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 MyWindSock

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

Revopoint Inspire 2 – 3D Scanner

paschmann

3D scanner has intrigued me for a couple of years. The lure of being able to quickly and easily recreate something from a physical scan seems like the holy grail of self sufficiency and reducing your reliance on manufacturing or availability. 1.) With a couple of bike related projects that I want to tackle, 2.) the prices of the devices falling easily with the reach of consumers and 3.) the reasonable accuracy you get thanks to the underlying technology – it seems like it was the right time to get started.

Firstly – the device has been awesome, doing exactly what I anticipated and expected. Albeit a slightly cumbersome and slow learning process to understand the nuances of these devices. It is not the technology but rather the workflows involved to turn a “simple” scan, into a “simple” digital representation. There are a variety of steps in different tools and software, and the reality is that, as always, the devil is in the details. read more

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 AGI

The 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.