Open source components behind this

I’ve open-sourced the key pieces that power this system:

Claude OpenClaw Bridge https://github.com/ericblue/claude-openclaw-bridge

Memspan — persistent memory layer https://github.com/ericblue/memspan

MAC – Mac Agent Gateway https://github.com/ericblue/mac-agent-gateway

Habit Sprint https://github.com/ericblue/habit-sprint

There’s still a lot of experimentation happening in the OpenClaw ecosystem, but what’s exciting right now is how quickly people are building skills, integrations, and agent architectures on top of it.

Curious to see where this space goes next.

If you’re building agents, memory systems, or personal AI infrastructure, I’d love to hear what you’re experimenting with.

Earlier this week I shared a small local first pipeline I built to help me step back from the constant flood of AI news and find actual signal.

I decided to take it one step further.

I used to blog heavily from 2004 to 2012, and for a while experimented with LifeStreaming by curating links and articles I found interesting as I went. Over time that faded, mostly because the volume of information exploded.

AI Signal is an experiment in bringing that idea back using automation and AI assisted filtering.

Behind the scenes, it builds on the same framework I shared earlier:

– pulls discussions from communities I care about, starting with Reddit
– scores and ranks posts using real signals like engagement, recency, and depth
– runs a second pass analysis with explicit agent instructions to identify novelty, themes, and notable quotes
– publishes a short, readable digest instead of an endless feed

The goal is not to cover everything. It is to surface a small set of genuinely interesting stories based on the communities and topics I care about, and to make it fast to scan.

This is intentionally early, opinionated, and evolving. For now, it is an experiment in automated curation and AI assisted microblogging, and a way for me to blog again without turning it into another job.

If you are feeling the same AI news fatigue, you might find it useful too. This will also publish automated weekly summaries of the most interesting AI news every Tuesday.

If you want to build something similar, the core building blocks are open source and available in the original repo here:

https://github.com/ericblue/curated-post-digest

Feedback welcome!

Over the last couple of weekends, I ran into a problem that had been quietly bothering me for a while.

I’ve used ChatGPT heavily for years. Over time, it accumulated a lot of useful context: how I work, what I’m building, preferences, projects, and long-running threads that make future conversations faster and more relevant.

Recently, I started spending more of my day-to-day time in Claude Code. Almost immediately, I felt the reset. None of that history came with me.

Every new session meant starting over. Reintroducing context. Re-explaining projects. Rebuilding working memory that already existed somewhere else.

That was the initial motivation behind memspan.

The Problem

LLMs don’t persist memory across sessions in any meaningful, portable way.

Some platforms offer memory features, but those memories are siloed. Your ChatGPT memory stays in ChatGPT. Your Claude context stays in Claude. Each tool ends up holding a partial view of who you are and what you’re working on.

The result is fragmentation. You’re building parallel relationships with multiple assistants, none of which can see the full picture.

My initial goal was simple: move my identity and long-term context from OpenAI into Claude Code so I wouldn’t be starting from zero again. Once I started working on it, it became clear the same approach could apply more broadly. This wasn’t just a Claude problem. It was a portability problem across LLMs and agent workflows.

The Approach

memspan is a file-based, portable memory system for LLMs.

There’s no database. No service. No backend. Just structured files that you own.

It extracts identity, saved memories, and selected conversation history from tools like ChatGPT, organizes them into Markdown and JSON, and lets you load that context into Claude Code sessions when needed.

Everything is opt-in. Nothing is automatic. You decide what context gets loaded and when.

The system is built around a few simple principles:

• File-based: Memory lives in plain files you can read, edit, version, and back up.
• Portable: Not tied to a single platform or vendor.
• Selective: You load only the context that makes sense for the session you’re in.

A Simple Memory Model

To keep things usable without exploding token usage, memspan uses a three-tier memory model:

Core identity
Always-available context about who you are, how you work, and how you prefer to communicate.

Project or domain memory
Session-selectable context tied to a specific project, framework, or area of work. You load this when you want deep continuity.

Historical archive
Longer conversation history stored outside the prompt and retrieved on demand when needed.

This structure keeps the day-to-day experience lightweight while still allowing full continuity when it matters.

How It’s Used

In practice, memspan fits into a few common workflows:

• For quick questions, you load just identity so responses are tailored but lightweight.
• For focused project work, you load identity plus project context.
• As you work, the assistant can detect notable information and offer to save it for future sessions.
• Over time, your memory archive grows organically rather than being rebuilt each conversation.

The goal is not unlimited memory. It’s continuity without losing control.

Implementation Notes

memspan is intentionally simple.

At its core, it’s a collection of scripts and conventions:

• Identity files stored as structured JSON
• Memories stored as dated Markdown entries
• Optional project files and conversation correlations
• A small wrapper script that loads selected context into Claude Code using --append-system-prompt

It does not interfere with Claude Code’s native features and does not replace them. Context loading is explicit and session-scoped.

Status and Next Steps

This is version 0.1.0.

It solves the core problem I set out to address: making identity and long-term context portable across tools. There’s plenty of room to extend it further, including things like on-demand retrieval, semantic search, or different agent integrations, but those are secondary.

For now, the focus is keeping it understandable, hackable, and useful.

Closing

As LLMs become more embedded in daily work, continuity matters. Starting from scratch every session doesn’t scale, and vendor-locked memory creates more problems than it solves.

memspan is a small attempt to address that by keeping memory local, portable, and under your control.

The project is open source and available here:

https://github.com/ericblue/memspan
https://memspan.ai

At the time, I had no grand vision—just curiosity and a desire to document my thoughts and pursue my passion across a range of topics. Fast forward to today, and I’m both surprised and deeply honored to see how my work has found its way into academia. My writings and open-source projects have been referenced by researchers and featured in books, academic theses, research papers, art exhibits (even before I found my own way into art years later), and research projects across the globe—26 citations and references from universities and organizations across 11 countries.

These contributions have touched on diverse topics, including the Quantified Self, Knowledge Management, Mind Mapping, EEG (Electroencephalography), Sleep Behavior and Monitoring, Wearable Fitness Technology, Information Visualization, and Learning. It’s amazing and humbling to see how these projects have been used in so many different ways, inspiring further research and creative endeavors. Many of these articles and open-source projects were spur-of-the-moment, late-night or weekend tinkerings, while others were multi-year journeys exploring niche areas of tech that fascinated me.

Some of the research projects and published papers include processing EEG data to control prosthetic limbs, improving software development processes through mind mapping, exploring personal knowledge management with my multi-year side project (the Personal Memex), and even an art exhibit visualizing brainwave data.

Although I don’t blog as much these days—most of my writing now happens on social media—I’m still actively creating and sharing open-source projects. It’s incredible to see that the work I started years ago continues to be used and appreciated by others. My hope is that these contributions will keep finding new homes in unexpected places, inspiring and supporting others in their journeys.

To celebrate this milestone, I’ve published a summary and a list of all these cited works on GitHub. So, as I continue to create and share, I encourage others to do the same. Follow your curiosity, embrace your passions, and put your work out there. You never know the lasting impact it could have on people and projects you never imagined!

Github: https://github.com/ericblue/my-cited-works

This past weekend I spent time diving deeper into OpenAI’s GPT-4 Vision capabilities. I figured this would be a good excuse to blend my interests in software development, AI/LLMs, and artwork. I wanted to see how quickly and effectively GPT-4V (gpt-4-1106-vision-preview) could analyze artwork given an uploaded image. I primarily wanted to do this for experimenting with my own artwork, but also interested in discovering what type of critique and feedback it would give on famous artist’s work.

For this project, GPT-4 is really doing all of the heavy lifting. With a carefully-crafted prompt, getting analysis feedback in a standard JSON format each time, I was able to reliably get back data that could be assembled into a very basic UI. I decided to use Python and Streamlit (https://streamlit.io/) to build a basic prototype, and released the code as open source to Github.

Check out this quick overview video:

The Art Analyzer App

Art Analyzer is an app that uses GPT Vision (See: OpenAI Platform) to identify artwork from images and AI language models like GPT-4 to provide detailed critiques of paintings, drawings, and other visual art forms. Users can upload an image of a piece for review, and the app will generate an analysis of the artwork covering composition, use of color, brushwork/texture, emotional impact, originality/creativity, and recommendations of similar artists and paintings.

This app is available to download and run locally, and also published at Streamlit – https://artanalyzer.streamlit.app/. An OpenAI API key is required to run and requires a subscription. You can obtain your own key at https://platform.openai.com/api-keys (Note: this key is not saved in *any* way, and after page reload will have to be entered again).

For quick examples, see:

• Vincent van Gogh
• David Hockney
• John Atkinson Grimshaw
• Eric Blue [me]

Van Gogh’s – A Starry Night

The Results – Summary, Critique, Similar Artists, Similar Paintings

Prompt Tuning

Along with the heavy lifting of this app coming from GPT-4 Vision APIs, the other secret sauce is the prompt used to analyze the image. Interestingly enough, the creation of this prompt was assisted by ChatGPT. What started as a question, turned into a complex prompt. Unfortunately with the complex prompt and in turn complex output, there was an easy way using this method to parse the output. So, going back to ChatGPT again to fine tune, I asked for the prompt output in JSON format.

What we effectively have here is a JSON definition of the output, with mutliple prompts included in the JSON itself. I did notice that periodically the results would have additional commentary at the end, so I included some verbiage in the prompt to ONLY include JSON as output. I think this approach is very powerful.

Closing Thoughts

Can we really use AI as an art critic or expert? Yes and No. This app is a fun experiment to see how far along GPT Vision has come, and the results are generally very good and surprisingly detailed. But, if you are looking to analyze and critique artwork in a more official capacity feel free to defer to art historians, experts, and enthusiasts.

For the time being, this is a great showcase of the power of GPT Vision and LLM (Large Language Models). I can see many use cases where this sort of data and knowledge aggregation can be used to do analysis, research and incorporate this into personal workflows for creating new works of art.

This quick app was created and released as open source. Hopefully this example be expanded and also spark some new ideas for others.

Links

GitHub – https://github.com/ericblue/art-analyzer/

StreamLit Demo – https://artanalyzer.streamlit.app/

You can also check out more of my own artwork at https://www.instagram.com/ericblueart/ and https://ericblueart.com/.

It’s been quite a while since I’ve blogged, or published an open source project! With ChatGPT taking the world by storm, and finding myself using it daily, I decided to do something I’ve done before – opening up an SMS interface using Twilio.

When you’re on the go and just need a quick interaction, nothing beats sending a text message and getting a quick response.

ChatGPT-Twilio-Java – Provides integration with ChatGPT via Twilio allowing text message (SMS) and voice interactions with ChatGPT’s AI chatbot

This web application, written in Java using the Micronaut (https://micronaut.io/) framework provides API endpoints to support both SMS and Voice interactions via Twilio (in progress). And uses OpenAI’s APIs and supported engines (text-davinci-003) and models (currently tested with gpt-3.5-turbo).

This app has also been released as a Docker image and tested on Heroku.

Check out:

https://github.com/ericblue/ChatGPT-Twilio-Java

I particularly like Granville Redmond (who I posted about back in Oct when I discovered him) – born in PA, moved to CA and the LA area (and have seen a focus on him in the Laguna art museums – right down from where I live now). He was famous for both Impressionism and Tonalism styles and focused on California landscapes.

I’m going to make this the focus of my art style for the next month or two and see where it goes. These are the last 6 pieces I’ve done – and the last one (top left) is a study and variation on Redmond’s Moonlit Pond painting. The others are originals (one, inspired by another tonalist painter Charles Warren Eaton.

I spend my work day and virtually all my time in front of the computer. I consider this a great break – and ironically although these works are all digital (and some eventually turned into canvas or wood prints) – this feels like a relaxing break from the day to day tech work.

This is a good overview of Tonalism:

Tonalism is rooted in the French Barbizon movement, which emphasized atmosphere and shadow. The Tonalist style employs a distinctive technique by the use of color’s middle values as opposed to stronger contrast and high chroma. Resulting in a understated and compelling overall effect. The tonalist subject matter is never entirely apparent; their is no effort to communicate a message or narrate a story. Instead of relating a story, each sensitively chosen color, composition, and line is arranged to create an intriguing visual poem.”The interiors of tonalist paintings are generally elegant and sparsely decorated, tonally uniform, simplified and indistinct; the figures are usually presented alone in silent contemplation. Landscapes are typically luscious and luminous with evocative atmospheric effects featuring misty backgrounds illuminated by moonlight. Tonalists painters were drawn to both the natural and spiritual realms. They sought to awaken the viewers consciousness by shrouding the subject in a misty indistinct veil of emotionalism. The palette is minimal, characterized by warm hues of brown, soft greens, gauzy yellows and muted grays. Preferred themes were evocative moonlight nights and poetic, vaporous landscapes. Tonalist painters seemed to favored unconscious states and psychological experiences over reality.”

http://www.buildart.com/secret_of_tonalism.htm

Some more of my artwork is frequently posted at https://www.instagram.com/ericblueart/

For me, tracking data related to my overall health & fitness has been a long and amazing journey; starting off almost a decade ago in 2009 with my Total Recall post (http://eric-blue.com/2009/10/18/total-recall/), my idea on fitness tracking after waking up from a dream (http://eric-blue.com/2010/08/14/fitness-tracking-platform-personal-informatics-for-fitness/) and ultimately building a health & fitness data aggregation company – ActiveOS – http://activeos.com.

I’ve written every few years and given an update on my current tools, areas of focus, and discoveries I’ve made since I’ve been immersed in Quantified Self. Where I am today (2019) will be the focus of a more in depth future post, but for now I wanted to publish a snapshot of knowledge I shared back in 2015 on Quora (my areas of focus and discoveries are still very much in alignment since I originally authored that piece).

Some Background

I’ll try not to write a novel here.  But, some background context is helpful.  It’s been about 5 years since I immersed myself deeply into the Quantified Self movement.  I jumped in head first starting in 2010 and since have been very active in the community, global conferences, and helped start the Los Angeles QS Meetup group.  But, my entry into Quantified Self and eventually re-igniting my passion for health & fitness was an accident.  Since it all began I’ve learned quite a bit.

For many now, “Quantified Self” has become somewhat of a buzzword and is sometimes perceived as being synonymous with the wearables movement (tons of apps and sensors tracking virtually every aspect of your life).  It is that, but much more.  In terms of the genesis of QS and my experience with it, it started off as a very personal quest.  In 2010 I was very interested in knowledge management, learning, life logging and leaning very much towards introspective self-tracking (more knowledge, less action).  I was also in the worst physical shape of my life, and suffering from some serious back pain and flareups that was from an injury many years before.  Overweight and frustrated at the time, I first heard about Fitbit and bought it out of shear curiosity and had the idea that I would get a better understanding of my activity patterns and start building some better activity habits (morning walks, going to the gym more, getting up from the desk and moving more often, etc) so I could pull myself out of the seemingly  never-ending  injury cycle.  This started my shift of focus towards improving my health and ultimately tracking many areas of my life (sleep, nutrition, stress levels/mood, location, etc).

Much has happened in the 5 years since.  I’m now in the best shape of my life, my recurring back issues have been kept at bay (just a couple issues in the past 3 years), and I’m now training for a Tough Mudder in Oct (a 12 mile run and intense obstacle course that is grueling to say the least); something I never would have thought possible 5 years ago.  You could argue that all of this could have been possible with out a Quantified Self mindset/paradigm or the multitude of fitness sensors and apps.  But, I view all of these as support system that allows you to raise awareness of what’s going on in your life and to eventually help build healthy habits so actions become second nature.

Self-Tracking  / Fitness Pics Along the Way

What do I actually track?

Pretty much everything? No, really.  I do go in cycles of what I focus on but here’s the short list:

– General activity (steps per day, calories burned)
– Nutrition – detailed meal tracking on a daily basis and a focus on my macro-nutrient ratios (carbs vs. protein vs. fat)
– My resting heart rate on wake and through out the day
– Heart rate variability (a very useful tool for measuring stress and gauging impact from workout intensity)
– Location tracking – I’ve tracked pretty much everywhere I’ve been in the range of 5-30 second intervals since 2010 (using a dedicated gps sensor, various tracking apps, and logging places using FourSquare)
– Weight – lbs and also body composition (fat vs. lean muscle mass)
– Workouts – detailed workout logs for strength and interval training.  And, sensors tracking my works – from Fitbit and Garmin to Hexoskin where I’m tracking volumes of biometric data (heat rate, respiration rate, acceleration, location, etc)
– Blood work – I get my own blood tests about every 6 months and have been tracking various markers since 2009
– Sleep – I just do bed/rise time tracking now, but for over a year I did nightly sleep tracking using a Zeo so I could track sleep phases (Deep vs. REM vs. Light Sleep)
– Blood Pressure – I sporadically take my blood pressure… maybe once or twice a month
– Memories/Travel Journal – Back in 2008-2009 I built a semantic travel journal/diary that detailed every vacation and trip I had taken since 1995

What Have I Learned? / Top 10 Improvements

It’s hard to compress 5 years of  “in the QS trenches” into a top 10, but I’ll do my best.  Actually, I tend to think of this all in terms of what I’ve learned from the QS process (how I approach self-tracking) and the individual areas of my own life that I’ve improved.

1. I’m much more active in terms of general activity than I was before tracking – Now, you’re probably thinking how could I know how active I was before wearing some sort of pedometer like Fitbit.  Great question.  If I had to guess, over a period of a few years I was probably averaging between 3,000 – 4,000 steps per day (now that I have years of data I know what an average ‘work day’ activity pattern looks like).  Like many, being a knowledge worker and desk bound most of the day you spend time tied to a desk and slouched over in the ‘praying mantis position’ at the keyboard.  Since tracking my general activity, I go in cycles by try to aim for at least 8,000 – 10,000 steps per day. 
2. I’m a sleep mutant and use it to my advantage – Since tracking my sleep for over a year using the Zeo (now defunct, but a great product which was a single-channel EEG that measured brain activity while you sleep).  I actually discovered that I get between 25-33% more REM and Deep sleep than the average person EVEN with sleeping less hours.  So roughly speaking if I get 6-7 hours of sleep I’m getting as much quality sleep than somebody who sleeps 8 or more.  I spent some time trying to find the underlying cause, even switched out the Zeo and used different headbands and saw the same result.  It’s still a mystery, but I’m not complaining.  I generally wake on my own, feel well rested and am probably in the 90+th percentile in terms of great sleepers.  I’ll take it.
   
3. I still struggle with diet and food, but have found tracking my food to be beneficial.  Food is my weakness. I LOVE to eat.  I always say that if I didn’t workout so much and pay attention to what I eat, I’d easily be *a lot* bulkier.  Tracking all the meals you eat sounds painful and annoying.  Honestly, it can be sometimes.  I used to go in cycles where I would track my calories/macronutrients for 4-8 weeks at a time and give up.  I’m now going on doing this for a year straight and have a much better understanding of my eating patterns and what types of foods I eat that better fuels my performance.  Having tried many styles of diet (zone, paleo, low carb, intermittent fasting) and sometimes having an unhealthy focus on not eating enough (staying in a calorie deficit for way too long) I am now eating properly and in a way that fuels my performance.  I’ll more than likely stop doing this at some point and build strong habits so what I eat becomes second nature.  But, this process has been eye opening in terms of what I eat and how it impacts my sleep, stress and general performance.
   
4. Heart Rate Variability is both intriguing and very beneficial.  I won’t go into the details on HRV, but in a nutshell is a somewhat well studied measure of stress (physical+emotional) and higher HRV generally = good.  I’ve tracked HRV now for a couple years on a daily basis and use this as a tool to see how my nutrition (bad diet or even alcohol can effect HRV), sleep and workload impact my HRV.  Generally even a 5-20 point dip from one day to the next really is indicative if I feel sluggish or drained.  Heavy strength training and in particular deadlifts are taxing on your central nervous system, and even activities like HIIT, sprints and races have an impact.  I generally use my HRV score to gauge how I approach workouts – I sometimes tend not to hold back or skip workouts, but if my HRV is low I’ll proceed with caution.
   
5. I have a more balanced approach to my fitness training – After a lot of research into various workout and training regimens and trying to understand why I kept having years of back flareups I took an analytical approach to my training.  It sounds simple but I had *major* imbalances between my upper and lower body.  I was always generally strong with upper body – bench pressing and push-ups.  Until I took a closer look I didn’t realize how bad it was.  I was ~ in the 80-85th percentile in terms of upper body strength and barely in the 40th percentile for lower body (squat and deadlift capability).  Coupled with weak areas like tight hips and low glute activation, this was a recipe for major recurring disaster with my training.  Fast forward 5 years and my upper body strength has just improved slightly (no major need to drastically change there), but my lower body has improved to where I’m now at least in the 60-70th percentile in terms of strength standards.  In the past year I’ve also taken a very holistic approach to my training and have incorporated quite a bit of cardio and HIIT along with my strength training.  I’m a huge fan of the concept of concurrent training or being a ‘hybrid athlete’ – the best of both worlds; finding a balance between being strong and well conditioned.
   
6. I have a somewhat uncanny memory that I never realized I had before and it’s improved with practice – after spending time working on my travel journal, collecting photos from a 15 year period and writing down all of the places I’ve been, I realized I have a knack for recalling dates and times.  My memories seem very much anchored around this and I can quickly recall dates, places, conversations.  Ask me a date and I can often surprise or annoy with the detail.  I think the act of creating the travel journal for the 10-15 year period actually significantly strengthened this ability and made my memories even more crystal clear.
   
7. I never have to worry about remembering where I’ve been or traveling – This almost sounds like the exact opposite of #6.  I actually got into automatic geolocation tracking as a result of not wanting to have to write everything down immediately after I went on a trip.  I figured I could automatically collect data in real-time with little to no effort and analyze at a later date when it was convenient.  I was inspired by Gordon Bell from his book ‘Total Recall’.  He carried around a small high-precision GPS sensor that tracked his latitude/longitude every 5 seconds.  I took a similar approach and bough a dedicated device (QStarz GPS) and carried it around with me for 4 years.  Since then I’ve been able to write software to analyze all of the places I’ve been (restaurants, trips, average speed in traffic, etc) and have found some interesting patterns.
   
8. Tracking my blood work and understanding panels and reports has helped improve my health – Sure.  Doctors can do this for you, and you definitely should consult a professional for serious issues with your health.  But, it’s worth spending some time understanding key metrics like cholesterol, micronutrient levels and deficiencies and even healthy hormone ranges to see how you’re doing.  Since tracking in 2009 I’ve improved my blood glucose and cholesterol (usually somewhat higher even with healthy diet and life style), resolved some vitamin and micronutrient deficiencies, and helped improve my hormone levels naturally with diet and effective natural supplementation (zinc, magnesium, creatine, vitamin D, etc.)
   
9. Keeping track of your ‘personal bests’ helps immensely.  This is more of a fitness thing than Quantified Self approach.  But in a 5 year period I moved from being in a sedentary and very unfit state to now operating at a fairly high level in terms of strength and activity.  I started off with just tracking my morning walks and didn’t want to ‘break the streak’.  Once I started feeling better (great actually from walking) I started picking up a better habit of going to the gym.  2 days a week turned to 3 and eventually turned into 5 or 6.  I tracked my max # of pushups to 5K running times, bench press and deadlift records and now tracking my workout pace doing intense weekend workouts on the Santa Monica stairs.  I’ve learned to keep pushing myself little by little, gradually over time and look to keep improving and beating my own records.
   
10. Data is meaningless without comprehension and action.  This is coming from a self-professed technology and data geek who owns virtually every health & fitness tracking sensor out there and is building a business and platform in the space for doing health & fitness data aggregation & analytics.  Data and awareness of what’s going on is the 1st step in understanding.  Many people don’t always make the leap from being in data collect mode towards a ‘Now what can I do with this data and information?’ mindset.  Technology will help with this (finding the signal in the noise, finding the needle in the haystack, etc.).  But, at the end of the day data will not do the hard work for you.  You need to get up and move, build healthy habits and take massive action every day. 

The takeaway and issues?  This is something that will be different for each individual.  But, my advice would be to be curious, start small with what you’re interested in improving in your life.  Find ways to collect data (using sensors, wearables or apps) that are painless and ‘just work’.  Keep collecting, but also always keep action in mind.

Original Quora Post: https://www.quora.com/What-are-the-top-10-improvements-youve-made-as-a-result-of-being-immersed-in-the-Quantified-Self-movement

Blogging seems to be a thing of the past for many – our lives have been taken over by Facebook and other social media sites (myself included). And, these have been the launching points of our social connections, musings and where our original content is centralized. I’m not sure that’s going to change anytime soon. But, I think there is still something to be said for being able to maintain your own online presence – Not only having some control over your personal brand, but having the opportunity to get back to creating more in depth, personal content; it’s length and focus might be a little too content heavy by most standards due to our limited attention spans these days. But, now more than ever, I think it’s important to be able to continue to share personal, in depth writings and keep a curated history of ongoing thoughts, interests and projects.

It’s interesting to look back at my personal bios from 2011, and then again back in 2004. My interests have shifted, of course, over the last decade since I dove into my 2nd major phase of blogging in 2008 (with lots of content on my big 3 concerns – technology, philosophy, and personal development). Will I continue to write about those things? Possibly. But, I’ve also carved out considerable mind share since then focusing on health & fitness (I’m a workout fanatic in such a way that my self from 2008 couldn’t have imagined), entrepreneurship, creativity & flow, sensor technology. And, who knows the next ‘big thing’ coming up. Here’s to 2019 and seeing where the next decade of content takes us!

Traqs, a platform company specializing in health & fitness data aggregation and insights, announced today its rebrand to ActiveOS. The rebrand aligns the company’s vision of providing powerful health & fitness tracking solutions to the mainstream, and also recognizing the emerging need for real-time and 24/7 group and enterprise-level solutions in the growing wearable and sensor markets.
 
http://www.prweb.com/releases/2016/12/prweb13912369.htm