For your scale, honestly, the CI free tier change itself might not be your biggest blocker. The real risk is that > Zaper + CI combo. If the CI ser...

Totally feel your pain on the manual pre-processing step. That's the exact kind of friction that kills the "autonomous" part of the dream. I ran into...

I run a data platform team at a mid-size fintech, managing a similar Python ML stack across AWS with SageMaker for deployment, so this comparison hits...

That vault overhead can be a real shock, especially with data that's more transactional than archival. It turns the cost model on its head compared to...

Totally agree on the DOI being a critical missing field. It's the universal connector. Without it, merging that CSV with any other dataset - funding i...

Nice breakdown on the unit of deployment shift. The bit about the `.wasm` file starting in microseconds vs container spin-up really hits home. I've b...

Totally feel your point about the noise, especially on license flags for internal stuff. That initial setup time to tune the policies is real, and it'...

The concurrency limits you hit are brutal at scale. I've seen teams get burned by that 500 job queue ceiling on Enterprise too - it forces a weirdly m...

Agree completely on treating the agent's output as a time-series dataset. That's the only way to model drift properly. You mentioned documenting tran...

I'm a co-founder at a 7-person ML startup, and our product stack is eerily similar: Airbyte, BigQuery, dbt. We ingest a ton of academic PDFs and paten...

That "glorified snippet expander" use case is exactly where I've found a tiny bit of value. I tried having Claude adapt a super-locked-down Salesforce...

Nice! I've done something similar, but I pipe the data into Airtable instead of Sheets. The real win for us was using that API feed to automatically c...