u/MartinMalinda

Hello!

I've extracted one feature from my product and made it available for free public use.

It accepts an Airtable base schema and checks for things like:

• Broken references in formulas, such as references to deleted fields
• Unsafe formula operations that may result in NaN, Infinity, or invalid date values
• Inconsistent casing
• Inconsistent number and date configurations
• Stale field names - for example, a field still called Name (from Contact) even though the linked field is now called Owner
• And a number of other structural issues

The results are shown directly on a diagram of the base. If you click a formula or rollup field, it highlights the full chain of dependencies.

The audit itself does not use AI. All checks are deterministic and pre-built. The main Orloi product uses AI heavily, but I wanted this tool to be predictable and transparent (and free).

If you use the Paste schema option, everything is validated directly in your browser and nothing is sent to the server. If you connect Airtable directly, the base schema is processed but not persisted.

Hope this is useful. I'd be very interested in feedback, especially on additional checks that would be worth adding.

I'm also considering automation validation, although that would necessarily require an Airtable connection.

https://orloi.ai/airtable-base-audit/

Note: For very large bases, you'll currently need to use the Paste schema option because the connected version can hit server payload limits. A fix is planned.

What if your data in Airtable got checked in real time, the same way a grammar error gets a squiggly line in a document editor?

- No automation to setup or maintain
- It keeps working on all current and future tables

No hardcoded rules, it catches strange data and states you wouldn't think of happening in the first place.

This is now possible.

- My platform handles the wiring, watching data changes efficiently, creates the Validation field if it does not exist yet
- Jev - an efficient zero-shot classifier from TypeSafe AI - then receives the record state and assigns labels

How is the efficient data observation achieved? Via Airtable Web API webhooks. They are pretty powerful but can be a bit challenging to setup. Feel free to reach out if you need help with a feature like this.

Hello r/Airtable !

Airtable is very good at showing you the current state of your data. But when you need to understand what recently changed, its native history only takes you so far.

Orloi captures record changes in a Postgres database you control. It compacts that history, derives metrics and trends, and produces daily and weekly reports about what happened.

That makes questions like these much easier to answer:

How many times did this product’s price change?
Who owned this project before the current owner?
What happened to this subscription before it became delinquent?
How many transactions came from Stripe versus PayPal?
How frequently are this product’s tags changed?
What happened last week when I was on vacation?

As the history builds up, Orloi gets better at learning what normal activity looks like.
Orloi can then flag unusual API activity, invalid or unexpected data, and automations starting to spiral out of control. More history means a better baseline for deciding what is actually unusual.
The other part I’m excited about is what this history makes possible for AI agents.

Learning requires a retrospective and a feedback loop. A current snapshot tells an agent what is true now. A structured history can show it what changed, what happened before an outcome, which patterns keep recurring, and whether a previous action worked. That creates the possibility of automated calibration instead of agents repeatedly acting from the latest snapshot.

The Orloi dashboard can be embedded directly into Airtable as a custom interface. The underlying history stays in your own Postgres database, such as Supabase.

Because the data is already in your database, it is programmable from day one. A new report can trigger an n8n workflow, be passed to a Gumloop agent, or combined with the rest of your operational data.
Orloi does not have to expose another API for all of this.
Your database is the API.

The reports should become more useful with time. More history gives Orloi a stronger baseline for detecting anomalies and gives people and agents more context for reviewing what happened.

Reach out for a demo, collaboration or more information!

https://orloi.ai/

Hi!

My chrome extension allows to clip to different kinds of services and I just added support for Obsidian.

The official web clipper is great. Powersave just has different design and it may be more convenient for certain kinds of workflows. It lives in a side panel, so you can scroll, drag content, copy paste and so on.

You can set up multiple advanced autofills for different properties. If one autofill does not find a value, the extension tries the next one and so on.

And then there's an AI to categorize information to these fields. Completely optional.

And each "destination" in powersave can be duplicated, adjusted. So it's easy to switch between different configs. You might have different autofills set up for each, or each obsidian destination can save to different folder, vault and so on.

There's also an option to connect elsewhere, maybe even just send the website data to your own webhook URL.

I'm only a casual obsidian user so if there's anything I could help improve further, I'd be happy to hear about it.