Kelson Reviews Stuff - Page 24

Clean, stable, fast, free, no-clutter and no-nonsense RSS/Atom newsfeed reader for macOS and iOS. You can read articles in the application or open them in the web browser of your choice.

It’s an app, not a service, so it’s not tracking you or inserting ads. That also means there’s no web version. But it can sync over iCloud, and with a variety of web-based newsreaders (including Feedly, Feedbin, InoReader, and FreshRSS), so you can keep track of your subscriptions and read/unread articles across devices. External actions can be set up with any Share extensions installed on the system, and you can choose an alternate web browser to open posts.

Unfortunately it doesn’t sync with Nextcloud News (yet).

I haven’t used the iOS version, but I highly recommend the macOS application!

Simple web-based news reader for Nextcloud. Feels faster in the browser than some desktop apps I’ve used. Took me all of a minute to install on my Nextcloud server, and a few more to find the export button on Feedly. (Nextcloud imported my subscriptions perfectly, by the way.) In addition to running smoothly on the website, there are multiple clients for desktop or mobile access. RSS Guard is a solid option on Windows, macOS and Linux, and I quite like NewsFlash on Linux. Just be sure to use an app-specific password on each client – some won’t sync properly with the main login.

Mobile App

For my phone I went with NextCloud News for Android. Lightweight, fast, and stable. Works exactly like you’d expect, down to swiping gestures to save/archive posts from the list view, or flip through article view, and matches the system light/dark theme by default.

Super easy to set up if you already have the Nextcloud app on your phone.

You can also manage your subscriptions through the app, including autodetecting feeds on websites that support it, and importing subscriptions from an OPML file.

It’s hard to pin down Picard Season 2.

The opening episode should have been an opening 5 minutes. From there it turns into something that looks like a Mirror Universe story but isn’t. By episode 3 it’s a fix-the-timeline story set in the present day (more or less – the Europa mission is way too soon for real-world 2024, but Artemis II is targeted for roughly the same window!) interwoven with an intriguing story about mental illness and trauma.

The next few episodes are actually pretty good! Interesting concepts, social commentary, some mystery, character exploration, some nice references to Star Trek IV for the nostalgia, Brent Spiner as yet another ancestor of Dr. Soong.

But once they figure out what’s changed and what they need to fix, the story just turns into a messy kitchen-sink stew. Things make sense individually but the connections don’t, or vice versa. Overkill is the rule, as character developments are sidelined or discarded, thematic resolutions are undermined, and the timey-wimey ball unravels into a timey-wimey mess.

A fun, accessible introduction to how machine learning works…and how it sometimes doesn’t!

Still relevant despite recent advances in AI-generated imagery and text, because the new systems still work on the same principles as the ones that were around three years ago. They just have a lot more data and processing power. This also means they have the same limitations and blind spots. What was it trained on? How was it trained? (This is the most obvious way human bias can leak into an AI model.) How well is the goal specified? And of course, did the AI actually latch onto relevant details, or did it notice that all the training pictures labeled sheep had green fields and blue skies, and completely ignore the actual sheep?

These are things to keep in mind as we enter the landscape of generative AI tools like ChatGPT: You can train an LLM to write a book review, and it’ll give you a great piece of text that reads like a book review – but it’s not going to have actually evaluated the book. For that, you’d have to train another AI to categorize books as good, bad, interesting, dull, and so on. But even that can only be as good as its training data. (I don’t remember whether the classic phrase “garbage in, garbage out” is used anywhere in the book, but it still applies today!)

The author has a blog/newsletter, AI Weirdness, where she pushes AI over the edge to sometimes hilarious results.

I really wanted to like The Book of Boba Fett. I like Fett well enough and I like Fennec Shand…but I just couldn’t get into the story of him trying – clumsily – to become a crimelord.

The flashbacks showing his escape from the Sarlaac, his captivity and later acceptance into a Tusken tribe were fascinating, and I would cheerfully have watched more of that!

I think the problem with the show is that they put everything that audiences wanted out of a Boba Fett show into The Mandalorian. Cool armor and weapons, has his own code, always seen with the mask on, flies around the galaxy and gets into fights… They had to do something different for a Boba Fett show…but the approach they took just didn’t work for me.

(Spoiler: Also, I was so mad at Luke and Ahsoka in their guest spot. The two people in the galaxy best suited to understand that denying Anakin familial attachment is what enabled the dark side to get its hooks in him, and there they are repeating the old order’s mistake with their first student.)