I personally prefer leaving the measurement in the instructions for two reasons.
1. I'll often use the ingredients list (and quantities there) before cooking to ensure that I have everything I need ahead of time. Depending on what ingredient it is, I might not mise en place it. In those cases, a step that says "add ingredient" would require me to go reference the list in the beginning, losing a bit of context.
2. It's not often, but I've followed a few recipes that require a particular ingredient in 2 different steps in different amounts.
recipes that don't separate their ingredients into steps like crust/filling/icing when using common ingredients like butter are just poorly written recipes.
I've started building a similar app before, except with more of a focus on the timestamps than the text (prioritizing time tracking over note taking). I ended up switching gears to different side projects and never came back to this one.
That being said, a feature I'd begun to implement that you could consider adding is what I called Context Tags. It looks like in Notetime tags are applied to notes to provide better organization. Context tags in contrast would be applied to timestamped lines within the note. When moving to the next line, the new one would default to having the same context tags.
That's it! That's the whole feature. This let's a user tag the first line in a category, such as "work" or "project A", then gain that categorization for any subsequent lines (until the user specified a new category).
that's an interesting approach
this implies that the context can change from one subject to the other within a single note? is that a useful use-case to you?
Genuinely, I'd love to see this approach be taken: allow for a set of characters to be used as list delimiters. I personally like the set to be comma, semicolon, and newline, but of course this set would need to be varied depending on other syntax (e.g. in SQL, we wouldn't want semicolon to be used for this).
Having newline be a valid list separator is particularly nice because it solves the "trailing comma" and "comma-first" style workarounds in a visually elegant way. The newline already provides a visual separator; we can already tell that we're at the end of most lists by way of having another keyword appear next without needing to rely on a lack of commas, for example:
Totally agreed! It's a problem I'd love to see solved well. I've wanted a route planning / mapping app that can achieve advanced features for a few years now. I'd love to build it myself but honestly, I'd much more excited that it could exist rather than it existing under my control and for my profit.
* Multi-modal planning: where walking, transit, biking, and driving can be independently selected for each leg of the journey (in a multi-stop trip)
* Travel breaks with duration: for a multi-stop trip, allow the inclusion of estimated stop time (e.g. I plan to stop for a meal at a particular restaurant on my upcoming road trip and I intend for that stop to take 1.5 hours). That stop time would then affect the ETA for all stops along the way and may even impact which public transit options are available at later stops.
* Multi-day journeys: for more specifically planning road trips or vacations.
* Constraints on the journey: the most familiar of these (which is already somewhat implemented in the popular maps apps) is whether a business will be closed when you arrive. Others could involve weather predictions (on a multi-day, multi-stop trip I only want to go to the beach on a day it's not raining), daylight hours (I don't want to go to the park after dark), etc. I would expect this feature to let users provide their constraints and the planned route would warn users if their constraints aren't met.
* As mentioned in another comment, route comparisons: for single-stop or multi-stop trips, it should be incredibly simple to compare two or more routes. This should be possible prior to travel _and_ while en route. CityMapper provides some amount of this feature: while on a route, you can return to the form where you input your origin and destination and can get an idea of comparisons.
* Another comment (and the article) mentioned exploration while en route: it should be trivial to explore the map in a way that's completely unrelated to one's current trip. Similarly, it should be easier to search for locations that are nearby your route (that is, wouldn't take more than X amount of time out of your way).
An obvious stretch goal after having these features would be to allow for trip optimization given a number of modes of travel, destinations, timing constraints, ordering constraints (want to pick up food before I go to the park for a picnic, so restaurant must come before picnic), and even proposed start and finish times for a trip. This of course falls into the realm of the traveling salesman problem, but if I'm able to reasonable build a few proposals for routes manually (by setting arrival / departure times and choosing transport modes in my maps app) one stop at a time, an app could certainly check a few permutations.
I find myself being unable to search for more complex subjects when I don't know the keywords, specialized terminology, or even the title of a work, yet I have a broad understanding of what I'd like to find. Traditional search engines (I'll jump between Kagi, DuckDuckGo, and Google) haven't proved as useful at pointing me in the right direction when I find that I need to spend a few sentences describing what I'm looking for.
LLMs on the other hand (free ChatGPT is the only one I've used for this, not sure which models) give me an opportunity to describe in detail what I'm looking for, and I can provide extra context if the LLM doesn't immediately give me an answer. Given LLM's propensity for hallucinations, I don't take its answers as solid truth, but I'll use the keywords, terms, and phrases in what it gives me to leverage traditional search engines to find a more authoritative source of information.
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Separately, I'll also use LLMs to search for what I suspect is obscure-enough knowledge that it would prove difficult to wade through more popular sites in traditional search engine results pages.
> I find myself being unable to search for more complex subjects when I don't know the keywords, specialized terminology, or even the title of a work, yet I have a broad understanding of what I'd like to find.
For me this is typically a multi-step process. The results of a first search give me more ideas of terms to search for, and after some iteration I usually find the right terms. It’s a bit of an art to search for content that maybe isn’t your end goal, but will help you search for what you actually seek.
LLMs can be useful for that first step, but I always revert to Google for the final search.
Yeah this is exactly how I use LLMs + Google as well. I would even go further and say that most of the value of Google to me is the ability to find a specific type of source by searching for exact terminology. I think AI search is fatally flawed for this reason. For some things generic factual information is okay ("What's the capital of France?") but for everything else, the information is inextricably bound up with it's context. A spammy SEO blog and a specialist forum might have identical claims, but when received from the latter source it's more valuable, it's just higher signal.
Google used to care about this but no longer does, pagerank sucks and is ruined by SEO, but it still "works" because if you're good you can guess the kind of source you're looking for and what keywords might surface it. LLMs help with that part but you still need to read it yourself, because they don't have theory of mind yet to make good value judgements on source quality and communicate about it.
I also find some use for this. Or I often ask if there's a specific term for a thing that I only know generally, which usually yields better search results, especially for obscure science and technology things. The newer GPTs are also decent at math, but I still use Wolfram Alpha for most of that stuff just because I don't have to double check it for hallucinations.
You might like what we're building in that sense :D (full disclosure, I'm the founder of Beloga). We're building a new way for search with programmable knowledge. You're essentially able to call on search from Google, Perplexity other search engines by specifying them as @ mentions together with your detailed query.
Lately I've started using a barebones SQLite DB for this and a GUI DB editor program (TablePlus, which happens to have an iOS app as well).
I'd always relied on ORMs in whatever web application I used to interact with DBs for the most part, but I've recently been learning more about views, triggers, and more complex relations. It's been insightful and I've found that much of what I want from a program like BeeBase is covered by knowing more SQL.
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That being said, I'd love to see what you described too. I don't mean for this to be like the infamous "why do you need dropbox when you have rsync" comment. I just wanted to give an anecdotal alternative to use until someone creates what you described!
Set up should be simpler than needing to manually copy codes into your settings app.
When a QR code is present on screen that resolves to a TOTP seed, an additional context menu option should be present to "Add Verification Code in Passwords" or "Set Up Verification Code" or similar.
This is possible and relatively easy for Apple to do: for most (if not all) permissions, a declaration that you intend to ask for permission is required in the app's Info.plist manifest file.
When permission is requested and you've forgotten to declare that your app asks for it, the permission will be immediately denied without prompting the user.
In the US, we tax roads by usage via fuel (gasoline and diesel) tax [^1]. It's a simple solution: the more miles you drive, the more fuel you use; the more fuel you use, the more tax you pay. Vehicles that use more fuel per mile driven tend to be larger and thus cause more wear on the roads.
It's not without its faults though. Fuel usage isn't directly related to cost of road maintenance, it's just a very rough approximation. Fuel usage has mattered less and less over the past couple of decades with hybrids and EVs – though this is addressed in some places by imposing an extra EV tax (since EV drivers would pay no fuel tax but would still cause wear on the roads).
A lot more wear on the roads in a lot of cases, as it's exponential (~fourth power) with respect to weight and EVs weigh a lot (~30% more than a comparable ICE car).
Great article! I'd never considered the mathematical risks associated with this.
Now I'm curious what the numbers would look like if it were a cultural norm for everyone (who passed the screening exam) to donate their kidneys in this non-directed fashion. My incredibly unscientific gut-feeling, back-of-the-napkin math seems like it would be plausible to reduce kidney failures to zero in no time: most people have two functioning kidneys and there are significantly less than 50% of people that need donated kidneys. Of course that gut-feeling math doesn't factor in the increase in risk for the donors (the radiation during screening, botched surgeries, etc), but those risks seem low enough that I imagine we'd net positive on a large scale.
1. I'll often use the ingredients list (and quantities there) before cooking to ensure that I have everything I need ahead of time. Depending on what ingredient it is, I might not mise en place it. In those cases, a step that says "add ingredient" would require me to go reference the list in the beginning, losing a bit of context.
2. It's not often, but I've followed a few recipes that require a particular ingredient in 2 different steps in different amounts.