In just about a year, Bluesky has doubled its userbase from 20 million to 40 million. Last year, it benefitted from “the wake of Donald Trump’s re-election as president, and Elon Musk’s continued degradation of X, Bluesky welcomed an exodus of liberals, leftists, journalists, and academic researchers, among other groups.” Writing in his Platformer newsletter, Casey Newton reflects back on the year, surfacing up the challenges Bluesky has tried to solve in reimagining a more “feel-good feed.”

It’s clear that you can build a nicer online environment than X has; in many ways Bluesky already did. What’s less clear is that you can build a Twitter clone that mostly makes people feel good. For as vital and hilarious as Twitter often was, it also accelerated the polarization of our politics and often left users feeling worse than they did before they opened it.

Bluesky’s ingenuity in reimagining feeds and moderation tools has been a boon to social networks, which have happily adopted some of its best ideas. (You can now find “starter packs” on both Threads and Mastodon.) Ultimately, though, it has the same shape and fundamental dynamics as a place that even its most active users called “the Hellsite.”

Bluesky began by rethinking many core assumptions about social networks. To realize its dream of a feel-good feed, though, it will likely need to rethink several more.

I agree with Newton. I’m not sure that in this day and age, building a friendlier, snark- and toxic-free social media platform is possible. Users are too used to hiding behind keyboards. It’s not only the shitposters but also the online mobs who jump on the anything that might seem out of the norm with whatever community a user might be in.

Newton again:

Nate Silver opened the latest front in the Bluesky debate in September with a post about “Blueskyism,” which he defines as “not a political movement so much as a tribal affiliation, a niche set of attitudes and style of discursive norms that almost seem designed in a lab to be as unappealing as possible to anyone outside the clique.” Its hallmarks, he writes, are aggressively punishing dissent, credentialism, and a dedication to the proposition that we are all currently living through the end of the world.

Mobs, woke or otherwise, silence speech and freeze ideas into orthodoxy.

I miss the pre-Elon Musk Twitter. But I can’t help but think it would have become just as polarized and toxic regardless of Musk transforming it into X.

I think the form of text-based social media from the last 20 years is akin to manufacturing tobacco in the mid-1990s. We know it’s harmful. It may be time to slap a big warning label on these platforms and discourage use.

(Truth be told, I’m on the social networks—see the follow icons in the sidebar—but mainly to give visibility into my work here, though largely unsuccessfully.)

In a very gutsy move, Grammarly is rebranding to Superhuman. I was definitely scratching my head when the company acquired the eponymous email app back in June. Why is this spellcheck-on-steroids company buying an email product?

Turns out the company has been quietly acquiring other products too, like Coda, a collaborative document platform similar to Notion, building the company into an AI-powered productivity suite.

So the name Superhuman makes sense.

Grace Snelling, writing in Fast Company about the rebrand:

[Grammarly CEO Shishir] Mehrotra explains it like this: Grammarly has always run on the “AI superhighway,” meaning that, instead of living on its own platform, Grammarly travels with you to places like Google Docs, email, or your Notes app to help improve your writing. Superhuman will use that superhighway to bring a huge new range of productivity tools to wherever you’re working.

In shedding the Grammarly name, Mehrota says:

“The trouble with the name ‘Grammarly’ is, like many names, its strength is its biggest weakness: it’s so precise,” Mehrotra says. “People’s expectations of what Grammarly can do for them are the reason it’s so popular. You need very little pitch for what it does, because the name explains the whole thing … As we went and looked at all the other things we wanted to be able to do for you, people scratch their heads a bit [saying], ‘Wait, I don’t really perceive Grammarly that way.’”

The company tapped branding agency Smith & Diction, the firm behind Perplexity’s brand identity.

Grammarly began briefing the Smith & Diction team on the rebrand in early 2025, but the company didn’t officially select its new name until late June, when the Superhuman acquisition was completed. For Chara and Mike Smith, the couple behind Smith & Diction, that meant there were only about three months to fully realize Superhuman’s branding.

Ouch, just three months for a complete rebrand. Ambitious indeed, but they hit a homerun with the icon, an arrow cursor which also morphs into a human with a cape, lovingly called “Hero.”

In their case study writeup, one of the Smiths says:

I was working on logo concepts and I was just drawing the basic shapes, you know the ones: triangles, circles, squares, octagons, etc., to see if I could get a story to fall out of any of them. Then I drew this arrow and was like hmm, that kinda looks like a cursor, oh wow it also kinda looks like a cape. I wonder if I put a dot on top of tha…OH MY GOD IT’S A SUPERHERO.

Check out the full case study for example visuals from the rebrand and some behind-the-scenes sketches.

In graphic design news, a new version of the Affinity suite dropped last week, and it’s free. Canva purchased Serif, the company behind the Affinity products, last year. After about a year of engineering, they have combined all the products into a single product to offer maximum flexibility. And they made it free.

Of course then, that sparks debate.

Joe Foley, writing for Creative Bloq explains:

…A natural suspicion of big corporations is causing some to worry about what the new Affinity will become. What’s in it for Canva?

Theories abound. Some think the app will start to show adverts like many free mobile apps do. Others think it will be used to train AI (something Canva denies). Some wonder if Canva’s just doing it to spite Adobe. “Their objective was to undermine Adobe, not provide for paying customers. Revenge instead of progress,” one person thinks.

Others fear Affinity’s tools will be left to stagnate. “If you depend on a software for your design work it needs to be regularly updated and developed. Free software never has that pressure and priority to be kept top notch,” one person writes.

AI features are gated behind paid Canva premium subscription plans. This makes sense as AI features have inference costs. As Adobe is going all out with its AI features, gen AI is now table stakes for creative and design programs.

In thinking about the three current AI-native web browsers, Fanny on Medium sees what lessons product designers can take from their different approaches.

On Perplexity Comet:

Design Insight: Comet succeeds by making AI feel like a natural extension of browsing, not an interruption. The sidecar model is brilliant because it respects the user’s primary task (reading, researching, shopping) while offering help exactly when context is fresh. But there’s a trade-off — Comet’s background assistant, which can handle multiple tasks simultaneously while you work, requires extensive permissions and introduces real security concerns.

On ChatGPT Atlas:

Design Insight: Atlas is making a larger philosophical statement — that the future of computing isn’t about better search, it’s about conversation as an interface. The key product decision here is making ChatGPT’s memory and context awareness central. Atlas remembers what sites you’ve visited, what you were working on, and uses that history to personalize responses. Ask “What was that doc I had my presentation plan in?” and it finds it.

On The Browser Company Dia:

Design Insight: Dia is asking the most interesting question — what happens when AI isn’t a sidebar or a search replacement, but a fundamental rethinking of input methods? The insertion cursor, the mouse, the address bar — these are the primitives of computing. Dia is making them intelligent.

She concludes that they “can’t all be right. But they’re probably all pointing at pieces of what comes next.”

I do think it’s a combo and Atlas is likely headed in the right direction. For AI to be truly assistive, it has to have relevant context. Since a lot of our lives are increasingly on the internet via web apps—and nearly everything is a web app these days—ChatGPT’s profile of you will have the most context, including your chats with the chatbot.

I began using Perplexity because I appreciated its accuracy compared with ChatGPT; this was pre-web search. But even with web search built into ChatGPT 5, I still find Perplexity’s (and therefore Comet’s) approach to be more trustworthy.

My conclusion stands though: I’m still waiting on the Arc-Dia-Comet browser smoothie.

Building on Matthew Ström-Awn’s argument that true quality emerges from decentralized, ground-level ownership, Sean Goedecke writes an essay exploring how software companies navigate the tension between formalized control and the informal, often invisible work that actually drives product excellence.

But first, what does legibility even mean?

What does legibility mean to a tech company, in practice? It means:

• The head of a department knows, to the engineer, all the projects the department is currently working on
• That head also knows (or can request) a comprehensive list of all the projects the department has shipped in the last quarter
• That head has the ability to plan work at least one quarter ahead (ideally longer)
• That head can, in an emergency, direct the entire resources of the department at immediate work

Note that “shipping high quality software” or “making customers happy” or even “making money” is not on this list. Those are all things tech companies want to do, but they’re not legibility.

Goedecke argues that while leaders prize formal processes and legibility to facilitate predictability and coordination, these systems often overlook the messier, less measurable activities that drive true product quality and user satisfaction.

All organizations - tech companies, social clubs, governments - have both a legible and an illegible side. The legible side is important, past a certain size. It lets the organization do things that would otherwise be impossible: long-term planning, coordination with other very large organizations, and so on. But the illegible side is just as important. It allows for high-efficiency work, offers a release valve for processes that don’t fit the current circumstances, and fills the natural human desire for gossip and soft consensus.

Slow and steady wins the race, so they say. And in Waymo’s case, that’s true. Unlike the stereotypical Silicon Valley of “Move fast and break things,” Waymo has been very deliberate and intentional in developing its self-driving tech. In other words, they’re really trying to account for the unintended consequences.

Writing for The Atlantic, Saahil Desai:

Compared with its robotaxi competitors, “Waymo has moved the slowest and the most deliberately,” [Bryant Walker Smith] said—which may be a lesson for the world’s AI developers. The company was founded in 2009 as a secretive project inside of Google; a year later, it had logged 1,000 miles of autonomous rides in a tricked-out Prius. Close to a decade later, in 2018, Waymo officially launched its robotaxi service. Even now, when Waymos are inching their way into the mainstream, the company has been hypercautious. The company is limited to specific zones within the five cities it operates in (San Francisco, Phoenix, Los Angeles, Austin, and Atlanta). And only Waymo employees and “a growing number of guests” can ride them on the highway, Chris Bonelli, a Waymo spokesperson, told me. Although the company successfully completed rides on the highway years ago, higher speeds bring more risk for people and self-driving cars alike. What might look like a few grainy pixels to Waymo’s cameras one moment could be roadkill to swerve around the very next.

OK, so there’s workslop, but there’s also general AI slop. With OpenAI’s recent launch of the Sora app, there going to be more and more AI-generated image and video content making the rounds. I do believe that there’s a place for using AI to generate imagery. It can be done well (see Christian Haas’s “AI Jobs”). Or not.

Casey Newton, writing in his Platformer newsletter:

In Sora we find the entire debate over AI-generated media in miniature. On one hand, the content now widely derided as “slop” continually receives brickbats on social media, in blog posts and in YouTube comments. And on the other, some AI-generated material is generating millions of views — presumably not all from people who are hate-watching it.

As the content on the internet is increasingly AI-generated, platforms will need to balance how much of it they let in, lest the overall quality drops.

As Sarah Perez noted at TechCrunch, Pinterest has come under fire from its user base all year for a perceived decline in quality of the service as the percentage of slop there increases. Many people use the service to find real objects they can buy and use; the more that those objects are replaced with AI fantasies, the worse Pinterest becomes for them.

Like most platforms, Pinterest sees little value in banning slop altogether. After all, some people enjoy looking at fantastical AI creations. At the same time, its success depends in some part on creators believing that there is value in populating the site with authentic photos and videos. The more that Pinterest’s various surfaces are dominated by slop, the less motivated traditional creators may be to post there.

Definitely use AI at work if you can. You’d be guilty of professional negligence if you don’t. But, you must not blindly take output from ChatGPT, Claude, or Gemini and use it as-is. You have to check it, verify that it’s free from hallucinations, and applicable to the task at hand. Otherwise, you’ll generate “workslop.”

Kate Niederhoffer, Gabriella Rosen Kellerman, et. al., in Harvard Business Review, report on a study by Stanford Social Media Lab and BetterUp Labs. They write, “Employees are using AI tools to create low-effort, passable looking work that ends up creating more work for their coworkers.”

Here’s how this happens. As AI tools become more accessible, workers are increasingly able to quickly produce polished output: well-formatted slides, long, structured reports, seemingly articulate summaries of academic papers by non-experts, and usable code. But while some employees are using this ability to polish good work, others use it to create content that is actually unhelpful, incomplete, or missing crucial context about the project at hand. The insidious effect of workslop is that it shifts the burden of the work downstream, requiring the receiver to interpret, correct, or redo the work. In other words, it transfers the effort from creator to receiver.

Don’t be like this. Use it to do better work, not to turn in mediocre work.

Workslop may feel effortless to create but exacts a toll on the organization. What a sender perceives as a loophole becomes a hole the recipient needs to dig out of. Leaders will do best to model thoughtful AI use that has purpose and intention. Set clear guardrails for your teams around norms and acceptable use. Frame AI as a collaborative tool, not a shortcut. Embody a pilot mindset, with high agency and optimism, using AI to accelerate specific outcomes with specific usage. And uphold the same standards of excellence for work done by bionic human-AI duos as by humans alone.

The web is a magical place. It started out as a way to link documents like research papers across the internet, but has evolved into the representation of the internet and the place where we get information and get things done. Writer Will Leitch on Medium:

It is difficult to describe, to a younger person or, really, anyone who wasn’t there, what the emergence of the Internet — this thing that had not been there your entire life, that you had no idea existed, that was suddenly just everywhere — meant to someone who wanted to write. When I graduated college in 1997, the expectation for me, and most wanna-be writers, was that we had two options: Start on the bottom rung of a print publication and toil away for years, hoping that enough people with jobs above you would retire or die in time for you to get a real byline by the time you were 40, or write a brilliant novel or memoir that turned you into Dave Eggers or Elizabeth Wurtzel. That was pretty much it! Then, suddenly, from the sky, there was this place where you could:

• Write whatever you wanted.
• Write as long as you wanted.
• Have your work available to read by anyone, anywhere on the entire freaking planet.

This was — and still is — magical.

The core argument of what Leitch write is that while the business and traffic models that fueled web publishing are collapsing—due to changing priorities of platforms like Google and the dominance of video on social media (i.e., TikTok and Reels), the essential, original magic of publishing on the web isn’t dead.

But that does not mean that Web publishing — that writing on the Internet, the pure pleasure of putting something out in the world and having it be yours, of discovering other people who are doing the same thing — itself is somehow dead, or any less magical than it was in the first place. Because it is magical. It still is. It always was.

Ian Dean, writing for Creative Bloq, revisits the impact the original TRON movie had on visual effects and the design industry. The film was not nominated for an Oscar for visual effects as the Academy’s members claimed that “using computers was ‘cheating.’” Little did they know it was only the beginning of a revolution.

More than four decades later, TRON still feels like a moment the film industry stopped and changed direction, just as it had done years earlier when Oz was colourised and Mary Poppins danced with animated animals.

Dean asks, now what about AI-powered visual effects? Runway and Sora are only the beginning.

In the scenario “AI 2027,” the authors argue that by October 2027—exactly two years from now—we will be at an inflection point. Race to build the superintelligence, or slow down the pace to fix misalignment issues first.

In a piece by Derek Thompson in The Argument, he takes a different predicted AI doomsday date—18 months—and argues:

The problem of the next 18 months isn’t AI disemploying all workers, or students losing competition after competition to nonhuman agents. The problem is whether we will degrade our own capabilities in the presence of new machines. We are so fixated on how technology will outskill us that we miss the many ways that we can deskill ourselves.

Degrading our own capabilities includes writing:

The demise of writing matters because writing is not a second thing that happens after thinking. The act of writing is an act of thinking. This is as true for professionals as it is for students. In “Writing is thinking,” an editorial in Nature, the authors argued that “outsourcing the entire writing process to LLMs” deprives scientists of the important work of understanding what they’ve discovered and why it matters.

…

The decline of writing and reading matters because writing and reading are the twin pillars of deep thinking, according to Cal Newport, a computer science professor and the author of several bestselling books, including Deep Work. The modern economy prizes the sort of symbolic logic and systems thinking for which deep reading and writing are the best practice.

More depressing trends to add to the list.

Is the AI bubble about to burst? Apparently, AI prompt-to-code tools like Lovable and v0 have peaked and are on their way down.

Alistair Barr writing for Business Insider:

The drop-off raises tough questions for startups that flaunted exponential annual recurring revenue growth just months ago. Analysts wrote that much of that revenue comes from month-to-month subscribers who may churn as quickly as they signed up, putting the durability of those flashy numbers in doubt.

Barr interviewed Eric Simons, CEO of Bolt who said:

“This is the problem across all these companies right now. The churn rate for everyone is really high,” Simons said. “You have to build a retentive business.”

In an announcement to users this morning, Visual Electric said they were being acquired by Perplexity—or more accurately, the team that makes Visual Electric will be hired by Perplexity. The service will shut down in the next 90 days.

Today we’re sharing the next step in Visual Electric’s journey: we’ve been acquired by Perplexity. This is a milestone that marks both an exciting opportunity for our team and some big changes for our product.

Over the next 90 days we’ll be sunsetting Visual Electric, and our team will be forming a new Agent Experiences group at Perplexity.

While we’ve seen acquihires and shutdowns in either the AI infrastructure space (e.g., Scale AI) or coding space (e.g., Windsurf), I don’t believe we’ve seen one in the image or video gen AI space have an exit event like this yet. Obviously, The Browser Company announced their acquisition by Atlassian last month.

I believe building gen AI tools at this moment is incredibly competitive. I think it takes an even stronger stomached entrepreneur than in the pre-ChatGPT moment. So kudos for the folks at Visual Electric for having a good outcome and getting to continue to do their work at Perplexity. But I do think this is not the last that we’ll see consolidation in this space.

Tim Berners-Lee, the father of the web who gave away the technology for free, says that we are at an inflection point with data privacy and AI. But before he makes that point, he reminds us that we are the product:

Today, I look at my invention and I am forced to ask: is the web still free today? No, not all of it. We see a handful of large platforms harvesting users’ private data to share with commercial brokers or even repressive governments. We see ubiquitous algorithms that are addictive by design and damaging to our teenagers’ mental health. Trading personal data for use certainly does not fit with my vision for a free web.

On many platforms, we are no longer the customers, but instead have become the product. Our data, even if anonymised, is sold on to actors we never intended it to reach, who can then target us with content and advertising. This includes deliberately harmful content that leads to real-world violence, spreads misinformation, wreaks havoc on our psychological wellbeing and seeks to undermine social cohesion.

And about that fork in the road with AI:

In 2017, I wrote a thought experiment about an AI that works for you. I called it Charlie. Charlie works for you like your doctor or your lawyer, bound by law, regulation and codes of conduct. Why can’t the same frameworks be adopted for AI? We have learned from social media that power rests with the monopolies who control and harvest personal data. We can’t let the same thing happen with AI.

In my most recent post, I called out our design profession, for our part in developing these addictive products. Jeffrey Inscho, brings it back up to the tech industry at large and observes they’re actually publishers:

The executives at these companies will tell you they’re neutral platforms, that they don’t choose what content gets seen. This is a lie. Every algorithmic recommendation is an editorial decision. When YouTube’s algorithm suggests increasingly extreme political content to keep someone watching, that’s editorial. When Facebook’s algorithm amplifies posts that generate angry reactions, that’s editorial. When Twitter’s trending algorithms surface conspiracy theories, that’s editorial.

They are publishers. They have always been publishers. They just don’t want the responsibility that comes with being publishers.

His point is that if these social media platforms are sorting and promoting posts, it’s an editorial approach and they should be treated like newspapers. “It’s like a newspaper publisher claiming they’re not responsible for what appears on their front page because they didn’t write the articles themselves.”

The answer, Inscho argues, is regulation of the algorithms.

I’m happy that the conversation around the design talent crisis continues. Carly Ayres, writing for It’s Nice That picks up the torch and speaks to designers and educators about this topic. What struck me—and I think what adds to the dialogue—is the notion of the belief gap. Ayres spoke with Naheel Jawaid, founder of Silicon Valley School of Design, about it:

“A big part of what I do is just being a coach, helping someone see their potential when they don’t see it yet,” Naheel says. “I’ve had people tell me later that a single conversation changed how they saw themselves.”

In the past, belief capital came from senior designers taking juniors under their wing. Today, those same seniors are managing instability of their own. “It’s a bit of a ‘dog eat dog world’-type vibe,” Naheel says. “It’s really hard to get mentorship right now.”

The whole piece is great. Tighter than my sprawling three-parter. I do think there’s a piece missing though. While Ayres highlights the issue and offers suggestions from designer leaders, businesses need to step up and do something about the issue—i.e., hire more juniors. Us recognizing it is the first step.

Josh Miller, CEO, and Hursh Agrawal, CTO, of The Browser Company:

Today, The Browser Company of New York is entering into an agreement to be acquired by Atlassian in an all-cash transaction. We will operate independently, with Dia as our focus. Our objective is to bring Dia to the masses.

Super interesting acquisition here. There is zero overlap as far as I can tell. Atlassian’s move is out of left-field. Dia’s early users were college students. The Browser Company more recently opened it up to former Arc users. Is this bet for Atlassian—the company that makes tech-company-focused products like Jira and Confluence—around the future of work and collaboration? Is this their first move against Salesforce? 🤔

Simon Sherwood, writing in The Register:

Amazon Web Services CEO Matt Garman has suggested firing junior workers because AI can do their jobs is “the dumbest thing I’ve ever heard.”

Garman made that remark in conversation with AI investor Matthew Berman, during which he talked up AWS’s Kiro AI-assisted coding tool and said he’s encountered business leaders who think AI tools “can replace all of our junior people in our company.”

That notion led to the “dumbest thing I’ve ever heard” quote, followed by a justification that junior staff are “probably the least expensive employees you have” and also the most engaged with AI tools.

“How’s that going to work when ten years in the future you have no one that has learned anything,” he asked. “My view is you absolutely want to keep hiring kids out of college and teaching them the right ways to go build software and decompose problems and think about it, just as much as you ever have.”

Yup. I agree.

Jessica Davies reports that new publisher data suggests that some sites are getting 25% less traffic from Google than the previous year.

Writing in Digiday:

Organic search referral traffic from Google is declining broadly, with the majority of DCN member sites — spanning both news and entertainment — experiencing traffic losses from Google search between 1% and 25%. Twelve of the respondent companies were news brands, and seven were non-news.

Jason Kint, CEO of DCN, says that this is a “direct consequence of Google AI Overviews.”

I wrote previously about the changing economics of the web here, here, and here.

And related, Eric Mersch writes in a LinkedIn post that Monday.com’s stock fell 23% because co-CEO Roy Mann said, “We are seeing some softness in the market due to Google algorithm,” during their Q2 earnings call and the analysts just kept hammering him and the CFO about how the algo changes might affect customer acquisition.

Analysts continued to press the issue, which caught company management completely off guard. Matthew Bullock from Bank of America Merrill Lynch asked frankly, “And then help us understand, why call this out now? How did the influence of Google SEO disruption change this quarter versus 1Q, for example?” The CEO could only respond, “So look, I think like we said, we optimize in real-time. We just budget daily,” implying that they were not aware of the problem until they saw Q2 results.

This is the first public sign that the shift from Google to AI-powered searches is having an impact.

I enjoyed this interview with Notion’s CEO, Ivan Zhao over at the Decoder podcast, with substitute host, Casey Newton. What I didn’t quite get when I first used Notion was the “LEGO” aspect of it. Their vision is to build business software that is highly malleable and configurable to do all sorts of things. Here’s Zhao:

Well, because it didn’t quite exist with software. If you think about the last 15 years of [software-as-a-service], it’s largely people building vertical point solutions. For each buyer, for each point, that solution sort of makes sense. The way we describe it is that it’s like a hard plastic solution for your problem, but once you have 20 different hard plastic solutions, they sort of don’t fit well together. You cannot tinker with them. As an end user, you have to jump between half a dozen of them each day.

That’s not quite right, and we’re also inspired by the early computing pioneers who in the ‘60s and ‘70s thought that computing should be more LEGO-like rather than like hard plastic. That’s what got me started working on Notion a long time ago, when I was reading a computer science paper back in college.

From a user experience POV, Notion is both simple and exceedingly complicated. Taking notes is easy. Building the system for a workflow, not so much.

In the second half, Newton (gently) presses Zhao on the impact of AI on the workforce and how productivity software like Notion could replace headcount.

Newton: Do you think that AI and Notion will get to a point where executives will hire fewer people, because Notion will do it for them? Or are you more focused on just helping people do their existing jobs?

Zhao: We’re actually putting out a campaign about this, in the coming weeks or months. We want to push out a more amplifying, positive message about what Notion can do for you. So, imagine the billboard we’re putting out. It’s you in the center. Then, with a tool like Notion or other AI tools, you can have AI teammates. Imagine that you and I start a company. We’re two co-founders, we sign up for Notion, and all of a sudden, we’re supplemented by other AI teammates, some taking notes for us, some triaging, some doing research while we’re sleeping.

Zhao dodges the “hire fewer people” part of the question and instead, answers with “amplifying” people or making them more productive.

Yesterday, OpenAI launched GPT-5, their latest and greatest model that replaces the confusing assortment of GPT-4o, o3, o4-mini, etc. with just two options: GPT-5 and GPT-5 pro. The reasoning is built in and the new model is smart enough to know what to think harder, or when a quick answer suffices.

Simon Willison deep dives into GPT-5, exploring its mix of speed and deep reasoning, massive context limits, and competitive pricing. He sees it as a steady, reliable default for everyday work rather than a radical leap forward:

I’ve mainly explored full GPT-5. My verdict: it’s just good at stuff. It doesn’t feel like a dramatic leap ahead from other LLMs but it exudes competence—it rarely messes up, and frequently impresses me. I’ve found it to be a very sensible default for everything that I want to do. At no point have I found myself wanting to re-run a prompt against a different model to try and get a better result.

It’s a long technical read but interesting nonetheless.

Jay Hoffman, writing in his excellent The History of the Web website, reflects on Kevin Kelly’s 2005 Wired piece that celebrated the explosive growth of blogging—50 million blogs, one created every two seconds—and predicted a future powered by open participation and user-created content. Kelly was right about the power of audiences becoming creators, but he missed the crucial detail: 2005 would mark the peak of that open web participation before everyone moved into centralized platforms.

There are still a lot of blogs, 600 million by some accounts. But they have been supplanted over the years by social media networks. Commerce on the web has consolidated among fewer and fewer sites. Open source continues to be a major backbone to web technologies, but it is underfunded and powered almost entirely by the generosity of its contributors. Open API’s barely exist. Forums and comment sections are finding it harder and harder to beat back the spam. Users still participate in the web each and every day, but it increasingly feels like they do so in spite of the largest web platforms and sites, not because of them.

My blog—this website—is a direct response to the consolidation. This site and its content are owned and operated by me and not stuck behind a login or paywall to be monetized by Meta, Medium, Substack, or Elon Musk. That is the open web.

Hoffman goes on to say, “The web was created for participation, by its nature and by its design. It can’t be bottled up long.” He concludes with:

Independent journalists who create unique and authentic connections with their readers are now possible. Open social protocols that experts truly struggle to understand, is being powered by a community that talks to each other.

The web is just people. Lots of people, connected across global networks. In 2005, it was the audience that made the web. In 2025, it will be the audience again.