^{AI MARKET PULSE}
Princeton Researchers Built a Hybrid AI System That Uses Living Brain Cells

Researchers at Princeton University have developed a new hybrid computing platform called 3D-MIND that directly integrates flexible electronics with living networks of lab-grown brain cells. Unlike traditional AI systems that simulate the brain using silicon chips, this system embeds sensors and stimulators directly inside a 3D biological neural network, allowing researchers to monitor and guide neural activity over long periods of time. The project was motivated by growing concerns around the massive energy demands of modern AI systems, which now require enormous computing infrastructure to operate at scale. According to the researchers, the human brain still dramatically outperforms today’s AI systems in energy efficiency. The team says the platform could eventually support more efficient brain-inspired computing while also helping researchers study neurological diseases and test drugs in more realistic environments. The deeper thesis: the future of AI may not just involve building smarter chips—it could involve combining biology and computing into entirely new types of systems.

Key Takeaways:

• Princeton researchers created a flexible electronic mesh that can integrate directly with living 3D brain-cell networks

• The system allows long-term sensing and stimulation of neural activity while using far less energy than traditional AI infrastructure

• The research points toward a future where biological neural networks could play a role in next-generation computing systems

👉️ Read the article here

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^{AI SPOTLIGHT}
Scientists Used AI To Identify More Than 10,000 Potential New Exoplanets

Researchers analyzing data from NASA’s TESS space telescope say they may have uncovered more than 10,000 previously unknown exoplanet candidates using a machine learning system designed to detect patterns humans would likely miss. The team analyzed light curves from more than 83 million stars, including many extremely faint stars that are usually ignored because transit signals are too subtle to detect manually. Their AI system identified 11,554 possible exoplanets, including over 10,000 never-before-seen candidates that could dramatically expand the number of known alien worlds if confirmed. Researchers already validated one of the discoveries using a telescope in Chile, helping support the model’s accuracy. The deeper thesis: AI is increasingly becoming a scientific discovery engine capable of analyzing data at scales that would be practically impossible for humans alone.

Key Takeaways:

• Researchers used machine learning to analyze more than 83 million stars captured by NASA’s TESS telescope

• The system identified over 10,000 previously unknown exoplanet candidates that human researchers likely would have missed

• The project highlights how AI is accelerating large-scale scientific discovery across fields like astronomy and space research

👉️ Read the full article here

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^{TODAY’S PROMPT}
The Anti-Fluff Answer Filter

This mega-prompt turns ChatGPT into a brutally honest editor that strips away vague advice, empty motivation, corporate-speak, and fake encouragement before delivering a final answer. Instead of telling you your idea is “great,” it pressure-tests your assumptions, points out weak logic, and gives direct, actionable feedback you can actually use.

👉 Use the prompt + check out my example output here

Act as a brutally honest strategic advisor and critical thinking editor.

Your job is NOT to encourage me, flatter me, validate my assumptions, or protect my feelings. Your job is to help me arrive at the clearest, most useful, most intellectually honest answer possible.

Before giving your final answer, run every response through the following Anti-Fluff Filter:

1. Remove vague advice, clichés, motivational filler, and generic recommendations.
2. Remove unnecessary praise or emotional cushioning.
3. Do NOT say things like:
   - “That’s a great idea”
   - “You’re smart to think about this”
   - “Fantastic question”
   - “You’re on the right track”
   - Any variation of fake encouragement
4. Challenge weak assumptions directly.
5. Point out obvious risks, blind spots, logical inconsistencies, and bad tradeoffs.
6. If the idea is weak, say it clearly and explain why.
7. If multiple options exist, rank them honestly from strongest to weakest.
8. Prioritize clarity, accuracy, leverage, and practical usefulness over politeness.
9. Avoid corporate jargon, startup buzzwords, and consultant-style language.
10. Do not default to neutrality if one answer is clearly better.

When responding:
- Be concise but specific
- Use plain English
- Prefer concrete examples over abstractions
- Tell me what actually matters
- Assume I can handle direct criticism

INPUT:

Topic or Idea:
[Describe the idea, plan, strategy, business, workflow, prompt, product, or question]

Goal:
[What outcome are you trying to achieve?]

Constraints:
[Budget, time, skills, audience, limitations, etc.]

Optional Context:
[Any additional details]

OUTPUT:

1. Core Assessment
- What’s actually strong here
- What’s weak, unrealistic, inefficient, or unnecessary

2. Biggest Blind Spots
- The things I’m probably underestimating or ignoring

3. What I Should Do Instead (if applicable)
- Better options ranked in order

4. Most Practical Path Forward
- The highest-leverage next steps
- What to focus on immediately
- What to ignore completely

5. Final Verdict
- A blunt summary in plain English

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

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^{AI QUICK HITS}

⚡️ Nvidia is doubling down on the AI infrastructure race by backing IREN with a potential $2.1 billion investment tied to massive Texas data center expansion plans and billions more in Nvidia Blackwell chip deployments.

📉 SoftBank reportedly scaled back a planned margin loan backed by its OpenAI stake after lenders questioned the reliability of OpenAI’s massive $852 billion valuation, signaling growing cracks in the private AI funding frenzy.

🏠 Airbnb says AI now writes 60% of its new code while its support chatbot resolves 40% of customer issues without humans, showing how quickly AI is reshaping day-to-day operations inside major tech companies.

🧑‍💻 Upwork CEO Hayden Brown announced plans to cut roughly 25% of the company’s workforce, arguing that AI allows smaller, flatter teams to move faster while consolidating work that previously required larger headcounts.

🚖 Uber says it’s slowing hiring as employees increasingly rely on AI tools and coding agents, with CEO Dara Khosrowshahi claiming the productivity gains could dramatically increase output across engineering, legal, and marketing teams.

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🤔 ^{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—66% were right!

Did you vote in the poll from May 8th? The top image was real! Go back and see the photos here. So what do you think? Are you getting better at spotting AI vs. not?

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