jovdg

Might be the survivorship bias. Or "history is written by the victor".

Spark is a personal AI assistant. You store information about important (future) events in a local database. Spark sends this information to an AI API and compiles a summary of the events for you.

> 1/ would this be able to generate a QR code for recurring payments?

Maybe the underlying technology is not really well explained: the code is generated "offline" and static. Nothing leaves your computer when you generated it. It can be used as often as you want, but will be for the same payment (amount, remittance, destination account). Many people can scan the same code to pay the same amount, or one person can scan the code on a recurring base (eg. you pay your internet invoice every month and it's a fixed price)

> 2/ the generated QR when read would translate into a payment order accepted by any online banking platform?

You need to scan the QR code with a "compatible" banking app. Not every bank app has support for this, and if they support it, they don't always support it equally. Many banks in Belgium (and at least all the bigger banks in this region of Europe) support it, but eg. the allowed characters in the remittance message varies...

I would be interested in knowing which banks do and don't support it, actually! One can have only so many accounts with different banks... In case it needs explaining, generating a code and scanning does not cost anything (except your time and electricity), unless you hit the "confirm" button on the payment dialog in your banking app...

Yes; could have sworn they had weight lifting by default, but apparently not. They have other indoor activities, however. But elsewhere in the thread, people have suggested many options...

I can access it from any of my devices (laptops, desktop, phone), and my family can use it too.

Check out FitoTrack

Do you mean some kind of "share with Social" button? Or do you mean anonymous access to your recent activities?

Yes, GPX based workouts. Did you look at FitoTrack?

Yes, as stated in the README... I would love to. There seem to be some libraries to parse the .fit file, so when I have more time, I'll take a look at that.

I did some evaluation of HTMX a few weeks ago; while I understand the appeal, it does not yet ... vibe with me. Also, the use for HTMX is still very limited in this project. I don't think this will really change, because the project is fairly focused and will remain so for the time being. Maybe there is an opportunity with the statistics and graphs? Like switch the buckets between km, miles, and number of those (1 km, 5 km, etc), of zoom to more years.

Would you mind cleaning up your script and open-sourcing it? It could be a sort of external component to many such projects...

Not sure exactly what you are asking about, so I'll be verbose :-)

I use an app called FitoTrack (also FOSS), which records my location while running. It stores my GPS position every few seconds. When I'm done, it auto-exports a GPX file to a folder on my phone, which is synced via syncthing. Then I (manually, for now) upload the file to my self-hosted workout tracker.

The GPX file contains some data per measurement point; this is the second point in some random GPX file:

      <trkpt lat="x" lon="y">
        <ele>56.72105575919558</ele>
        <extensions>
          <speed>1.8200000524520874</speed>
          <wstxns2:TrackPointExtension xmlns:wstxns2="gpxtxp"/>
        </extensions>
        <time>2024-02-28T09:28:26Z</time>
      </trkpt>

So it contains speed (average since previous point), elevation, location and time (offtopic: it took me a while to understand that this elevation is not the actual elevation above sea level; only yesterday I figured that out and fixed it in the code!).

The Go GPX library some of this information, and some extras (like max and min elevation, max speed, total up and down, etc. over the whole track). Then I perform some more calculations (like putting the points in buckets per km and per minute), and calculate the estimated location using a geocoder library.

Then, finally, to estimate the difference between walking, running, or cycling, I take the average speed and guesstimate from there. This may be wrong some times for some people, and could be improved on. Or maybe I should include an AI here? (just kidding)