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X sued the WFA in 2024 for conducting what it called a "systematic illegal boycott" of the platform after it saw a decline in advertising revenue following Musk's $44 billion takeover of the social network

If your training dashboard starts as one HTTP request and grows into athletes, activities, wellness, workouts, gear, and performance data, a hand-written fetch wrapper becomes expensive to maintain. Every new endpoint adds another URL, another response shape, and another place to get authentication or retry behavior wrong. This tutorial shows a small, reproducible path with intervals-icu , an open-source TypeScript client for the Intervals.icu API . The goal is not to build a complete training application. It is to establish a typed client, choose the right authentication boundary, call one service, and understand what changes when you move from version 1 to version 2 of the library. TL;DR Install the stable npm package, create an IntervalsClient with an API key or OAuth access token, and use service accessors such as client.athletes or client.activities. Version 2 uses typed service methods, retries selected transient failures, and defaults requests to the authenticated athlete. Prerequisites You need: Node.js 18 or newer. npm. An Intervals.icu account with an API key, or an OAuth access token for an application acting for other users. A TypeScript project that can run ESM modules. The published package is intervals-icu version 2.2.1, and its package metadata declares Node.js >=18.0.0. The repository is public and licensed under MIT. The examples below target that stable package version, not an unreleased default-branch change. Install the stable client Create a small project and pin the package version used in this tutorial: mkdir intervals-demo cd intervals-demo npm init -y npm install intervals-icu@2.2.1 npm install -D typescript tsx The package publishes both ESM and CommonJS entry points and exposes TypeScript declarations from its package root. Add a script so a .ts file can run without a separate build step: { "type" : "module" , "scripts" : { "start" : "tsx src/index.ts" } } Create the smallest useful client Create src/index.ts. Keep the credential outside

Elon Musk’s platform attacked the country’s “invasive” information-gathering powers as it demands the government drop efforts to strengthen its no-children policy.

Quando eu decidi aprender como a JVM funciona por dentro, eu precisava de uma linguagem simples o suficiente pra não atrapalhar o aprendizado. Algo onde eu pudesse focar na mecânica do compilador sem me perder na complexidade da linguagem fonte. Brainfuck foi a escolha óbvia. Esse é o primeiro post de uma série de três onde a gente vai construir, do zero, um compilador que transforma código Brainfuck em bytecode JVM executável. Sem dependências externas, sem framework, só Node.js puro. No final da série, você vai ter um compilador que gera arquivos .class válidos que rodam direto no java . O código completo está no GitHub . Nesse primeiro post, a gente vai construir o interpretador - que é a base pra tudo que vem depois. O que é Brainfuck Brainfuck é uma linguagem de programação esotérica criada em 1993 por Urban Müller. Ela tem 8 comandos . Oito. E ainda assim é Turing-completa - ou seja, em teoria, você pode computar qualquer coisa que qualquer outra linguagem computa. O modelo de execução é simples: Uma fita de memória com 30.000 células, cada uma armazenando um byte (0-255) Um ponteiro que aponta pra célula atual Entrada e saída (stdin/stdout) Os 8 comandos: Comando O que faz + Incrementa o valor da célula atual - Decrementa o valor da célula atual > Move o ponteiro uma célula pra direita < Move o ponteiro uma célula pra esquerda . Imprime o valor da célula atual como caractere ASCII , Lê um byte da entrada e armazena na célula atual [ Se a célula atual é zero, pula pro ] correspondente ] Se a célula atual não é zero, volta pro [ correspondente Qualquer outro caractere é ignorado - o que significa que você pode escrever comentários livremente no meio do código. Um exemplo simples Pra imprimir a letra "A" (código ASCII 65), você precisa colocar o valor 65 na célula e usar . : +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ . São 65 sinais de + seguidos de um . . Funciona, mas é feio. Uma forma mais elegante: ++++++++ [ > ++++++++ < - ] > +. O qu

Anthropic published Verbalizable Representations Form a Global Workspace in Language Models on July 6, and the vocabulary it introduced is suddenly everywhere: J-space, the Jacobian lens, a global workspace inside Claude. Most of the discussion so far is about interpretability and alignment auditing, which is fair, since that is what the paper is about. I had a narrower and more mercenary question: can the workspace tell an inference runtime which parts of the KV cache it is safe to throw away? Three days after the paper landed, the first pre-registered gate on that question passed. As of this week the signal has replicated on three models and ships inside EVOKE , my KV cache memory manager built on a forked llama.cpp. This post covers what J-space is, why it makes a good KV cache eviction signal, the numbers across Qwen2.5-7B, Qwen3-8B, and Qwen3-4B, and the caveat that comes with them. What J-space is, in one paragraph The Jacobian lens is the instrument and J-space is the phenomenon. The lens isolates directions in a model's residual stream that encode a token the model could verbalize next, and those directions form a low-dimensional workspace: roughly 10% of activation variance, concentrated in the middle layers, carrying whatever the model is "holding in mind" at each position. Anthropic's headline application is alignment auditing, reading reasoning the model never voices. What makes independent work possible is that they released companion code under Apache-2.0 along with fitted lens matrices for open Qwen models on Hugging Face , so anyone can apply the lens to an open-weights model on a single GPU. The systems problem: KV cache eviction Every long-running LLM session eventually outgrows its KV cache budget. An agent session in a coding harness crosses tens of thousands of cached tokens within a few turns, and something has to decide which entries stay in GPU memory. The standard answers, H2O and SnapKV, rank cache blocks by accumulated attention history: k

Novelists, journalists, and power LinkedIn posters are embracing first-person narratives and idiosyncrasies to avoid being mistaken for chat bots.

Daniel Doubrovkine explains why traditional LeetCode whiteboard interviews fail to evaluate senior engineering talent. He discusses his own experience bombing basic algorithm tests despite decades of leadership, and shares actionable frameworks for redefining the interview loop. Discover how evaluating human judgment, system design, and hands-on AI collaboration yields far better hiring signals. By Daniel Doubrovkine

Handling Asynchronous Webhook Notifications & Callbacks in Joget via BeanShell When integrating Joget DX with external platforms—such as payment gateways, SMS providers, or ERP systems—requests are often processed asynchronously. The external system accepts a request immediately and dispatches an HTTP POST webhook callback to Joget minutes or hours later when processing completes. Receiving webhook callbacks inside BeanShell API endpoints requires two key tasks: Safe Variable Type Coercion: Handling parameter arrays ( String[] ) versus single strings ( String ) safely without throwing ClassCastException . FormDataDao Persistence: Saving or updating the notification payload inside a Joget form database table using FormDataDao . In this guide, we'll write a defensive Java/BeanShell script that receives asynchronous webhook callbacks and logs them cleanly into Joget. Architecture Overview Webhook Endpoint: An external system hits your Joget API endpoint with callback parameters (e.g. process_id , status , response_payload , recipient ). Type Extraction: A safe helper function handles parameter type variations (whether passed via URL query params or JSON request bodies). FormDataDao Save: Instead of executing raw JDBC queries, the script uses FormDataDao to persist a FormRowSet directly into Joget's form storage engine. The BeanShell Script Place this code inside your API Builder BeanShell script or custom REST endpoint: import org.joget.apps.app.service.AppUtil ; import org.joget.apps.form.dao.FormDataDao ; import org.joget.apps.form.model.FormRow ; import org.joget.apps.form.model.FormRowSet ; import org.joget.commons.util.LogUtil ; import java.util.UUID ; // 1. Safe Type Extraction Helper public String safeExtract ( Object param ) { if ( param == null ) return "" ; try { if ( param instanceof String []) { String [] arr = ( String []) param ; return arr . length > 0 ? arr [ 0 ] : "" ; } if ( param instanceof String ) { return ( String ) param ; } } catch ( Throwable t

Generating Multilingual HTML Reports with Attachment Download Links in Joget Creating customized executive report summaries in Joget DX often requires more than simple database lists. Real-world business reports frequently need to join multiple tables, translate status labels based on the user's active locale ( #platform.currentLocale# ), and generate secure file download links for form attachments. In this guide, we'll build a Java/BeanShell script that queries main records and history logs, resolves internationalization ( i18n ) message keys dynamically, and generates interactive HTML reports embedded with secure attachment links. Key Components Dynamic i18n Translation: Uses AppUtil.processHashVariable("#i18n.key#", null, null, null) to convert database status codes into localized text matching the user's language setting. File Attachment Links: Formats secure file download URLs ( /jw/web/client/app/{appId}/{version}/form/download/{tableName}/{recordId}/{fileName} ) so users can open uploaded documents directly from the report summary. Multi-Table SQL Join: Merges main request details, audit transaction history, and custom review tables into a clean HTML document layout. The BeanShell Script Place this code inside a BeanShell Form Bounding Box or an HTML Report Generator tool step: import java.sql.Connection ; import java.sql.PreparedStatement ; import java.sql.ResultSet ; import java.net.URLEncoder ; import javax.sql.DataSource ; import org.joget.apps.app.service.AppUtil ; import org.joget.apps.app.model.AppDefinition ; import org.joget.commons.util.LogUtil ; // Helper: Resolve i18n hash variables dynamically public String getLocalizedText ( String messageKey ) { if ( messageKey == null || messageKey . isEmpty ()) return "" ; String hashVariable = "#i18n." + messageKey + "#" ; return AppUtil . processHashVariable ( hashVariable , null , null , null ); } String recordId = "#requestParam.id#" ; if ( recordId == null || recordId . trim (). isEmpty ()) { return "<di

How to Update Joget App Environment Variables Programmatically in BeanShell In Joget DX, App Environment Variables are commonly used to store global configuration values—such as API endpoints, tax rates, batch counter sequences, or feature flags. While administrators can update these variables manually through Joget App Center, enterprise workflows often need to update environment variables programmatically (for example, incrementing a daily batch sequence counter or updating an OAuth access token). In this guide, we'll write a short BeanShell script using Joget's EnvironmentVariableDao to fetch and update App Environment Variables dynamically. How It Works Obtain App Context: AppUtil.getCurrentAppDefinition() retrieves the active application definition. Access the DAO Bean: AppUtil.getApplicationContext().getBean("environmentVariableDao") retrieves Joget's internal DAO for environment variables. Load & Update: environmentVariableDao.loadById(envVarId, appDef) retrieves the target variable instance. Modifying .setValue() and executing environmentVariableDao.update(envVar) persists the updated value immediately. The Code Place this BeanShell snippet inside a BeanShell Tool workflow step or a Form Post-Processing Tool : import org.joget.apps.app.dao.EnvironmentVariableDao ; import org.joget.apps.app.model.AppDefinition ; import org.joget.apps.app.model.EnvironmentVariable ; import org.joget.apps.app.service.AppUtil ; import org.joget.commons.util.LogUtil ; public void updateAppEnvironmentVariable ( String variableId , String newValue ) { AppDefinition appDef = AppUtil . getCurrentAppDefinition (); if ( appDef != null ) { // Retrieve Joget's Environment Variable DAO bean EnvironmentVariableDao envDao = ( EnvironmentVariableDao ) AppUtil . getApplicationContext (). getBean ( "environmentVariableDao" ); // Load target environment variable by ID EnvironmentVariable envVar = envDao . loadById ( variableId , appDef ); if ( envVar != null ) { LogUtil . info ( "EnvVar Manager

Custom Cell Renderers & Action Buttons in Joget Spreadsheet Elements The built-in Spreadsheet Element in Joget DX provides a spreadsheet-like interface for managing tabular records inside forms. However, standard spreadsheet columns only support basic text or dropdown inputs out of the box. If you want to add row-level action buttons (like a Delete Row button) or turn plain cell text into an interactive Modal Popup Link , you can supply custom Handsontable renderer functions directly inside your Spreadsheet column properties. In this guide, we'll look at two practical examples: adding a custom row-deletion button and rendering interactive drill-down links. Example 1: Adding a Custom Delete Row Button In your Joget Spreadsheet element, open column properties for an action column and configure the custom renderer function below: {{ renderer : function ( instance , td , row , col , prop , value , cellProperties ) { // Render custom HTML button inside the cell td . innerHTML = " <button type='button' class='btn-delete-row'>Delete</button> " ; td . style . textAlign = " center " ; // Attach click handler to remove the target row from the Handsontable instance const btn = td . querySelector ( " .btn-delete-row " ); btn . onclick = function ( e ) { e . preventDefault (); e . stopPropagation (); // Get underlying Handsontable instance from the form field const hotInstance = FormUtil . getField ( " your_spreadsheet_field_id " ). data ( " hot " ); if ( hotInstance ) { hotInstance . alter ( " remove_row " , row ); } }; } }} Key Highlights: instance.alter("remove_row", row) removes the target row directly from the underlying data model. e.stopPropagation() prevents Handsontable from entering cell-edit mode when the button is clicked. Example 2: Interactive Drill-Down Popup Links To display a clickable link in a grid cell that opens a detailed record inside a Joget modal dialog (popup iframe), use this cell renderer: {{ renderer : function ( instance , td , row , col , prop , va

It feels bad enough when an open letter signed by leading economists warns that AI might steal your job. The fact it may soon be better than you at making dinner? Insult to injury. But that’s exactly what the company 1X promised when it showed off a pair of new, impressively dexterous (and, to some,…

TL;DR Eight engineering disasters. Zero arithmetic errors. Every single one was caused by two numbers — both correct, both carefully computed — meaning different things on opposite sides of a software interface. One cost $65 billion. Another killed 28 soldiers because 0.1 can't be represented in binary. The fix is never the math. The fix is the label. There is a particular kind of silence in a control room when someone realizes the number on the screen is in the wrong unit. It lasts about two seconds. Then it's replaced by the kind of noise nobody wants to hear. On September 23, 1999, that silence happened at the Jet Propulsion Laboratory in Pasadena, California. The Mars Climate Orbiter had just disappeared behind the planet. Telemetry showed the spacecraft at 57 kilometers above the surface. It was supposed to be at 140. The silence was four seconds long. Then someone said "oh no" — the official NASA transcript uses a stronger word — and $327 million of aluminum, titanium, and human effort disintegrated into the Martian atmosphere. What follows are eight stories about the same bug, wearing different uniforms. Some are famous. Some you've never heard of. Two of them are pure software failures that every developer who's ever written for (let i = 0; i < 10; i += 0.1) has come within a rounding error of replicating. 1. The Patriot Missile — When 0.1 Is Not 0.1 (1991) Let's start with the one that belongs in every CS curriculum. Because this isn't a "unit conversion" error in the traditional sense — nobody confused meters and feet. The error was in the way a computer counted time. And it killed 28 American soldiers in a warehouse in Dhahran, Saudi Arabia. The MIM-104 Patriot missile system tracks incoming targets using a phased-array radar. The radar scans the sky, and the fire-control computer predicts where the target will be when the interceptor arrives. That prediction depends on knowing exactly when the radar echo returned. Time is measured by the system's interna

Prólogo No início da faculdade, séculos atrás (papo de 2021, foi quando entrei na he4rt...

I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about. So here is my honest take on where things actually are. The Problem With How We Talk About AI Agents Everyone is calling everything an "agent" right now. A function that calls a tool? Agent. A chatbot with memory? Agent. A script with a loop? Agent. This dilution is not just semantic. It is causing real engineering mistakes. When you do not have a precise definition for what you are building, you end up over-engineering simple pipelines and under-engineering genuinely complex ones. I have seen teams spend weeks adding "agentic" orchestration to workflows that would have been fine as a single well-structured prompt. Here is the definition I keep coming back to: an agent is a system that has an objective, not just an instruction. It decides what to do next. It handles failure. It knows when it is done. Everything else is just a fancy function call. 🟢 If your system needs a human to tell it each step, it is not an agent. It is a chat interface. 🔵 If your system can recover from a failed tool call and try a different approach, you are getting somewhere. ✅ If your system can decompose a goal into subtasks and delegate them, that is the real thing. What Is Actually Happening in Production Right Now The honest picture from teams I follow and talk to: Most real agent deployments are narrow. They do one thing well. Customer support triage. Document extraction. Code review on a specific codebase. They are not general-purpose reasoning engines. They are purpose-built pipelines with some intelligence in the decision layer. The teams getting good results are not chasing the latest model release. They are obsessing over: ☑️ Tool design -- what can the agent actually call, and how clean is the interface ☑️ Failure handling -- wh

I spend a lot of time in the AI space -- reading papers, building things, talking to engineers who are actually shipping. And there is a gap between what the demos show and what production systems actually look like that nobody is being fully honest about. So here is my honest take on where things actually are. The Problem With How We Talk About AI Agents Everyone is calling everything an "agent" right now. A function that calls a tool? Agent. A chatbot with memory? Agent. A script with a loop? Agent. This dilution is not just semantic. It is causing real engineering mistakes. When you do not have a precise definition for what you are building, you end up over-engineering simple pipelines and under-engineering genuinely complex ones. I have seen teams spend weeks adding "agentic" orchestration to workflows that would have been fine as a single well-structured prompt. Here is the definition I keep coming back to: an agent is a system that has an objective, not just an instruction. It decides what to do next. It handles failure. It knows when it is done. Everything else is just a fancy function call. 🟢 If your system needs a human to tell it each step, it is not an agent. It is a chat interface. 🔵 If your system can recover from a failed tool call and try a different approach, you are getting somewhere. ✅ If your system can decompose a goal into subtasks and delegate them, that is the real thing. What Is Actually Happening in Production Right Now The honest picture from teams I follow and talk to: Most real agent deployments are narrow. They do one thing well. Customer support triage. Document extraction. Code review on a specific codebase. They are not general-purpose reasoning engines. They are purpose-built pipelines with some intelligence in the decision layer. The teams getting good results are not chasing the latest model release. They are obsessing over: ☑️ Tool design -- what can the agent actually call, and how clean is the interface ☑️ Failure handling -- wh

Docker published the second entry in its Coding Agent Horror Stories series on July 20, and the operational read is short: on a stock developer laptop, an AI coding agent runs with the engineer's filesystem permissions and the engineer's credentials, with nothing sitting between it and the host. The post frames a scenario in which the agent deletes production and works backward through why that outcome is not exceptional. Docker names the piece as part two of a series that will cover six categories of coding-agent failure. What the post actually claims Two claims carry the argument. First, the agent inherits the developer's shell posture: whatever the developer can touch on disk, the agent can touch; whatever token is exported into the environment, the agent can spend. Second, that default is not a sandbox. Docker's phrasing is that nothing sits between the agent and the host unless the operator puts it there. The piece does not attribute the scenario to a named incident; it is a category, not a case study. Anyone extrapolating specific companies, victims or numbers is filling in blanks the source did not. The runner problem, one hop to the left For CI operators this shape is familiar. A self-hosted Actions runner or a Jenkins agent that mounts the workspace, holds a checkout token and can call the host shell is a service you already isolate on purpose. You isolate it because the workflow you invited in is not always the workflow that runs. You isolate it because the token in the environment can do more than the job description. You isolate it because rollback of a bounded container is cheaper than reasoning about everything a process touched on a shared box. A coding agent living on the developer laptop occupies the same trust position, one machine earlier in the pipeline. It reads and writes the working tree. It holds session credentials to cloud APIs, the cluster and the registry. It executes instructions the developer did not always write, sometimes routed from

A forgotten password, a security plugin that blocked your own IP by mistake, a plugin bug that turns the admin screen white — the causes vary, but the result is the same: you can’t log in to wp-admin. Note: WP-CLI is a command-line tool for managing WordPress, invoked as wp . It operates directly on the server, without going through a browser. This is exactly the situation where WP-CLI is useful. It works here because it never touches wp-login.php — it reads and writes the WordPress database and filesystem directly, so a broken login screen doesn’t affect it at all. Why WP-CLI keeps working when wp-admin doesn't A normal login follows the path: browser → wp-login.php → authentication → wp-admin. If anything along that path is broken — a plugin throwing a fatal error during authentication, a security plugin blocking your IP, a fatal error in the admin theme — the login itself can’t complete. WP-CLI connects over SSH and reads/writes the wp_users and wp_options tables (and the filesystem) directly. The code in wp-login.php is never executed, so problems on that path don’t matter. This does require that SSH access itself still works — on most hosting providers, SSH is a separate access path from the admin dashboard, so it usually still works even when wp-admin doesn’t. Scenario 1: Forgotten password # List administrator accounts wp user list --role = administrator --fields = ID,user_login,user_email # Overwrite the password directly wp user update 1 --user_pass = 'a-strong-new-password' wp user update writes the new password directly to the database row — no password-reset email, no token, no waiting on a delivery that might land in spam or not arrive at all. Scenario 2: A security plugin blocked your own IP Login-attempt-limiting plugins occasionally misclassify legitimate activity as an attack and add the working IP to a block list. # Deactivate the plugin responsible for the block wp plugin deactivate <plugin-causing-the-lockout> # Re-enable it later, after reviewin

In a new disclosure, OpenAI says its agent used exposed logins to gain access to at least four “publicly available services” in its unhinged quest to solve a test.

Introduction Small teams don't need GitFlow or other complex branching models. They need a workflow that's easy to understand, quick to execute, and minimizes merge headaches. Here's a practical workflow I've used with teams of 2-8 developers. The Core Idea: Main and Short-Lived Feature Branches We keep it simple with one long-lived branch ( main ) and short-lived feature branches. Every change starts from main and is merged back as soon as it's ready. git checkout main git pull git checkout -b feature/my-feature Branch Naming Convention Use a consistent prefix to keep branches organized: feature/ for new features fix/ for bug fixes chore/ for maintenance tasks Example: feature/user-authentication , fix/login-error The Workflow Step by Step 1. Start from an Up-to-Date Main Before creating a branch, make sure your local main is up to date: git checkout main git pull --rebase 2. Create a Feature Branch git checkout -b feature/awesome-feature 3. Make Small, Frequent Commits Commit early and often. Each commit should represent a logical unit of work. git add . git commit -m "Add user model with email validation" 4. Push and Open a Pull Request Even if the branch isn't finished, pushing early allows others to see your progress. git push -u origin feature/awesome-feature Then open a PR against main . Keep PRs small (under 400 lines if possible). 5. Keep Your Branch Updated If main moves forward, rebase your branch to avoid conflicts later: git checkout feature/awesome-feature git rebase main # resolve conflicts if any git push --force-with-lease --force-with-lease is safer than --force because it prevents overwriting others' work. 6. Code Review At least one other team member reviews the PR. Look for logic errors, readability, and test coverage. 7. Merge via Squash Merge When the PR is approved, use squash merge to keep main history clean: git checkout main git pull git merge --squash feature/awesome-feature git commit -m "Add awesome feature" Or use the GitHub/GitLab squ