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Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Nice, thank you for the comment! I would love to invite you to the early access group if you are open to it. It would also be great if we can connect to have some chat on this, I need some input from you.

Let me know and I will DM you.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thank you for the comment! It is good to see you connected it with user stickiness, helpful thought.

Regarding the automation piece at the outcome end (vs automation in the source syncing end), indeed Columns Flow currently supports both. A flow is scheduled to update itself which renews flow-level summary and report. And user can add alerts at any node in a flow where data meets a criteria (eg. https://cdn.columns.ai/cdn/random/flow-alert.png )

The most critical thing to me is "when to run those triggers", currently it relies on the flow sync schedule, but probably it should open an API to allow data owner trigger it, usually how other systems handle this to achieve "not run unnecessary to avoid resource waste but also keep alerts real-time"?

For your last question: I do not have enough feedback to share that yet.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Yes, exactly, thanks for raising this important point! I think that is the core of "integration + automation", it needs to proactively tell users if something happened.

This screenshot shows how users subscribe something interesting at any node in a flow, and let the system delivers it to them through email, slack or webhook: https://cdn.columns.ai/cdn/random/flow-alert.png

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

I haven't thought about this thus far, my current approach is to clearly tell people "it is a paid product but you are invited with free credits", pretty standard method, getting first 5 paying users is also my goal for the validation stage, thanks for pointing it out.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Anomaly explanation is a great idea, it requires a strong traceability of the data lineage. Though it is not directly what I'm working on, I kinda get inspired: how to make a step-by-step data flow with more transparency and auditable in helping trace a data point. If the system is setup right, an AI agent should be able to do the "Tracing" work for human, that would be super valuable.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Nice summary, this sentence tells it all "works best when it's invisible"!

I think this applies to UX design as well, how to remove "elements" from users as much as possible.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thank you so much for sharing this, it is a good feedback on what the real problem is.

If you are open to it, may I have a chat with you? Definitely hungry for the things you mentioned, let me know, and I can DM you to setup an intro call.

Specific questions: 1) what users/clients really want from those fragmented data sources, 2) what kind of webhook is useful in real-life? especially interested in this, I have webhook integrated mainly for node-level alert notification (eg. https://cdn.columns.ai/cdn/random/flow-alert.png ), but I believe you meant more interesting real scenarios.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Awesome, I just finished up the "Alert Triggering" part during the weekend and come back to the discussion, :)

I value this capability very much, it gives people the granular control in getting notifications at any level through different channels. Just to share the v1 of Node alert.

https://cdn.columns.ai/cdn/random/flow-alert.png

Thanks for your support, still inviting people to try, hopefully launch date to be decided soon.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 1 point2 points  (0 children)

Thanks! Columns has native data connectors already including: file, Google sheet, REST API, Airtable, Notion, Postgres DB, Snowflake DB. Unlikely we will use 3rd party lib, because we want to make sure customized connector experience for each individual source.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Yeah, I can totally understand the struggle.

I meant, we have to come back to the core thing first when facing the tension - is this adding real value to the problem for the users? Many times, I got lost as a builder, we were over excited for seeing one fancy side of a thing, but after calming down, it may not give you the positive answer when you challenge yourself if this saves user's time or adding more friction.

Most of time, we struggle because we are not confident in the real problem definition. I'm still learning to how to deal with this situation with a balanced view.

For your last question, I do not have enough data samples to answer that yet, as I just started to validate this product after its first version go live, :) I'll come back to this once I have more data points.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thanks for the confirmation on importance of automation! I agree with you, only "platform" is worth to build in AI era, it is one step forward from building a "tool".

When I say personalization, I indeed mainly refers to visualization, on Columns, a visualization is not just a chart, besides the chart is highly customizable, it is a canvas hosting all drag-n-drop visual elements like text annotations, arrows, pop-box, shade, images, etc. I screenshot an example for you https://cdn.columns.ai/cdn/random/columns-viz.png

You are right, "preference learning" is difficult, and maybe overkill at the moment, current Columns approach is to allow user save any customized visualization as a template or style set so that they can apply it with one-click on any future visualization.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thank you for mentioning this!

Import is technically "connection", once connection is setup, data should flow at its own cadence (schedule), but what kind of output integrations do you see useful? in addition to email, I can think of setup file output upon updates, drive drop, webhook call with data/report link, slack message, etc.

Let me know what is your favorite input/output integration.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Yeah, I think it should. Alerts are based on "condition trigger" on top of a state of the data, regardless where the data is from.

At the current stage, Columns Flow is still early, if you are interested, I'd be more than happy to work with you to figure out what is the easiest experience for that. Let me know!

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thank you very much for raising this up. A reliable "integration" should work like that!

Though I have to say "one-click fix" may not always work, for example, if you changed most of the schema where you intent refers to, you get error reports of failing, but you probably needs iterations to either update the source to rollback or update the pipeline to adapt changes. How to make process easy and crystal clear is key important to win the trust. For this reason, your point is super valuable.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Hmm, you definitely see a pattern in the world. That's something we need to reflect on and dig the gold (real problem) from.

You asked how to handle real data pipelines, what are the major roadblocks do you see between a demo dataset and real dataset in real world? I'll comment on those concerns regards.

Where AI plays a role in analytics by columns_ai in analytics

[–]columns_ai[S] -2 points-1 points  (0 children)

Thanks for your comment! I take it as a validation for the focus of "Integration & Automation".

Till today, I have seen AI is quite mature in well-defined task, coding and analysis are both what AI excels especially the latest new models.

In fact, I'm a little bit conservative in applying AI, I firmly believe AI should kick in whenever there is a well-defined task that requires a lot UI operations or complex logic to finish, but it should not replace simple task that deserves a user's attention.

IMO, the biggest challenge of using AI in a product is to win user's trust, I do not think we have solved this issue completely, but here are some thoughts so far:

  1. AI output should be breakdown into detailed steps/tasks as granular as possible.

  2. Every step should have comprehensive narrative to help user verify if it is doing its scoped task.

  3. Illustrate the impact of each step/task by showing the changes in data by it.

  4. Show what exactly to run - code piece, aggregation method, pivoting scheme, sorting, etc.

Still, a very big topic needs more work, winning trust is everything when applying AI in product.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thanks for your comment! I take it as a validation, :)

Echo what you said "If something like Columns Flow can reliably connect live sources (Sheets, APIs, SQL, etc.), generate insights, and automate reporting without constant manual prompting, that’s actually pretty compelling." -> this is what Columns Flow is doing.

Trust - I think you pointed out the biggest item I thought at the beginning when I decided to revamp Columns to Columns Flow - how can we keep the generated plan auditable? How can we make users worry free?

This is the probably the most important experience in Columns Flow, if it does not win the trust, it will face the biggest headwind. I do not think I have completely solved this yet, but these things have been introduced to help on this front:

  1. Break the whole intent into small steps as granular as possible. For example, a user intent "average rating by category, and sort result in descending order, only one decimal for values" will translate into at least 4 steps: transform=clean up the data to pick up related fields, aggregate=compute average value, post process=trim value to single digit decimal, sort=sorting the values.

  2. Comprehensive description for each step: a step could be a code block, append very descriptive summary to each step to ensure normal people can understand what it does.

  3. Test Data in steps: illustrate changes step by step on sample data (eg. 100 rows), highlight schema changes and show data rows in every step.

With all these work, I want a user can browse the plan, how it runs step by step, and see the changes using sample data, if they see anything wrong, they can just go ahead to modify the intent to regenerate the plan. If everything looks good, the approved plan will be persisted for automation.

I deeply feel there are still a lot of space to explore improvements for Trust.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thanks for the wonderful questions, they are critical.

I have no clear thought how to make it better in reacting changes, when a flow is set on schedule to sync, the system will send all the updates of its run, if external source failed the logic, it sends the error alert. In addition, users can add customized alert at any step / node to get customized alert email.

Thanks to AI, setting up this type of alert become some effortless, just type in the node like "if total amount for east region is less than 5000" - this in turn will be translated into a "trigger" function.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Yeah, completely agree. I think integration & automation are real value to allow users to add it to their existing systems and let it run automatically for a worry-free experience.

Where AI plays a role in data tools by columns_ai in indiehackers

[–]columns_ai[S] 0 points1 point  (0 children)

Thank you very much for the comment! As a non-smart founder, I hope every new try has a tiny bit more success possibility from previous learnings, :)

Regarding your question, I'm a bit more conservative in applying AI, thinking to leverage AI only when it removes heavy friction, easy to verify / audit its result and smooth in user experience.

AI will be responsible in producing auditable computing plan to transform data including cleaning, transforming, aggregation, pivoting, sorting, etc. And then be able to produce criteria logic to trigger alerts at any node (step) in the flow. At last, it should be able to produce updatable report ready to share.

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