> Back to the dashboard experiment: after you applied the Bonferroni correction you got... nothing.
I guess you got something: Users are not sensitive to these changes, or that any effect is too small to detect with your current sample size/test setup.
In a startup scenario, I'd quickly move on and possibly ship all developed options if good enough.
Also, running A/B tests might not be the most appropriate method in such a scenario. What about user-centric UX research methods?
I agree. Hamburger menus aren't any better than menu bars. It seems like an example where design has more importance than function.
My alternative to the menu bar would be a search bar that allowed me to search in a Google style everything related to that program: functions, features, shortcuts, and documentation.
File | Edit | View | etc. is not the right choice for every program.
This is where it’s useful for the menu bar to be system-owned rather than the responsibility of individual programs (whether that be a global bar as in macOS or attached to window decorations). That would make it easy to implement a toggle that hides menubars either globally or on a per-app basis and enable a Unity-type shortkey-summoned HUD to be used instead.
For some reason I tend to forget about task management apps. So whenever I got back to them, it was depressing to see all the unfinished tasks that I haven't touched.
My solution is extreme time boxing. Every Sunday, I sit down and time box the next week. Work and personal stuff goes into the same calendar. I've learned to keep enough slack to not get stressed out. I also know how much unplanned time I need to coordinate with co-workers.
It's kind of absurd that less freedom when to do things, gives me more happiness.
In the beginning I thought that this cannot work because sometimes you just need uninterrupted time to finish something. However, such uninterrupted long spans don't exist anyways for many people. There are stand-ups at work, you have appointments with some of your team members and at 5pm you need to pick up your kids.
I understand, and I've tried this for a long time, but unfortunately it didn't work very well for me.
First, the level of uncertainty is very high right now, and I can't afford to have a fixed calendar for the whole week. In fact, it's pretty rare that I manage to stick to the same schedule for even half a day. :X
Also, it's often extremely difficult to estimate how long certain tasks will take because they involve both spending time with people (who, by definition, are highly unpredictable) and thinking strategically (which can take anywhere from 2 minutes to 2 hours).
That said, I do relate to the idea that "less freedom" can lead to "more happiness"! I try to apply this as much as possible when it makes sense, especially in my personal life.
Yes, this was also a concern when I started this approach. However, restricted time can also be a yard stick for accomplishing something in a short amount of time. But there are, of course, other factors that influence the success of such a method.
I wouldn't say that I was a very chaotic person but after moving several times it felt like it takes longer to find some special tool than buy it new. So I created a little program to keep track of all my stuff [1]. It took quite a while to put everything in there but it helps me to check for a tool if a friend asks for something. Also, I like to be aware of what I own and what I should give away because I don't need it anymore.
The one logistical change I made that solved this for me was that whenever I have to look for something, as soon as I find it, it gets moved to wherever the first place I looked for it was. I take that as an indication that my brain's default sorting system thinks it belongs there.
I also have a tendency to go through everything I own once every couple of years and get rid of anything a past version of me thought a future version of me might want to do, but future me does not want to do, though, so the total amount of "stuff" I have is pretty manageable to start out with.
Brilliant idea (moving the found item to the location you first looked at)! If you ever write a blog post about your org system/such tricks I'd happily read it :)
I don't know if there's more to it than these two sides of the same coin, but every time I move house, when I pull something out of the box and go to put it "away," wherever it's going to stay from then on, I close my eyes for a second and think, "If all I knew was that I had already unpacked this, but I couldn't remember where I'd put it, where would I go look for it first?" and then I go put it there and that becomes its home.
I just spent fully 15 minutes trying to find anywhere there's a simple list of what the 5 rules are. Couldn't get AI search bots to pull it up for me either.
I don't know whether I'm more worried for her, that her work is somehow disorganized enough for this not to be screaming obvious, repeatedly, throughout her blog, or worried for the state of my ability to search the internet anymore, when I used to find it to easy to find specific things like lists of rules online.
Still haven't actually found the list, but I'm done trying.
You implicitly assume that a good's price should be based on the amount of effort that was put in.
My impression is rather that the price of goods is based on how much people appreciate it. To me it better explains reality with the absurd prices for some stuff.
Lately, I've been thinking that LLMs will lift programming anyways to another level: the level of specification in natural language and some formal descriptions mixed in. LLMs will take care of transforming this into actual code. So not only users don't care about programming but also the developers. Switching the tech stack might become a matter of minutes.
If it simply generates code from natural language then I am still fundamentally working with code. Aider as an example is useful for this, but anything that isn't a common function/component/class it falls apart even with flagship models.
If I actually put my "natural language code" under git then it'll lack specificity at compile time likely leading to large inconsistencies between versions. This is horrible user experience - like the random changes Excel makes every few years, but every week instead.
And everyone that has migrated a somewhat large database knows it isn't doable within minutes.
I don't think one would put only the specification in Git. LLMs are not a reliable compiler.
Actual code is still the important part of a business. However, how this code is developed will drastically change (some people actually work already with Cursor etc.). Imagine: If you want a new feature, you update the spec., ask an LLM for the code and some tests, test the code personally and ship it.
I guess no one would hand over the control of committing and deployment to an AI. But for coding yes.
I guess the standard answer would be a foundation. In some countries, there are minimum capital requirements for foundations but I don't think it's the case for the US. So some thousands of dollars should be enough to keep a website running forever and also hire web developers and accountants every now and then to maintain it.
I would have thought there is a standard solution, a lot of people want to keep web sites active indefinitely, it is a common use case. I feel like setting up my own foundation would be attempting to re-invent the wheel.
Setting up a trust is more common than a foundation. But the concept is similar - you create a legal entity that will use your estate's resources to carry on whatever work you direct it to. You will appoint a trustee to take over the work when you die, and it will all be put together by an attorney into legal documentation that is filed with your local governments.
It will cost you a few thousand dollars for the legal work, and significantly more if you want it to last forever, as you need to fund it with enough money that it will not run out.
But I'm not sure keeping a web site alive forever is actually a common use case. That would be an interesting question to ask your attorney when you hire one.
Humanity has bootstrapped itself out of a lot of BS over the centuries. There's a mechanism for discarding bad ideas. For example:
Badly-designed boats just don't return.
Ill-designed protection of cities means they'll be conquered.
Scientific ideas that do not corroborate, will be discarded.
etc.
Our current approach to AI doesn't have this mechanism. In the past, humanity just implemented ideas: a city was built according to some weird idea and lasted centuries. So the original idea would spread and be refined by further generations.
I guess we need to bring such a mechanism into the loop.
There has definitely been research for simulating physics based on observation, especially in fluid dynamics but also for rigid body motion and collision. It's important for robotics applications actually. You can bet people will be applying this technique in those contexts.
I think for real world application one challenge is going to be the "action" signal which is a necessary component of the conditioning signal that makes the simulation reactive. In video games you can just record the buttons, but for real world scenarios you need difficult and intrusive sensor setups for recording force signals.
(Again for robotics though maybe it's enough to record the motor commands, just that you can't easily record the "motor commands" for humans, for example)
It's called predictive coding. By trying to predict sensory stimuli, the brain creates a simplified model of the world, including common sense physics. Yann LeCun says that this is a major key to AGI. Another one is effective planning.
But while current predictive models (autoregressive LLMs) work well on text, they don't work well on video data, because of the large outcome space. In an LLM, text prediction boils down to a probability distribution over a few thousand possible next tokens, while there are several orders of magnitude more possible "next frames" in a video. Diffusion models work better on video data, but they are not inherently predictive like causal LLMs. Apparently this new Doom model made some progress on that front though.
Howver, this is due how we actually digitize video. From a human point a view, looking in my room reduces the load to the _objects_ in the room and everyhing else is just noise ( like the color of the wall could be just a single item to remember, while otherwise in the digital world, it needs to remember all the pixels )
I guess you got something: Users are not sensitive to these changes, or that any effect is too small to detect with your current sample size/test setup.
In a startup scenario, I'd quickly move on and possibly ship all developed options if good enough.
Also, running A/B tests might not be the most appropriate method in such a scenario. What about user-centric UX research methods?