Hope I’m able to write a fitting tribute for mine too someday. It’s been hard (emotionally) to put into words the impact she had on us and the world around her.
82 karma · joined December 13, 2021
Hope I’m able to write a fitting tribute for mine too someday. It’s been hard (emotionally) to put into words the impact she had on us and the world around her.
Makes me think that I should get my remarkable modded as well to post things I write to my website directly, incorporating some ideas from this. Thank you for sharing!
(IV with microbubbles that they can trace as it flows through the brain & some extra imaging algorithms)
Thank you for sharing
So yes, take the day off but the models still need you to steer them when you’re back
The other problem of who owns the relay where the data is stored still exists. One way to solve this is a scheduled query of your data and keeping a local dump
[1] https://chatgpt.com/share/682499c9-0578-800e-aa5f-664a9a6a74...
...based on code generated from an agent that understands it.
...I think cursor editor gets closer to this experience but for a single task
A core reason why we need to do as many trials as possible for different interventions is that there's no one-size-fit all approach. I believe that some things will work for some and not others.
The bottleneck is in having a standard framework to validate the impact of interventions and understanding "whether or not it works for you" with empirical evidence (not anecdotes)
The number of years a person has lived is a good metric but that means if we're trying something new now, we need to wait for X years before saying this has an effect. X in your example being someone reaching 120years.
We can't wait that long :)
Now that we have this. We can use it as a reference for future longitudinal studies with larger groups of people.
For participant 7 (who is an experienced cold plunger) we could see clear changes in their alpha power (dropping during the stroop task - attention & rising during resting state/cold plunge - being more relaxed)
With Fusion you get to understand shifts in your behaviour and activities using personalized prompts. We combine prompt responses with your sleep, activity and heart rate data from wearables. One thing we knew from the start is that there's no one-size fits all approach to managing personal wellbeing.
We're building Fusion Copilot to provide recommendations based on your context.
Using this as example, here's how an insight like this comes about:
- user downloads data from different sources to device
- We use activitywatch (https://activitywatch.net/) to know what activities I'm doing on the computer + when it happens and auto categorize into different groups (e.g neurotech, angel investing etc)
- Exercise/high intensity training data is obtained from Oura ring (https://cloud.ouraring.com/docs/activity) - e.g `activity.high` (number of mins of high intensity training in a day), `activity.met_1min` (metabolic equivalent of activity in 1min granularity) variables
After pre-processing, we combine these features (amongst others), and the calculate correlation matrices + conditional probability to see the impact of given variables on desired outcome.
It still pretty early days and we hope to generate higher signal predictions from aggregated sources trying out different modelling techniques.