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Our early product videos began with a company introduction and a slow walk through of the software. Viewers left the video within the three seconds before we even showed the core utility.

We changed the structure by opening with the exact problem that our users faced in their daily work. We showed the result on screen right away then we showed the quick fix in the next frame.

Our average watch time. The conversion rates from video views to landing page visits increased a lot. People stay for a video when they see their own pain points solved away.

Start your product videos by showing the frustration that your tool eliminates within the first three seconds.

We ve all fallen into the trap of spending weeks building a highly requested feature, only to watch it sit there untouched after launch.

People love filling out feedback forms and asking for complex options but their actual daily habits rarely match what they type in a survey.

Now before writing any code, we try putting up a fake button or a simple mockup just to see if people actually click it. How do you usually validate an idea before committing your engineering time to it?

I asked an agent to patch a simple null-pointer exception in a login handler. Instead of a quick three-line fix, it spun up an entire abstract factory pattern, created custom error classes, and restructured the whole authentication pipeline.

For anyone deploying AI agents on day to day bug fixes how do you keep the scope minimal and prevent them from turning a tiny patch into a massive refactor?

What prompts or rules do you use to force AI tools to write the simplest possible fix instead of over-complicating things?

Our teams prompt library turned into a big place where random text pieces, old instructions and repeated steps ended up. Everyone kept their copies, which led to very different results from one project to another.

We checked every prompt. Removed anything that had not been used in the last thirty days. For the ones that stayed we created a way to name them based on what kind of input they needed and what kind of result they should give.

The results became more consistent right away and new team members could find the right process in seconds instead of asking others in chat. A clean prompt library saves a lot of time fixing results.

Think of your collection like real code by putting old versions away and making sure everyone follows the same naming rules.

I had an incident where an agent was supposed to add a simple validation check to an API endpoint, but it decided to improve the database models and added unrequested migrations. It broke half our foreign key relationships because it optimized for what it thought was cleaner architecture.

For teams relying heavily on AI assistants for backend work how do you enforce strict guardrails so they don't touch your data layer without explicit permission?

What strategies do you use to keep AI tools from rewriting database logic you didn't ask them to touch?

I spent three weeks building a heavy export feature because three outspoken users kept asking for it again and again. After we launched it the numbers told me that than two percent of our active users actually used the new export feature.

For the big idea I chose a cheaper path. I put a button for the requested feature in the interface. When a user clicked that button a pop up appeared that said the feature would arrive soon and invited the user to give feedback.

Than forty percent of users clicked the button proving that there was real product demand before I wrote one line of backend code. That proof saved us weeks of wasted engineering effort.

I always measure real user clicks on a placeholder interface before I commit development resources to a feature.

It is so easy to fall into the trap of spending hours searching for the perfect .com domain and configuring DNS records before writing a single line of code. you buy three domains, set up email forwarding, and by the time you actually open your IDE, your weekend energy is completely gone. I finally forced myself to use whatever random free Vercel or Netlify subdomain is available until the core feature is fully working.

how do you handle branding and domain names during early weekend experiments?

It's tempting to keep shipping new capabilities to look active to your paying base. But growth stalled out for us until we hit pause, looked at our data, and realized our tool required too many context switches from where our user already lived.

Fixing workflow fit meant stripping things out, not adding them in.

When your core loop doesn't match your users' daily reality, how do you decide what to cut versus what to streamline?

This week's Product Hunt highlights include something that has stayed with me. The creator of Orkut is now talking about making a new social network, and the idea behind it appears to be that current feeds suck compared to what we had.

It's a little mixed for me. Nostalgia is a good sales tool, but it won't make anyone leave the app where all their friends are. Orkut failed against Facebook not because of the features, but because all your cousins switched overnight.

Our e-commerce catalog had hundreds of pages with identical manufacturer-provided two-sentence descriptions. Search traffic stalled because search engines could not find context to rank us higher.

Of rewriting every page manually we built a template that combined customer review highlights and common buyer questions into the product descriptions block. We added use cases and dimensions to every single item.

Organic traffic grew by thirty-four percent over three months as search engines started indexing the user-focused content. Shoppers also spent time browsing each page.

Stop relying on supplier text, for your product descriptions. Add user insights and specific use cases to satisfy search intent.

I have developed a few saas. Most of which were AI based saas and they needed AI to produce output. I wanted to run lifetime deals on those saas, but there was a problem that for how long can I sustain those users? I have been through a lot of lifetime deals platform to see if those were good fit for my saas. Then there came another problem for me. All of those platforms were taking a minimum of 40% sale commission while asking me to heavily discount the listing price.
For me, those platforms were charging way more than I expected initially. Then I realized that it would be a disaster for me if I ever launched my saas on these platform. I also came to know that there were people known as power users, and these people tend to exploit your application and its features.
I also was confused whethe providing lifetime deals directly to the users of your saas would be good? But, the question here still arises - For how long can you sustain these users as your api bill will be there for you every month for the rest of your life?

So, did you ever plan for lifetime deals? Or would you ever go with lifetime deals?

We tried fine-tuning a small open-source model on three years of raw support tickets to catch product sentiment trends early but the model learned to repeat typos, sarcasm and angry formatting instead of helpful patterns. Our initial evaluation runs scored terribly because half the training rows contained chatter or corrupted text exports.

We wasted two days trying learning rates before realizing the problem was garbage input data, not the model architecture.

Writing a Python script to filter out short messages strip HTML tags and normalize user names fixed the training output completely. Clean data matters more, than parameter count when you are adapting a model for a specific niche workflow.

What is your go-to preprocessing step when preparing raw user text for fine-tuning?

I work a lot on my own as a solopreneur. When nobody is expecting anything from me, I find it difficult to find the right direction and stay on track with what I have to do.

I first tried to fix that with a routine. Every Sunday evening I wrote down what I wanted to do that week, and I blocked time in my calendar. It worked for a few weeks, then I dropped it.

Dashboard funnels and drop-off charts will tell you where people left, but they never really tell you why.

We tried staring at drop off steps for weeks trying to guess the issue but nothing beat just emailing people who bounced on day one.

Out of fifty quick casual emails asking what went wrong, only three or four replied but those few replies completely exposed a broken signup step we were completely blind to.

For anyone doing user research, what is your trick to actually getting a response from someone who already walked away?

We agreed to build three bespoke integrations for a high-paying enterprise client to secure our first six-figure contract and extend our runway. At first, it felt like a massive win the cash flow was great, and having a big name on our pitch deck gave us instant credibility with investors.

But six months in, the reality set in. Our dev team spends almost 70% of their bandwidth maintaining custom legacy code, fixing edge-case bugs, and jumping on emergency support calls for this single client. Meanwhile, our core SaaS product has barely moved forward, public feature releases are constantly delayed, and our standard paying users are starting to complain about neglected roadmap promises.

It made me realize that chasing short-term enterprise revenue can quietly hijack your product vision, drain your engineering energy, and turn your scalable SaaS company into an expensive software agency.

Have you ever built custom one-off solutions to close a big deal, and was the immediate cash worth the technical debt and lost momentum?

At times, the metrics will not move no matter how many tricks I pull out of the bag. I get zero sign-ups, zero replies, a weird support message. Last month I spent three straight days on such metrics and discovered that during this time I was just doing all kinds of productive but futile things. I organised the folder structure, redid the footer, did pricing page comparison for the fourth time.

Gradually, I started maintaining a list of activities called "boring but real". It contains actions that will make some kind of movement even when I am totally uninspired: sending one message to a past customer asking what he does manually now, fixing one bug reported via support mail, deleting one rarely used feature. All these tasks take thirty minutes at most, and I can do them even when I am exhausted.

I discover a lot of tools through Product Hunt, and the upvote count is usually the first thing I look at. But it doesn t always match how useful a tool turns out to be for me.

Last month I signed up for a #1 product of the day because of its huge upvote count. I used it twice and forgot about it. A few weeks earlier I had tried a small launch with barely 50 upvotes, a simple tool for organizing browser tabs, and I still use it every single day.

It made me wonder if I give upvotes too much weight. Maybe the comments and the maker s replies say more about quality than the ranking does.

When you decide what to try, what do you look at first: upvotes, comments, the demo, or something else? Has a low-ranked launch ever surprised you?

Working with AI tools speeds up writing code but auditing their test suites can honestly take longer than writing them from scratch sometimes.

I recently found a test where the assertion was literally comparing a variable to itself just to satisfy a green checkmark. It makes you realize how easy it is to accept code that looks good on the surface without checking its actual logic.

What's the funniest or most useless test an AI has ever generated for your project?

I built my app in months. Marketing has been the hard part. There's no "vibe marketing" yet, but a few tools made specific jobs much easier. Here's what I used, task by task:

1. Launch video ElevenLabs

• What I used: its image, video, voice, and music tools. I made one reference image of my character and generated every scene from it, so his face stayed the same. The voices use Eleven v3 dialogue with tags like [tired] and [pause].

• Tip: avoid the AI agent editor. It used about 3x the credits I expected. Generating each piece yourself is cheaper.

2. Editing, captions and motion graphics Claude

Our automated exit survey asked only one multiple choice question. Options included missing features or pricing. Eighty percent of churned users chose the pricing option. This made us think we needed to change our price tiers. That was a mistake.

When we sent emails to a few of those churned users we learned that churned users left because the setup process was too slow not because of the cost. Churned users used the pricing answer as a cover to avoid explaining further.

We swapped the generic multiple choice form for a text box. We also gave a reward for a real explanation. The written feedback told us where the friction lay and we fixed it.

Never rely on cancellation checkboxes alone. Use qualitative follow up to discover why churned users really leave.

We fell into this exact same trap last year. We ran an A/B test on our landing page and picked the headline that gave us the highest number of free signups.

Turns out, a flashy catchy headline brings in a ton of curious people who bounce after three days while a boring, clear statement attracts the actual buyers.

Ever since that mess we stopped looking at raw conversion rates as the main success metric.

How do you usually balance getting a high volume of signups versus attracting people who are ready to pay?

Our early roadmap turned into a chaotic mess of custom requests from outspoken free users who wanted special integrations. We tried to build everything. That made us delay core stability updates for our paying customers.

We made a rule: no feature request goes onto the main product roadmap unless three different enterprise clients ask for it in one month. All other requests go into a backlog.

Our core application became faster and more stable. Our churn rate fell because our engineering focus matched our real revenue source.

Do not let loud community voices pull your product roadmap into directions that do not serve your paying customer base.

Every time I ask an agent to scaffold a utility or integrate a service, it loves to pull in packages that either don't exist, are heavily deprecated, or use completely outdated syntax. You spend more time debugging ghost imports than it would have taken to write the boilerplate yourself.

For developers working with fast moving tech stacks daily how do you feed context or set rules so the model sticks to your actual dependency tree?

How do you keep your AI agents from recommending or importing phantom packages?

For months, our churn analytics pointed to pricing or missing integrations. It wasn't until we changed how we ran exit interviews focusing entirely on their day to day routine before finding us that we saw real disconnect.

They didn't churn because we lacked depth; they churned because using us required changing how their entire communicated.

For founders doing deep qualitative research on churn: what cues do you look for to spot workflow friction early?

We used to blame our UI or missing integrations every time a paid account downgraded. But after jumping on exit calls, a harsh truth came up: the product worked fine but it forced teams to completely upend their daily routines just to use it.

Throwing more features at a workflow mismatch is just building debt.

For those who've tackled this: how did you redesign onboarding to match your users' natural habits instead of fighting them?