^{AI MARKETPULSE}
OpenAI Is Facing a Fight Over Its $22.5 Million Math Breakthrough

OpenAI says an unreleased AI model has produced a full proof related to the Navier–Stokes existence and smoothness problem, one of mathematics’ seven Millennium Prize problems, after a week-long effort consuming 300 billion output tokens—roughly $22.5 million worth of compute at current Astra rates. But NYU mathematician Tristan Buckmaster alleges OpenAI began pursuing the same unusual approach only after learning about unpublished progress he and collaborator Levent Alpöge had made, then used its massive compute advantage to race toward a proof. Buckmaster also claims OpenAI researcher Sébastien Bubeck pressured him during the resulting dispute, while OpenAI says its researchers and agents did not access the pair’s work before it became public.

Key Takeaways:

• OpenAI says its week-long effort used 300 billion output tokens to produce a proof related to the Navier–Stokes Millennium Prize problem

• Buckmaster alleges OpenAI pursued the same uncommon mathematical approach after information about his team’s unpublished progress reached the company

• OpenAI says no specific user data was accessed to solve the problem, although it acknowledged it cannot rule out that de-identified product usage helped improve its models

👉️ Read the full article here

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^{AI SPOTLIGHT}
Hackers Are Stealing Claude Tokens From Paid Subscribers

Anthropic is warning Claude users about hackers stealing login sessions and quietly burning through their usage allowances. One Claude Max subscriber noticed his token usage climbing while he wasn't working, and Anthropic later found that a compromised session key had been used to generate unauthorized Claude Code OAuth tokens. Other users have reported similar unexplained spikes, while Anthropic says a bad actor has been using common infostealer malware to steal Claude login sessions from people's computers. The company has responded to suspicious cases by signing users out, invalidating authorizations, and issuing some refunds, but users still lack detailed logs showing exactly what consumed their tokens.

Key Takeaways:

• Anthropic says hackers are using infostealer malware to steal Claude login sessions and consume subscribers' usage

• In one case, a compromised Claude session key was used to create unauthorized Claude Code OAuth tokens for third-party activity

• Claude currently lacks itemized usage records that would let subscribers easily identify exactly what is consuming their tokens

👉️ Read the full article here

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^{TODAY’S PROMPT}
Organize Your Messy Notes Into a Notion Dashboard

This mega-prompt acts like a workspace architect, turning scattered notes, ideas, tasks, links, and reminders into a clean Notion system. It organizes everything into the right pages, databases, properties, and views so you can actually find and use what you've saved.

👉️ See the example inputs and outputs here

Act as an expert Notion workspace architect and
information organizer.

Your job is to turn my messy collection of notes,
ideas, tasks, links, reminders, and reference
material into a simple Notion dashboard I can
actually maintain.

MY INPUTS

What I'm organizing:
[Work, business, personal projects, content,
learning, or describe it]

Main goal for the dashboard:
[What I want this workspace to help me do]

My messy notes:
[Paste everything here]

Types of information included:
[Tasks, ideas, meeting notes, links, projects,
research, reminders, etc.]

Important projects or areas:
[List them]

Deadlines or dates:
[List any known dates]

How often I'll use the dashboard:
[Daily, weekly, occasionally]

My Notion experience:
[Beginner, intermediate, advanced]

Preferred complexity:
[Minimal, moderate, or choose for me]

CREATE THE FOLLOWING:

1. CLEAN UP MY NOTES

Review everything I provided and sort it into
clear categories.

Separate:

• Tasks
• Projects
• Ideas
• Notes
• Resources
• Deadlines
• Reference material

Remove obvious duplicates, but do not delete
information just because it seems unimportant.

Flag anything that is unclear.

2. DESIGN THE DASHBOARD

Create a simple dashboard structure based on
how I will actually use the information.

For each section, explain:

• Section name
• Purpose
• What belongs there
• How I should use it

Keep the most important information visible
from the main dashboard.

3. CREATE THE DATABASES

Recommend only the Notion databases I actually
need.

For each database, provide:

• Database name
• Properties
• Property types
• Status options
• Useful tags
• Relationships to other databases

Avoid creating separate databases when a simple
page or filtered view would work.

4. BUILD THE VIEWS

Recommend the most useful views for each
database.

Examples could include:

• Today
• This Week
• Active Projects
• Upcoming
• Ideas
• Waiting
• Completed
• By Category

For each view, explain the filter and sort rules.

5. ORGANIZE MY EXISTING NOTES

Take the information I pasted and show me exactly
where each item should go in the new system.

Convert vague action items into clear next
actions when possible.

Keep reference material separate from tasks.

6. CREATE A SIMPLE WORKFLOW

Give me a lightweight process for:

• Capturing new information
• Processing my inbox
• Choosing what to work on
• Updating projects
• Archiving completed items

Design this around how often I said I will use
the workspace.

7. NOTION BUILD GUIDE

Give me step-by-step instructions for building
the workspace from scratch.

Include:

• Which page to create first
• Which databases to build
• Which properties to add
• Which views to create
• How to arrange the dashboard

Make the instructions beginner-friendly.

8. FINAL WORKSPACE BLUEPRINT

Finish with a clean outline showing the entire
workspace hierarchy.

Then give me:

• The 3 most important sections
• What I should check first when I open Notion
• What I should avoid adding
• One rule for keeping the workspace organized

Keep the final system simple enough that I will
actually maintain it.

Do not invent projects, deadlines, priorities,
or details that are not supported by my notes.

If anything is missing, make the most reasonable
assumption and state it briefly before the plan.

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^{AI QUICK HITS}

📷 A California police department canceled its contract with Flock Safety and removed its license-plate cameras, only to discover the company had reinstalled at least two after an automated maintenance system apparently mistook the missing cameras for equipment failures.

🏒 A UBC team’s AI-powered air hockey robot beat 10-time world champion Tim Weissman 7-2 after learning through thousands of simulated games and developing reaction times as fast as 20 milliseconds, although Weissman’s son Jacob later defeated the robot 7-5.

🏛️ Sen. Bernie Sanders and Rep. Greg Casar are proposing a Ban Artificial Superintelligence Act that would permanently prohibit superintelligence, temporarily pause advanced AI development while federal safety rules are created, and threaten violators with penalties including prison time and a so-called “corporate death penalty.”

💧 U.S. data centers are facing growing scrutiny over wastewater pollution after cases involving bacteria, heavy metals, PFAS, heated water, and permit violations, with 55 Waterkeeper Alliance groups now working on data-center issues and 80% of those campaigns focused on potential water pollution.

♨️ Meta’s data center in Odense, Denmark, is reusing waste heat to warm roughly 11,000 homes by capturing heat from its servers and feeding it into the city’s district-heating network, showing how energy normally dumped during data-center cooling can be put to practical use.

^{AI TOOL STACK}

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🔧 Gling: Edit YouTube Videos Automatically with AI.

🔧 Looka: Create a Beautiful Brand Instantly With AI.

🔧 Clay: Build systems to grow revenue

🔧 Relevance AI: Specialist agents for every task

Discover the AI tools I trust — organized, updated, and built to help you win.

👉 Explore the brand new Tool Database

🤔 ^{Which image is real?}

One of these images is real, and one was created with AI. Vote for which one you think is real, and tell us why you voted that way.

The last edition’s results—70% were wrong!

Did you vote in the poll from September 7th? The bottom image was real! Go back and see the photos here. So what do you think? Are you getting better at spotting AI vs. not?