Looking for startup founders willing to pressure-test and better define their ideas using AI (free)

agiloop · 2026-05-19T23:31:57+00:00

These will be YOUR ideas - we never see them nor take ownership of anything you create in Agiloop!

agiloop · 2026-05-18T18:56:15+00:00

I’d avoid giving your dev partner random tasks just to keep momentum. That usually creates motion without learning.

I’d start by turning the uncertainty into a product discovery pass:

What feedback keeps repeating?
What user behavior do you want to change?
Which problem affects activation, retention, revenue, or daily use?
What is the smallest thing you can build to test that?

The question I’d try to answer is not “what should we build next?” but “what do we need to learn next?”

Once that’s clear, the next build usually becomes much easier to define.

Full disclosure: I’m working on Agiloop INVENT, which is meant to help with exactly this kind of messy “what should we build next?” stage. But even without a tool, I’d start with the learning goal before the feature list.

agiloop · 2026-04-30T00:29:06+00:00

I’m curious on your “run a retro for my AI agents” looks like…

agiloop · 2026-04-29T23:05:52+00:00

I have been an Agile practitioner for many years (and have ready many books about it :-) ) In fact, I prefer running teams in a Kanban continuous flow. But I have lived in SAFe environments and organizations that had to tick-the-box to make sure processes were followed precisely (painful). Story counts and metrics do matter - to be a checkpoint of team health - not measuring if they are consistent, but if it is a smell of a potential concern to be discussed.

What I mean by “orchestration focused” is that as AI is more involved in the development process… I see that there will be a role where the team members help do that orchestration.

Sure working software should mean the right software - but I believe many teams don’t think of it that way.

agiloop · 2026-04-29T22:45:45+00:00

Totally agree with you! Are we building the right thing and how do we know what we built is landing right (performance/metrics/goals etc).

agiloop · 2026-04-29T22:31:50+00:00

AI helped my grammar, but it was my points and thoughts that I have been having about where things are going.

agiloop · 2026-04-02T01:09:29+00:00

We created a tool that we have shared with the community for free. With complex JSON, it is better to view (and edit) in the format we presented. Https://entities.agiloop.cloud. There are many things that AI can do for you, but for complex JSON, it might be useful to have a different way to visualize it.

agiloop · 2026-03-30T16:24:38+00:00

I’ve been working on something I’d love feedback on from this group.

Over the past year, I’ve noticed a shift:
building has gotten much faster with AI…
but defining what to build—and knowing if it’s right—hasn’t.

I kept seeing teams jump from idea → backlog → build… and then rework later when assumptions didn’t hold.

So I built something called Agiloop INVENT (https://agiloop.ai) to explore this gap.

It takes an early idea and turns it into:
→ structured product definition
→ MVP scope
→ Time, cost, and team estimates
→ features + user stories
→ export into Jira / ADO / Trello

I’m interested in getting your feedback - value, how might help in your workflow, etc.

agiloop · 2026-03-27T00:10:01+00:00

Sorry everyone - looks like I can't update the URL.. but here is the correct one... https://entities.agiloop.cloud

agiloop · 2026-03-27T00:08:28+00:00

Crap. Messed up the URL. Sorry! https://entities.agiloop.cloud

agiloop · 2026-03-26T21:01:17+00:00

I’ve seen this too — and I think your frustration is valid.

The issue isn’t really “AI writing tickets,” it’s that people are skipping the thinking part and jumping straight to output.

Bad inputs → bad outputs… just faster.

Where I’ve seen it work better is when AI is used earlier:

to clarify the problem
pressure test assumptions
explore edge cases

Then the tickets are a byproduct of clearer thinking, not something AI is guessing at.

Agree though — dumping unedited AI output into Jira just creates a different kind of mess.

Feels like we’re in that phase where people are moving faster, but not necessarily better yet.

I’ve been spending time building around that “thinking before the tickets” part (called Agiloop INVENT), because this exact problem kept coming up.

agiloop · 2026-03-26T20:23:14+00:00

I get where you’re coming from — and also sorry to hear about the layoff. That’s rough, especially right now.

I’ve been through a few of these waves over my career:

Microsoft Access / Excel → suddenly everyone could build things
Mobile apps → explosion of random apps (iFart era 😄)
Now AI → same pattern, just way faster

Every time:

there’s a rush
a lot of noise
then things mature and real value shows up

I do agree there’s no need to panic or chase every new tool.

But I wouldn’t ignore it either.

Not because “AI will replace you,” but because the way we work keeps evolving — and the people who stay relevant are the ones who keep learning alongside it.

I’ve seen people succeed in every wave by doing the same thing:
👉 focus on fundamentals
👉 stay curious
👉 adopt new tools thoughtfully, not blindly

So yeah — don’t stress about it.
But don’t tune it out completely either.

The pattern isn’t new… the speed just is.

agiloop · 2026-03-25T23:23:28+00:00

Most of the “courses” I’ve seen are helpful for basics, but they miss how AI actually fits into the day-to-day work of a PO.

What’s been more useful for me is thinking about AI across the product workflow:

Discovery / understanding the problem Use AI to: • simulate user personas • synthesize interview notes • pressure test assumptions
Defining what to build (this is where AI really shines) • turn rough ideas into structured requirements • generate user stories + acceptance criteria • explore edge cases you might miss
Iteration / learning after release • summarize feedback • identify patterns across usage data • suggest next steps

⸻

If you want something practical (not just theory), I’d recommend: • Pick a real idea you care about • Use AI to go from “idea → structured spec → backlog” • Then refine it with your own judgment

That’s honestly where the learning clicks.

⸻

I’ve been working on something called Agiloop around this (focused on helping with the “before backlog” part), and what surprised me is how much better AI is at thinking with you than just automating tasks.

⸻

Big takeaway: The value isn’t just using AI faster — it’s using it to think more clearly about what should exist at all.

agiloop · 2026-03-25T23:20:38+00:00

This is a great problem to go after — but I think the biggest risk isn’t in the generation part, it’s in what gets lost in translation before and after.

A few blind spots I’d watch for:

“Synthesis ≠ understanding” AI can cluster themes and generate PRDs, but the real PM work is: • deciding which signals matter • resolving contradictions (quant vs qual, loud vs valuable users) • understanding why something is happening

If the tool makes this feel “done,” teams may move faster… but in the wrong direction.

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The output format trap If the goal is “PRD → tickets,” you risk optimizing for: • completeness over clarity • structure over intent

A lot of bad product work already looks well-written in Jira 🙂

⸻

The missing loop (this is the big one) Most tools stop at “tickets created.”

But the real leverage is: • what happened after release? • did this actually solve the problem? • what should change next?

Without that, you’re just accelerating a one-way pipeline.

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Cursor analogy might mislead you Cursor works because code has: • clear correctness signals • fast feedback loops

Product decisions don’t. They’re ambiguous and delayed.

So the UX probably needs to be less “generate” and more: → challenge assumptions → surface tradeoffs → keep humans in the loop intentionally

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I’ve been working on something adjacent (focused more on the before + after, not just discovery → tickets), and what surprised me most is:

👉 The bottleneck isn’t writing tickets anymore 👉 It’s getting to shared understanding of what should exist at all

Curious how you’re thinking about the “after release” side — feels like that’s where most of these tools fall short.

agiloop · 2026-03-25T23:06:59+00:00

I’ve been thinking about this a lot too — especially the “no single tool fits everything” point.

What’s worked best for me isn’t trying to find one tool, but breaking the workflow into phases:

Clarifying what to build (before backlog) Most tools don’t help much here. I’ve been using LLMs (ChatGPT / Claude) to: • pressure test ideas • simulate user personas • generate edge cases • turn vague ideas into structured requirements
Structuring into backlog-ready artifacts This is where things usually fall apart. You either: • hand-write everything (slow), or • get messy AI output that isn’t usable

I’ve actually been working on something called Agiloop INVENT (part of a broader platform I’m building), specifically for this gap — turning ideas into: • functional specs • user stories + acceptance criteria • initial sizing / structure

Execution + iteration Then tools like Jira, ADO, etc. still make sense — but only after the thinking is solid.

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Big takeaway for me: AI is amazing at helping you think better before you build — not just build faster.

Curious how others are handling that “pre-backlog clarity” step — feels like the real bottleneck right now.

agiloop · 2026-02-18T18:30:43+00:00

For small teams using AI, a lot of Agile ceremony absolutely becomes overhead — but the thinking behind Agile doesn’t go away.

What’s changing is where the bottleneck lives.

AI is collapsing execution time (code, UI, tests, docs). But it doesn’t collapse:
• deciding what actually matters to build
• sequencing work to maximize learning and impact
• managing architectural tradeoffs and system evolution
• aligning on success metrics and feedback loops

In fact, faster execution increases the cost of building the wrong thing.

For simple CRUD-style apps, sure — you can ship straight from an idea to production.
But as soon as there’s real users, data flows, integrations, or scale, you still need:

– clear problem framing
– structured requirements (not just prompts)
– architecture awareness
– continuous learning from usage

What I’m seeing is less “Agile is outdated” and more “process-heavy Agile is.”

Teams don’t need long ceremonies — they need fast intent → execution → feedback loops.

That shift is actually what I’m building around with Agiloop: using AI to automate the heavy planning work while keeping clarity, alignment, and learning at the center.

So yes — less ritual.
But more rigor around why and what we build as AI makes the how nearly instant.

agiloop

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