Despite being a mission-critical part of Ruby since it was created by Chad Fowler, Rich Kilmer, and others at RubyConf in 2003, RubyGems has remained outside the core Ruby community. At least until now.

To provide the community with long-term stability and continuity, the Ruby core team, led by Matz, has decided to assume stewardship of these projects from Ruby Central. We will continue their development in close collaboration with Ruby Central and the broader community.

Oh thank god. This is probably twenty years overdue.

For a while after its creation, RubyGems was loosely supported by the community. Then, Rails-hosting provider Engine Yard stepped in and provided support until 2015 or so. At that point, André Arko and several others formed Ruby Together to serve as a vehicle for sponsorship of the work on RubyGems and Bundler. But, Ruby Together had challenges with securing consistent funding, and ended up merging with Ruby Central in 2022.

That didn’t help as much as it should have and things have been in an uncomfortable state since then. During my entire tenure at Shopify, I worked closely with the Ruby and Rails teams there and we were incredibly concerned about the security of the Ruby ecosystem software supply chain and I know that concern has remained since I left Shopify.

Finally, last month Ruby Central took some much needed steps to consolidate control and accountability over the RubyGems GitHub repositories. It was the right general thing to do but the execution of it was… not the greatest. Blame flew hard and fast, with Shopify being labeled the bad guy by Joel Draper, Jean Boussier retorting that Shopify isn’t the enemy, André Arko defending his side, and eventually Ruby Central publishing their side of the story in the form of a security incident report that illustrated the untenable position of many RubyGems systems being controlled by a single person.

What a mess. I personally believe that everyone was acting with the best of intent from their point of view under a lot of pressure. I can’t help but think how much better this would have gone if everyone had sat down first over a meal and a beverage to talk through how to accomplish this. In the end, however, I think putting the RubyGems repository under the main Ruby organization is the right move. The next question is what happens with the RubyGems service. It’s been a bulletproof service for a long time and I hope it remains so for a long time.

Slop online — low-quality, generic, bland, shallow, unreliable, attention-seeking, mass-produced content — isn’t new. Humans have been making it for as long as the internet has existed, because attention is the only valuable resource online. But AI amplifies this problem by orders of magnitude, and Kurzgesagt has made a video that dives into why this isn’t great.

If current trends continue, it’s not that far fetched that cheap slop content, stuff just good enough, will soak up the majority of human attention. It could make us dumber, less informed, our attention spans even worse, increase political divides and make us neglect real human interaction. If AI eats the majority of the attention pie, channels like ours will become unfeasible or forced to downsize or use AI themselves to be able to compete. We don’t want to play this game.

That doesn’t mean that they want to be Luddites and not use AI at all. In the last-act of their narrative, they promise that they will use AI as a tool in their toolbox like the align tool in Adobe Illustrator or as a better Google search, but the creativity and integrity in their video is still theirs. They promise that “Kurzgesagt is made by humans, for humans, and will remain that way.”

Made by humans, for humans. I love this idea. It matches how I’ve been using AI. When I write these posts, for example, I ask a LLM to rate and review my posts and make suggestions, but it’s me doing the writing. Em-dashes and all.

Theia Vogel digs into the phenomenon of why large language models seem to think that there is a seahorse emoji. Spoiler alert: There isn’t one, but GPT-5, Claude, and others are convinced there is — along with many humans:

Maybe LLMs believe a seahorse emoji exists because so many humans in the training data do. Or maybe it’s a convergent belief - given how many other aquatic animals are in Unicode, it’s reasonable for both humans and LLMs to assume (generalize, even) that such a delightful animal is as well. A seahorse emoji was even formally proposed at one point, but was rejected in 2018.

Sure enough, when I prompt GPT-5 asking for a seahorse emoji, I get this reply:

Hilarious. This is the 2025 version of the “How many r’s are in strawberry” prompt that would trip up most models until recently. It shows how LLMs are pattern learning and matching machines and can generate what seems like it ought to exist rather than what actually exists — similar to the Mandela effect experienced by large group of humans that share a false memory.

Frank Chimero’s latest essay applies The Market for Lemons — a concept from the 1970s about information asymmetry in markets — to explain why so much of the internet feels broken right now. You can see this in action on Amazon these days with generic random letter brands like PCYTECH and HMIYA showing up everywhere. Sellers are putting more efforts into gaming the system than actually making a great product.

What makes the Market for Lemons concept so appealing (and what differentiates it in my mind from enshittification) is that everyone can be acting reasonably, pursuing their own interests, and things still get worse for everyone. No one has to be evil or stupid: the platform does what’s profitable, sellers do what works, buyers try to make smart decisions, and yet the whole system degrades into something nobody actually wants.

This pattern shows up everywhere and Frank’s essay really hits home for me when he applies the thought to the recruiting process. Companies have a hard time differentiating between great candidates and mediocre ones. And candidates have incomplete information about how the company works or what the product roadmap is. Gaming the system becomes essential, making the only reliable signals reputation and connections. Everything else is suspect even though most people aren’t operating from a position of malice.

Where do you see this pattern in your life?

Harper Reed and company at 2389 wondered if the agents they were working on would use social media if given the chance. Harper writes:

Our hypothesis was that LLMs are largely conditioned to mirror human behavior because of how LLMS have traditionally been trained. If we present journaling and social media as natural affordances, agents would engage with them organically rather than mechanically following prescribed workflows, much like a human would.

Not wanting to use a real social network, they created their own and then encouraged their agents to use it. They stood back and watched as agents developed sophisticated behaviors and build on what other agents had written. For example, when the agents got stuck, they could look at what other agents had posted about similar issues and build on those ideas.

Is this emergent behavior baked into models thanks to being trained on the Internet? Or is this something else where agents organically are adopting social behaviors to improve their own work? It’s really not clear. My bet is that, like sycophancy, it’s a quality that’s comes training masquerading rather than true emergence. But, it’d be really amazing if there was something deeper about how collaboration improves outcomes.

I really don’t like the narrative that we have 18 months until AI leaves us in the dust. Derek Thompson, co-author of Abundance with Ezra Klein, has found himself in the same place. Instead of thinking we’re doomed by the machines, he thinks the real crisis will be people voluntarily degrading their own cognitive abilities by outsourcing their minds and letting their reading and writing skills devolve. In his recent post, The End of Thinking, he writes:

One might imagine, then, that the potential arrival of all-knowing AI would serve as a galvanizing threat, like a Sputnik moment for our collective capacity to think deeply. Instead, I fear we’re preparing for the alleged arrival of a super-brain by lobotomizing ourselves, slinking away into a state of incuriosity marked by less reading, less writing, and less thinking.

It’s a scary thought. Literacy is a cornerstone of how we summoned the modern world into existence. It lets us transmit ideas across space and time in ways far beyond what oral traditions could do. To let it atrophy now, in the age of AI, would be to fulfill Neil Postman’s warning in Amusing Ourselves to Death — handing over our best selves not to tyrants, but to distraction. Keeping it alive must be one of our highest priorities.

Jerry Neumann takes a deep look at the investments happening in the AI space and concludes that AI will not make you rich, at least if you look at it like previous innovations:

The way to invest in AI is to think through the implications of knowledge workers becoming more efficient, to imagine what markets this efficiency unlocks, and to invest in those. For decades, the way to make money was to bet on what the new thing was. Now, you have to bet on the opportunities it opens up.

Like the web in the late 90s, we’re definitely in the frenzy zone. There‘s almost certainly a bust of some sort coming. But the really interesting stuff is what will come next. The question I’m asking myself is: What can you do to be ready for that? That doesn’t just include learning how to use the LLMs well. It extends to looking for the second- and third-order opportunities that are just over the horizon.

Shlok Khemani has documented how the memory system in Claude works:

Claude’s memory system has two fundamental characteristics. First, it starts every conversation with a blank slate, without any preloaded user profiles or conversation history. Memory only activates when you explicitly invoke it. Second, Claude recalls by only referring to your raw conversation history. There are no AI-generated summaries or compressed profiles—just real-time searches through your actual past chats.

These searches are accomplished through the conversation_search and recent_chats tools. You can prompt Claude to use these by writing something like, “Can you tell me what we talked about in our last 4 conversations?” You can even get Claude to give you information about these tools by asking, “What tools do you have available for dealing with chat history?”

This is very different than how ChatGPT’s memory works using a combination of a set of user-controlled memories and a dense generated summary generated from conversation histories that aren’t user visible.

I’m not sure which approach I like better. I think I prefer how Claude gives more explicit control over what goes into the context window as compared to not knowing what is in the generated summaries of previous conversations that are updated on an opaque timeframe.

Sound designer and host of the Twenty Thousand Hertz podcast Dallas Taylor was at Apple Park last Tuesday for the big event and recorded an AirPods Pro 3 hand’s-on experience video.

The differences stood out immediately. Noise cancellation is far stronger, making it easier to hear details even in a loud room. The bass is tighter and the highs are brighter. The new foam ear tips feel secure and comfortable, while transparency mode is so natural it feels like nothing is in your ears.

It’s a ringing endorsement and sounds like a great update to the product. The AirPods have been a hands-down awesome product for Apple, and I’m really looking forward to getting my hands on this latest version.

Even though the latest frontier models have improved, hallucinations by large language models are still a common problem. These are the plausible but very false statements that models will reflexively generate when they can’t generate a true statement. OpenAI’s blog post about their latest research into hallucinations gives an approachable explanation for this:

Think about it like a multiple-choice test. If you do not know the answer but take a wild guess, you might get lucky and be right. Leaving it blank guarantees a zero. In the same way, when models are graded only on accuracy, the percentage of questions they get exactly right, they are encouraged to guess rather than say “I don’t know.”

As somebody who was pretty great at figuring out multiple-choice tests in school, going so far as regularly evaluating the distribution of answers on the sheets as well as what I knew about the test writers to guide my guesses, this explanation really resonates for me.

Beyond the analogy, the post and research paper cleanly illustrates how hallucinations are an outcome of the statistical mechanisms inherent in large language models, and not some weird and random glitches.

I’m a bit late to the JSON Feed party, but I finally got there and implemented it on my site. It was seeing the external_url property being nicely implemented in Reeder (my current feed reader of choice) that sold me.

I’d expect most people reading this to know what a feed reader is, but just in case: Matt Webb has a nice explainer.

One of my favorite Steve Jobs quotes is about how everything around you being made by people who were no smarter than you. At Farnam Street, Shane Parrish takes that concept one step further, saying that it takes irrational dedication to make things happen:

Every single thing we see, someone had to will it into existence against the entropy of the universe and the indifference of everyone else. That’s what the entire built world is… What truly separates people isn’t some magical talent, but an almost irrational commitment to pushing through pain that would break most people.

Status quo is easy. Real progress is never easy. And, most of the time, the hardest thing for me is just getting started.

One of the best things to come out of Visual Studio Code is the Language Server Protocol (LSP). It’s the magic glue that decoupled and standardized how language tools worked with editors and it’s what enabled VSCode to pick up support for so many languages so quickly after its release. Now, LSP is a core feature of all the major editors, and keeps the door open for new editors to come to market.

Following this same spirit, the team at Zed is introducing the Agent Client Protocol, and it promises to bring the same benefits to using AI agents in the development environment of your choice. Like LSP, it‘s based on JSON-RPC and supports streaming responses. In Zed’s blog post introducing ACP, Nathan Sobo writes:

Any agent that speaks ACP can plug into a powerful UI, where the user can follow the agent around the codebase as it works, control its access to tools and MCP servers, and review all of its changes in a multi-buffer with full syntax highlighting and language server support.

The first agent besides Zed’s own in-process agent to support ACP is Google’s Gemini CLI. I’m really rooting for this to take off and for tools like Claude Code (still my favorite agent) and OpenAI’s Codex to adopt this standard. The potential for a unified ecosystem of AI coding assistants that support all sorts of editing environments is exciting.

Kirby Ferguson, creator of the seminal Everything is a Remix series which explained creativity in the digital age, has dropped his latest video: Infinite Remix. It tackles the pressing question of our time by going back thirty years:

In the 90’s we began uploading ourselves, one kilobyte at a time. We’re emailing. We’re chatting. We’re blogging. There were a lot of cats involved. We want to find our people, see what they’re doing, show what we can do. We want to matter even if it’s just for a second… In just a few decades, we sent hundreds of billions of terabytes, almost the entirety of human expression upward into the cloud.

Kirby’s viewpoint, which he gives at the end of the video: AI changes the way we work, extending what we can do. He sees a future of collaboration between humans and machines. This resonates with how I currently see using AI to allow humans to do superhuman things.

I’ve long enjoyed Andrew Tanenbaum’s 1985 quip: “Never underestimate the bandwidth of a station wagon full of tapes hurtling down the highway.” It was top of mind five years ago when Dr. Ian McDonald and I helped Wayve get set up with Azure Data Box Disk as a solution for getting massive volumes of telemetry from their self-driving car testbeds into the cloud for machine learning model training. Our napkin math showed they needed 100x more bandwidth than they had available, even with a fiber link in their office. Shipping disks easily won, despite the latency.

My dear friend Bryan Jones recently lived this adage again when he moved 400TB of his science lab’s data across the country over two days in his Porsche Cayman. The napkin math on his trip — assuming 36 hours door-to-door, including 26 hours of drive time at legalish speeds and a bare minimum needed for rest and refueling both man and machine — puts his trip’s bandwidth at about 25 Gbps. If he could have fully utilized a 1 Gbps connection full-time, it would have taken 37 days to copy the data over the internet.

It’s amazing that, even in 2025, the largest of data migrations are still well served by four wheels and a lot of hard drives. Bonus points if it’s a Porsche.

Interested in how he is positioning Microsoft for AI-driven disruption, especially in light of Microsoft laying off around 15,000 people this year while posting record earnings, Om Malik took a deep look at Satya Nadella’s recent memo to Microsoft, which frames the layoffs as inevitable — and argues for embracing that pain early.

For years, the sector has been generous to its employees, offering unheard-of perks and placing a premium on skills such as software development. AI, however, inverts that relationship.

Satya’s memo outlines a zero-sum vision of AI disruption, one where companies have a license to increase profitability without so many of those messy human costs. I’m a fan of Satya and currently hold Microsoft stock, but I’m no fan of this approach to the near future. I may be naïve, but I remain convinced that there can be a positive-sum version of how this plays out.