John Crowley, 83, acclaimed American fantasy novelist, died in August 2026 in Conway, Massachusetts. He was the author of 14 novels and 10 volumes of essays/short fiction and was praised as “arguably the most original and accomplished American fantasy writer ever.” He is best known for *Little, Big* (1981), which Harold Bloom called a "neglected masterpiece," one of the 20th century's best novels. The novel centers on the marriage of Smoky Barnable and Daily Alice Drinkwater set against urban decay, a fascist U.S., and a rural manse that serves as a gateway to fairies.
Crowley’s work is marked by recurring themes of narrative shaping perception, the blur between fantasy and reality, and the idea that there is “more than one history of the world.” His first novel, *The Deep* (1975), is a Discworld novel loosely based on the Wars of the Roses. Even his more realistic work reflects this preoccupation, as *Four Freedoms* (2009) is an alternate World War II home-front history.
Crowley’s work is marked by recurring themes of narrative shaping perception, the blur between fantasy and reality, and the idea that there is “more than one history of the world.” His first novel, *The Deep* (1975), is a Discworld novel loosely based on the Wars of the Roses. Even his more realistic work reflects this preoccupation, as *Four Freedoms* (2009) is an alternate World War II home-front history.
This New York times article from June 15, 1989 by Julie Lew explores the launch of Maxis Software's *SimCity*, a pioneering simulation game that tasks players with building and managing complex urban environments. Eschewing traditional "win or lose" mechanics, the game encourages experimentation through zoning, taxation, and infrastructure management to create thriving cityscapes. From surviving historical disasters to constructing digital utopias, the software is praised for its sophisticated modeling and potential as a legitimate tool for teaching urban planning.
This deep dive from *The New York Times Magazine’s* 1619 Project explores the dark origins of America's sugar industry, tracing its evolution from a luxury commodity to "white gold" fueled by the brutal exploitation of enslaved labor.
The article examines how the high-intensity demands of sugarcane production shaped Louisiana's economy and left a legacy of racial hierarchy that persists today—not only through systemic economic barriers preventing Black land ownership but also in the disproportionate impact of sugar-related health issues, such as obesity and diabetes, on African American communities.
The article examines how the high-intensity demands of sugarcane production shaped Louisiana's economy and left a legacy of racial hierarchy that persists today—not only through systemic economic barriers preventing Black land ownership but also in the disproportionate impact of sugar-related health issues, such as obesity and diabetes, on African American communities.
>"For us to trust it on certain subjects, researchers in the growing field of interpretability might need to learn how to open the black box of its brain."
As AI shifts from predictable programs to autonomous neural networks, it has become harder for creators to understand how models reach conclusions. This "black box" problem creates risks in high-stakes fields like medicine and national security, where unaccountable decisions can be life-altering. While interpretability research uses tools like sparse autoencoding to peer inside these systems, the process remains experimental and inconsistent. Researchers are racing to build a reliable toolkit to move from mere observation toward true scientific comprehension.
Key Points:
* Evolution of Complexity: AI has moved from rule-based logic to massive neural networks that learn autonomously, making internal processes difficult to trace.
* High Stakes: Opacity limits AI adoption in critical sectors like healthcare, law, and defense.
* Interpretability Challenges: Current methods for explaining model behavior are often unreliable or prone to deception.
* Potential for Discovery: Emerging tools have already begun uncovering scientific insights, such as new biomarkers for diseases.
* A Developing Science: The field is in its infancy, transitioning from trial-and-error toward a structured scientific discipline.
As AI shifts from predictable programs to autonomous neural networks, it has become harder for creators to understand how models reach conclusions. This "black box" problem creates risks in high-stakes fields like medicine and national security, where unaccountable decisions can be life-altering. While interpretability research uses tools like sparse autoencoding to peer inside these systems, the process remains experimental and inconsistent. Researchers are racing to build a reliable toolkit to move from mere observation toward true scientific comprehension.
Key Points:
* Evolution of Complexity: AI has moved from rule-based logic to massive neural networks that learn autonomously, making internal processes difficult to trace.
* High Stakes: Opacity limits AI adoption in critical sectors like healthcare, law, and defense.
* Interpretability Challenges: Current methods for explaining model behavior are often unreliable or prone to deception.
* Potential for Discovery: Emerging tools have already begun uncovering scientific insights, such as new biomarkers for diseases.
* A Developing Science: The field is in its infancy, transitioning from trial-and-error toward a structured scientific discipline.
>from the exit-statements dept.
Long-time tech journalist Clive Thompson interviewed over 70 software developers at Google, Amazon, Microsoft and start-ups for a new article on AI-assisted programming. It's title?
"Coding After Coders: The End of Computer Programming as We Know It."
Published in the prestigious New York Times Magazine, the article even cites long-time programming guru Kent Beck saying LLMs got him going again and he's now finishing more projects than ever, calling AI's unpredictability "addictive, in a slot-machine way."
In fact, the article concludes "many Silicon Valley programmers are now barely programming. Instead, what they're doing is deeply, deeply weird..."
Long-time tech journalist Clive Thompson interviewed over 70 software developers at Google, Amazon, Microsoft and start-ups for a new article on AI-assisted programming. It's title?
"Coding After Coders: The End of Computer Programming as We Know It."
Published in the prestigious New York Times Magazine, the article even cites long-time programming guru Kent Beck saying LLMs got him going again and he's now finishing more projects than ever, calling AI's unpredictability "addictive, in a slot-machine way."
In fact, the article concludes "many Silicon Valley programmers are now barely programming. Instead, what they're doing is deeply, deeply weird..."
This 2003 New York Times article details the emergence of "Hiptop Nation" (hiptop.bedope.com), an early example of mobile blogging and social networking centered around the T-Mobile Sidekick (also known as the Hiptop). The site, founded by Mike Popovic, allowed Sidekick users to share real-time experiences – often with accompanying photos – creating a uniquely immediate and visual online community. Unlike traditional blogs focused on reflection, Hiptop Nation emphasized present experiences, like commutes or current events (a turkey deal before Thanksgiving!). The article highlights the site’s surprising civility, attributed to users sharing photos alongside their posts, and details a successful Halloween scavenger hunt organized via the Sidekick's capabilities. Ultimately, the article frames Hiptop Nation as a demonstration of how empowering technology can lead to unexpected and "amazing stuff," foreshadowing the broader social media landscape to come – and offering a nostalgic glimpse into the early days of mobile connectedness. It also briefly touches on the challenges of content moderation, referencing a quickly-addressed instance of inappropriate content.
The article discusses how Sam Altman, CEO of OpenAI, strategically outmaneuvered Elon Musk to become a key player in President Trump's administration regarding AI policies. Despite being a Democratic donor and critic of Trump, Altman leveraged his relationships with key figures like Doug Burgum, Larry Ellison, and Masayoshi Son to secure a $100 billion AI infrastructure project called Stargate. This project, announced by Trump, positions OpenAI at the center of the administration's AI agenda, aiming to stay ahead of China in AI development. The article also highlights the complex political maneuvers and funding challenges Altman faced, including a temporary firing from the OpenAI board and the need to secure investments from multiple sources.
- 2x,:1lb bag lentils, 2x broth, 3 carrots, 2 onions, 6 garlic, 1tbsp cumin seeds (not ground), 1/8 tsp Kerala Chile, ginger powder, ginger paste, labneh
- no water
- try insta pot
- fry garlic 30s only, onions 7-8 min
- immersion blend in pot; don't overdo it
- no water
- try insta pot
- fry garlic 30s only, onions 7-8 min
- immersion blend in pot; don't overdo it
