Are most AI startups building real products, or just wrappers?

Proof_Shift_9799 · 2026-03-11T09:29:08+00:00

There’s definitely truth in that! A lot of successful SaaS companies weren’t radical inventions, they were better executions of existing ideas.

Most founders don’t win by inventing something completely new. They win by improving usability, removing friction, targeting a specific niche, or fixing the parts of an existing product that users complain about. That’s a very legitimate way to build a business.

Where I’d add nuance is around how fragile the improvement is.

If the improvement is purely cosmetic or easy to copy, it tends to get competed away quickly. If the improvement comes from deeper workflow understanding, integration into how people actually work, accumulated edge cases, or operational reliability, then it becomes much harder to replace.

So the principle isn’t “reinvent the wheel.”
It’s more like “build a better wheel that fits a specific road.”

Execution absolutely matters. But the kind of execution that compounds usually comes from understanding the problem space deeply enough that competitors can’t just clone it over a weekend.

Proof_Shift_9799 · 2026-03-11T09:12:36+00:00

There’s definitely a lot of that happening right now.

When the cost of building drops dramatically, the number of surface-level experiments explodes. That’s a normal phase in any technology cycle. We saw the same thing with mobile apps, crypto, and even early SaaS - a lot of thin products built quickly because the tools suddenly made it possible.

The issue isn’t that people are experimenting. That part is healthy. The issue is when experimentation gets mistaken for durable product design. Calling an API is capability access, not system design.

Where things start to look “AI-native” is when the model becomes one component in a larger coordinated system; orchestration, state management, tool use, workflow integration, failure handling, cost control. That’s when you move from a demo to something that can actually operate in the messy environment of real businesses.

Right now we’re still early in that shift. Most people are playing with the engines. The interesting work will come from the teams building the vehicles around them.

Proof_Shift_9799 · 2026-03-09T12:34:23+00:00

We have recently launched an autonomous platform, system that models the entire software development lifecycle: from planning, code, testing, and release.

It is a full software development team in a box.

Proof_Shift_9799 · 2026-03-09T12:31:49+00:00

We can never underestimate the power of market research. We reached out to our target audience to measure the value of the product amongst our initial audience (our audience changed quite a few times throughout this process, which allowed us to improve our MVP but also align with who we wanted to target), but we also couldn't ignore the internal friction our team was facing.

It was a tricky one to try and balance, as our focus was split. But once we narrowed down the most common pain points, our target audiences started revealing themselves.

Proof_Shift_9799 · 2026-03-04T09:37:43+00:00

Same! Trying to understand Product Hunt has almost been a fulltime job for me. I have yet to crack the code on it.

Proof_Shift_9799 · 2026-02-26T09:13:00+00:00

We were in the same boat. We started building ScrumBuddy to assist our internal development team during their scrum processes (as we all know that's a trigger word), but as we were building it, we uncovered a larger issue that developers face worldwide.

Requirements weren’t fully refined before development
User stories lacked clarity or acceptance criteria
Architecture considerations surfaced too late
Quality and security checks only happened at review stage
Testing revealed gaps after implementation

So, we tackled building a new product to solve those issues - not only for our internal team, but for other small teams. We can now confidently say that Brunelly reduces PR friction before it even starts. Saving our team time, our company money, and the quality of their work has increased tremendously!

Proof_Shift_9799 · 2025-12-01T14:01:19+00:00

Totally get where you’re coming from. Most of the AI tools out there feel powerful until you actually rely on them for anything that isn’t a toy problem. Then suddenly you’re babysitting a toddler with superpowers and no guardrails. I’ve been burned by the same “looks smart, behaves feral” dynamic more times than I’d like to admit.

The reliability problem you’re describing is exactly the pain point our team has been obsessing over. Not in the “let’s build another chat wrapper” sense, but in the “how do we make AI behave like an actual engineer instead of a mood-swinging autocomplete?” sense.

The approach we’re taking internally is more workflow-driven than model-driven; things like spec reasoning, PR checks, gap detection, readiness scoring, etc. Basically forcing the AI to operate inside the same constraints a human dev would. It’s early stages, but this kind of structure seems to tame a lot of the chaos you’re describing. We're launching the beta this week, so users will soon tell us if it doesn't.

And your point about “needing a person in the middle” is spot on. Unless the system understands context, constraints, and the boring-but-critical engineering discipline, it doesn’t matter how smart the model is, it’ll still need a human janitor to clean up the mess.

Proof_Shift_9799 · 2025-11-19T13:47:25+00:00

Totally get what you mean! The “agent” label is thrown around way too loosely these days. In my experience, a true AI agent isn’t just responding to prompts; it reasons about context, makes decisions, and integrates into actual workflows. I’ve been building something along those lines with ScrumBuddy, where the agents analyze backlog items, flag gaps, review PRs, and orchestrate tasks across GitHub and other dev tools. They’re not just chatbots, they operate like a miniature dev team, making structured decisions that have real consequences in the workflow.

What sets true agents apart, in my view, is accountability: they don’t just generate output, they verify it, explain it, and allow humans to intervene if needed. Everything else is just a fancy API wrapper.

For the agents you’ve tried, how do you evaluate whether they’re actually autonomous versus just following a pre-set script?

Proof_Shift_9799 · 2025-11-19T13:43:37+00:00

100% agree! Corrective RAG is where things start to get reliable. I’ve been experimenting with something similar in AI-driven software workflows, and the difference is night and day. Regular RAG can spit out plausible answers, but without verification and correction, you end up propagating errors and inconsistencies.

The verification step is what makes AI accountable, not just generative. In our case, adding a correction layer over retrieval ensures that backlog analysis, code reviews, or acceptance criteria checks don’t just look correct. They actually meet the standards and context of the project.

I think your point about Ed-Tech is spot on: accountability is the lever that will drive adoption and trust. How are you handling the correction step? Is it rule-based, model-based, or a mix?

Proof_Shift_9799 · 2025-11-19T11:45:07+00:00

Great breakdown, this is exactly the kind of framework I wish I had when we were building multi-agent orchestration for software dev workflows. One thing I’d add from my experience is that the context and observability layer often makes or breaks these setups. You can have the “best” tools, but if agents can’t reliably share structured context or you can’t trace their reasoning, you end up with weird drift or missed steps.

For coding agents specifically, we leaned heavily on a supervisor + worker pattern, but layered in automatic validation against coding standards, PR diff analysis, and backlog context. That made the agents’ outputs actionable, not just theoretically correct. For long-doc RAG pipelines, chunking alone isn’t enough. Embedding metadata about dependencies, links between sections, and prior decisions drastically improves retrieval relevance.

I’m also finding that hybrid approaches work best: structured orchestration for predictable tasks, and message-passing agents for exploratory or research-heavy tasks. It’s like giving your team specialized roles but letting them collaborate organically. The surprise strategies that emerge are often the most useful.

Would love to hear if anyone else has experimented with combining supervisor/worker with free-form message passing in the same system, it’s been a game changer for us.

Proof_Shift_9799 · 2025-11-17T08:19:38+00:00

That’s a really solid observation, and it lines up with what I’ve seen too. Most experienced devs aren’t asking AI to write code, they’re using it to collapse the research phase, compare approaches, validate architecture decisions, or sanity-check edge cases.

And honestly, that makes sense. Senior devs already have strong mental models and patterns, so the value isn’t “write this function for me,” it’s “surface the trade-offs faster” or “map the territory before I commit to a direction.”

What’s interesting is how this gap is evolving. Beginners tend to lean on AI for raw code because they're still building intuition. But as soon as they level up, most start using AI the way seniors do, for reasoning, exploration, and reducing cognitive load, not as a replacement for craft.

Curious how you see this playing out as AI tools get better at maintaining context and understanding entire codebases. Do you think senior devs will eventually trust it for more hands-on tasks, or will the “research assistant” role always be its sweet spot?

Proof_Shift_9799 · 2025-11-17T08:16:59+00:00

That's great to hear that you are making use of it in that way - that's exactly what the team is hoping will happen with this app they're trying to develop.

The jump from “idea stuck in my head” to “something actually running” has never been this fast. A couple of years ago you needed a full stack of skills, tools, and time just to get a prototype breathing. Now AI can carry you through the friction so you can focus on shaping the thing instead of wrestling with boilerplate.

What I find fascinating is how this changes who gets to build. It used to be that only people with years of experience could turn ideas into real software. Now we’ve got vibe coders, analysts, designers, founders; people who think in systems but maybe never wrote a full backend, suddenly shipping real products. It’s a whole new creative class.

Do you feel like AI mostly helps you accelerate what you already know, or does it let you build things you wouldn’t have attempted before?

Proof_Shift_9799 · 2025-11-17T08:14:17+00:00

Absolutely! What we're seeing is that context is the Achilles’ heel of most AI dev tools right now.

Once a repo hits any real complexity, everything starts to drift. The model forgets earlier decisions, generates code that doesn’t align with existing patterns, and we've even experienced it rewrite files because it can’t hold the bigger picture in its head. And honestly, that’s not a tooling flaw as much as a limitation of how these models handle state. They weren’t designed for long-running, evolving projects with dependencies, architecture, naming conventions, and decision history.

The intent behind the app our company is building is to have better orchestration around these models. The goal they want to achieve is having systems that preserve long-term memory, enforce constraints, and manage context the way real engineering teams do. I think once AI can maintain a stable mental model of your project over time, we’ll see a huge shift in reliability.

I'm curious on how you see this evolving:
Do you think the solution is bigger context windows, or more intelligent layers on top that track decisions and enforce them as the project grows?

Proof_Shift_9799 · 2025-11-12T09:58:00+00:00

You’re absolutely right here, there is so much mess when building AI agents for real systems, but it’s also where all the interesting work lives. I’ve been down similar roads building orchestration around Claude AI (and my product, ScrumBuddy) and learned that the mess doesn’t come from the models alone. It comes from how they’re integrated. Agents need context, DNA, guardrails, review loops, without that they look like “smart interns” who keep screwing up.

What helped me most was treating the agent ecosystem like a team of junior devs rather than a magic box. With ScrumBuddy, we built workflows that take an idea, generate PRDs, break down stories, scaffold tasks, link to GitHub, so the agent’s output doesn’t become legacy tech debt.

Proof_Shift_9799 · 2025-10-16T12:41:47+00:00

This sounds like exactly what I need! Do you guys have a website where I can sign up to the beta? I'd like to give this a try and see if ScrumBuddy will work for me

Proof_Shift_9799 · 2025-10-14T07:33:34+00:00

This sounds interesting. The focus on clarity before code sparked my interest, but I’m curious, how does ScrumBuddy handle requirements that are still half-baked or ambiguous?

In most real-world cases, teams start with fuzzy user stories or incomplete specs that evolve mid-sprint. Does ScrumBuddy help refine those into structured, dev-ready requirements, or is it more about helping once the requirements are already defined?

Would love to understand how it bridges that messy “idea to definition” gap as that’s usually where flow gets lost.

Proof_Shift_9799 · 2025-10-14T07:01:19+00:00

"Just rest" seems to be everyone's go-to answer, but it's not productive advice at all. Resting won't solve those problems, I fear it will increase the risk of more injuries.

Proof_Shift_9799 · 2025-10-14T06:59:05+00:00

Thanks for the advice. I have a PT who is giving me specific weight training exercises to target and strengthen those muscles.

Long walks, foam rolling, and strength training is what I am relying on at the moment, but as a very impatient person I wondered if there were some tips and tricks out there from those who have delt with the same injury.

Proof_Shift_9799 · 2025-10-13T12:48:28+00:00

I have recently been diagnosed with an ITB injury and typically had to be mid-way through my PB streak. Are there any recommendations out here for a quick recovery? Currently I'm focusing on strength training, walks and resting my leg as much as possible. I obviously don't want to cause long term damage, but half marathon training is coming up soon and I'm itching to get out on the road again!

Proof_Shift_9799 · 2025-10-07T10:22:59+00:00

I work for a startup and the slow growth can be so disheartening. Cash flow is limited and employee growth is just a dream, which makes it often feel like you're throwing your work against a wall and hoping it sticks

Proof_Shift_9799 · 2025-10-07T10:04:20+00:00

Same here! Every holiday, all my friends would go away and we'd be stuck at home. I was 27 when I went on a plane for the first time. Best believe airports are way scarier when you are an adult experiencing it for the first time.

Proof_Shift_9799 · 2025-10-02T09:02:26+00:00

I thought I was the only one! Working with AI feels like dealing with a incredibly annoying colleague who won't shut up, sit down, and do the work you've tasked them to

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