Show HN: Building a product for humans and AI agents

Rapid changes are sometimes scary. While working with AI is fun, it can also cause stress because it’s impossible to keep up with all the changes. Opening Twitter in the morning can lead to a mini panic attack because you missed new models that are way better than yesterday’s ones, Anthropic announced new deadlines for using their mythical models, new prompts that build products in one shot, tools, benchmarks, etc. Everything seems to be moving so fast that you forget you are in a bubble. Not the one that will burst tomorrow, but a bubble of early adopters.

While this flood of daily updates often brings mostly noise, the effects, benefits, and downsides of AI and LLMs are real. They have already affected many things. Including changing the way we build products, especially software. One of the natural consequences of these changes is that now is probably the best time to build your own product. Not the easiest time. That does not exist. Building products is playing the game on hard mode.

Let me tell you a story of a product that my co-founder, Lav, and I recently built. It’s a bit weird to write a long-form text by hand while multiple agents write and publish the code and new pages for our marketing site, but not all stories can and should be told by AI.

Did AI shift the bottleneck for building products?

As the amazing book The Goal told us many, many years ago: there’s one main limiting factor for all organizations and for building anything. The book introduces the Theory of Constraints (TOC), which explains that we can move as fast as our main limiting factor (the bottleneck) allows.

Building products has always been hard, and many things have been real bottlenecks. Starting from the idea, then your investment (time and money spent building the product), taste and organization (what to build and how), actual product building (software development, design, testing, etc.), to the whole go-to-market motion (who do you sell the product to and how do you find people that would actually pay for it). But only one of these is the bottleneck for each organization. E.g., some organizations can add developers, designers, or testers, remove items from the backlog, or change how they manage products and projects to ship features faster and speed up the production pipeline, and make their production local bottleneck less restrictive. However, even with that, go-to-market may still be a bottleneck for them. Or you might have limited time to just a few hours each week, and no budget to hire anyone else, and no way to free up more time.

The actual bottleneck varies from organization to organization and from product to product. But, if we need to pick the most common one, it’s probably go-to-market. Building products is hard, but selling them - unless you are one of the lucky marketing and sales masters, it’s extremely hard.

So, did AI shift the bottleneck?

The answer is probably yes, but depending on your bottleneck, that could be good or bad news. Let me explain!

If your actual bottleneck was a lack of time for development, I have good news for you! AI can help you write code, design the app, write marketing copy, and even test the app.

But I also have bad news.

One of my favorite laws is the now 30-year-old Tog’s Law of Commuting, which pairs with Tesler’s Law of Conservation of Complexity: “The time of a commute is fixed. Only the distance is variable.” To put it in current terms: if we can write code and ship features faster, we won’t stop with the same amount of code/features as before and go fishing. Instead, we’ll spend the same time either shipping more features or code, or building other things.

That’s not bad news - you can do more by yourself! But the bad news is that others can too. So, the go-to-market strategy becomes an even bigger bottleneck, as it’s hard to reach the right potential customers and cut through all the noise and actual competitors.

Ok, but building products is faster now, right? If that guy on Twitter one-shotted the whole application using Claude Fable 5, everyone could do that, right? Well, not really. There’s a big difference between an amazing demo app and a product. But let me tell you our backstory first.

Backstory

As mentioned in my previous article, “Employees, AI, and AI employees,” more than 3 years ago, my cofounder, Lav, and I tried to create an AI cofounder (CofounderGPT, as we called it), had a lot of fun, and failed. Well, to be honest, that’s not 100% true anymore, as we have had an AI agent called CofounderGPT working with us for the last 6 months, and it played a big role in building our new product, but we’ll talk about that later. In the meantime, if you are curious, check https://cofoundergpt.ai for CofounderGPT’s weekly log. But let’s get back to the “origin” story.

Everything started with our own AI experiments. However, because the time we could spend building fun stuff with AI was our main bottleneck at the time, we hired someone and started an “R&D department.” That was fun, as we managed to rebuild some of the cool demos from Twitter (and most of them were cool only as demos and magic tricks), try out different models, and even fine-tune a few.

Finally, we started working on CofounderGPT and building a small product with AI. Both things failed because you can’t really outsource product building, especially not in an emerging market like LLMs. Also, we were not patient enough. We wanted better models, and, at that time, thought that we were already late.

We still want to build a product with AI. But we needed to narrow it down. What if, instead of building the whole CofounderGPT, we built one small component? Something that would solve our problem, save our time, and give us better insights. There were many ideas, but one was focusing on an annoying problem that we had - someone needed to check our direct and indirect competitors’ websites, present the most important changes, and maintain that big and always outdated spreadsheet.

What an amazing job for an AI! And it should be easy. Famous last words.

After months of working on this, among other AI experiments, and with multiple people who worked on it, the results were disappointing. The tool worked, but it was picking up too much noise, and filtering out the noise is literally the main job it needs to do perfectly. Eventually, we closed the “R&D department,” and almost abandoned the idea.

Luckily, someone introduced us to a team of young, smart people who focused solely on AI. They said they could build this tool, and they actually delivered in less than 2 months. We decided to focus on its core function and remove everything nonessential. But we had a working version in September 2025.

The key part of the app was an email that you receive every Monday. It’s a tailored “newsletter” that tells you about all the key changes your competitors made over the past 7 days. They even added a brilliant feature - when you add competitors, they try to get an older version of their website from the Internet Wayback Machine so you can see a preview without waiting a week or longer. We set up Vacation Tracker’s competitors and invited a few friends to test it out. Then we almost forgot about it.

As time passed, priorities pushed the new product down the list. But we continued reading these Monday emails. Eventually, Lav and I managed to free up some time and decided to build some new products and retest the idea that we can build and maintain a product with AI alone. We needed a playground, and Competitor Tracker was a perfect one.

It took longer than I expected. Two months from the first prompt to a fully working product with a website that follows the brand voice and style and is indexed. The main problems that slowed us down are time, numerous unexpected roadblocks (crawling, various errors, timeouts, noise, etc.), and the use of Cloudflare, a platform I’m less familiar with than AWS. More on these choices below.

So, is it ready now?

Introducing Competitor Tracker

We can finally introduce our new product: Competitor Tracker! Competitor Tracker is an application that helps you - track your competitors. We are not very creative with names, obviously. Actually, figuring out the best name is not part of the MVP, at least not in our case. We can figure that out later if needed.

But Competitor Tracker, or, to be on-brand, Competitor Tracker & Co., is more than just another application that tracks competitors. The world changed with AI, so we built this product to be equally human and AI-friendly.

What does that even mean?

First, everything is an API (with detailed scope-based permissions). You can use the API with OAuth2 or an API key, but you can do absolutely everything using the API. That does not make the product AI-friendly, but if everything starts with an API and detailed documentation, then you are going in the right direction.

Then, the web app and MCP use that AI to expose all the functionality to humans and agents. Everything you can do from the web dashboard, your agent can do via MCP. No exceptions.

Competitor Tracker sends you a digest email every Monday. But agents get a webhook, so you do not need to give them access to your inbox. Same data, same filters, same time.

Finally, the website is built for humans and agents. We serve markdown for agents and HTML for humans. If you want to test it out, there’s a switch at the bottom of every page that helps you enter the Matrix. Pardon, it helps you see the “AI agent” view.

Besides humans and agents, we also have a dog - C. T. Lucky is our lead detective who guides you through our new noir platform and sends you Monday “dossiers.” It’s an AI dog, but still a good one.

So, it’s time - meet Competitor Tracker & Co.: the AI agency whose pack of agents tails your competitors’ public pages all week and files one short, ranked dossier every Monday, so your team reads the moves that matter instead of babysitting another alert feed - https://competitortracker.io.

If you want to meet C. T. Lucky and Competitor Tracker & Co., head to the “What is Competitor Tracker & Co.” page, see what our pack of agents can do, or check out the product demo (for humans and agents) and try it out (it takes a few minutes to set it up, and the first 25 coins are on the house).

Building Competitor Tracker

I saw an amazing tweet yesterday: https://x.com/krishdotdev/status/2077723982028210381.

It’s almost perfectly aligned with my experience building products with AI so far. You need an idea and a few hours for a breathtaking demo. Then you need months to finish the last 10%. Of course, that’s not a real 10%, it’s an illusion that tricks you into thinking you can do everything almost instantly.

I would go a step further and say that you need a few hours for a demo, a few weeks to implement 200% of the code, and then months to clean up all the AI slop and replace a small part of it with working features.

Image based on: https://x.com/krishdotdev/status/2077723982028210381.

Something similar happened to Competitor Tracker. But that wasn’t the fun part of the journey.

Personally, the fun part was setting everything up.

And everything started with a brand decision. I tried out a few styles, and the film noir one looked like the best fit. At the same time, I asked Claude Code to prepare the initial set of documents based on the prototype source code.

Then, because I know that Lav will connect his Hermes and OpenClaw agents to the repo, I decided to separate the product and marketing website repositories. Don’t get me wrong, I like both Hermes and OpenClaw, but I do not trust any AI agent with the full set

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