Connect with the Product Hunt community

It took me a good while to figure this out. When my programming agent makes an error and I point out the wrong part, the correction doesn't vanish inside the chat log. It's saved as a file. So the next session never trips over the same issue twice.

So far the agent has written 100 incident notes. It also keeps memory notes in the project, one fact for each file. Each time a session starts, a one-line index of those notes gets loaded. If the same mistake happens again, it becomes more than a mere memory. It becomes a hook. And that hook runs on every tool call and every reply, regardless of whether the agent remembers it. I currently have 63 hooks.

A few of those hooks came from pretty ordinary situations. After long sessions in another language, the agent started replying in English. According to the note, the more English documents it had read recently, the higher the chance it would drift. So now, whenever a reply comes out in English, one of the hooks forces a rewrite.

Another time it claimed something was fixed without ever testing it. That led to a rule: it must write down exactly how it checked. Dash usage also got restricted.

In the beginning, we focused heavily on smoothing out our signup flow, and activation rates looked great. But a few weeks in, we realized users were dropping off right after completing onboarding.

Getting users to see the initial value is one thing, but keeping them coming back to perform managing early retention: what onboarding or product changes made the biggest difference in turning initial activation into a daily or weekly habit?

We pull job posts from a few dozen boards and company career pages, and each one is keyed on its source plus that source's own id. That works inside one source and does nothing across them: the same opening can arrive from the company's own page, an aggregator and a recruiter repost, each with a slightly different title and location line. Matching on company plus title looks obvious until you meet one company with two genuinely separate Backend Developer openings in two cities, next to one opening titled three different ways. We still haven't found a rule we trust, so for now we let duplicates leak through rather than risk merging real jobs away. If you run anything that aggregates listings, products or events from many places, which signal ended up being the one you trust for deciding two records are the same?

My app runs inside another company's app marketplace, so before anyone can try it they have to create an account on a platform they've probably never heard of. My first Reddit posts got about 380 views between them, and the install count didn't move.

For those of you building plugins, GPTs, Shopify apps, or anything else that lives inside someone else's platform: what helped people get past that first step? A short demo video, a sample they can see without an account, something else?

Three launches on the board today price in credits, and the complaint underneath each one is the same.

Solid's reviews say credits burn while fixing errors the tool itself caused. Speechka charges 1 credit per minute of translation mode, so you pay full freight for the call where the other person does most of the talking. GBrain bundles $100 of usage into a $199 seat, which is fat margin or underwater depending on which model the user happened to pick that week.

We spent months building a loyal free user base who loved the core utility. But when we introduced paid tiers, conversion was much lower than we expected not because the value wasn't there but because users had already built daily habits around the free version.

Locking existing features behind a paywall creates friction, but keeping too much in the free tier slows down revenue growth.

For founders who have successfully monetized a free tier: how did you decide where to draw the line between free usage and paid conversion?

Month a partner platform changed their API payload structure without telling anyone turning a regular user ID field into a nested object. That small change broke three days of data across our CRM and email marketing systems.

Of going through thousands of bad records in the database one by one we created a simple validation middleware between our webhook receiver and our internal processing queue. Every incoming payload now goes through a schema check before it reaches any business logic.

If a field does not match the schema we expect the middleware moves the payload to an error log table. Sends a message to our developer channel on Slack right away. This small change has saved us a lot of time that we would have spent fixing data

What error handling strategies do you use to keep your automated workflows safe, from API changes?

It s funny how all our automated tests can pass with 100% green checks and yet a simple user action like hitting the browser's Back arrow completely breaks the app experience.

I spent hours debugging why an onboarding wizard kept dragging finished users backward because of how router pushes stack up in Next.js. Unit tests just don't catch browser level history states unless you're running heavy end-to-end browser tests.

For UI flows with multiple steps do you rely on tools like Playwright to test history behavior or is this just one of those things you catch manually by testing it yourself?

early on every request feels urgent because there are so few users. one person asks for something twice and it starts to feel like the roadmap. but a loud user and a common need look the same when you only have a handful of people talking to you. my view is the request matters less than the problem under it, and if three people describe the same problem in different words thats worth more than one person asking for the exact feature five times. curious how others draw the line. do you have a number, a rule, or is it mostly gut until you have enough users to see patterns?

I feel like internal documentation has become a losing battle lately. Code changes endpoints move around, and setup configs tweak every few days but no one wants to pause momentum just to write static markdown files. Eventually onboarding becomes a game of "ask the lead dev on Slack" because the official docs are useless.

We're trying to figure out if there's a lightweight habit that actually sticks like auto generating API docs from code comments or keeping docs right inside the codebase itself.

If you work on a fast moving project what's one actual practical workflow that kept your docs from falling behind without feeling like a chore?

Something I keep running into: the work gets approved, everyone's happy, and then a week later comes "can we just tweak one small thing?" Then another. None of them are big on their own, but together they quietly eat the margin on the project.

I'm curious how others deal with it:

• Do you include a set number of revision rounds in the original scope, or handle each request as it comes?

• Is there a point where you start quoting post-approval changes as new work, and how do you say that without souring the relationship?

• Does writing down exactly what was approved (and when) actually help, or do clients just ignore it?

Would love to hear what's worked for you, and what backfired.

Google Search Console told me one of my blog pages had 2,246 impressions over 28 days for the query "workout app development", sitting on page one. Semrush has no US volume figure for that query at all. When I split the same query by device in Search Console, 19 of those 2,246 impressions were mobile.

So I checked a second one. "ai manufacturing cost reduction case study": roughly 825 impressions a month in Search Console against Semrush's 320 a month, and 1 mobile impression.

Moving upmarket brings a wave of requirements around compliance, audit logs, and granular permission controls.

Implementing these systems takes months of engineering bandwidth, which pulls focus awat from the snappy lightweight experience our core self serve user signed up for.

For teams expanding upstream: how do you manage this split focus without diluting your original product identity?

Ngl, I'm kinda tired of AI case studies that read like fairy tales. Pilot, rollout, ROI, done.

The numbers tell a different story. MIT's NANDA report (2025) found about 95% of gen AI pilots showed no measurable P&L impact. Gartner expects 40%+ of agentic AI projects to get scrapped by the end of 2027. So where do all the messy middles go?

I spent months on acquisition. Ads, content, outreach, the whole playbook. New signups every week.

Churn ate all of it. I was filling a bucket with a hole in the bottom and calling it growth.

Our earliest adopters built impressive custom habits around our product's initial limitations, using spreadsheets and manual exports to fill the gaps.

When we finally shipped the native automation to replace those hacks ,engagement actually dropped for a week because their familiar routine were disrupted.

For teams launching core updates: how do you introduce structural product changes without alienating users who rely on the old way?

Whenever we run a contest or promo to reward our community with free access the biggest headache is always our existing paid subscribers. They're the ones who support us the most so ignoring them or making the process complicated feels wrong. But auto extending active paid plans manually is quickly becoming an operational hassle.

I m curious if anyone has found a clean automated solution for this.

Do you pause their recurring billing for a month gift them an upgraded feature tier instead or is there a simpler workflow I m missing? Would love to know how other founders structure this to keep subscribers happy without messing up billing records.

There s so much noise right now about how AI lets you pump out 10x more video content in half the time. But honestly most of the fully generated stuff I see feels lifeless and gets scrolled past instantly.

I find these tools great for brainstorming concepts or grabbing quick placeholder visuals but pulling together a real story still takes manual tweaking and human intuition.

For those making content regularly have these speed up tools actually helped your quality or just pushed you to create more noise?

Every bug report, logged. Date, user, severity, what broke.

The pattern was not what I expected. Half of them were not bugs at all. They were confusion. People using the product in a way that made sense to them, hitting a wall, and reporting it as broken.

Working with enterprise tools all day means my brain is fried by the time I get home but I really don't want my Python skills to gather dust.

I tried tackling massive ML projects on the side before but I always end up quitting halfway because they're too heavy. I'm trying to find a balance maybe a lightweight automation or a simple local AI tool that I can hack away at for 30 minutes a day.

What small coding projects did you build that actually held your interest?

Every time I look at pre built AI roles for things like customer support or lead qualification the sales page makes it look effortless.

But in reality as soon as you connect your own data and approval rules you realize your internal process is way too messy for a plug and play bot. You end up spending hours re configuring everything anyway which makes me wonder if starting from scratch with modular tools is just better from the start.

For anyone who brought AI into their daily ops did buying a pre packaged role save you time, or did you end up building your own logic?

When we landed our first big client paying $2,000/month, we were ecstatic. They were a massive logistics company, and they had one specific requirement: they needed a completely custom reporting dashboard that synced with their proprietary internal software.

We didn't want to lose the deal. So, my co-founder and I spent six weeks building this custom integration. It was a massive hack duct-taped together with webhooks and custom database queries. But it worked. The client was happy, and we had our first real revenue coming in.

Then the nightmare started.

Because we hardcoded this custom feature into our core app, every time we tried to push a new update, the custom dashboard broke. We spent the next three months firefighting bugs for this one client instead of improving the product for our other 150 users. Our overall churn rate ticked up by 5% because our standard features were falling behind, and the codebase became a mess to maintain.

Six months ago, we were desperate for cash flow to keep the lights on. We decided to run a Lifetime Deal (LTD) campaign. We thought it was a win-win: we get a quick cash injection, and we get hundreds of users to give us feedback.

It worked... at first. We made $5,000 in a single week. I felt like a genius.

But the reality check hit us hard over the next 90 days:

• Our support tickets exploded by 400%. LTD buyers were incredibly demanding, asking for custom features and 1-on-1 onboarding calls for a product they paid $49 for once.

• Our server costs tripled. Many of these users were uploading massive files and using our API heavily. We were now paying AWS $300/month to host users who would never give us another dime in recurring revenue.

• Our real MRR stalled. We got so bogged down supporting the LTD crowd that we completely stopped our outbound marketing and sales efforts.

We recently had a small discussion about bottom spacing in a B2B product.

The layout was using 80px of bottom padding, and the feedback was that it felt a little cramped. So we started looking at 160px instead.

My first thought was whether doubling the spacing was really necessary.

But looking at different screens made me question the idea of having one fixed value in the first place.

agents made prompt edits scary for me. tweak one system prompt and a tool call 3 steps later starts failing. what helped: prompts live in git, every change runs against a small fixed set of real traces before shipping, and the last good version is always one revert away. curious what others do here, do you run evals on every prompt change or only the big ones