Kappa90

Thank you, if you happen to try it out or have ideas, feel free to contact me at: em at dinobase dot ai

Right now schema changes create new columns, I’m working on reconciling old columns, which right now are not dropped.

The annotation/semantic layer agent creates a new description of the schema on sync, which represents the current state, but that includes stale columns as of today, data is not dropped.

I’ll implement automated schema migrations in the next week or so!

It's not explicitly stated in the benchmarks README, good catch.

80% of the benchmark questions are aggregations, 16% are multi-hop, 4% are lookups/subqueries.

Multi-hop is where LLMs struggle the most (hallucinations, partial answers), and aggregations is where you get the most token efficiency, since you skip on pagination which you need with APIs/MCPs that don't provide filters.

DuckDB exposes Postgres SQL, and most coding LLMs have been trained on that.

Of the small models I tested, Qwen 3.5 is the clear winner. Going to larger LLMs, Sonnet and Opus lead the charts.

Hey, one of the devs working on PostHog AI here.

The main reason why we advocate for a single-loop is because subagents suffer from the telephone game problem: context is lost when the interface is just an agent-to-agent prompt.

We are still working on subagents for parallelization, but if you can stick to the main loop, everything works much better.

Used Copilot to write a user auth migration script. It silently reset 2FA settings for ~3k users with OAuth accounts because it didn't handle NULL vs empty string edge cases in our legacy DB schema. Classic "garbage in, garbage out" situation.

Found out two weeks later when angry users couldn't log in during peak hours. Damage: 4 hours of downtime, one very grumpy security team, and a new "no AI for auth code" policy.

There seems to be a peak in traffic I guess due to HN, apologies! If you have any further problems it would be really helpful if you'd let me know at [email protected]

Indeed, there is no other tool out there automatically linking documents for you, as far as we know.

The idea is that Saga helps you resurface connections and ideas without having to do the manual work yourself.

This allows for a more serendipitous discovery of knowledge while you're crafting content or researching a topic.

Areas where we see great promise are: - analyzing interviews and notes for product and user research - automatically generate company or support wikis from docs - discover insights while writing meeting/team notes and documentation

Currently beta testing realtime collaborative editing and we'll start pushing towards integrating with other tools after that.