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‘Popa’ Botnet Linked to Publicly-Traded Israeli Firm

For the past four years, a sprawling Android-based botnet called Popa has forced millions of consumer TV boxes to relay Internet traffic linked to advertising fraud, account takeovers, and mass data-scraping efforts. This week, researchers from multiple security firms concluded that the Popa botnet is linked to NetNut, a "residential proxy" provider operated by the publicly-traded Israeli firm Alarum Technologies Ltd [NASDAQ: ALAR].

2026-06-19 原文 →
AI 资讯

A year of Walmart Plus is half off ahead of Prime Day

Do you often find yourself shopping at the big blue, or perhaps you’re just looking for an alternative to Amazon? Either way, Walmart is currently offering a year of its Walmart Plus subscription for $49, which typically costs $98. The membership includes perks like free delivery on orders over $35, free next-day and two-day shipping […]

2026-06-19 原文 →
AI 资讯

Firefox’s new home page widgets are helping me focus

I launched Firefox this morning to find some new blocks on my home page. The widgets that are currently rolling out add sports scores, time zones, a focus timer, and a checklist, which are already some of my favorite new Firefox features in years. I usually have Focus Friend open on my phone when I […]

2026-06-19 原文 →
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The Onion’s rebooted InfoWars is coming July 2nd

The Onion's InfoWars officially has a launch date: On July 2nd, the conspiracy network previously run by Alex Jones will return as a comedy and media platform. The reboot comes more than a year and a half after news broke that the satirical news site was working to acquire the property owned by Jones, a […]

2026-06-19 原文 →
AI 资讯

7 Alternatives to Building SaaS Backlogs That Never Get Finished

Most SaaS ideas don’t fail because of bad ideas. They fail because the execution gets stuck in an endless setup loop. You start with energy, then slowly get buried in: auth systems, billing, dashboards, SEO, analytics, and infrastructure decisions. By the time the “real product” should begin, momentum is already gone. Here are 7 practical alternatives to building SaaS in a way that never gets finished. 1. Nexora (start with a working SaaS foundation) Instead of rebuilding everything, Nexora gives you a production-ready base so you can focus on actual features. Includes: Authentication system Stripe billing User dashboards SEO pages Blog + docs structure Clean Next.js architecture 🔗 https://nexora.collabtower.com/ 👉 Best for founders who want to ship instead of setup. 2. Build-from-scratch Next.js projects The most common approach. You get: Full control Flexible architecture But you also get: Weeks of setup Repeated boilerplate work High chance of burnout before launch 3. SaaS boilerplates (minimal versions) Lightweight starter kits with: Auth Basic UI Simple Stripe setup But usually missing: Real dashboards SEO systems Production-level structure 4. Supabase-first builds Backend-focused setups. You get: Database Auth APIs But still need to build: Billing UI system Marketing pages SaaS structure 5. Low-code SaaS tools Fast visual builders. Pros: Quick UI creation No heavy coding Cons: Limited flexibility Hard to scale complex SaaS logic Platform dependency 6. AI-generated starter apps AI tools can scaffold SaaS apps instantly. Pros: Fast starting point Cons: Inconsistent structure Requires cleanup Not production-ready out of the box 7. Tutorial-based SaaS builds Many developers still learn SaaS by following tutorials step-by-step. Pros: Educational Cons: Slow Fragmented Hard to turn into real production apps Final takeaway Most SaaS workflows fail before launch because they repeat the same mistake: They start from zero every single time. That creates unnecessary setup

2026-06-18 原文 →
AI 资讯

Securing AI-Generated Bash Scripts Before You Run Them

Bash is the easiest language for AI to write and the easiest language to get devastating output from. A 20-line script that "just cleans up old files" can recursively delete a home directory because the model assumed a variable would always be set. A "simple log shipper" can write your secrets to a remote server because the model used set -x for debugging and forgot to remove it. I have run AI-generated bash that I should not have. Most engineers I know have too. After enough close calls, there's a short checklist that catches the worst of it. This is that checklist. The five things to check before running any AI-generated bash 1. Does it start with a strict pragma? The first lines of any non-trivial bash script should be: #!/usr/bin/env bash set -euo pipefail IFS = $' \n\t ' What each does: set -e — exit on any command failure. Without this, a failure in line 5 doesn't stop the script from happily running lines 6-50. set -u — error on undefined variables. This is the one that saves you from rm -rf $UNDEFINED/ . set -o pipefail — propagate failures through pipes. Without it, failing-command | grep something succeeds because grep succeeds. IFS=$'\n\t' — sane field splitting. Defends against word-splitting bugs in filenames. If the AI-generated script doesn't have these, add them and re-read the script. You'll often discover bugs the pragma now flags. 2. Is every variable expansion quoted? # Wrong rm -rf $TARGET_DIR # Right rm -rf " $TARGET_DIR " The wrong version is what causes the "I deleted the root directory" stories. If $TARGET_DIR is empty or contains a space, the command becomes rm -rf (delete current directory) or rm -rf foo bar (delete two unintended things). Models default to the wrong version about half the time because the right version is harder to write in chat ("escape the quotes!") and the wrong version is what most blogs show. Fix: When reading AI bash, mentally check every $VAR for quotes. Add them if missing. This is the single biggest source of bas

2026-06-18 原文 →
AI 资讯

I built a CLI that generates .env files so I never read docs again

# EnvForge BETA v1.1 ⚡ Structured .env scaffolding for modern applications. Generate, validate, and protect environment variables for 14+ services – without ever opening a docs page. Github repo [ https://github.com/Jos3456/envforge ] NPM version (https://img.shields.io/npm/v/envforge-dev) MIT License (https://img.shields.io/badge/License-MIT-yellow.svg) ## Installation bash npm install envforge-dev **Requirements:** Node.js 18 or later. --- **## Quick Start** bash # 1. Generate an .env file and choose your providers envforge init # 2. Fill in your actual credentials envforge fill # 3. Check everything is set correctly envforge validate ## All Commands ### Scaffolding Command What it does envforge init Create a new .env by selecting providers interactively envforge add <provider> Add variables from a specific provider to your existing .env envforge preset Generate a .env from a popular stack preset envforge example Create a safe‑to‑commit .env.example file envforge fill Interactively enter values (secret keys are masked) envforge list Show all built‑in and custom providers ### Guardrails Command What it does envforge validate Check that all required variables are filled in envforge scan Detect secret keys accidentally exposed in frontend code envforge hook install Install a pre‑commit hook that runs validate + scan ### Customisation Command What it does envforge provider add Create a custom provider template envforge registry update Download the latest providers from the community registry ## Built‑in Providers Category Providers Database Supabase, Neon, MongoDB Atlas Auth Clerk, Auth0, Firebase AI OpenAI, Anthropic (Claude) Payments Stripe Email Resend, SendGrid Storage Cloudinary, AWS S3 / Cloudflare R2 Other Vercel Missing a provider? Add your own with envforge provider add or contribute one to the community. ## Framework‑Aware Scanning Use --framework for smarter detection: # Next.js specific rules (app/ vs pages/, "use client") envforge scan --framework next Th

2026-06-18 原文 →
AI 资讯

Agent Framework RAG for Agents: Giving Your Agent the Right Context

This is Part 13 of my series on the Microsoft Agent Framework. You can read the original post over on lukaswalter.dev . In the previous article , we looked at workflows. Workflows make sense when the process itself needs structure: state, checkpoints, events, human approvals, and resumable execution. This post is the bridge from Agent Framework into RAG. I plan on doing a full RAG deep dive sometime later. The practical question for now is smaller: How do I connect an Agent Framework agent to private application knowledge without stuffing every document into the prompt? For agents, RAG is less about adding more text and more about giving the agent a controlled retrieval path. The agent should fetch the right context at the point where it needs it. Agents do not know your private data Your company documents, product catalog, tickets, rules, policies, runbooks, and internal knowledge base live outside the model. The model has generic knowledge. Your application has private knowledge. Treat those as separate systems. You can paste some private data into the prompt, and for a demo that may be enough. But this falls apart quickly: full documents are expensive to send repeatedly long prompts are fragile stale documents may sit next to current ones users may not be allowed to see every source long context still needs selection The last point is easy to underestimate. A larger context window lets you send more text. It does not decide which text is correct, current, relevant, or permitted. Do not give the agent all knowledge. Give it the right context at the moment it needs it. Retrieval owns that job. The minimal RAG shape The basic RAG loop is small: user question -> retrieve relevant chunks -> pass chunks to the agent -> agent answers using that context For documents, the longer pipeline usually looks like this: documents -> chunks -> embeddings -> vector store -> search -> retrieved context -> agent response Documents are split into smaller chunks. Those chunks are embe

2026-06-18 原文 →