😺 Google sued the people spamming your phone

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ICYMI, a few months ago we asked how we could better help our readers learn AI, and what kind of learning format would be most useful to you. Since then, we built The Neuron Academy for readers who love the newsletter but still feel like they’re underusing AI at work. It’s a hands-on learning library for the practical AI skills knowledge workers actually need: better prompts, custom agents, interface explainers, and AI workflows for research, planning, writing, and analysis.

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Here’s what happened in AI today:

• 😸 Google sued the phishing ring that's been flooding your phone with scam texts — and they used Gemini to build it.

• 📰 Meta hid military-grade facial recognition software in an app on 50M phones, from a Pentagon contractor. Then deleted it.

• 📰 Fully autonomous drones killed Russian soldiers in Ukraine — the first confirmed battlefield deaths from AI-controlled weapons.

• 📰 Meta launched AI Mode on Facebook, surfacing answers from public posts instead of search results.

• 📰 ChatGPT hit 1 billion monthly app users, but enterprises still have hard questions about AI trust and ROI before committing.

…and a whole lot more that you can read about here.

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P.S: Love robots? We’re starting a new robotics newsletter! Sign up early here.

😸 The Scam Texts Flooding Your Phone Were Built With Google's Own AI

You've seen the texts. "Your package is held." "Your toll is overdue." "Suspicious activity on your account." You probably deleted them without clicking. Smart. Because the operation behind them just got sued by Google and the numbers are staggering.

Google filed a lawsuit against Outsider Enterprise, a China-based cybercrime network accused of using Gemini (Google's own AI) to mass-produce phishing websites and flood American phones with fraudulent texts. It's the first time Google has ever sued bad actors specifically for misusing Gemini to run scams.

Here's what happened:

• In just two weeks in May, Outsider Enterprise sent 2.5 million scam texts to Android users, generating 55,000 spam complaints.

• The FBI estimates the group has stolen 3.87 million credit card numbers and caused roughly $1.9 billion in losses since July 2023.

• Google identified over 9,000 fake websites and 1.5 million fraudulent URLs linked to the operation.

• The group used Gemini to generate HTML code for convincing fake sites impersonating Google, YouTube, the US Postal Service, banks, and toll agencies.

• They ran it like a franchise: a subscription phishing toolkit starting at $88/week on Telegram, with 290+ pre-built templates any non-technical criminal could deploy in minutes.

Why this matters: Before AI, building a convincing fake website took a coder and a few hours. Outsider Enterprise cut that to minutes, then sold the ability to anyone with $88 and a Telegram account.

The business model here is the alarming part. Outsider Enterprise also sold the toolkit to other criminals. Any non-technical buyer could log into Telegram, pay $88/week, pick a template impersonating their bank or the USPS, and start harvesting credit cards. Gemini was the production engine that generated convincing fake pages faster than any human team could.

Google is now coordinating with the FBI and has partnered with AT&T, T-Mobile, and Verizon to block the traffic. It's also backing seven bipartisan bills in Congress targeting AI-enabled scams.

Our take: The uncomfortable detail is that Outsider Enterprise used Google's own infrastructure throughout with Gemini for content, Google Cloud to host the fake sites, Google Drive to store the stolen data. Google suing them is the right call. But it's also a useful reminder that the same AI tools making your work faster are being rented out on Telegram for $88 a week to steal credit cards.

The scam texts will slow down. The toolkit still exists.

On a tangent, if you want to understand how AI models get stacked and routed under the hood, we broke down OpenRouter's new Fusion API, which lets you run prompts through multiple models at once and synthesize the best answer. Good explainer on where AI infrastructure is quietly heading. Read it here.

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🎓 Make your agent write its own goal

So, a /goal is your AI agent’s job description for the task you want it to do: what it’s trying to do, what “done” looks like, and what boundaries it should respect. Use this in Claude Code or Codex when you want an agent to handle project-based work instead of dumping everything into a normal ChatGPT window.

Pietro Schirano (creator of MagicPath) shared a tiny agent workflow that solves a real delegation problem: before you ask the agent to do the work, ask it to define the goal itself (including for writing sub-agent goals).

His move is simple: he “basically never” writes his own /goal anymore. He asks Codex to write one for itself, plus one for each agent it spawns, meaning each helper agent gets its own target before the work begins.

The best version of this is human-reviewed autonomy: let the model draft the target, then you tighten the constraints so the agents stay on track.

As Steven Cheng pointed out in the replies, spawned agents can drift into weird edge cases without human-set boundaries, so that kinda sanity checking of the bots matters.

Try this before any multi-step agent task:

Before starting, write your own /goal for this task.

Task: [describe the task]
Context: [paste files, docs, requirements, or links]
Constraints: [scope limits, style rules, deadlines, things to avoid]
Definition of done: [what success looks like]

Return:
1. Your main /goal.
2. 3-5 success criteria.
3. Boundaries you should not cross.
4. If you spawn helper agents, write a separate /goal for each one.
5. Ask me to approve or edit the goals before execution.

Do not start until I approve the /goal.

Total AI beginner? Start here (goes with this video).

Have a specific skill you want to learn? Request it here. 

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5. Cartesia upgraded its voice stack with Sonic 3.5 for faster generated speech and Ink 2 for transcription that knows when a speaker is done; free to try, then $5/mo.

6. Arcade gives your AI agents a secure action layer so they can take real actions, like accessing tools, approving permissions, and logging what happened, without turning the enterprise stack into a haunted Roomba (raised $60M).

This video is going bonkers on YouTube…

This is seriously one of the biggest surprise hits we’ve had in awhile… Jeff does a great job of explaining computers, GPUs, and the next kind of computer he’s building… there’s a reason the YT algo has been loving this one. Check it out here.

📰 Around the Horn

• New Scientist reported the first known battlefield deaths from fully autonomous drones, after Ukraine reportedly used AI-controlled drones to kill Russian soldiers in 2024.

• New Scientist reported the first known battlefield deaths from fully autonomous drones, after Ukraine reportedly used AI-controlled drones to kill Russian soldiers in 2024.

• Meta secretly licensed facial recognition software from Rank One, a Pentagon contractor whose CEO ran the FBI's biometric database division, embedded it dormant in the Meta AI app on 50M+ phones, then deleted it the day after WIRED broke the story.

• Anthropic is in damage-control mode in Washington after the White House banned foreign access to its two newest frontier models, Fable 5 and Mythos 5, days before the company filed SEC documents ahead of its IPO.

• Meta launched AI Mode on Facebook, a chatbot-style search that synthesizes answers from public posts, Groups, and Reels instead of returning a list of links, powered by Meta's Muse Spark model.

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📖 Tuesday Tool Tip

So “vibe-coded” apps (made entirely with AI writing all the code) can look finished while still leaving the front door, side window, and surprisingly thick raccoon-sized crawlspace wide open.

Today’s tool tip: use AI as a pre-launch security reviewer before you put a project on the public internet. A Reddit user recently vibe-checked (hacked) a bunch of vibe-coded sites and kept seeing the same problems: no rate limits, no email verification, and API keys exposed where anyone could grab them.

So if you’re building your own software with AI, make sure you run this before launch:

1. Ask AI to inspect your codebase for public forms, signup, login, password reset, and API calls.

2. Make it check for rate limits, email verification, server-side validation, and protected database rules.

3. Tell it to flag any secret keys in frontend code. Publishable keys are okay only when the backend rules are locked down.

4. Ask for fixes ranked by “could cost me money,” “could leak user data,” and “could annoy real users.”

Basically: treat “it works” and “it is safe to ship” as two different checkboxes.

You are my pre-launch security reviewer. Review this app for production-readiness issues, focusing only on defensive improvements.

Check for:
1. Rate limits on signup, login, password reset, contact forms, and expensive API routes.
2. Email verification after account creation.
3. Secrets or paid API keys exposed in frontend code.
4. Server-side validation for every user action.
5. Access-control bugs, especially whether one user could request another user’s data.
6. Supabase/Firebase rules, including RLS or equivalent database protections.
7. HTTPS, minimum TLS 1.2, SPF/DMARC for email, and DNSSEC if relevant.
8. Places where I built auth, payments, or account systems from scratch when a trusted service would be safer.

Return:
- Critical issues to fix before launch
- Medium-risk issues to fix this week
- Exact files or settings to inspect
- Safe implementation steps
- A final “ship / do not ship” recommendation

A Cat’s Commentary

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