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p/general
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B C
I supported GitScrum early on by purchasing their $299 Lifetime Deal (which is a massive investment here in Vietnam, equal to 2 months of a worker's salary). Back then, the product was broken, incomplete, and barely usable. We bought it hoping to support them and grow together.
Fast forward to today: they have completely locked down the old platform so we can't even access it anymore. They rebranded to an "AI Agents" platform and told lifetime deal holders that our licenses do not transfer. If we want to use anything, we have to pay all over again.
Taking people's money for an unfinished product, promising lifetime access, and then completely locking us out to force a new paywall is pure scam behavior. Beware of their products and future deals!
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Yasmina
Tried building a rag app to help people navigate confusing government paperwork like pension files and certificates.
The model handled plain text fine, but started making up entire steps whenever it hit messy or blurry scanned pages.
Akhilesh
It s so easy to focus on parts of a product that are simple to measure. We recently celebrated a feature launch because our dashboard showed great engagement but user interviews told a completely different story.
People were using the new tool simply to work around an annoying bug earlier in their workflow.
The data made us feel good, but it masked the core issue users were actually facing daily.
What methods do you use to catch hidden workflow pain points before you mistake good analytics for true user satisfaction?
Sidra Arif
I spend a lot of time using video editing software and creating AI media for UGC style content. Lately I have studied engineering in depth to get the exact results I need.. I wonder: should a good AI tool someday remove the need, for complicated slash commands and wild prompt formulas? At the moment it feels as if you must take a course just to make a decent thumbnail or video asset. Do you think the future of AI media tools will depend on users becoming prompt engineers or will founders build invisible AI that simply knows what the user wants through a normal clickable interface?
Henan
I m an independent developer based in France and I currently have a few SaaS products at different stages.
For one product, the brand is already decided and I ve filed a trademark application.
For two others, the products are not fully validated yet. My plan is to first reach out privately to potential business customers, get some early users or paying customers, and see whether there is real demand before investing more heavily in the brands.
The names would still be checked beforehand for obvious trademark conflicts, domain availability, and existing companies/products.
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Luther Werington
Building an entire product solo means every midnight bug and silent design choice falls entirely on you.
Without a teammate to bounce ideas off shipping features can feel like guessing in the dark, even when everything runs smoothly.
Lately I ve had to slow down and write out my rationale for big updates before implementing them treating my past self like an auditor.
It gives me a bit more confidence but I still miss having a real code review process.
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Hisashi Space
It's been about a month since I started posting as a founder in English. Blog posts go to Dev.to and Indie Hackers, and I post on X, Threads, Instagram and Facebook too. The one I'm stuck on is LinkedIn.
I've always posted there in Japanese, and I have around 5,000 followers, but almost no connections in the English-speaking world. LinkedIn isn't that big in Japan, and a lot of people here aren't comfortable with English. If I suddenly switched to English, I think a lot of my existing connections would just tune out.
I thought about a separate account just for English, but it looks like LinkedIn only allows one account per person. If they built in auto-translation the way X has, the language barrier would mostly go away.
Anyone else dealing with this? How did you handle it?
Ghost Kitty
When I first started using agents I thought the answer was simple give them more tools and they can do more useful work. But the more access they get the more I start thinking about permissions costs data and what happens when something goes wrong. Having more capability also means having more things to keep an eye on. Where do you draw the line between making an agent powerful enough to be useful and giving it too much freedom?
Seeing people sign up share the product and talk about it felt like a huge win at first. But after the excitement settled down I realized attention and real usage were two very different things. The numbers looked good for a moment but they didn t tell me much about what happened afterward. How do you judge a launch once the initial excitement is gone?
There have been features I knew weren t doing much I but removing them felt wrong after all the time that went into building them. So instead of admitting they weren t useful I kept making small changes and hoping people would eventually care. How do you know when something needs another chance and when you re just trying to justify the work you already put into it?
Acquiring new users feels like momentum, but retaining them reveals whether you built a gimmick or a workflow.
Early on, we assumed cancellations happened because we lacked features X, Y, or Z. Once paid revenue was on the line, customer feedback shifted: users loved the concept, but it didn't naturally slot into their daily tools (Slack, Notion, or email).
Fixing how your software fits into someone's existing 9 to 5 workflow is exponentially harder than building a shiny new dashboard.
K S DEEPAK
There seems to be a huge divide in micro-SaaS right now between:
Offering a generous free-forever tier to build word-of-mouth and top-of-funnel traffic.
Hard 7-to-14 day free trials with a strict paywall to weed out free-riders early.
I'm currently mapping out pricing for a utility-focused mobile/web app where the core daily actions cost almost nothing in compute, but predictive analytics and data exports add up.
The fear with freemium is supporting thousands of users who never convert, but the fear with hard paywalls is high bounce rates before users form a daily habit.
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Miles Monroe
We built an automated setup to cut down on repetitive team tasks and speed things up. It handled basic cases fast, but the moment tiny exceptions popped up, it broke or got things wrong.
Instead of saving hours, our team now spends that time reviewing and fixing the automated outputs. It didn't solve the workload, it just moved the effort to a different step.
What rules do you follow to decide which processes stay manual versus which ones get automated?
We set up an aggressive AI bot to handle support tickets and on paper it looked like a huge win ticket numbers dropped by 40%.
Turns out the bot wasn't fixing anything. It was just sending users in circles with generic help articles until they gave up and canceled their accounts. Saving money on support ended up costing us way more in lost customers.
How do you stop automated bots from blocking people when they actually need to talk to a human?
Nimra Tariq
I recently automated my product demo video pipeline using AI to generate high-CTR video thumbnails and UGC-style covers. At first it looked like a big win click-through rates went up by than 35 percent.
Then I looked at the analytics a week later and the average watch time had dropped a lot. The AI-generated covers were making people expect something that the real product video didn't deliver. This caused a lot of people to leave away. I was focused on getting the click. I hurt viewer trust and made people stop watching.
For people who make video content for product launches how do you find the balance, between AI visuals that get clicks and real content that keeps people watching?
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Hambali Salisu
Our team launched a feature and the stats looked amazing on paper clicks were up and daily usage was high.
But when we actually talked to users, they were still frustrated by an earlier step in the flow. Turns out good numbers On a dashboard just show what people click not if their actual headache got fixed.
How do you stop yourself from only fixing parts of the app that are easy to track?
Cole Simmons
We tracked what users did in their first session and correlated with 30-day retention.
Users who imported a CSV: 18% of new signups, 74% retained at 30 daysUsers who did not import: 82% of signups, 31% retained
A massive difference. CSV import was the single strongest predictor of retention we had ever found. We promoted it everywhere.
Then we looked at the users who started an import and did not finish. 61% of import attempts failed or were abandoned. Among those users, 30-day retention was 9%. Lower than users who never tried.
Brady Wilfahrt
It s super tempting to jump between different launch platforms and tactics when trying to find early traction.
But broad reach doesn't mean much if people don't stick around.
When I started I used to feel productive whenever I had a full day. More calls more tasks more things to fix. But lately I ve noticed that some of those busy days barely moved the business forward. Sometimes one important decision is worth more than ten small tasks. How do you tell the difference between actually making progress and just keeping yourself busy?
noor abid
Azam Sultan
The speed of going from zero to a working demo with AI is addictive. But the real headache starts when you have to refactor that code after two weeks of user feedback.
I realized that without strict naming rules good docs and clear testing modifying early AI code takes twice as long as writing it manually.
Now I force myself to slow down right after the initial build to clean up the logic before touching any new features.
Do you clean up your AI-generated code right after the prototype works, or do you wait until things start breaking?
Khadija Abdi Hassan
Everyone talks about how fast AI lets you spin up a MVP but nobody warns you about iteration two.
Building from scratch takes a few hours going back weeks later to edit old code gets confusing fast. I've stopped treating the first generated version as a finished product and started looking at it as just a rough design draft.
At what point do you guys stop prompting for new features and start manually locking down the codebase structure?
We logged connection speed metadata across 2.1 million sessions over 60 days.
Under 5 Mbps: 24%5 20 Mbps: 37%20 100 Mbps: 28%Over 100 Mbps: 11%
61% of sessions were under 20 Mbps. Our median asset payload per page was 4.2 MB. On a fast connection that was invisible. On a 5 Mbps connection it was 7 seconds of loading. Our office had gigabit fiber. Our users had hotel wifi, mobile hotspots, and shared connections in buildings with too many people and not enough bandwidth.
We built a low-bandwidth mode that loaded a text-first version with images on demand. Median payload dropped to 480 KB. Session length in the under-5-Mbps group rose 34%, and bounce rate in that group dropped from 51% to 28%.
We measured cursor dwell time on destructive action buttons across 220,000 sessions.
Median hover time before clicking delete: 11.2 secondsMedian hover time before clicking save: 0.8 secondsUsers who hovered then navigated away from delete: 19%Users who hovered delete, clicked, and then undid: 14%
Users were not deleting carelessly. They were agonizing. Eleven seconds of hovering was a decision, not a reflex. And
