rebeccaskinner

I think you can find a technology useful and think that it will continue to improve and solve some problems while simultaneously being frustrated at the technology being used badly and applied inappropriately.

on-device filtering is the right way to go, but it shouldn’t be a matter of sending metadata about the user to a service- instead services should provide standard metadata about the content to the device and the device can choose what, if anything, to display.

This preserves privacy better by keeping more information about the user local, and gives people better tools to decide what metadata categories they want to filter- for their kids and for themselves.

I don’t think pricing is the only problem with theaters. Especially over the last 15 years or so they’ve been increasingly competing with alternative ways of watching movies, and for a lot of people watching at home wins at any price.

Fancier theaters like the Alamo draft house seem to be trying to complete with watching at home in some ways, but for the most part theaters seem to just be doubling down on the parts if the experience that were already decisive- namely getting louder and adding bigger screens. That might tempt the people who already like what theaters have to offer into going slightly more often, but I think it’s made even more people stop going at all.

It is pretty riddled with ads though. By default the Home Screen shows the tv all and it’s completely crammed with ads.

I sometimes talk with ChatGPT in a conversational style when thinking critically about media. In general I find the conversational style a useful format for my own exploration of media, and it can be particularly useful for quickly referencing work by particular directors for example.

Normally it does fairly well but the guardrails sometimes kick even with fairly popular mainstream media- for example I’ve recently been watching Shameless and a few of the plot lines caused the model to generate output that hit the content moderation layer, even when the discussion was focused on critical analysis.

I shared the story as I remember it. Memory is imperfect. It's been years since I deleted my account, and I don't have the luxury of access to server or moderation logs.

What I do remember unambiguously is being an active member of the site, contributing regularly and in good faith, being accused of spamming, and the general feeling of hostility that I got from the site.

I was a pretty active member in the comments for a long time and left a few years ago after getting chastised by a moderator and accused of spamming for sharing a link to a blog post I had written, even though the content was purely technical, not promoting any product, and does not contain ads or monetize content in any way.

My impression is that the site was actively looking for any possible reason to remove people from the platform. It’s their site to moderate as they wish, but that’s not a community I want to continue participating in.

The only acceptable number of ads is zero.

I worked on a foveated video streaming system for 3D video back in 2008, and we used eye tracking and extrapolated a pretty simple motion vector for eyes and ignored saccades entirely. It worked well, you really don't notice the lower detail in the periphery and with a slightly over-sized high resolution focal area you can detect a change in gaze direction before the user's focus exits the high resolution area.

Anyway that was ages ago and we did it with like three people, some duct tape and a GPU, so I expect that it should work really well on modern equipment if they've put the effort into it.

Although thus isn’t directly related to the idea in the article, I’m reminded that one of the most effective hacks I’ve found for working with ChatGPT has been to attach screen shots of files rather than the files themselves. I’ve noticed the model will almost always pay attention to an image and pull relevant data out of it, but it requires a lot of detailed prompting to get it to reliably pay attention to text and pdf attachments instead of just hallucinating their contents.

I’ve had similar experiences where AI saved me a ton of time when I knew what I wanted and understood the language or library well enough to review but poorly enough that I’d gave been slow writing it because I’d have spent a lot of time looking things up.

I’ve also had experiences where I started out well but the AI got confused, hallucinated, or otherwise got stuck. At least for me those cases have turned pathological because it always _feels_ like just one or two more tweaks to the prompt, a little cleanup, and you’ll be done, but you can end up far down that path before you realize that you need to step back and either write the thing yourself or, at the very least, be methodical enough with the AI that you can get it to help you debug the issue.

The latter case happens maybe 20% of the time for me, but the cost is high enough that it erases most of the time savings I’ve seen in the happy path scenario.

It’s theoretically easy to avoid by just being more thoughtful and active as a reviewer, but that reduces the efficiency gain in the happy path. More importantly, I think it’s hard to do for the same reason partially self driving cars are dangerous: humans are bad at paying attention well in “mostly safe and boring, occasionally disastrous” type settings.

My guess is that in the end we’ll see less of the problematic cases. In part because AI improves, and in part because we’ll develop better intuition for when we’ve stepped onto the unproductive path. I think a lot of it too will also be that we adopt ways of working that minimize the pathological “lost all day to weird LLM issues” problems by trying to keep humans in the loop more deeply engaged. That will necessarily also reduce the maximum size of the wins we get, but we’ll come away with a net positive gain in productivity.

Looking at my own use of AI, and at how I see other engineers use it, it often feels like two steps forward and two steps back, and overall not a lot of real progress yet.

I see people using agents to develop features, but the amount of time they spend to actually make the agent do the work usually outweighs the time they’d have spent just building the feature themselves. I see people vibe coding their way to working features, but when the LLM gets stuck it takes long enough for even a good developer to realize it and re-engage their critical thinking that it can wipe out the time savings. Having an LLM do code and documentation review seems to usually be a net positive to quality, but that’s hard to sell as a benefit and most people seem to feel like just using the LLM to review things means they aren’t using it enough.

Even for engineers there are a lot of non-engineering benefits in companies that use LLMs heavily for things like searching email, ticketing systems, documentation sources, corporate policies, etc. A lot of that could have been done with traditional search methods if different systems had provided better standardized methods of indexing and searching data, but they never did and now LLMs are the best way to plug an interoperability gap that had been a huge problem for a long time.

My guess is that, like a lot of other technology driven transformations in how work gets done, AI is going to be a big win in the long term, but the win is going to come on gradually, take ongoing investment, and ultimately be the cumulative result of a lot of small improvements in efficiency across a huge number of processes rather than a single big win.

It would be much less interesting than the actual chat histories. My experience with chatGPTs memory feature is that about half the time its storing useful but uninteresting data, like my level of expertise in different languages or fields, and the other half it’s pointless trivia that I’ll have to clear out later (I use it for creating D&D campaigns and it wastes a lot of memory on random one-off NPCs).

Maybe it’s my use of it, but I’ve never had it store any memories that were personally identifiable or private.