ogundipeore

You can already do this with EEGs for low-ish cost

Yeah, they’re using butterfly network chips for the ultrasound but with some additions.

(IV with microbubbles that they can trace as it flows through the brain & some extra imaging algorithms)

These are really beautiful. I stumbled on it this morning when I woke up & took a nap afterwards…ended up dreaming about my mum and giving her a warm goodbye hug.

Thank you for sharing

I agree with the premise of time away being easier. I don’t think the models/harnesses are there yet. There’s still a good amount of human input required to generate quality work.

So yes, take the day off but the models still need you to steer them when you’re back

what’s the recommended for scenarios like this? Add a skill scanner that admins can configure?

Great suggestion. Thank you for sharing!

I always wondered why people don’t adopt protocols like Nostr as much? At least with nostr you know you can use your private key on any other platform that supports it as a valid auth.

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

Did this with ChatGPT[1] and it was quite helpful. That said using neetcode.io is what I found most helpful

[1] https://chatgpt.com/share/682499c9-0578-800e-aa5f-664a9a6a74...

context: a future I'd want re:coding experience is receiving pull requests for the issues I have in my code repo

...based on code generated from an agent that understands it.

...I think cursor editor gets closer to this experience but for a single task

We should definitely aim to do more studies that account for different people's conditions. As long as limitations are called out then it's fine imo.

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)

Your points are valid. However, I think it's important that we have standard frameworks for evaluating the impact of new interventions that aim to improve wellbeing (lifestyle changes, pills, neuromodulation)

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 :)

Yeah, it depends but to answer the "does it help me" question, we need a framework to measure individual changes over multiple cold plunges.

Now that we have this. We can use it as a reference for future longitudinal studies with larger groups of people.

Agreed! I had a similar feeling of accomplishment when I did my first cold plunge too.

Some participants were doing cold plunges for the first time and others had done it before.

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)

yep, this trial was to see if there were any neural effects. We plan to do a follow up study with a much larger cohort of people and also over many days. This will provide extra context for solid conclusions

haha yep that's correct. Observing how a single individual responds over time along side other contextual metrics of behavior, productivity will help nail down "why a person responds well vs. doesn't"

Fusion helps you track and identify stressors that impact your mental health, physical health and productivity. We provide AI coaching to help you manage them also.

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.

thanks for catching that! this was a typo, fixed now :p

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.