Author: paschmann

LinkedIn: The Science of Push

paschmann

Probably not the final cover, this is a Photoshop edit with Gemini background.

Probably one of the most nerve wracking things I have ever done is made this post. It was in my “drafts” for a while, the post went through a bunch of variations and changes, but “promoting” myself is something I have never enjoyed doing. How do you share something you have spent a considerable amount of time and passion on, without sounding arrogant. How do you share something in today’s age, without it sounding like it was created by AI, or is it even relevant if it was, or in some peoples eyes, wasn’t? Never the less, I hit the share button and basically nothing happened, which is what I was hoping. My plan was always to simply stake a claim. Put it out there to the world I had done this research and think it would be valuable to share. It was also a line in the sand for me to move my ass and get this thing completed … hopefully it does not take six more years to get it there. read more

LinkedIn: Mobile & Agentic AI

paschmann

Seeing all these posts about #SAP hashtag#TechEd makes me think …. remember when mobile access was a “nice-to-have” for select employees? Road Warriors, C-Suite, Field Techs …

Those days are over.

As we enter the era of agentic AI, every employee is becoming an orchestrator … managing teams of AI agents, approving tasks, and overseeing processes that never sleep. The definition of productivity now means directing AI operations, not just GSD. These processes don’t wait for you to return to your desk.

Enterprise mobility isn’t about convenience anymore—it’s about keeping pace with AI-speed business.

The question isn’t whether to invest in this trend. It’s whether you can afford not to.

What’s your organization doing to prepare for this shift?

#EnterpriseMobility #AI #FutureOfWork #DigitalTransformation #Productivity read more

Cycling CdA Analyzer

paschmann

I’ve been a BestBikeSplit user for years now. It’s been great for predicting race times and power requirements beforehand, and for analyzing my CdA after races. But at $20 per month, the cost started to feel steep for how often I actually used it.
That’s when I decided to create my own version using Claude. The project works surprisingly well, with results that match up closely with other paid subscription tools.

Check it out on Github here:

GitHub – paschmann/CDAAnalyzer: CDA Analyzer similar to Best Bike Split or MyWindSockCDA Analyzer similar to Best Bike Split or MyWindSock – paschmann/CDAAnalyzer

2025 – Ironman 70.3 – North Carolina

paschmann

4 years in a row! A pretty solid training block going into this race despite some last minute travel to Germany for work.

2025 NC HIM Goal Plan vs Actuals
– Swim: 26:00 (Actual: 25:55, 140th OA)
– T1: 5:00 (Actual: 4:30)
– Bike: 2:10 (235W, 245Nor AVG < 160BPM) (Actual: 2:19, 150HR, 234W Avg, 239WNor, 18th OA)
– T2: 2:00 (Actual: 1:30)
– Run: 1:29 (6:35 @ 168bpm) (Actual: 1:39, 8W, 159HR, 60th OA)
– Finish: 04:20:00
– Result: #3 AG, 20 OA, AG Winner 4:15?, OA Winner 3:59?
– Training Load:
– CTL: 75 → 98 (Peak) 99 (Race)
– Bike Load: 46 (Peak) 45 (Race)
– Run Load: 35 (Peak) 36 (Race)
– Biggest Week: 13.1 Hours, 864 TSS
– Recovery Week: ~9 hours
– Avg Week: ~12 hours

A Definition of AGI

paschmann

An interesting paper which outlines a model for quantifying the concept of AGI, which is useful in determining the advancement of AI solutions.

A Definition of AGIThe lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today’s specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly “jagged” cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.