I would like to start a thread for mobile apps which folks in the quantified self community might find useful.

To get us started, today I was introduced to an iPhone app called “LongevLab”. From what I have read, there are plans for an Android app in the future.

Here is a screenshot from the app’s website:

What other apps do you use for self quantification purposes?

Deep!

I’ve been trying to reach Mark to ask permission to quote the talk in my upcoming book on Quantified Self and Personal Science, but all my routes have hit dead ends. Anybody in touch with him?

Gary

Both Abbot and Dexcom now have direct-to-consumer sensors/apps, Lingo and Stelo. Abbot’s Lingo appears to be identical to their FreeStyle Libre sensor, minus the annoying alerts. Both cost around $50 for one two-week sensor, which is just slightly more than what a FreeStyle Libre had cost me. So I decided to get both a Lingo and a Stelo, and wear them next to each other for two weeks. Here’s what I found:

• Both apps can export a CSV file with blood sugar readings, but neither include meals and activities in the export. Stelo does at least export a PDF report that includes everything.
• Lingo’s readings were slightly above my venous blood sugar when fasted (as was often the case with the FreeStyle Libre), and Stelo added another ~5mg/dL on top of that, so not bad. But overall correlation between the two devices was not so great (r=0.79), even after accounting for a 5-minute lag with the Stelo (r=0.815). Lingo’s readings looked more plausible (none over 180 or under 70mg/dL, and no gaps), and it had me in the target range (70-140mg/dL) for 97.8% of the time (vs 93.1% with the Stelo), so I think I prefer the Lingo
• Both apps were really nice: Readable graphs, easy food logging, automatic syncing of activities from other apps etc. It’s amazing what can be done when every small change doesn’t need to be re-evaluated for submission to the FDA!

I’m Danqing, a Master’s student researcher at Technical University of Munich in Germany. I’m now conducting my Master’s thesis on how women experience and use period and fertility tracking apps in their everyday lives.

I’m looking for women who use (or have used) period or fertility tracking apps for more than 6 months and would be willing to participate in a confidential interview (approximately 30-45 minutes). If you are interested or would like more information, please kindly reply to this post or send me a private message, so I can provide you with more information about the study and the interview.

Have a nice day
Danqing

While we have incredibly granular data for our sleep (Oura/AutoSleep) and workouts (Strava), I felt that sex tracking was mostly relegated to simple “tick-boxes” in period trackers or apps that felt insecure or gimmicky. I wanted something that treated it like an actual health metric—tracking duration, heart rate variability, intensity, and context (solo vs. partner)—without sending that sensitive data to a random third-party server.

I built “Do?” (iOS & watchOS) to solve this, and I just pushed a major update (v1.1.0) focusing on hardware stability and real-time feedback.

The Data Model & Tech Stack:

• Privacy Architecture: The app uses SwiftData for local persistence and CloudKit Private Database for sync. This means I (as the developer) literally cannot see anyone’s data; it lives only on your device and your personal iCloud.
• Biometrics: It captures Active Energy, Heart Rate (Avg/Max), and Duration via HealthKit and Watch sensors.
• Contextual Metadata: Tags for behaviors, locations, and protection methods.
• Visualization: It generates heatmaps to visualize frequency over time, helping to identify patterns or seasonal changes in activity.

Recent Engineering Hurdles:

One of the biggest hurdles was the reliability of WatchConnectivity during long sessions where the phone might be in another room. In the latest update (v1.1.0), I completely refactored the sync logic to handle “standalone” Watch recording. Even if the connection drops, the Watch buffers the biometric samples and syncs them once reconnected.

I also implemented Live Activities & Dynamic Island support. From a QS perspective, this allows for a low-friction “glanceable” status of the session duration/HR without the need to interact with the device UI.

Seeking Community Feedback:

I’m looking for input from the QS community on the data side:

1. Correlations: I’m currently syncing to Apple Health. Are there specific correlations you’d find interesting to see analyzing this data against? (e.g., Sleep quality vs. Evening activity).
2. Data Portability: Currently, I support .dobackup files. Would there be interest in a raw CSV/JSON export for use in tools like Excel or Notion?
3. Subjective vs. Objective: Is the current biometric set enough, or do you find value in subjective mood/energy ratings for this specific category of tracking?

The app is called “Do?”. You can find it on the App Store here: ‎Do? Track Intimate/Sex Passion App - App Store

I’m mostly interested in hearing if this fits into anyone else’s “Quantified Self” dashboard and what features you would prioritize to make the data more actionable.

Thanks!

Most performance tracking is either purely biometric (HRV, sleep) or purely task-based. I’m interested in the Interplay among my attributes - i.e. how they act as multipliers for one another and my core metric of interest: My amount of high-quality output (admittedly subjective, but I’m working on refining it )

The Logic: By maximizing these internal attributes, one maximizes every driver of success within their direct control. While you can’t control “Stochastic Shocks,” (e.g. market crashes or hospitalization) you can ensure your “Human Kernel” is at 100% capacity to capture luck when it strikes.

Garbage-in-garbage-out consideration: Assigning values to control theory variables like “rate of decay” and “recovery constant” in the domain of psychology can admittedly get very fuzzy, but by using a “Gray Box” approach—back-testing subjective scores against “ground-truth” output—the system self-corrects. Even with self-reporting bias (specifically, me grading my own performance), modeling these attributes provides higher-fidelity insights than unmodeled intuition.

I have two questions for this group:

1. Why isn’t this more common? Performance psychology regimens for business & work are still dominated by isolated prescriptions for optimizing attribute or attribute (e.g. grit, discipline, etc.). Why don’t we see more rigorous/scientific models in the field of self-improvement? I know “quick fix” solutions will always dominate this market, but surely there’s some demo that’d be interested in this approach if it worked - are modeling these attributes unlikely to help somebody significantly move the needle towards business success?

2. Has anyone else here attempted to quantify psychological attributes? I’d be interested in learning what challenges you faced!

The sleeping position experiment involving an AMG8833 IR thermal sensor from August, 2025 has taken a fruitful turn.

Instead of using the single AMG8833 camera to measure my body heat, I am changing course. I have invested in 8 Force-Sensitive Resistor (FSR) square pads from Adafruit to measure where my body weight is positioned while sleeping.

In essence, as an alternative to mounting a thermal camera above my bed, I will be using pressure sensors below the mattress.

Here is a screenshot of the parts I will be building with:

And here is the hardware I purchased on Amazon to help secure the FSR pads to my bed frame:

I had tinkered with the AMG8833 IR thermal camera (including soldering) but ran into connectivity issues while using two different Raspberry Pi computers. So I am pivoting towards an alternative approach in order to complete this self-quantification experiment.

This undertaking has been on my mind for over a year. It is time to complete it and move on to other, even more interesting measurements and automations.

I am 100% added sugar free, the only sugars I get are the ones naturally in the foods I eat, and mostly right now I don’t eat those anyway - being on keto/IF pretty much eliminates those for now.

And the only milk I get is after it has been fermented, which changes its nature pretty radically.

What was going on that ground flax caused your gut to bleed? That sounds intensely interesting.

I do love your analogy and enjoy knowing what’s working for you. I recently discovered by accident that although ground flaxseed would formerly cause my gut to bleed, I can now ingest 32 g/day (more of a medical level of ingestion) and it immediately changed how my GI tract works (when every other soluble and insoluble food did not). So, I suggest taking a look at the research. I’m more than 12 years into a serious gut journey and was pleasantly surprised. Being open minded (forever) seems to be an advantage.

Best of luck to you!

My working model is that the gut behaves like a living farm:

• Microbial guilds instead of crops

• Ferments as inoculants

• Fibers and polyphenols as soil amendments

• Meal timing and substrate sequencing as irrigation and nutrient pulses

• Telemetry (glucose, ketones, BP, weight, subjective markers) as the feedback loop

What I’m doing right now

I’m running a daily rotation of DIY Home-made ferments and purchased substrates, including:

• Kefir

• Yogurt, Bulgarian style

• Natto

• Mixed vegetable ferments (6–8 tbsp/day), multiple varieites

• Fermented Hot Sauce

• Ginger/turmeric ferments

• Lacto-fermented ginger drink

• A structured prebiotic blend (psyllium, inulin, acacia, resistant starch, flax)

• Polyphenol inputs like pomegranate peel and black cumin and matcha tea

• A high‑diversity plant intake, with a garden and an orchard in the startup phase now

The goal is a stable, high‑diversity ecosystem that can handle metabolic stress, dietary shifts, and aging without collapsing into inflammation or dysbiosis.

What I’m tracking

I’m logging:

• Waking glucose

• Post‑coffee glucose

• Meal‑timed glucose curves

• Weight, waist, BP, Heat rate

• Subjective markers (energy, digestion, sleep)

• Ferment batches and substrate timing

• Fasting windows and feeding pulses

• I’m also following a keto IF style diet and losing weight (30lbs down so far)

I’m especially curious about how different ferments behave as inoculants — which ones “take,” which ones seem transient, and which ones shift metabolic markers in a measurable way, which ones I haven’t tried but should…

Why I’m posting

I have heard that others here are experimenting with ferments, fibers, microbiome tests, or metabolic tracking. I’d like to compare notes with anyone who’s:

• Trying to cultivate specific microbial guilds

• Using ferments as targeted inoculants

• Sequencing fibers or substrates intentionally

• Tracking metabolic responses to different ferments

• Or just contemplating a more ecological approach to gut health

What’s working for you? What surprised you? What didn’t behave the way you expected?

Looking forward to hearing from others who are cultivating their own internal ecosystems.

I’ve spent the last month prototyping a cover using Encapsulated Silica Aerogel (thermal conductivity ≈0.015W/m·K).

The Goal: Keep the watch under 110°F for a full 40-minute session.

It’s a ‘Dead-Front’ design—no screen distractions, just Siri for timers and continuous heart-rate tracking. I’m making a small batch of 10 ‘Founder’s Beta’ units for field testing this month.

If you’re an ‘Optimizer’ who hates losing data, would you actually use something like this, or is it just me? Love feedback at this point"

Here’s a Science News article about us: How patient-led research could speed up medical innovation

Here’s a personal fav of mine. Cutoffs for iron for anemia are too low. Lives and their quality are being destroyed because even though research shows much higher iron levels are needed for men and women, many orgs and practitioners do not (or cannot with insurance?) provide the treatment needed.

Any supplement though … how do you get around ‘not providing medical service’ legally?

What would you want to test first?

Thanks!

As part of learning more about myself through data, I thought it would be interesting to visualize Pi-hole DNS queries made during a given computer session as a network graph. So I developed a Python/JavaScript application (or workflow) to accomplish this.

In the screenshot below you will see a feature the frontend offers is centering on a given DNS query and visualizing all of the associated (connected) DNS queries. Each node with half a dozen data points available to interact with.

There are many other tools woven into this program. Such as domain filtering, session roadmaps, metadata analysis and a bunch more.

The software is a lot of fun to use. It was even funner to build.

Although not the easiest to set up, It offers a lot of customization and integrates well with existing home lab setups, while being fully free and open source and transparent. The project README has an extensive documentation. Unlike Strava or other similar application tracking only recorded exercises, this project can extract everything garmin watches collect, including raw HR, sleep scores, HRV, Steps, Breathing rate, SpO2 and all.

I think none of the three risks you outlined seem to invalidate the idea on their own, but the first one (misdiagnosis) does feel like the most structurally sensitive. People don’t just misidentify their own challenges, they often become attached to those interpretations. Burnout versus “lack of discipline” is especially delicate, because applying grit-based tools to the wrong condition can backfire. This is why it may be important for the system to rely less on early labels and more on observable behavioral patterns over time, allowing insights to emerge gradually and gently challenge the user’s own assumptions.

On the compliance side, the issue may not be willpower itself but how the app is perceived psychologically. If several well-timed interventions are ignored in a row, the app risks being reclassified in the user’s mind as optional advice rather than meaningful support. Your point that even partial compliance still creates value makes sense, but long-term retention will likely depend on how adaptive and non-judgmental small the interventions feel in moments of vulnerability.

The “reliable narrator” problem also feels real, but more as a natural market filter than a fundamental blocker. This kind of product will likely resonate first with people who already have a reflective relationship with their inner states. The subtler risk isn’t intentional inaccuracy, but emotional reinterpretation of events over time. Without surfacing contradictions and recurring patterns, the system could unintentionally reinforce distorted narratives rather than clarify them.

Beyond these three, a few quieter risks stand out, I believe. One is identity reactance. If users begin to feel “corrected” rather than supported, they may disengage emotionally. Another is the precision of emotional timing. The effectiveness of real-time interventions depends on arriving at just the right moment. There is also a longer-term risk of over-reliance, where the system could unintentionally weaken the user’s own regulatory capacity unless there is a clear path back toward autonomy. Finally, without visible long-term cause-and-effect feedback, users may credit themselves for successes while attributing failures to the app, which can slowly erode trust.

I think the core idea feels strong and what you’re building seems to go beyond a typical self-improvement app toward something closer to a temporary cognitive support system. It seems like its long term success will likely depend on whether users experience it as strengthening their inner agency rather than replacing it. Good luck!

I’m working on a self-improvement app for improving grit, discipline, motivation and more. I’m looking for critical feedback here, specifically regarding the behavioral assumptions I’m making.

None of this tackles “abnormal” psychology like clinical disorders - it’s more for performance enhancement.

The Context: Traits like grit and discipline correlate with success, but executing the necessary behaviors to “grow” these incurs a high tax on cognitive load and willpower. For example, pushing through failure with sheer grit & willpower requires draining executive function to override the brain’s natural desire to stop, leading to increased cognitive load and willpower depletion.

My app reduces this friction by acting as an external “executive function” coach/therapist in your pocket with AI - telling you what to do at inflection points throughout the day. It works by:

1. Ingesting your thoughts, emotions, and daily events via voice notes. Obviously a prereq is that the user must be a generally “mindful” person to notice these things.

2. Daily priming: Outputting specific Implementation Intentions (e.g., “If X happens, then I will do Y”) based on your specific weaknesses and recurrent failures.

3. Real-time Intervention: When a known “trigger” event happens (e.g., I encounter a setback), the app reminds you of: the cost of not executing the relevant intervention (e.g. Deep Breathing) and the benefit of performing the intervention, based on your history of performing interventions & their success rates.

4. Prescribes the best interventions: based on trial and error. An intervention I’ve found most valuable when I lose motivation to work after encountering a setback is to start work is to “just work for 5 mins, if you’re not motivated after that you can stop” (I always end up working well past 5 mins).

The Request: I’ve identified 3 major “Risky Assumptions” that could kill this product. I’ve also listed my “counters” (why I think it might still work).

I need to know: Do these assumptions invalidate the idea? And what other risks am I overlooking?

Risk 1: The Clarity & Diagnosis Issue

• The Assumption: Users can accurately diagnose their own issues (e.g., lack of grit vs. burnout) and select the right starting “intervention/tool set” to iterate on.

• The Fear: If users can’t self-diagnose, the interventions will be misplaced.

• My Counter: I plan to help identify common anomalies that would indicate traditional tools may not work for a given subject based on comprehensive questionnaires (for burnout vs grit - I might ask “How refreshed are you?” to identify whether burnout is an issue). Assessment can be self-reviewed and eventually be reviewed by a human psychologist for soundness.

• If the user and their conditions seem normal, the app starts by prescribing the most effective/common tools for a given domain. While such a generalized approach won’t make any “superhumans”, I’m hoping it’ll significantly move the needle for folks.

Risk 2: The Compliance & Willpower Issue

• The Assumption: Users will listen to the app in real-time.

• The Fear: When a user is “vulnerable” (e.g., just received bad news), they may simply lack the willpower to execute the intervention, even if the app reminds them.

• My Counter: The goal isn’t 100% compliance. If the app makes a user more likely to make the right decision than they would have been without it, it provides value.

Risk 3: The “Reliable Narrator” Problem (Garbage In / Garbage Out)

• The Assumption: The data the user puts in is accurate enough to yield good outputs.

• The Fear: Since the app doesn’t have access to actual thoughts (only what is dictated), efficacy depends on the user’s mindfulness and self-awareness.

• My Counter: This is a scaling problem. While the general population might struggle, those who are “mindfulness” may benefit the most.

Questions for you:

1. Do any of these risks seem insurmountable despite my counters?

2. Are there other “silent killers” in this workflow that I’m missing?

Thanks for the help!

I’m O+ as well; I’d love to donate but am pretty sure donation alone would cause disability (again, as I consider daily naps, not being able to work in afternoons/evenings, and painfully difficult cognitive function to be disability).

I strive for optimal health because my optimal health appears to be much lower than that attainable by others, but I am certainly curious the extent to which others with robust optimal health are affected by changing iron and ferritin. As iron is not a switch, not fuel for the car, I am certain it must affect everyone. Maybe you would not notice it until you are an astronaut in space, but maybe it would be a strange advantage then (though there appear to be no advantages with both RBC destruction and iron accumulation).

When you have noticeable symptoms, is hemoglobin low, or just ferritin? My understanding is that low iron stores isn’t an acute problem, but it can turn into one if ignored.

Iron is used metabolically for so many things, partly as another person notes for muscle capacity/endurance. It affects cognition, as well. That would be where I’d start: what does brain science say about ping ponging iron levels? Personally, a consistently robust storage of iron appears to bode well for the best health outcomes (in absence of iron overload disorders or other genes that inhibit iron utilization).

I don’t know your motivations for donating, and if you have a rare blood type, most certainly you are doing much good. As someone with some defect in iron retention, I can attest to noticeable cognitive effects that are ameliorated by iron monitoring and supplementation.

Maybe I’ll just do 60 days of supplementation after future donations, or perhaps I should reduce donations to once a year, if that’s all I can handle without having to supplement.

Also, have you looked at genetic contributions to your iron supplementation and results? Caffeine usage?

I’m notoriously a failure in keeping up with discourse, but I’ve found all those to be important considerations.

ejain:

In any case, I now have fewer reservations about supplementing with iron: I’ll try 25mg iron bisglycinate, and see where that gets me in 6 months…

After supplementing with iron for 6 month, my ferritin levels are at 103ng/mL, which is getting close to the upper level of ideal. I’ll resume donating and stop supplementing, and see where that gets me in another 6 months…

This is also where I completely agree with you about AI. As you said, when you ask the right questions, AI can surface factors you may not have even considered. Things like weather and air quality, humidity, sleep quality, stress levels, travel, daily activity, caffeine or alcohol consumption, hormonal cycles, and even timing of meals are often overlooked, but they can have real relationships with biomarkers. When these are analyzed together, the insights become much more meaningful.

Your experience actually reinforces why I believe this approach is so important. This kind of real-life insight is very valuable to me and my project. Thank you again for taking the time to share your experience!

Of course, there weren’t enough.

I mean, the markers itself tell a story. Like, AST and ALT enzymes are released in the bloodstream when liver cells die. So, it’s a good biomarker to follow how the liver is doing. Similarly, ESR and CRP are inflammation markers. Etc.

But they mention nothing of the why it evolved the way it did.

From the lab reports, I only get the values, the numbers, which I then entered into Excel and that would create trends/graphs over time.

The story of why the values were increasing or decreasing had to be interpreted though. In some cases, that story could be pretty straight-forward: I start taking Milk Thistle supplements (Silymarin as the main bioactive ingredient) and two weeks later my LFTs (liver function tests) are drastically improved. Well, the story is easy to interpret then.

In many cases though, it required hundreds of hours of reseach and reading on the Internet (research papers etc) to understand the illness and how to approach it and what could have an effect etc.

Essentially, that’s pretty much why you normally need a doctor to interpret results! You need that deep knowledge to understand what ‘s going on.

Nowadays though, I fill like AI could fill the gap on many aspects. I used to spend so many hours reading papers, first to understand how the liver works, how the immune system works, what the common triggers of autoimmune illnesses are, etc. Then, for each food or supplement or intervention I would hear or read about, I would research that specific topic in depth. Many “popular” remedies were “snake oils”, like health trends but not supported by peer-studies and such. Some though, sometimes, were extremely promising and supported by many studies.

Had I had AI at the time, I think my research would have drastically sped up.

Nowadays, I’m still testing but I take supplements in combination, not just testing them separately. And I do use AI a lot to interpret the results. Because some supplements that I knew for sure improved my liver when taken individually, when combining them with other supplements, the results were different… And using AI definitely helps me understand the interactions at play better. It’s a great tool in speeding up research.

So, for your app, I would suggest adding AI to interpret the results. That could greatly help.

OzQuantified:

I’m just curious what I could do my data that could interest anyone.

I’m actually developing an app right now, and maybe not your data itself, but your experience while tracking it could really help me. Especially around the gap between tracking and understanding. Did you ever feel like the tools you used (Excel, graphs, etc.) weren’t enough to uncover the full story behind your biomarkers? And were you also using any analogue methods like keeping notes in a notebook alongside your digital tracking? I would love to hear about your experience!

I’m actually curious about your process while building Staqc, so I wanted to ask a few things:

• Did you conduct any user research while building Staqc? If so, what did you discover users struggle with the most when tracking their health data?

• How do people feel about sharing their health data on the platform?

I’ve been self-tracking for nearly a decade, and my habit became even more serious after my epilepsy diagnosis. For anyone who doesn’t know, epilepsy actually has hundreds of different types, so I needed to understand my own pattern. But no doctor could offer a definitive answer.

So I started tracking everything:

• sleep

• medication

• stress

• nutrition

• screen time

• weather

• daily routines and notes

Over the years, I compared data across different apps, exported screenshots, layered graphs, and even digitized pages from my notebooks. After about three years of long-term correlations, I was finally able to identify my primary trigger.

The result: I’ve been seizure-free for two years!
Self-tracking truly changed my health and my life.

Now, as a designer researching data visualization, I’m studying how people make sense of their scattered data across apps, devices, and notebooks. I’m also working on an app for this, and I’d love to hear how you all deal with it.

Looking forward to connecting and learning from your experiences.
Nice to meet you all!

With this post I would like to express the following, “There is an array of quantified self projects I have worked on, but haven’t discussed.” I would like to now provide context for this statement.

Measuring Character Interactions

A first example of an undisclosed QS project is my “character interactions” application. Which takes a .txt file as input, and generates a (JSON file and) network graph visualization featuring all direct interactions between characters therein as output.

As an example, here is a screenshot of output from William Gibson’s “Neuromancer”:

If you are a fan of cyberpunk novels, you have probably read “Neuromancer”. What this output taught me is Molly (with nearly twice as many interactions than other characters) is the center of the story, not Case.

Expanded Audio Analyses And Visualizations

Another example is having added 11 new metrics to the “Detailed Audio Analyses and Visualizations” (DAAV) program, mentioned in the first post at the top of this thread. There are now 20 audio features measured for each sound file processed.

Here is a screenshot from the live demo of DAAV:

This data-rich application is fueling a much larger project. One where video and audio are intelligently fused together programmatically, into entirely new experiences. More to come on this project at a later date.

Dozens Of Other Examples

Since starting this thread, I have developed 80+ unique software programs, each with some form of logging or JSON output. Other specific examples I am especially proud of include PDF Finder, AV-Sync and many, many others. Each with a unique purpose:

In the long-run I am interested in integrating my love for data analysis and automation into biofeedback systems. Which has been a dream of mine since I was in college. And is something I can now imagine (in part) because of the development this thread has inspired.

I hope those who are following along enjoy the learning taking place as much as I am/do.

Also, a big thank you to those who have encouraged me here. It has been a wonderful journey so far. There is much more to come.

AlexKChen:

Oh A LOT of resources, may have to break this down. I’m most interested in using video games for quantified self, though a lot of gaming APIs come initially from RL AIs on them

Pymetrics Games - 5 Steps to NAIL them EVERY SINGLE TIME!

x.com for a lumosity game or game not easily in their database? And honestly, after digging into all this data-heavy stuff, I like to unwind a bit with some casual gaming — lately, I’ve been spending time on 1win. It’s a great way to relax after going down the quantified-self rabbit hole.

https://openai.com/research/neural-mmo

https://openai.com/research/gym-retro

https://openai.com/research/openai-baselines-ppo

Joseph Suarez has some packages + reading group

https://openai.com/research/openai-gym-beta

Intro and Screen reading - Python plays Grand Theft Auto V p.1

Scaling laws for single-agent reinforcement learning | DeepAI

https://openai.com/research/scaling-laws-for-reward-model-overoptimization

x.com (he will update his tutorial to use codebases way more elegant than tensorflow)

Arthur W. Juliani, PhD (awjuliani.github.io)

SuperMemo Guru - supermemo.guru

===

PIECES PER SECOND (PPS) in tetris - I know someone who tracks them in response to sleep quality and modafinil

Strooper | Andy Kong => HE is totally the essence of the quantified self

That’s a really interesting mix of resources — definitely a lot to unpack there. Using video games as a tool for quantified self is a fascinating idea, especially since they naturally generate rich behavioral data. Tracking metrics like PPS in Tetris or reaction times in cognitive games can give some surprisingly good insights into attention, fatigue, and even sleep patterns.

The connection between reinforcement learning environments (like Gym Retro or Neural MMO) and human performance tracking is a cool overlap too. The same frameworks used for AI agents can be repurposed to measure and model human learning curves.

If you’re diving into this area, it might help to narrow your focus first — maybe pick one domain (e.g., cognitive performance, motor response, or decision-making under stress) and build from there. Quantified gaming can get overwhelming quickly, but the potential for self-analysis and experimentation is huge.

From my own use of a smartwatch and home pulse oximeter during COVID times, I learned they’re great for tracking trends, but they can’t replace lab tests or professional diagnosis. The best approach seems to be combining data from these devices with regular medical advice. For those interested in how personal health tools tie into broader healthcare planning and protection, https://premierpmi.co.uk/ has some practical information about integrating personal monitoring with formal medical coverage.

Key Insights

• Sleep efficiency rose from 90.4% to 94.5% (+4.1 pp)

• Average sleep duration increased from 7h 15m to 7h 26m (+11m)

• Wake after sleep onset decreased from 50m to 27m (−23m)

• REM sleep increased from 55m to 1h 05m (+10m)

• Deep sleep was slightly lower (1h 27m → 1h 22m, −5m);

• Average SpO₂ improved (96.5% → 97.4%)

I mostly need it for better sleep at night, occasional naps during travel, and blocking light when working irregular hours. Comfort and light-blocking are my top priorities, but breathability is also really important.

Tempur-Pedic-Sleep-Mask

https://www.amazon.com/Tempur-Pedic-Sleep-Mask-Size-Navy/dp/B0027OUUFW?

Contoured Sleep Mask for Side Sleeper

https://www.amazon.com/Contoured-Sleep-Mask-Side-Sleepers/dp/B0F67CS2X3?

There are so many options out there, memory foam, silk, cotton, adjustable straps — it’s hard to know which ones are really worth it.

Has anyone here found a sleep mask that truly helps them sleep better? Which ones stay comfortable all night and don’t feel too tight or hot?
I’d really appreciate hearing about your experiences and recommendations.

Thanks in advance!

Similar to my last update in this thread, while using the Divine API platform, I built an application rooted in Western Astrology. This software is a little different from my last update, though.

First, the Divine API is pinged using Python, with values representing myself (or anyone) such as the date and location of my birth. JSON is returned with positions of and other metadata about various celestial objects; looking something like this:

The frontend of my WIP application takes the saved JSON file and creates a unique UI for understanding interactions between the planet(oid)s. Among half a dozen other major features.

For example, the slider at the top center of the user interface moves the celestial bodies (graphed in the center of the screen) around the signs of the zodiac. It can auto-advance. Detailed information about each body is available upon being hovered over. Along with other interesting, interactive measurements and displays.

There is a lot more I could say about this application, but I think the screenshots and descriptions above speak volumes. I don’t know of anything else like this tool. More to come of this soon, probably.

On A Personal Note

Regardless of one’s perspective, there is value in seeing this kind of app in operation. This is the kind of real-time open source intelligence once exclusive to rooms filled with scholars, priests and kings.

With today’s tools, said information is available to essentially anyone, eventually to everyone. In such a way that those few who once had exclusive access, would downright envy.

The relevance of these projects to self-quantification is immense, in my opinion. Just to be able to play with astrological data like this, in a way that creates new meaning, is pretty cool.

My next step along these lines of thinking, if there is to be more, is to combine this application with my previously mentioned “AI Oracle” software. So I can get on-demand advice from a locally deployed LLM about specific (future, present and/or past) planetary positions without having to resubmit API requests. We live in amazing times.

Peter_Stakoun:

I recently developed a mobile app that I’ve been using to quantify my goals and track my personal growth. Although this started as something I was building because I wanted it for myself, it’s been valuable enough for me that I want to start sharing it and developing it further. I want to offer white glove onboarding to anyone who wants to try it out and work closely with the community to build this into something more full-featured and accessible. You can install the beta on iOS at and if you are interested in hopping on a call and working together to get it set up to match what you’re looking for, please feel free to shoot me an email at [email protected]. I am looking for as much feedback as I can get, so please reach out if anything! For anyone interested in creating or improving similar apps, exploring professional mobile app development solutions from https://www.cogniteq.com/mobile-app-development can really help with scalability and performance optimization.

That sounds like a great project, Peter! I really like the idea of using an app to quantify personal growth — it’s something a lot of people struggle to track consistently. Offering white-glove onboarding is a smart move too; it helps build trust and engagement early on.

I’ll definitely check out the beta and send you feedback after trying it out. It’s always exciting to see developers creating tools they actually use themselves — that kind of authenticity usually leads to better design and usability.

We’re still early in our journey, but we believe that understanding your body should be as natural as checking the weather. And we want to build this future together with a curious, engaged community from day one.

We’re launching in 2026 — but right now, we’re opening our waitlist for early supporters, testers, and anyone excited about the future of personal health technology.

Join the waitlist here: https://teliot.eu — and become part of shaping what’s next.

Thank you to everyone who’s supported us so far — this is just the beginning.

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