MIT Tech Review
How courts are coping with a flood of AI-generated lawsuits
Federal courts are seeing a surge in AI-generated filings from self-represented litigants, forcing judges to adapt their workflows and standards to an unprecedented volume of machine-assisted legal documents. While AI tools have lowered the barrier for ordinary people to bring cases forward, they have also introduced new challenges around accuracy, hallucinated citations, and case viability. The trend is reshaping how courts think about access to justice and the limits of technology in legal proceedings.
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The LLM warnings Google fired Timnit Gebru over have all come true
Google fired AI ethics researcher Timnit Gebru in 2020 after she co-authored a paper warning about the risks of large language models, including bias, misinformation, and the dangers of deploying systems that weren't fully understood. Four years later, those exact concerns have materialized into mainstream crises spanning hallucination, copyright disputes, and the entrenchment of societal biases at scale. The episode stands as a stark reminder that silencing inconvenient research doesn't make the underlying problems disappear.
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The Verge
Nvidia is already planning N2X and N3X chips — the goal is the Star Trek computer
Nvidia is playing the long game on AI PCs. CEO Jensen Huang confirmed at Computex 2026 that at least two additional chip generations — N2X and N3X — are already in the pipeline beyond the debut RTX Spark. The stated endgame is nothing less than a Star Trek-style computer, signaling Nvidia's serious, sustained commitment to the consumer laptop silicon market.
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Alphabet’s record-breaking $85B raise for Google’s AI business is a helluva good signal
Alphabet's $85 billion stock sale for Google's AI operations marks the largest raise of its kind, sending an unmistakable signal that institutional investors have serious conviction in AI's commercial future. The sheer scale of demand suggests this isn't speculative froth — it's calculated, long-term capital deployment. For the broader AI sector, this is the clearest endorsement yet that the money is real and the appetite is growing.
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I built a vulnerable app and spent $1,500 seeing if LLMs could hack it
A developer intentionally built a vulnerable web application and ran it against multiple LLMs to test their real-world offensive security capabilities, spending $1,500 in API costs across the experiment. The results offer a rare empirical look at how well current AI models perform at identifying and exploiting actual vulnerabilities rather than theoretical ones. For security teams weighing AI-assisted pentesting, the findings carry practical weight.
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