Hi All,
For advanced users who have their own API key for other LLMs?
Check out this page Intervals ICU Coach – AI App to run your reports.
It will store your intervals token securely, and can easily be removed after, but never stores your API key.
Supporting right now:
Gemini (tested)
Claude
OpenApi (tested)
I’ve also now a working MCP server that can be queried successfully for the reports. Needs a couple of files in your Claude folder (desktop Claude app only) for it to work but again it offers nice reporting. If you are interested let me know and I’m happy to share.
Clive
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Is it possible to have a link to download a markdown or semantic file of the report? Maybe saved locally?
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Added JSON and MD Download link.
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Great and thanks, working with Gemini 
Much appreciated
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@Clive_King whilst this is great as I can now take the markdown and use with RAG. It has a cycling bias in it’s structure and analysis through Tier0 to Tier3, I think?, is there consideration to adapt to more endurance disciplines of cycling, running and swimming in there. Meta data in report states Discipline: Cycling (perhaps remove from report as not all are purely cyclists). Asking as many I see in the forum looking for and asking with regard to triathlon. Great initiative to open up to more Models, thank you.
Hi Simon, that bias is weighted, its certainly not just for cycling, my running and skiing have become primary also at different times of the year.
I will have a look at discipline again but its is largely driven by the data I receive.
Ultimately where I’m headed now is really using AI to augment the data, thats the real value add, along with the ability to create workout plans based on my performance intelligence engine. This is now working very well.
The API I created for the headless LLM integration is just a framework to prove the app can be implemented with anything.
I’m trying to be LLM agnostic but to be honest they are all at different levels of maturity and OpenAI has stole the march so far at the cost integration level with the ability to host an app inside OpenAI. Whilst its slightly proprietary (aka days of Apple) it works really well and is very popular for that reason.
I love the anthropic Claude LLM interpretation but like, Gemini and the standard LLM API approach that comes at a great cost and the response times are much slower at lower tier levels. There are a few others now starting to make waves.
I am working on improving my MCP manifest, so that when LLM adoption and maturity improves I can be ready for that as well. How this market shapes with MCP is still to be determined.
For now you have the raw data output. Appreciate your feedback nonetheless.
Clive
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Thanks Clive, As a triathlete, runner and cyclist depending of time of year, running now with maintenance for swim/bike, report meta for me in Discipline is “cyclist”, even though is only 3 times a week v 6 times for running, hence the query.
I did fork your github to get to run locally, although python code, LLM integration, RAG are still new to me, A steep learning curve and I had just got to the point of getting a working markdown, when you added your page to work with other LLM’s. Could have saved me some effort haha, but there again has been eye opening chatting with Chatgpt/Gemini/Claude and learning. Thanks again for the work done for others like myself to be able to use. Cheers, Simon
