Author: paschmann

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

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 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.