I have some ideas I’d like to release, but LLM api pricing and sudden traffic from sites like this one seem scary.
I'm considering switching to DeepSeek since it's way cheaper. I'll swap once I'm done testing out the API. You can use your own hosted LLM but it's not worth it at this moment.
That’s the tricky part about building LLM apps. I’d love to hear more from Indie devs because money is absolutely a bottle neck here.
For fun:
You don’t need an LLM for some of your calls I think. “Where is the Eiffel Tower”, Eiffel Tower is a NER that small NLP libraries can extract. Then it’s a simple long/lat lookup. You might be able to re-route 20% of your calls to a no-cost backend call.
Don't forget to put up a "donate" button.
CPU inference for LLMs takes forever (you'll get like 1tk/s on CPU) and limits you significantly in terms of model size/quality. You'll lock up all of your cores to provide service for a single user at a snail's pace.
I don't think it should even be considered as an option
I asked it:
"Show me all the train yards in New York."
It only identified seven of them when there are many more:
https://en.wikipedia.org/wiki/List_of_New_York_City_Subway_y...
Then when I tried to copy and past my prompt from the history it did not display the full prompt and had no option to copy it to the clipboard.
Seems like it should be useful beyond demos but we aren't sure what those use cases would be. Just need to wait for AGI and then..
I have actually spent about 20 minutes now and I can't think anything worth asking the model in this context.
Almost as if it needs data sanitization.
I guess in this case it might be a flaw of the underlying data input though. If the locations aren't tagged then it's not going to pick them up.
So it's probably similar to google maps but the search is just more configurable.
e.g. "i'm traveling to Tokyo this summer, show me good areas to live in if i want to run 10k every day through nature, as well as work out of highly rated cafes.".
i want to see good areas highlighted on the map. and even better would be integrating with Airbnb / Yelp / Google business ratings, etc. to show places i can rent in those areas.
another e.g. "best times to land in XYZ city if i want to avoid traffic getting to ABC". - to check this now i have to toggle some dropdowns in Google maps. natural language is a much better "interface" for most of what i want to do with maps.
Also, if you run out of free Mapbox credits, feel free to change the basemap to openfreemap.org (I'm the creator).
How's it work under the hood?
Then threw in some common google maps searches and got fun results.
"airport" => LAX
"restaurant" => The Clove Club in London
"coffee shop" => Philz in SF
It doesn't seem to take into account the current map location, so I wonder how the randomness works.
I did notice randomness too, going on a limb to say it's standard LLM randomness.
https://osmfoundation.org/wiki/Licence/Attribution_Guideline...
It would be cool for planning travels! I'll look into it.
Sadly in my prompt it replies with absolutely invented data. Same prompt, three times gives 3-4 different results, that era simply not true.
Prompt: show me the best glass factories in valsassina, italy. (there are no glass factories, so it suggest glass worker that contains wrong coordinates and invented names)
Repeating the prompt alternates between Madeira and Açores which is again, technically correct, but in this case not the best kind of correct.
I searched "show me 5 parks in SF", and all 5 were in the wrong spot. For example:
Lafayette Park Lafayette & Gough St, San Francisco, CA 94109 Latitude: 37.7955 Longitutde: -122.4668
Here is a guide: https://osmfoundation.org/wiki/Licence/Attribution_Guideline...
Use local storage to save the search history.
The i icon does nothing.
Have a open external window icon next to location/landmark title that searches Google for that location
e.g. "show me all IT companies in <suburb where I live>" should show my company (and only my company) - but it shows two other companies that aren't actually in the same city and draws the POI on a sugar beet field near <suburb>
Is the LLM perhaps only trained on english language geo data?