This page gives you the five checks that tell you whether that number is right. They take about two minutes, they need no tools beyond the database you already have, and they catch the four mistakes AI-written SQL actually makes. The order matters. The checks are arranged cheapest first, so the first one costs a single row count and the last one costs a short conversation. Most wrong queries fall to the first two. The short version. A query that runs has only passed a grammar check. The number is right when the rows, the filters and the denominator match the question you asked. The database only takes a query as far as the first gate. Why a query that runs can still be wrong Before the list: what do you think the database actually checks when it accepts a query? Grammar. That is the whole list. Spell a table name wrong and you get an error. Sum the wrong column, join in a way that doubles rows, or filter after grouping when the question needed it before, and you get a clean result set with a wrong number in it. Every mistake on this page is valid SQL. AI assistants add one specific difficulty: their queries are fluent. The aliases are tidy, the formatting is clean, and the shape looks like something a careful person wrote. Fluency reads as correctness, and it is not the same thing. Treat an AI query the way you would treat a first draft from a new colleague: with respect, and with the row counts open. The table the examples run on Everything below runs on one small shop dataset, so every number can be checked by hand. Thirteen orders in July, five customers, and a refunds table where two orders were refunded in two parts. Eleven of the thirteen orders are completed; one is refunded, one is pending. There is also a staff_accounts table listing internal accounts, and it contains one NULL row, because real lookup tables usually do.

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you will be running real SQL against a real database with nothing installed, and you will know which of the free browser tools suits which job. You will also know the thing none of them puts on the front page: some of them run entirely inside your browser, and some upload whatever you paste to a stranger's server. That difference decides what you are allowed to practise on. Here is what to actually do today. If you want a database already loaded and questions already written, open sql-practice.com . If you want to create your own tables and share the result with someone, open DB Fiddle . Both start working immediately with no account. The short version: browser-only tools keep your data on your machine, server-backed tools do not, and neither kind is the right place for anything from work. Where the data goes is the one idea that should drive your choice, so it gets the picture. The original carries a diagram here. In words: Two panels side by side, each drawn as a laptop outline containing a browser window. In the left panel a small data box sits inside the browser window, with a short circular arrow looping back into itself, showing the data never leaves the laptop. In the right panel the same data box has a long arrow leading out of the laptop, across a gap, and into a separate server rack drawn beyond the laptop's edge, with a copy of the data box now sitting in the rack as well. The original box remains, showing the data has been copied out rather than moved. Every tool below was opened and checked on 8 August 2026. These sites change often, so the descriptions describe what was actually on screen, and anything I could not confirm by looking is not claimed here. 1. Run your first query, right now Before the explanation: what do you think has to exist on your computer for a SELECT statement to return rows? The honest answer is nothing at all, and that surprises people who have sp

By Michael Nocito , data analyst · Published August 8, 2026 By the end of this page you will have a real database sitting on your own computer, with 11 tables, 3,503 tracks and 412 customer invoices in it, and you will have run a query that proves every table arrived intact. Then you will run a join across two of those tables, which is the thing a single spreadsheet can never teach you. It takes about five minutes and costs nothing. Here is what to actually do today. Download the Chinook database file, open it in DB Browser for SQLite, and run one query that counts the rows in every table. If the counts match the ones printed below, you have a working practice environment and you can stop shopping for one. The short version: get Chinook_Sqlite.sqlite , open it, count the rows, then join two tables. Northwind and Sakila are the other two names you will see, and there is a table further down saying when each is the right pick. The reason a sample database beats the CSV you already have is one idea, so it gets the picture. The original carries a diagram here. In words: Two panels side by side. The left panel holds a single grid of rows and columns, standing alone with nothing attached to it. The right panel holds four smaller grids arranged around each other. A highlighted column at the edge of each small grid is joined by a solid line to a matching highlighted column on a neighbouring grid, so all four grids are wired together into a connected shape. The left panel has no lines at all, because there is nothing for a line to reach. Every number on this page is real. I downloaded Chinook v1.4.5 and Northwind on 8 August 2026 and ran each query with SQLite 3.51.1. The counts, the outputs and the row multiplication are what came back, not what should have come back. If you have no database software at all yet, how to set up a SQL database is the fifteen-minute version of that step, and this page picks up right after it. 1. Why one CSV is not enough Before the explanation:

The US Department of Justice announced on Friday that TikTok will pay $400 million to settle a lawsuit filed in 2024 over allegedly violating the Children's Online Privacy Protection Act (COPPA). In the lawsuit, the DOJ alleged that TikTok collected data from children without notifying parents or obtaining consent and did not delete the accounts […]

Git is a version control system. Version control, also known as source control, is the practice of tracking and managing changes to software code. Version control systems are software tools that help software teams manage changes to source code over time. Git is used for: Tracking code changes Tracking who made changes Coding collaboration Setting up a new Repository A Git repository is a folder that Git tracks for changes. The repository stores all your project's history and versions. Add files to the folder. The following describes how to set up a new repository: Git Init Initializes git user@localhost $ git init This creates a hidden folder called .git inside your project. This is where Git stores all the information it needs to track your files and history. To see which files are in your project folder, use the ls command: user@localhost $ ls To Check if Git is tracking your new files: user@localhost $ git status The files here could either be tracked or untracked:- Untracked Files Files you've created or copied into the folder, but haven't told Git to watch. Tracked Files Files that Git is watching for changes. To make a file tracked, you need to add it to the staging area. Git Staging Tells Git exactly which files you want to include in your next commit. user@localhost $ git add . Common Commands git add . Stages all new, modified, and deleted files in the current directory and its subdirectories. git add <file> Stages a specific file. git add -A (or --all) Stages all changes across the entire repository, regardless of your current folder location. git add -u Stages modifications and deletions of already-tracked files, ignoring completely new (untracked) files. git add *.txt Stages all files matching a specific pattern (e.g., all text files). Git Commit A commit is like a save point in your project. It records a snapshot of your files at a certain time, with a message describing what changed. user@localhost $ git commit -m " Describe your changes" Pushing Chan

A newly resolved proposal would allow us to select classes that are a prefix for variations with a wildcard, like .prefix-* . Resolved: CSS Class Prefix Selector originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Over the past few days, a number of prominent filmmaking content creators including Matti Haapoja and Sam "Kold" Kolder have posted videos of themselves demonstrating what's possible with AI platform Higgsfield. The videos highlight Higgsfield's recently added Seedance 2.5 functionality and pitch these technologies as the future of video production. In response to these videos, […]

One broken window invites another TL;DR: Don't reuse an existing getter to bolt on new business logic from outside the object. Problems 😔 Duplicated business rules Broken encapsulation Scattered comparison logic Hidden domain knowledge Fragile refactoring Law of Demeter violation Solutions 😃 Add real behavior methods Keep comparisons inside object Pass collaborators, not primitives Reserve getters for rendering Follow tell, don't ask Refactorings ⚙️ Refactoring 027 - Remove Getters Maxi Contieri Maxi Contieri Maxi Contieri Follow Apr 18 '25 Refactoring 027 - Remove Getters # webdev # programming # beginners # java 3 reactions Add Comment 17 min read Refactoring 013 - Remove Repeated Code Maxi Contieri Maxi Contieri Maxi Contieri Follow Jun 16 '24 Refactoring 013 - Remove Repeated Code # webdev # beginners # programming # tutorial 2 reactions Add Comment 3 min read Context 💬 An object exposes a getter for one legitimate reason: some other part of the system needs to read that value, usually to display it. Getters are a code smell, but this one gets a pass, for now. Later on, you discover that you need new business logic that depends on the same value. You already have the getter, so you write a function outside the object that calls it and does the comparison itself, breaking the encapsulation principle. Someone else needs slightly different logic based on the same value. They also call the getter and write their own version of the comparison. Now two places decide what that value means , and neither of them is the object that owns it. Typical. You didn't add a second getter this time. You reused the first one, because it was already there. That's the trap. The getter existed for one reason, and you let it justify skipping the real fix: a method on the object that answers the question itself, instead of handing out the raw value for every caller to interpret on their own. Sample Code 💻 Wrong 🚫 // Food needs to show its use-by date on the shelf // label, so useByDate(

Patreon has announced a number of new and overhauled features that are designed to "build a better network - and a better internet," according to CEO Jack Conte. The Patreon roadmap includes discovery algorithm updates, platform and security improvements, and new features for both creators and fans. In a lengthy blog post, Conte outlines each […]

Security researchers at Wiz recently examined S3-compatible object storage services across six popular neoclouds, revealing significant security gaps compared to Amazon S3. While S3 has become the de facto standard for object storage, most services lack several of AWS's security protections. By Renato Losio

Building fully autonomous "AI agent teams" with automated code merging often introduces subtle architectural defects, circular refactoring loops, and codebase degradation. Two agents running in parallel do not constitute independent ground truth: if the author agent makes a logical error, a reviewer agent operating on similar prompt foundations may easily overlook it. A reliable multi-agent workflow is built not on the illusion of full autonomy, but on strict role separation: a dedicated implementer (Author), an independent verifier (Reviewer), and a human developer who makes the final merge decision (Decision Maker). 1. Role Boundaries: Author, Reviewer, and Human Engineer In an effective AI development workflow, each participant has a closed, well-defined scope of responsibility: Role Core Responsibility Input Artifacts Output Artifacts Author Agent Code implementation, local unit tests Task description, completion criteria Git branch, diff, focused test suite Reviewer Agent Edge case discovery, regression checking Git diff, handoff card, verification commands Structured review checklist (Pass/Block) Human Decision Maker Architectural validation, final merge Reviewer summary, CI/CD status Manual merge to main branch 2. Context and Workspace Isolation Never run the author and reviewer agents within the same working directory or shared conversation thread. Isolate them across three operational levels: Session Context : Independent conversation threads prevent mutual hallucination and circular confirmation. Filesystem Isolation : Separate Git worktrees ensure the reviewer inspects only committed diffs without dirty working tree state. Environment Security : API keys and runtime credentials remain in environment variables and are never passed in prompt text. Worktree Preparation Commands: git worktree add ../agent-author -b feat/payment-retry git worktree add ../agent-reviewer feat/payment-retry [!IMPORTANT] API Configuration : Each Claude Code session operates using

Top Vector Databases for AI Agents in 2026: Qdrant vs Pinecone vs Weaviate vs PgVector vs Milvus Persistent memory is the foundation that turns a stateless LLM into a continuously improving, autonomous agent. In 2026, selecting a vector database is no longer just about raw Approximate Nearest Neighbor (ANN) speed. For AI agents, the critical requirements have shifted to: Payload & Metadata Filtering : Can you filter by tenant_id , user_id , and timestamp during vector graph traversal without sacrificing recall? Hybrid Search (BM25 + Dense Vectors + Sparse SPLADE) : Combining exact keyword matching (for code symbols and error codes) with semantic understanding. Multi-Tenancy & Memory Namespacing : Safely isolating memory blocks across thousands of users and sessions. Billion-Scale Quantization (Product Quantization & Scalar Quantization) : Slashing RAM costs by 75–90% in production. This guide provides a comprehensive architectural comparison of the top 5 vector databases for AI agents in 2026. Head-to-Head Comparison Matrix Feature / Metric Qdrant Pinecone (Serverless) Weaviate PgVector (PostgreSQL) Milvus Primary Architecture Rust-native, disk-backed Fully managed serverless Go-native, modular RAG PostgreSQL extension Distributed cloud-native Open Source Yes (Apache 2.0) Proprietary SaaS Yes (BSD-3) Yes (Open Source) Yes (Apache 2.0) Payload Filtering Exceptional (HNSW custom payload indexing) Good (Metadata filtering) Strong (Inverted index + HNSW) SQL WHERE clause Strong (Partition keys) Hybrid Search Native (Dense + Sparse vectors) Native hybrid Native BM25 + Vector SQL text search + pgvector Native multi-vector Quantization Scalar & Product Quantization (Binary) Automatic serverless compression PQ, BQ, SQ Halfvec, Binary Quantization Scalar / Product Quantization Best Fit High-performance agent memory & self-hosted RAG Zero-maintenance cloud SaaS GraphQL & multi-modal search Unified relational + vector apps Ultra-large enterprise (100M+ vectors) 1. Qdrant: The

A blank application window is not a diagnosis. It is only a symptom that tells you the process reached a different stage than an installer that never launched. The most useful first step is to stop applying fixes and record what Windows is actually doing. 1. Define the failure boundary Treat these as separate cases: No window and no lasting process: investigate the installer, security blocking, architecture, and missing runtime dependencies. A window frame appears but the content area is blank: investigate the rendering layer and online content initialization. The entire window stops responding: capture hang evidence instead of reinstalling a rendering runtime. The UI appears but sign-in or online panels spin forever: check network and account services separately. This boundary prevents a common mistake: diagnosing every white or empty interface as a WebView2 failure. 2. Record the process tree Close the application completely, including any tray process, then start it once. Open Task Manager and note the start time of the main process. Check whether child processes such as msedgewebview2.exe appear at the same time. The presence of a WebView2 process is evidence that the runtime participates in the session. Its absence does not automatically prove that WebView2 is broken; the application may have failed before reaching that stage or may use another rendering stack. 3. Check the installed WebView2 version without downloading anything On Windows, the Evergreen Runtime version can often be read from registry locations under EdgeUpdate. The exact location can vary by installation scope. Use read-only inspection first, and confirm that a non-empty version value is present. Do not install several copies from random download pages. If repair is eventually necessary, use Microsoft's distribution channel and document the version before and after the change. 4. Correlate the failure with Windows logs Reproduce the blank window once, then open Event Viewer and inspect entries

Survivors reconnect with those who saved them in National Geographic's 9/11: Reunited .