areddyfd

5 ms·

1.
by areddyfd 8mo ago
How we think about it? It asks clarifying questions before generating anything. It explains every step, what it’s doing and why. It keeps humans in control of execution. Counterintuitive truth is that users trust it more when it slows down.
2.
by areddyfd 8mo ago
While analyzing data you tweak logic, redefine metrics, and try again. Yet we often run this entire process directly on full datasets. You usually don't need all the data Early on, you’re testing logic, not scale and a small, truly ran
3.
How do you validate AI-generated data transformations before prod?(yorph.ai)
3 points by areddyfd 8mo ago|1 comments
4.
by areddyfd 8mo ago
The hard part about analyzing data isn’t generating a a series of analysis steps, it’s proving it’s correct. Our current approach: - Sandboxed / sample runs on smaller datasets before full execution - Step-level transparency: summaries
5.
by areddyfd 11mo ago
Good question - Currently our approach to semantic layer creation is based on source data, interaction with the agent, and publishing workflows. We recompute the semantic layer every time a user takes an action that fits in those categories
6.
Show HN: Yorph AI – a data engineer in your pocket(yorph.ai)
6 points by areddyfd 11mo ago|1 comments
7.
by areddyfd 1y ago
www.yorph.ai
8.
Lessons Learned Building Reliable Multi-Agent Systems(youtube.com)
4 points by areddyfd 1y ago|2 comments
9.
by areddyfd 1y ago
We are getting ready to launch our agentic data platform and wanted to share what we think are the most important things we've learned. Turns out that building a reliable agentic system is largely about good engineering fundamentals an
10.
by areddyfd 3y ago
Damn interesting proposition considering that the folk might just feed in all sorts of data into chatgpt without considering the implications