I added a new feature to Micro.blog Premium today called domain name aliases. Often when people host their blog on Micro.blog, they later change their mind about the primary domain name for their blog. Now you can more easily redirect multiple domain names to the same blog so no previous links break.

For example, maybe you start with the subdomain micro.yourdomain.com but later want it to just be the root domain yourdomain.com. Micro.blog handles HTTPS and redirecting any path in the URL between domain names.

Here’s a screenshot with a couple test examples for my blog. You can find this feature under Account → New Domain Alias:

This option is available for Micro.blog Premium subscribers. Micro.blog Premium also includes support for up to 5 separate hosted blogs and any number of single-page websites.

This is an excellent post by Molly White about the potential conflict between making the world’s knowledge more accessible through AI and the risk of destroying the foundations for open content on the web:

The true threat from AI models training on open access material is not that more people may access knowledge thanks to new modalities. It’s that those models may stifle Wikipedia and other free knowledge repositories, benefiting from the labor, money, and care that goes into supporting them while also bleeding them dry.

She also gets at something I tried to articulate in one of my posts last year about putting up roadblocks for crawlers. We don’t want to make the web worse in the process of protecting content from AI training. Molly again:

Often by trying to wall off those considered to be bad actors, people wall off the very people they intended to give access to. People who gate their work behind paywalls likely didn’t set out to create works that only the wealthy could access. People who implement registration walls probably didn’t intend for their work to only be available to those willing to put up with the risk of incessant email spam after they relinquish their personal information.

AI companies are moving so quickly that it’s going to take the open web and standards organization a little time to catch up. It’s not hopeless, though. Personally, I do want all of my blog posts — and the entire content of my book Indie Microblogging — available for AI models. But if other writers feel differently, there should be steps they can take without also taking a step back from the open web.

I believe all these things:

• AI models with all the world’s information are an incredible resource and will transform education and how we work.
• AI training should respect how authors intend for their content to be used without forcing authors to mangle their own content.
• AI companies shouldn’t take from the open web without giving back in citation links and money to authors and organizations.
• AI slop will become a problem for both users and AI training, so we need a web filled mostly with human-generated content.

I remain optimistic in part because despite how divisive AI has become, this year is also seeing an amazing return to open web principles. More people are blogging. More social networks are based on open protocols. We need to be thoughtful in how we navigate all of this, finding the right balance with AI training that doesn’t undermine what we love about the open web.

We are so used to being able to do almost anything in software. For example, today I was looking at how long it takes to load the list of your blog posts in Micro.blog. It’s a couple seconds, but I’d like it to be half a second. I know from many years of web experience that there is no technical reason why it can’t be faster. Just a little more caching and database optimizations.

With the Apple Intelligence and Siri delays, people have speculated on why it’s late. Maybe it’s about getting the security right. Maybe it’s just buggy and taking longer than expected.

But what if Apple has discovered that it’s not actually possible? AI is entirely new, with new requirements that stress the limits of hardware. Apple is attempting to cram a clever intermingling of data and Siri features into 8 GB of RAM. As a comparison, the largest version of DeepSeek R1 can only be run on a brand new Mac Studio with the M3 Ultra and 512 GB of RAM.

Apple does have an out if on-device models fall over: private cloud compute. But scaling that out to hundreds of millions of iPhone users goes well beyond what Apple had presumed was needed when they talked last year about ramping up production of M2-based servers for AI.

If Apple needs to lean on the cloud to really make Siri work, I think it will be the largest server undertaking that Apple has ever attempted. And they need to balance this with their commitments to energy use and the environment. This is not something you just spin up out of nothing.

Another path would be to simplify their approach, starting with a more manageable set of tasks that the new Siri could do. Something that fits within the limits of iPhone hardware and a realistic deployment of new servers. Apple could focus on making Siri a little more capable and more reliable, saving some of the harder challenges for later. Most people have no idea what Apple promised last year, despite the TV ads, so a reset of expectations could get Apple back on track.

We don’t know what’s going on inside Apple. Apple Intelligence might need a little more time or much more time. The only truly worrying scenario would be if the sunk cost fallacy is blinding them to how badly they are stuck.