Jacob Schroeder sharing his 25 lessons on money and meaning. This lesson regarding raising kids stood out for me.

The cost of children is an admission to adventure, love, pain, joy, despair, loss, fulfillment – all that life can and should be. Then one day it’s over. The ride comes to a stop – hopefully, much later than sooner – and that emptiness is a bittersweet debt. It is a debt that can never be repaid. You are left desperately wishing to repay it only to take it out again so you can relive it all over, desperately wishing to take out a second mortgage on all the spills, the cuts and bruises, the breaks, the heartaches, the tears, the smiles, the hugs, the laughs, the I love yous and the goodbyes, enough to get you angry at the unfairness of it all.

I am still on this adventure.

This comment by nicbou who is sharing their thoughts on the layoffs among technical writers due to AI.

I write documentation for a living. Although my output is writing, my job is observing, listening and understanding. I can only write well because I have an intimate understanding of my readers’ problems, anxieties and confusion. This decides what I write about, and how to write about it. This sort of curation can only come from a thinking, feeling human being.

I revise my local public transit guide every time I experience a foreign public transit system. I improve my writing by walking in my readers’ shoes and experiencing their confusion. Empathy is the engine that powers my work.

Most of my information is carefully collected from a network of people I have a good relationship with, and from a large and trusting audience. It took me years to build the infrastructure to surface useful information. AI can only report what someone was bothered to write down, but I actually go out in the real world and ask questions.

I have built tools to collect people’s experience at the immigration office. I have had many conversations with lawyers and other experts. I have interviewed hundreds of my readers. I have put a lot of information on the internet for the first time. AI writing is only as good as the data it feeds on. I hunt for my own data.

People who think that AI can do this and the other things have an almost insulting understanding of the jobs they are trying to replace.

I would implore you to read the comments in the Hacker News thread. A lot of folks are having a feeling that there’s no going back and it reminds me of this—we are in a market of lemons.

Carette Antonin reflecting on the recent viral tweets of Jaana Dogan and Andrej Karpathy and how ‘Influentists’ are hyping up AI only to add context later on, which ends up deflating the hype.

This pattern of “hype first and context later” is actually part of a growing trend.

I call the individuals participating to that trend “The Influentists”. Those people are members of a scientific or technical community, and leverage their large audiences to propagate claims that are, at best, unproven and, at worst, intentionally misleading.

But how can we spot them?

I personally identify these “Influentists” by four personality traits that characterize their public discourse.

The first is a reliance on “trust-me-bro” culture, where anecdotal experiences are framed as universal, objective truths to generate hype. This is a sentiment perfectly captured by the “I’m not joking and this isn’t funny” tone of Rakyll’s original tweet, but also the dramatic “I’ve never felt that much behind as a programmer” from Andrej Karpathy’s tweet. This is supported by an absence of reproducible proof, as these individuals rarely share the code, data, or methodology behind their viral “wins”, an omission made easier than ever in the current LLM era. And finally, they utilize strategic ambiguity, carefully wording their claims with enough vagueness to pivot toward a “clarification” if the technical community challenges their accuracy.

Weimin Chu has documented the scale of renewable energy in China in series of photographs. Yale Environment 360 showcases these photographs along with the energy capabilities of these renewable energy sources.

Last year China installed more than half of all wind and solar added globally. In May alone, it added enough renewable energy to power Poland, installing solar panels at a rate of roughly 100 every second.

The massive buildout is happening across the country, from crowded eastern cities increasingly topped by rooftop solar panels to remote western deserts where colossal wind farms sprawl across the landscape.

“From the ground, it’s hard to grasp the scale of these power plants,” said Chinese photographer Weimin Chu. “But when you rise into the air, you can see the geometry, the rhythm — and their relationship with the mountains, the desert, the sea.”

Matheus Lima sharing his thoughts on processing everything at 2x, just because you can.

Life happens at 1x. Every conversation you’ve ever had. Every walk, every meal, every meaningful experience. None of it comes with a speed dial. We’re biological creatures wired for real-time processing. When someone speaks to you in person, you don’t get to fast-forward through the parts you find boring.

There’s something strange about trying to shortcut how humans communicate. A podcast is just a conversation you’re eavesdropping on. The pauses, the rhythm, the way someone builds to a point. That’s all part of it. Speed it up and you get the words, sure. But you lose the texture.

Your brain needs empty space too. This is the part we’ve collectively forgotten. Boredom is a feature, not a bug. It’s where our best ideas — like starting this blog! — come from. It’s where you actually process what you’ve learned, make connections, have original thoughts. Constant consumption, even sped up, leaves no room for any of that. You need to be bored.

The irony is that consuming faster often means processing less. You’re optimizing for throughput when you should be optimizing for understanding. All those 2x podcasts blur together into background noise. What did you actually retain? What changed how you think? It’s empty calories. It’s fake productivity.

Morgan Housel shares his theory about nostalgia.

I have a theory about nostalgia: It happens because the best survival strategy in an uncertain world is to overworry. When you look back, you forget about all the things you worried about that never came true. So life appears better in the past because in hindsight there wasn’t as much to worry about as you were actually worrying about at the time.

Ha!

Bryan Cantrill reflecting back on his days when he decided to become a software engineer.

When I entered university in 1992, it didn’t feel like the right time: the economy for new grads was very grim — and I knew plenty of folks who were struggling to find work (and accepting part time jobs that didn’t need a college degree at all while they searched for something better). I never doubted going to school, but I also have never taken a job for granted.

When I fell in love with computer science as an undergraduate and realized that I wanted to become a software engineer, it didn’t feel like the right time: Ed Yourdon had just written “The Decline and Fall of the American Programmer”, which boldly told any young computer science student that they were wasting their time — that all programming jobs would be done by cheap labor abroad. This argument felt wrong, but I was too in love with computer science to be talked out of it anyway.

When I decided that I was specifically interested in operating systems kernel development, it definitely didn’t feel like the right time: the conventional wisdom in the mid-1990s was that operating systems were done — that Unix was in decline and that the future clearly belonged to Microsoft. I ardently disagreed with this, and my conviction in 1996 brought me to the one company that unequivocally shared it: Sun Microsystems.

This is so relevant in today’s uncertain times. And this reminds me of the book Same as ever by Morgan Housel.

Gokul Rajaram explaining the difference between dashboard product and pipes product, and why it is important to identify early on which product are you are working on.

Every startup needs to make a choice: is their product a dashboard product or a pipes product?

Dashboard products are used directly and regularly by end users as their primary interface for accomplishing tasks. The goal for these products is to get customers to live in the product. The primary North Star metric for these companies is active users (daily / weekly / monthly, depending on the natural frequency of customer usage for the category). Facebook’s first product (aka Facebook :)) was a dashboard product.

Pipes products are used in the background to process transactions, data, payments, etc, and customers rarely interact with them directly after initial setup. The goal for these products is to for their customers to send as much of their data / payments / etc through them. Their North Star metrics is a volume metric (eg GPV). Databricks’ core product is a pipes product.

Companies can have both types of products in their portfolio. For example, ChatGPT is a dashboard product while OpenAI’s APIs are a pipes product. However, a given product has to determine which camp it’s primarily in.

This excerpt from research paper by Koyeli Sengupta, Srushti Gandhi and Alokananda Rudra on highlighting the importance of making autistic individuals aware of their diagnosis.

Self–advocacy is possible only when autistic individuals are aware of their diagnosis and cognizant of their strengths and differences (Shore, 2004). Knowledge about their diagnosis helps create an empowering positive autistic identity (Cooper et al., 2017, Oredipe et al., 2023) rather than fostering an image of a broken neurotypical (Almog et al., 2024). An increased understanding of one’s condition among autistic adults is associated with enhanced self-understanding, awareness, and self-compassion (Crompton et al., 2020, Leedham et al., 2020), with opportunities to belong to a community by connecting with other autistic individuals (Hickey et al., 2018, Tan, 2018)_.Studies also suggest that the earlier individuals know their diagnosis, the greater the association with a more positive disability identity (Corden et al., 2021) and sense of self (Oredipe et al., 2023, Smith et al., 2018), while later knowledge of diagnosis was associated with experiences of grief for the pre-diagnosis years when autistic individuals struggled and blamed themselves for the challenges. (Leedham et al., 2020)_.

Emily M. Bender and Nanna Inie sharing an approach on how we should be talking about AI.

A more deliberate and thoughtful way forward is to talk about “AI” systems in terms of what we use systems to do, often specifying input and/or output. That is, talk about functionalities that serve our purposes, rather than “capabilities” of the system. Rather than saying a model is “good at” something (suggesting the model has skills) we can talk about what it is “good for”. Who is using the model to do something, and what are they using it to do?