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AI and the rise of the universal entertainment app

Over the past decade, streaming platforms competed by dominating individual formats like music, video, podcasts, or audiobooks. Now, as AI makes it easier to create, organize, and recommend content, those distinctions are fading, pushing companies like Spotify, Netflix, YouTube, and TikTok to become all-purpose entertainment destinations instead.

2026-07-22 原文 →
AI 资讯

Clinejection: How a GitHub Issue Title Compromised an AI Coding Assistant Used by 5M Developers

TL;DR In December 2025, Cline — an AI coding assistant with over 5 million users — gave an AI agent (Claude) write access to triage GitHub issues, including permission to run shell commands. A misconfigured trigger condition let any GitHub user invoke the workflow. What followed was a four-hop supply chain compromise that ended with a malicious npm package silently installing a second AI agent on user machines. I broke down the full chain in video form: Watch the episode Below is the chain, hop by hop. Hop 0: The setup The triage automation was configured with broad tool permissions and a trigger condition open to any GitHub user — not just contributors. That second part is the root cause: it opened the trigger to unauthenticated input. (Exact config values are in the Confirmed Artifacts section below.) Hop 1: Prompt injection via issue title The issue title itself was never sanitized before reaching the model. No first-party source has published the exact injected payload verbatim — GHSA doesn't disclose it — so any reconstruction here is illustrative, not confirmed. What's confirmed is the mechanism: an untrusted string reached the model with tool access already granted. Hop 2: Cache poisoning The injected instruction deployed a tool (multiple independent postmortems — Snyk, Cloud Security Alliance — name it "Cacheract") that flooded the CI cache with over 10GB of junk data, evicting legitimate entries through standard LRU eviction. Hop 3: Nightly workflow inherits the poisoned cache The nightly release workflow restored that poisoned cache around 2 AM UTC and ran inside it — handing over three publish tokens. (Names confirmed across GHSA and multiple independent sources — see below.) Hop 4: Publication cline@2.3.0 went live on npm with a postinstall script that silently installed a second package globally — an AI agent, installed by an AI agent, with no user consent. This line is a direct quote from Cline's own security advisory, not a reconstruction. It's the st

2026-07-19 原文 →
AI 资讯

Apple Music is getting a price hike

Apple Music is more expensive now. In the US, an individual plan now costs $11.99 per month, a $1 bump up from the previous $10.99 price. A family plan now costs $19.99 per month, up from $16.99, and a student plan costs $6.99 per month, up from $5.99. Apple, in a statement to Music Business […]

2026-07-18 原文 →
AI 资讯

WhatsApp Automation for Small Businesses in 2026: AI Replies, Lead Capture & Tiered Commissions

Your customers would rather message you on WhatsApp than fill in a contact form. That's fine at ten conversations a day. At a hundred, messages get missed, nobody knows which rep is on which deal, and at month-end somebody rebuilds the commission sheet by hand and gets it wrong. The usual answer is a $49–$499/month WhatsApp SaaS platform, priced per seat, with your customer data living in someone else's database. This post is the other answer: the same workflow on Google Sheets + Apps Script — and the one piece I see teams get wrong every single time, with the code to fix it. Where DIY WhatsApp automation actually breaks It isn't the messaging. Wiring a WhatsApp webhook into a sheet is a couple of hours of work, and I've written that build up separately — the webhook, the AI reply, and the lock that stops two reps chasing the same lead are all in Build a WhatsApp Sales Inbox in Google Sheets . I won't repeat it here. The part that breaks is the commission math . Someone writes =IF(revenue>10000, revenue*0.08, revenue*0.05) into a column, and three things kill it: A single sale spans two tiers — the formula charges the whole amount at one rate. The tiers change in July , and now every historical row recalculates at the new rate. A customer refunds in August on a sale from June, and nobody can unwind it without breaking the audit trail. So that's what this post builds: a tiered commission engine that survives rule changes and refunds. 1. Put the tiers in a table, never in a formula This is the whole trick. Make a Commission Rules tab, one row per rule: rule_id | rep_id | effective_from | effective_to | tier_1_cap | tier_1_pct | | | | | tier_2_cap | tier_2_pct | tier_3_pct --------+----------+----------------+--------------+------------+------------+----------- R1 | ALL | 2026-01-01 | | 10000 | 0.05 | | | | | 50000 | 0.08 | 0.10 R2 | rep_ayse | 2026-06-01 | | 10000 | 0.06 | | | | | 50000 | 0.09 | 0.12 rep_id is either a specific rep or ALL (the house default). Percenta

2026-07-17 原文 →