mritchie712

tldr: this caches your S3 data in EFS.

we run datalakes using DuckLake and this sounds really useful. GCP should follow suit quickly.

I think this is a move to get business users paying consumption (per token) pricing for codex instead of a flat rate.

Hannes (one of the creators) had a pet duck

what's the use case for cortex? is anyone here using it?

We run a lakehouse product (https://www.definite.app/) and I still don't get who the user is for cortex. Our users are either:

non-technical: wants to use the agent we have built into our web app

technical: wants to use their own agent (e.g. claude, cursor) and connect via MCP / API.

why does snowflake need it's own agentic CLI?

claude code solved this about a month ago

I think you're reading it exactly right

It's half the price per token. Not all tokens are generated equally.

We've (https://www.definite.app/) replaced quite a few metabase accounts now and we have a built-in lakehouse using duckdb + ducklake, so I feel comfortable calling us a "duckdb-based metabase alternative".

When I see the title here, I think "BI with an embedded database", which is what we're building at Definite. A lot of people want dashboards / AI analysis without buying Snowflake, Fivetran, BI and stitching them all together.

us-east-2 has been down for over 2 hours now

> when you connect a warehouse like Snowflake, BigQuery, or Postgres

I'm curious what others are seeing connecting AI tools to Snowflake. Snowflake charges $3 per compute hour and it's pretty easy for an agent to run dozens of queries asynchronously.

As others have mentioned, if you want a notebook, compare this hard against Hex. It's unclear what LiveDocs would give you over Hex (cheaper maybe?).

ps - if you don't have Snowflake / data warehouse yet, we give you a full data platform (data lake + pipelines + dashboards + agent) at https://www.definite.app/.

thanks! It's been a long, painful 3 years getting here.

also, who pays for "a data visualization" SaaS?

I guess they mean BI, but for a company of any scale, they aren't paying for a chart, they're paying for a permissions system, query caching, a modeling layer, scheduling, export to excel, etc.

Stand alone BI tools are going to struggle, but not because they can easily be vibe coded. It'll be because data platforms have BI built-in. Snowflake is starting down this direction and we're (https://www.definite.app/) trying to beat them to it.

yeah, I wanted a better terminal for operating many TUI agent's at once and none of these worked because they all want to own the agent.

I ended up building a terminal[0] with Tauri and xterm that works exactly how I want.

0 - screenshot: https://x.com/thisritchie/status/2016861571897606504?s=20

varied day to day

sued for what?

if the bank makes mistakes in fraud, they just eat the cost.

in 2024, yes.

what AI are you using where this still works?

I worked in the fraud department for for a big bank (handling questionable transactions). I can say with 100% certainty an agent could do the job better than 80% of the people I worked with and cheaper than the other 20%.

Piling on to the vendor pitches here:

We give you all of this in 5 minutes at https://www.definite.app/.

And I mean all of it. You don't need Spark or Snowflake. We give you a datalake, pipelines to get data in, semantic layer and a data agent in one app.

The agent is kind of the easy / fun part. Getting the data infrastructure right so the agent is useful is the hard part.

i.e. if the agent has low agency (e.g. can only write SQL in Snowflake) and can't add a new data source or update transformation logic, it's not going to be terribly effective. Our agent can obviously write SQL, but it can also manage the underlying infra, which has been a huge unlock for us.

this is cool, but:

> This replaces about 500 lines of standard Python

isn't really a selling point when an LLM can do it in a few seconds. I think you'd be better off pitching simpler infra and better performance (if that's true).

i.e. why should I use this instead of turbopuffer? The answer of "write a little less code" is not compelling.

also migrated, but to duckdb.

It's funny to look back at the tricks that were needed to get gpt3 and 3.5 to write SQL (e.g. "you are a data analyst looking at a SQL database with table [tables]"). It's almost effortless now.

happy middle ground: https://www.definite.app/ (I'm the founder).

datalake (DuckLake), pipelines (hubspot, stripe, postgres), and dashboards in a single app for $250/mo.

marketing/finance get dashboards, everyone else gets SQL + AI access. one abstraction instead of five, for a fraction of your Snowflake bill.

the "Prompt Management" part of these products always seemed odd. Does anyone use it? Why?

Added link above!

I made something called `ultraplan`. It's is a CLI tool that records multi-modal context (audio transcription via local Whisper, screenshots, clipboard content, etc.) into a timeline that AI agents like Claude Code can consume.

I have a claude skill `/record` that runs the CLI which starts a new recording. I debug, research, etc., then say "finito" (or choose your own stopword). It outputs a markdown file with your transcribed speech interleaved with screenshots and text that you copied. You can say other keywords like "marco" and it will take a screenshot hands-free.

When the session ends, claude reads the timeline (e.g. looks at screenshots) and gets to work.

I can clean it up and push to github if anyone would get use out of it.

to keep your mac awake:

    caffeinate -di

tacking on to the "New Kind Of" section:

New Kind of QA: One bottle neck I have (as a founder of a b2b saas) is testing changes. We have unit tests, we review PRs, etc. but those don't account for taste. I need to know if the feature feels right to the end user.

One example: we recently changed something about our onboarding flow. I needed to create a fresh team and go thru the onboarding flow dozens of times. It involves adding third party integrations (e.g. Postgres, a CRM, etc.) and each one can behave a little different. The full process can take 5 to 10 minutes.

I want an agent go thru the flow hundreds of times, trying different things (i.e. trying to break it) before I do it myself. There are some obvious things I catch on the first pass that an agent should easily identify and figure out solutions to.

New Kind of "Note to Self": Many of the voice memos, Loom videos, or notes I make (and later email to myself) are feature ideas. These could be 10x better with agents. If there were a local app recording my screen while I talk thru a problem or feature, agents could be picking up all sorts of context that would improve the final note.

Example: You're recording your screen and say "this drop down menu should have an option to drop the cache". An agent could be listening in, capture a screenshot of the menu, find the frontend files / functions related to caching, and trace to the backend endpoints. That single sentence would become a full spec for how to implement the feature.

> Why LLMs Suck at OCR

I paste screenshots into claude code everyday and it's incredible. As in, I can't believe how good it is. I send a screenshot of console logs, a UI and some HTML elements and it just "gets it".

So saying they "Suck" makes me not take your opinion seriously.

The only SaaS that AI has eaten for us is Retool. It wasn't the cost (we were paying < $200 per month). Retool has become more clunky to use vs. writing code with AI to solve the problems we used Retool for.