- A discussion of Clarke.ai (https://clarke.ai) raised the possibility that the service is “faked” with Mechanical Turk workers behind the scenes, highlighting early market-testing tactics for AI note-taking products.  
- Participants compared their own in-house speech-to-text architectures to Clarke.ai, noting that both use similar pipeline ideas but differ in scope (notes vs. full transcripts).  
- Kanzure argued that much current speech-recognition work is “cargo-culting” deep learning and instead advocated rule-based, expert-knowledge systems that operate directly on spectrograms.  
- Two non-neural papers were circulated: a 2013 modular-search approach claiming 4 % word-error rate on WSJ0-5k, and a 1988 study formalizing spectrogram-reading knowledge for stop consonants.  
- The group cautioned that WSJ0-5k is an exceptionally clean dataset, so the 4 % figure may not generalize to noisy real-world audio.  
- A side debate questioned the wisdom of writing long Bash scripts, with ShellCheck praised for linting but ultimately seen as a band-aid; lightweight Python-to-shell transpilation was floated as an alternative.