Timeline

Sixty events, each with its own illustration. This timeline can be read in chronological order or in reverse, whichever feels better. A few days of work, already against the background of first interviews.

• While putting it together, I rethought a lot. I do not regret a single decision. The idea of working for myself again is now planted too deeply to disappear.
• I invite you to live through these two years with me. Maybe you will find something useful here for yourself too. Though one thing I have already learned is that other people's experience rarely teaches enough on its own, and most things still have to be tested firsthand.
• Think of it as a window into what solo AI product building actually looks like, with the tasks, the decisions, and the dead ends included.

Everything is ready for Google Ads except for one detail: Google does not believe my company is actually called AI Apps.

• Under Google's rules, there are two ways to verify a business name: provide trademark documents or use the domain name. I chose the second path and ran into complications I did not expect.
• For the fourth Monday in a row, the same email arrives: 'we're working on it, another 3 to 5 business days.'
• You never know in advance where the process will stall. This time it stalled here. So I wait.

January through May. Four and a half months. A major product pivot, dozens of experiments, and a lot of hard-earned experience in distribution, video, and AI-agent workflows. I am satisfied.

• The last decision of this iteration is to gather the whole story into a timeline on the site.
• What comes next is work search, interviews, and reflection: what worked, what did not, and what I would do differently.
• There are already thoughts about the next turn. But first, I need stability.

I go to Reddit for data. I get the data. I also get both bans.

• In r/jira, I run a poll about clarification overhead around tasks. Twenty votes produce a useful picture: for most people, more than 10% of work time disappears into everything around tasks, and for some it is close to half. A moderator decides it is advertising. Ninety-day ban.
• In r/polls, I get 179 votes. Seventy-one percent say they do not use AI on principle because they do not want slop. A great snapshot of how the average Reddit user feels about AI. Permanent ban: a moderator decides I am a bot. The evidence? Long dashes. Support never replies.
• I am happy with the experiments. The pain point is confirmed. The channel is not.

Not ads, content. A person looks for an answer, finds an article, and the product is already there inside the path.

• Everywhere, the product placement stays soft, with links to other articles too. Not 'buy this', more like 'interested? here is more.'
• A couple of hours per article: images, editing, translation. The first pass covers the basic circle of questions and turns into more than a hundred SEO pages.
• I give the experiment a month and watch what happens.

If I cannot pour full energy into the product, I need channels with a long tail. A blog is the obvious choice.

• I want to write articles that are genuinely useful and still index well, not just content for the sake of content.
• Like the rest of the site, it all launches in 13 languages from day one. One article turns into 13. It sounds like a good idea. Later I learn that translating articles burns more tokens than writing code.
• This is a bet on time, not reach. One strong article can keep bringing traffic for years.

The first attempts to tell the story for free, through communities and articles.

• A spotlight in Atlassian Community gets the product into Atlassian's partner-product newsletter.
• A Reddit post goes into a product megathread, partly for SEO. I had also read that AI models learn well from Reddit, so I wanted to leave a trace there too.
• A Medium article becomes the first real test of a proper article format. That one is for SEO too.

The site was originally meant for several products. Now I rebuild it around one: Just.

• From scratch: a hero section with the demo launching right from the page, a showcase of use cases, a pricing calculator, an AI provider block, and contact points.
• I build it properly from the start: localization into 13 languages and all the search essentials: canonical URLs, hreflang, robots.txt, sitemap, JSON-LD.
• That was exactly what had been missing before. Now Google Ads, SEO, and GEO all become possible. One day I may split the site back into several products, but only after those products actually exist.

If LinkedIn does not work, I need a different channel. Google is more interesting because it is not cold outreach. It is a meeting with someone who is already looking for a solution.

• I open Google Ads, start setting things up, and hit a limitation: an Atlassian Marketplace page does not count as mine. The domain belongs to someone else, so the campaign will not pass.
• The marketplace is good for organic discovery, but paid traffic needs a domain I actually own.
• Which means I need a real site. That becomes the next step.

The full founder demo runs about six minutes. Interesting, but too long for ads. In one day I make a short 25-second version.

• The previous video took two or three weeks. This one takes a day.
• Same product, different tempo: a short format for ads and the landing page.

The day after the big release, I run one more quick LinkedIn Ads test just to see what changed. Nothing did.

• I stupidly forget to disable LinkedIn Audience Network and end up with 120 visits that all show zero seconds on site in Google Analytics. That kind of traffic is useless to me.
• Ads shown directly inside LinkedIn look somewhat better, but still grim: CPM around $42, CTR around 0.3%, CPC around $15.
• Even if I pushed it down to $5 to $10 per click, it would still be too expensive for a first-pass test. The economics do not work. Time to look for other channels.

Good AI asks before it answers. I knew that already, but the implementation used to feel too heavy. With coding agents, that is no longer true.

• When Insights senses that a task may be underspecified, it opens a clarification stage: five questions about the non-obvious parts that can radically change the implementation. Checkboxes, radio buttons, free-form answers. And the loop can run again and again.
• Less than two weeks from idea to several working iterations. A year earlier this would have triggered a small personal crisis.

I spent a year building depth and ended up with something closer to an IDE. The data says people install it and never come back. So the decision becomes obvious: the product has to create value fast, not after five minutes of setup.

• The era of shorts, reels, and Twitter logic is only accelerating. A product that unfolds too slowly already loses in that environment. Value has to show up immediately and in a form that is easy to digest.
• That is exactly where the Insights plan comes from: a simpler first step, a stronger result format, and more proactive AI.
• At this point it is still only a plan. It will take two months and around 20 prototype iterations before it becomes what it eventually becomes.

Jira's built-in analytics were almost useless, and 2 to 5 organic installs a month did not feel like enough reason to dig deeper. But while preparing the move toward Insights, I decide to fix that.

• I bring in PostHog and do it properly from the start: full compliance, GDPR support, no prompt collection, no content collection, no API keys. User corporate data should be untouchable. I should only see usage metrics for my own product.
• Even with traffic this small, one pattern becomes clear: most users install the product and never actually enter it.

The work at hooh is over, and Just is ahead without split attention. But this is not a return to the old plan.

• Half a year of parallel work is behind me. For the first time in a while, all focus is back on my own thing.
• The key conclusion from the hooh period is blunt: a strong product can lose before a person ever reaches its core. Entry matters more than the engine.
• Insights stops being a loose idea and becomes a concrete direction: repackage the product around a clearer entry point, a stronger result, and more proactive AI.

Explicit message roles, control over what flows between steps, and real pricing with cache taken into account.

• Messages inside a step now have explicit USER, ASSISTANT, and SYSTEM roles, so context no longer has to be guessed.
• A step result can be passed forward as a dialogue, as final output only, or cut off from the next step entirely.
• For Anthropic and OpenAI, cached tokens and real price now appear right in the interface. In practice, that turns Just into a real LLM backend, the kind hooh had hired a separate person to build around.

From November on, the plugin lives inside hooh.com and gets used in a real project. The first honest test outside demo data.

• Inside hooh I use it as my own controllable AI builder, even though the original idea was for it to help with Jira tasks.
• When I need to write Jira tickets, I still reach for ChatGPT because the habit is already there. My prompt is one or two A4 pages of half-structured thoughts about a feature.
• In practice I am delegating the writing of the ticket to an assistant after handing over most of the detail. A big spec for a complex feature takes one or two hours. Just can do all of that too, just not as comfortably, and when there is a choice... well.

The money runs out. Product work becomes secondary, and the path back to full-time employment starts coming into view.

• While interviews are still underway and no offer is signed yet, there is a narrow window left.
• I use it to finish the major Claude Code update before switching context.

Claude Code is not just faster. The quality jumps in places where I did not expect it to.

• Before that I had used Cursor and AI plugins for JetBrains. They worked. But Claude Code changed both quality and speed at once.
• The agent's clarifying questions lead to cleaner and more precise results than a one-shot prompt. That was genuinely surprising.
• It was one of those moments when you fall in love with what AI can do all over again. If the earlier wave felt like the first space age, this one felt like the second.

Google Drive was a strong integration that I never managed to ship.

• File search and filtering, context from Docs and Sheets, images from Google Docs, comment threads: all of it worked locally.
• Access to all files turned out to be too sensitive a scope for Google, and verification never went through.
• I rolled the feature back without ever releasing it. A good experiment with a bad ending.

Google demanded a real site, and that is how aiapps.me is born.

• Google Drive verification required a domain, legal pages, and ownership confirmation. There was no way around it.
• So I put together a minimal landing page: product description, policy, terms. Not really branding yet.
• Sometimes a product site is born not from ambition, but from bureaucracy.

Morning: idea. Evening: product live on the Marketplace.

• This is Just: AI Auto Labels, a simple Jira plugin that uses AI to assign labels automatically.
• The repo starts at 12:15. By 15:49, only small fixes are left. Code and testing take around two hours.
• Most of the time goes not into code, but into packaging: logo, Marketplace page, and app setup.

xAI joins the stack, and it arrives together with a bigger wave of new models from everyone else too.

• xAI enters with six models at once: Grok 4, Grok 3, Grok 3 Mini, Grok 3 Fast, Grok 3 Mini Fast, and Grok 2 Image for image generation.
• o3-pro, Claude Opus 4, Sonnet 4, Haiku 3.5, Gemini Flash Lite Preview all land in the same update.
• The gallery moves to Claude Sonnet 4, which becomes the default model for templates.

I finally solve a pain that existed from day one: data migrations without direct access to customer databases.

• Forge does not give direct access to customer databases. Every schema update meant web triggers, custom runs, and workarounds. That had been true since the first version.
• Forge SQL fixes the core issue: normal migrations and sane support for existing accounts.
• The move touched the whole server side, and it took weeks of careful work not to break current users. Anyone who has ever migrated from NoSQL to SQL will understand.

Four models from OpenAI and four from Google land at once: eight additions in a single release.

• o4-mini, GPT-4.1, GPT-4.1 mini, GPT-4.1 nano from OpenAI. Gemini 2.5 Flash, 2.5 Pro, 2.0 Flash, 2.0 Flash Lite from Google.
• Model selection gets search and reset, so browsing old model names no longer feels like scrolling a junk drawer.
• This also becomes the first step toward images: Jira attachment read access and the groundwork for generation.

After LinkedIn it becomes obvious that the real problem is activation, not the channel.

• The product's value has to become visible before the user closes the tab. That turns out to be harder than it sounds.
• The working hypothesis is that the page needs a much clearer demonstration. Video would be perfect, but too expensive and too hard at this point, so I postpone it for a year.
• This is the first real shift toward sales thinking: building is not enough, you also have to be able to sell what you built.

A month of paid ads leads to a sober look at what is actually not working.

• Five waves, more than 40 ad messages, audiences, and formats. The result: 12 installs for $1,300. Conversation ads did best at around $30 per install in the best campaign, but the overall outcome was still weak.
• The biggest insight was structural: the buyer is not the same person as the future user. That breaks both targeting and messaging.
• Three diagnoses emerge: weak activation, a weak marketplace page, and weak product framing. Plus one honest question: maybe part of the problem is LinkedIn itself and my own distribution skills. None of the 12 installs really stuck.

A modest release, but every change lands squarely in day-to-day use.

• Chats now name themselves from the first prompt, like ChatGPT. No more faceless threads in the list.
• Archived scenarios disappear from quick menus, which means less noise and fewer accidental launches.
• The gallery gets a customer interview template, one more tool for real product work.

I test models against my own scenarios, and Claude 3.7 Sonnet unexpectedly wins across the board.

• I add new Anthropic models and immediately make Claude 3.7 Sonnet the default.
• The response quality is clearly better: more concise, more precise, and better at holding the scenario context. A free boost out of nowhere.
• It is slower and more expensive than OpenAI, but once the result is that much better, that stops being a serious argument. From that point on, I have a clear favorite in the AI race.

Task groups become available too, for scenarios that need a wider view of what is happening in the project.

• Tasks can now be pulled into context by sprint, release, label, status, type, or assignee.
• That unlocks new templates on top: release notes, sprint reports, and next-step scenarios.

A fourth provider arrives: Google. Model choice finally starts feeling broad for real.

• Gemini 2.0 Flash Exp, 1.5 Flash, and 1.5 Pro all become available inside the product.
• OpenAI, Anthropic, Mistral, Google. At this point the product is no longer tied to any single player.

At the end of the year, I finally sign the sale of AdSpoiler. The deal had been dragging on since September, and it solved a very basic question: what exactly I was going to live on while Just still could not.

• The money for development was running out, and so were my savings. A startup does not pay rent by itself.
• AdSpoiler, my ad analytics service, had become a frankly toxic asset by then: not a growth bet, just dead weight. Luckily, I managed to sell it for decent money.
• I split the sale with a partner, but my share was enough for roughly nine more months of startup runway. That became the plan.

A faster start: from folder to run in two clicks.

• A quick-launch window appears for scenario folders, with the right version visible immediately.
• From the same place I can access the last run, settings, and restart actions.
• Contexts move into a separate panel, and the interface gets cleaner.

Forge does not let one app work with Jira and Confluence at the same time, so I cut the path.

• A single Forge app cannot talk to multiple Atlassian products at once. That is a platform limitation, not my choice.
• Locally, with expanded permissions, it works. In the marketplace, it fails verification and does not pass.
• If the rule is no third-party workaround, then the only honest path is to remove it. Frustrating, but clear.

It is time to push the product outward, so I set a goal and start with the circle closest to me.

• The concrete goal: 50 installs in one month.
• In startup language this is FFF: Friends, Family, and Fools. Not the most demanding users, but the ones who give a product its first momentum.
• Around 20 people installed the plugin, but every install came from a very manual and very pedantic process: weeks of personal conversations, explanations, and persuasion.
• It was not an empty exercise. Some of them actually explored the product and gave feedback.

What should AI know before the very first question, and how do you even define that?

• The first context set looked like this: Jira issue, project description, product glossary, role, competitors, data sources. Raw building blocks, but already enough to start carving out a place for product memory inside the AI.
• Those blocks would change a lot later, but the core idea appears here: AI should not have to start from zero every single time.
• Confluence was already connected as a context source here. Later that path had to be closed because of platform limits.

I decide to test a personal thesis: one person with the right mix of skills can build a complete product.

• Before coding agents, the solo path required a rare combination: product, engineering, design, and marketing. That mix happened to come together for me, and AI agents only lowered the threshold further after that.
• I knew the threshold would drop sharply soon, and that my head start was small. But it existed, and I tried to use it.
• Every action became a product increment. Communication overhead inside the team: zero. The first time in my life. It was an oddly joyful state, almost childlike, and it made me want to keep working more and more.

There are entities, there is an interface, and the product starts feeling real.

• The key entities are defined: chat, action, step, message, context.
• Everything is built around real Jira tasks, not an abstract chat box.
• The strongest scenarios are requirements, alternatives, and planning.

While looking for work, a test assignment unexpectedly led me to a product idea I wanted to believe in for real.

• During my job search, I prepared my own transformation proposal for Aqua, a software testing company, as my view on how a business like that could evolve in 2024.
• The central idea was simple: do not duplicate infrastructure, move the product closer to Jira, where the workflow, the users, and the market already are. And of course, bring AI into it.
• During the presentation itself, I said that if they were not going to build it, I would. A week later, on the day of the rejection, it no longer sounded like bravado, and I started building Just.

January 2024. I write down two beliefs for myself and decide to build around them.

• First: over time, personal AI assistants will outperform humans in a large share of day-to-day work. I wrote that down not as an observation, but as a bet.
• Second: the human edge will last longest in vision and marketing. What we build and why still belongs to people. So does convincing others that it matters. AI will not replace that any time soon.
• The early majority will need at least five years to move into AI-native workflows, and the later majority even longer. People are more conservative in their habits than they seem. That whole transition window is an opportunity for anyone building tools inside it.

December 2023 became the first serious test: I broke product work into parts and checked what AI could handle and what it could not.

• The approach was simple: take real PM tasks such as competitor analysis or writing specs, and run them through AI as an experiment. Not a feeling, a test.
• The result showed up fast: one prompt, one answer. No memory, no web. A meaningful part of the job just fell out. Competitor analysis without current data is not really analysis.
• But the limitations also pointed to what would come next. It became clearer not only what was missing, but what direction the whole thing was moving in.

A course on product vision in December 2023 was not really about the product yet. It was about the fact that without vision at the top of the hierarchy, everything below gets built blind.

• The course laid the hierarchy out clearly: vision → strategy → goals → initiatives → releases → features → epics → tasks. Once you see it in action in big companies, it gets a little uncomfortable. You realize how much gets built blindly, including things you build yourself.
• That led to an awkward thought: I already had a position on AI, but it was nowhere written down. Intuition is not vision.
• So I sat down and made it explicit: where things are heading, what I believe, what I want to build, and why. Without that step, everything that followed would have felt far less grounded.

Until then, I had never really looked at Jira plugins as a market. Then I saw the numbers by accident and remembered the niche.

• Not everyone gets to see real Jira Marketplace numbers from the inside. I got lucky: while working at Railsware on Coupler.io, I was close enough to the TitanApps plugin line to see the monthly reports.
• The numbers were more convincing than I expected: a narrow Jira tool could make steady money without a huge audience or broad reach.
• At the time I was thinking about a completely different product. But the conclusion stuck with me: if there is one thing almost every software team in the world has in common, it is Jira. And the market around it has been alive for a long time.

One real task at work was enough for AI to stop feeling like a novelty and start turning into a habit.

• April 2023. ChatGPT had existed for only a few months. I was editing a Confluence page at work and asked AI to help with a paragraph, more out of curiosity than intent.
• The answers were inaccurate, but already useful enough to try again. And then again.
• Three months later AI was already part of my near-daily workflow. At the time, I still had no idea that this was only the beginning.