Use kimi 2.6 for planning and a cheap model (preferably local) for execution, and then kimi once again for reviewing it. Then finally I review the code. Saves a lot on tokens.
126 karma · joined June 4, 2015
In general, the goal is to use an org-wide installation method wherever possible, and record the identify of the user we are impersonating when ingesting data in the ACL. There are some gaps in the permission-gathering step in some of the connectors, I'm still working on fixing those.
Typical RAG implementations I’ve seen take the user query and directly run it against the full-text search and embedding indexes. This produces sub-par results because the query embedding doesn’t really capture fully what the user is really looking for.
A better solution is to send the user query to the LLM, and let it construct and run queries against the index via tool calling. Nothing too ground-breaking tbh, pretty much every AI search agent does this now. But it produces much better results.
I haven't directly compared against Elasticsearch yet, but I plan to do that next and publish some numbers. There's a benchmark harness setup already: https://github.com/getomnico/omni/tree/master/benchmarks, but there's a couple issues with it right now that I need to address first before I do a large scale run (the ParadeDB index settings need some tuning).
Currently permissions are handled in the app layer - it's simply a WHERE clause filter that restricts access to only those records that the user has read permissions for in the source. But I plan to upgrade this to use RLS in Postgres eventually.
For Slack specifically, right now the connector only indexes public channels. For private channels, I'm still working on full permission inheritance - capturing all channel members, and giving them read permissions to messages indexed from that channel. It's a bit challenging because channel members can change over time, and you'll have to keep permissions updated in real-time.
A platform for consulting aspirants to practice business case interviews.
Finding case prep partners is a major pain point for B-school students/consulting aspirants. Fortunately, frontier AI models are now good enough to function as surprisingly competent case interviewers.
That said, let me just go ahead and share the obvious: https://xkcd.com/936/
When you press backspace, the focus shifts to the previous cell, deleting the contents of the current cell. When you've entered < 5 characters in the current row, the focus is on the next empty cell. So the first backspace only takes you back to the previous cell, and the second backspace clears the last entered character.