Looks cool! It's a lot of work to get a full data stack set up and people are losing interest in stitching the pieces (ETL, warehouse, BI) together.
> Agents made bad inferences because they had no context on the business
We've been working on this since before the chatgpt launch.
We started with a semantic layer since there were already good open source options and LLMs at the time were good at writing the JSON (remember function calling?) to run a semantic query.
But as LLMs have gotten smarter and people wanted to do more data work in agents, we found we needed something more flexible, so we built an "Ontology" that lets you store all the terms you use in your company and connect them to the data points (e.g. tables, columns, metrics) that matter.
all of our customers are using Anthropic APIs for programmatic use. Codex and other providers let you use Oauth. But inside of a sandbox, you can technically use max plan since it is the same as using Claude locally.
Can you provide some info on how to use the max plan / ChatGPT pro subscription via OAuth? I was about to subscribe but wasn't entirely sure how to configure that
students get a couple free years of a Google One subscription which comes with access to the nicer models and some free claude credits to use inside Antigravity IDE. Though there's a small chance I'm wrong and everyone has access to those models.
The free tokens is the only reason why I've been using Antigravity. And after this god awful update (I had the exact same scenario as the article author) and once my free student plan runs out, I'm immediately switching to something else.
Makes me wish we could regulate these big tech companies and run them big fines whenever they abuse their monopoly by unnecessarily downgrading the experience for users. I hate how all their stocks continue to climb despite headline after headline of new updates breaking things for users. It's like they can just do whatever they want and never get punished for it meaningfully.
That was a surprising sentence. But, Antigravity is fine. I mean, I only open it because it's the only way Gemini is going to get tool use right on the first try, but it works like all the other VS Code forks (acceptable, not great). I don't mind using it, and if Google AI is your one AI subscription, then Antigravity is obviously the editor to use, since Gemini fails to play well with others.
Does this mean I can finally connect to a ducklake instnace hosted remotely? i.e. DuckLake is writing to disk on the remote server and my client is just a client.
Because rn even with Postgres as a catalog my client needs access to the underlying storage to use Ducklake.
Yes, Quack resolves this problem. In particular, your client (likely a DuckDB instance) will talk to a remote DuckDB that both has access to the underlying storage and can also serve as the catalog itself.
I think that Quack will become the primary option for a DuckLake catalog in the future, for several reasons. To list a few:
1. No type mismatches for inlining. If you use a non-DuckDB catalog, many types do not have a 1:1 mapping, which introduces additional overhead when operating on those data types.
2. You get the raw performance of DuckDB analytics (and now transactions) over the catalog. DuckDB reading DuckDB is simply faster than any of our Postgres/SQLite scanners.
3. No round-trip for retries. We can easily(tm) run the full retry logic on the DuckDB server side. Right now, these retries trigger multiple round trips for Postgres, making it a performance bottleneck for high-contention workloads.
We previously had our (https://www.definite.app/) agent write reports / dashboards in a YAML spec that would get rendered by our frontend framework (i.e. charts and tables).
For example, user says "build a report with revenue and orders by month and show 100 most recent orders". The agent would write a spec that would get rendered by our frontend.
This runs fast, but we were drowning in feature requests for what the framework could render (e.g. "I don't want labels here", "I DO want labels there", "can this chart be a heatmap", etc.)
A few months ago, we let the agent just write HTML instead. It takes longer to generate, but you get unlimited customization.
There are a host of issues with the new approach (non-technical users debugging a monstrous app they created), but net-net our customers like it much better.
I completely understand a "people who give a shit stick around" mentality if you work there, but you can't expect users who run a business on it to stick around if it's broken.
Correct, sorry I thought this was pretty obvious but in retrospect maybe not.
I'm not encouraging Mitchell to stay, I'm saying that my version of his post is about _me_ staying to make a brighter future, and adding my context on why I still believe that.
And finally I closed with "I hope we win you back" to be extra clear about it!
The problem is that you decided the correct place for your “version of his post” was in the comments to his version of his post. This rhetorically implies you’re offering your version as an alternative to his, and it explicitly sets up your version as a comment on his version.
And then you claim your version is only about you. Why post it as a reply, if that’s true? It would be off topic. But it isn’t off topic; because actually you are talking about OP in addition to yourself.
I don't like the "I canceled my x subscription" hype posts, but I did cancel Figma today. We've barely used it in months and this was the nail in the coffin.
> Agents made bad inferences because they had no context on the business
We've been working on this since before the chatgpt launch.
We started with a semantic layer since there were already good open source options and LLMs at the time were good at writing the JSON (remember function calling?) to run a semantic query.
But as LLMs have gotten smarter and people wanted to do more data work in agents, we found we needed something more flexible, so we built an "Ontology" that lets you store all the terms you use in your company and connect them to the data points (e.g. tables, columns, metrics) that matter.
https://www.definite.app/blog/ontology-ai-analytics
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