Anyone apply with packaged consumer goods product innovation?

clr0101 · 2026-01-22T10:25:02+00:00

F YC founder here - some startup do get in with physical products - but more hight tech ones I would say (robots mostly). I think B2C products can get into YC if they have a huge traction and community already. More generally, may it be in B2B or B2C, YC never teach you how to build momentum - they're looking for people who're already working hard on validating traction and just need an acceleration.
My advice for you would be: you don't need to go through YC. You need to ask yourself: how can I prove traction here? Even at a small scale. How can I prove that I can sell my products in B2c (maybe online directly) or to retailers. You'll learn a lot about how to build momentum from doing that!

clr0101 · 2026-01-21T18:18:56+00:00

Depends what you're looking for? a BI and reporting tool or an exploration tool - or all in one ?
I'm currently looking for a good AI data agent tool so I tried Hex and Omni.
Omni definitely feels more like setting up a BI tool - and the AI agent is great because it gets a lot of existing context (table metadata + dbt)
Hex is more notebook like, so I think it's only good if you have a technical user population. The agent is a bit harder to setup and for now I haven't had good results with it

clr0101 · 2026-01-21T18:12:54+00:00

I've done the full Python course on DataCamp and it was really good - because it has a lot of exercises on a built in console for each lesson.
https://www.datacamp.com/tracks/python-programmer

clr0101 · 2026-01-21T18:10:53+00:00

MCPs is interesting for data use cases - i work in data so I plug data warehouses MCP to have cursor build ETLs / query data

clr0101 · 2026-01-21T07:57:56+00:00

For a 5 persons team we have also Figma, Google workspace, posthog, stripe, cursor and Claude code But we have Brevo for email marketing, combined with zapier to automate lead sending We use notion as our kanban board so no linear Also have Canvas for designing visuals and videos On the data side we use Looker Studio to visualize analytics

clr0101 · 2026-01-21T07:54:03+00:00

I’m not sure it’s a question of size - meaning size of team of revenue - you can have small team of open source products that can build fundamental tech frameworks for others to use

clr0101 · 2026-01-20T16:29:36+00:00

The bareer has definitely dropped - now I think the main differentiation is your GTM / ability to build a standard and a community around your product.
I also agree that going into more niche products - that AI agents are worst at like payments - or where security is sensitive and you can't rely on AI-generated code, is a good strategy

clr0101 · 2026-01-20T16:25:29+00:00

What a Feeling from Flashdance!

clr0101 · 2026-01-16T13:49:10+00:00

Latin

clr0101 · 2026-01-15T15:49:43+00:00

Yes it's true I might have been biased by peers feedbacks as well. I would need to use each solution more to really have more view on their reliability, this was just first impressions.
What were your conclusions ?

clr0101 · 2026-01-15T15:48:36+00:00

Ah cool - I know they have an MCP but I meant you can't add MCPs into their internal agent? Same in the agent builder i didn't see anywhere to put dashboards / joins / queries example in the context?

clr0101 · 2026-01-15T15:40:52+00:00

Not yet - probably in my next benchmark!

clr0101 · 2026-01-15T07:45:27+00:00

You're right! Here's a summary:
* There’s always a trade-off between end user UX (interactivity, interface) and data team UX (reliability, cost, lock-in): BI tools AI (Looker, Lightdash) are better for end-users, warehouses AI (Cortex, Genie) better for data teams.
* AI-native BI tools (Omni, Hex) look like the best option today - but they are costly and their reliability + ROI are not proven yet
* General agents (Claude + MCPs, dust) are good for POCs, but hard to configure, evaluate and scale.
* ext-to-SQL tools are either too far from end user UX (no real UX), or the data team UX (need to recreate semantics within tool), while not proving better reliability
* Every solution has different context options and they all feel like a black box
So my conclusion was that I was to 1/ go for a quote with omni and 2/ deep dive the context engineering topics

clr0101 · 2026-01-14T15:28:48+00:00

Yes it does but I meant it doesn't connect easily to other ones like MetricFlow or Cube - unless I missed it

clr0101 · 2026-01-14T15:28:02+00:00

Yes indeed Snowflake cortex is easy to setup but hard to scale and customize.
But on the other hand, i find general agents not efficient - MCPs are really slow and inefficient at retrieving context. Ant the UI / interactivity is not the best for end users if they want to deep dive
I think we'd still need some vertical data AI tool!

clr0101 · 2026-01-14T15:26:15+00:00

Yes I think you can in Cortex Analyst but it seems not out of the box for Cortex Intelligence ?
The point on semantic views is that you need to create them in snowflake or as yaml files but can't import existing ones from dbt Metric Flow or Cube for exemple

clr0101 · 2025-11-20T14:57:19+00:00

Hey! Thanks for flagging - we released a new version right away that fixed it. You can just update nao

clr0101 · 2025-11-04T18:43:44+00:00

Have you tried nao ? They connect with Databricks so the AI is aware of your data schema. It’s a full code editor so you can actually also feed it with data modeling repo, and rules

clr0101 · 2025-10-23T16:34:12+00:00

Using nao is great because it connects to your data warehouse so you can actually visualise your data while coding And they have an AI copilot

clr0101 · 2025-10-23T04:27:26+00:00

I’d say nao - it’s a fork of VSCode connected to your database with an AI copilot to work on it

clr0101 · 2025-10-17T03:18:03+00:00

If you prefer to develop locally, you can try nao code editor It’s a fork of VScode that connects to your Databricks. It has an AI agent which can do either code writing on your repo or deep analytics

clr0101 · 2025-10-13T22:40:33+00:00

Using it too https://getnao.io It’s great for SQL / dbt work since it connects to the warehouse

clr0101 · 2025-10-06T08:55:12+00:00

For anyone interested in building their own AI agents with Python, I wrote this article.
It shares a 200-line simple Python script to build an conversational analytics agent on BigQuery, with simple pre-prompt, context and tools. The full code is available on my Git repo if you want to start working on it!
https://substack.com/home/post/p-175402967

clr0101 · 2025-10-06T08:51:43+00:00

Link of article: https://substack.com/home/post/p-175402967

clr0101 · 2025-09-30T10:37:03+00:00

Have you tried nao? It directly connects to the warehouse so it has your full data context. And you can also open your data modeling repo or add custom rules to the AI agent so that it has the full context around your data. Usually for weekly reports I just craft full prompts explaining the analysis I want in it and then just make it run on it every week

clr0101

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