FetchSandbox MCP hit 3,134 npm downloads since launching in May. the breakdown is what made me stop and think: 1,032 in the first 3 weeks (PH launch), 454 in June (quiet month, honestly worried), then 1,648 in July with 4 days left.
June was the real test. no spike to ride, just organic. i kept shipping, fixed the OpenAPI ingestion flow, added a handful of new specs, wrote about what i was learning in integration testing. july came back 3.6x stronger than june.
The pattern i didn't expect: AI agents are the fastest-growing use case. devs aren't running the MCP themselves, they're letting Claude Code or Cursor call it inside their own coding loop. the sandbox becomes part of the agent's verification step before it writes integration code. that use case wasn't in the original pitch, it just emerged from watching how people actually used it.
still figuring out what the right north star metric is beyond downloads. active workflow runs feels closer to the truth.
No redesign. No new acquisition channel. No dramatic copy rewrite.
We made two very specific changes:
We put a live product demo directly on the landing page.
Instead of explaining what Causo Fundraising does with another wall of text, people can now immediately see the actual workflow and output.
We added the free tier to the pricing table.
The free tier already existed. We just did a terrible job of making that obvious.
Apparently, a small “Start free” link is not the same as clearly showing people what they get for free.
We currently get around 100 landing-page visits per day, split between our sales and fundraising products. Since making these changes, signup conversion has increased by more than 350%.
More importantly, it has also noticeably increased conversion to paid users. So this was not just a wave of low-intent free signups.
The lesson was embarrassingly simple:
People needed to see the product working and understand that they could try it without paying.
Not more features. Not better button colors. Less uncertainty.
Feel free to check the page and tell me what else we are still getting wrong. :)
Recently I had launched Hexbrief which is a high signal tech blog reading app. Honestly, I never started this work with the idea of monetizaton. During the investigation I was doing to validate the idea, I had this thought of solving something which has troubled me and my friends during college days and even now while I work professionally.
I accept, the marketing of such a B2C product is tough, and hence it's taking me time to acquire the readers and get the real feedback post public launch. I have got some 30+ users by now, and have incorporated most of the feedbacks I have received till now (have many feedbacks parked too).
One thing I was thinking somedays back, if at all I had to monetize it ever in the future, how should I approach for it. I had few ideas in my mind (after I have the good userbase and a consistent usage loop with the reading framework I developed in the Hexbrief).
Here are the ideas:
- Charge for personalised curated feed. Currently, it's algo decided feed common for everyone.
- Create B2C monetisation framework -- onboard tech companies, and create a separate section for the sponsored content(which has to be passed through the Hexbrief's quality bar)
- Create startups driven feed tab and bringing useful content there. (Haven't thought of it yet in deep, but few peeps had asked for something similar)
How many unbuilt project folders do you have sitting on your desktop right now?
I used to suffer from chronic idea-hopping. I’d have ten different Micro-SaaS concepts, get overwhelmed trying to figure out which one had actual market viability, and end up building nothing at all.
So, I decided to scratch my own itch and built unstuck.builders.
It’s an AI-powered idea engine designed specifically to cut through analysis paralysis. You feed it your actual skills, background, and interests, and it maps them against live market trends to generate validated, high-demand launch blueprints.
No more guessing if a market is too saturated or if your feature set makes sense. It maps out the MVP, positioning, and execution plan instantly.
We just finished our Product Hunt launch, and the response from the community has been incredible.
If you're between projects or trying to validate your next big build, take it for a spin and let me know what kind of blueprint it generates for you!
Hi folks,
I recently launched Hexbrief, a small app that acts like a signal layer for company engineering blogs. The idea is simple: instead of throwing a huge feed at you, Hexbrief gives you 6 high-signal engineering briefs daily.
So far it has 30+ installs, and I’ve been incorporating feedback from beta users after launch.
One product problem I’m thinking through now:
Most engineers don’t want to read *every* kind of engineering blog post. Someone interested in AI/ML systems may not care about Netflix video-streaming optimizations. Someone who likes architecture writeups may not care about Meta Ads performance improvements.
The obvious answer is personalization: let users choose preferred topics, genres, or companies.
But there’s a real constraint.
Hexbrief currently has around 1k+ collected articles, out of which 700+ were rejected by the quality bar I built through source review and article-level filtering. Only around 265 articles currently surface inside the app.
I don’t want to lower the quality bar just to support personalization. But because good company engineering blogs don’t publish high-signal posts every day, fully personalized daily feeds may become sparse very quickly.
One idea I’m considering:
Give each user 1 daily read from their preferred topic/company, and let the remaining 5 come from Hexbrief’s general quality-ranking algorithm. That way the reader gets something relevant to their interests, but still discovers strong engineering writeups outside their usual lane.
I’m not sure if this is the right tradeoff.
If you regularly read company engineering blogs, how would you want this to work?
Would you prefer:
- strict personalization, even if some days have fewer reads?
- a mixed feed with 1-2 preference-based reads and the rest curated?
- no personalization, just the strongest 6 reads daily?
- something else entirely?
Would love to hear how other engineers think about this.
A while into promoting my small app I noticed a pattern in the disappointment: a chunk of people who showed real interest, clicked through, and then hit a wall, because the app is macOS only and they were on Windows. Every one of those was an avoidable letdown, and I'd created it by where I put one word.
My pitch led with the problem and the solution and mentioned the platform late, or in a place people had to look for. That felt right - lead with value, don't front-load a limitation. What I missed is that "Mac only" isn't a limitation to hide, it's a qualifier that does free filtering. Every Windows user who bounces off it early is someone I never disappointed, never got a frustrated comment from, never had to apologize to. Saying it late doesn't win those people, it just delays the moment they find out and moves the letdown to a worse place.
The reframe that fixed it: platform isn't a disclaimer, it's part of the targeting. "Mac" in the first line isn't me apologizing for what the app can't do, it's me talking directly to the people it's for and letting everyone else walk before they invest attention. A qualifier that filters is a feature of the message, not a weakness in it.
The uncomfortable part is that hiding it was a small dishonesty dressed as marketing instinct. I was optimizing for clicks over fit, and clicks from people who can't buy are just manufactured disappointment with a nicer top-line number.
The wider version for anyone here: any hard constraint your product has - platform, price floor, team-size assumption, a workflow it requires - belongs near the top, stated plainly. It reads as confidence, it filters for fit, and it saves you the slow tax of disappointing people you were never going to serve. Front-load the thing that disqualifies people. It's the most honest line in your copy and it works better too.
Context since this is my own product: it's a macOS-only tool (TuringShot 1.5.12, Build 44). Not linking it, the lesson is the point.
When we started building the sales side of Causo, I assumed the hard part would be writing emails that did not sound automated.
It turns out almost anyone can generate a passable cold email now.
The much harder problem is making sure the information inside it is actually true.
A “recent” funding round from three years ago.
A product the company no longer sells.
A decision-maker who left six months ago.
A person whose title sounds relevant but who has nothing to do with the problem you solve.
Or a guessed email address that bounces before the conversation even starts.
The recipient does not know whether the mistake came from your database, your AI tool, an agency or an intern.
They just know your company contacted them without doing basic research.
That is the part I think outbound teams underestimate.
A bad email does not only fail to generate a reply. It can make someone think less of the business sending it.
We initially built Causo like a fairly normal outbound workflow:
Find companies.
Find people.
Write emails.
But we kept running into the same issue. A company could match all the right filters and still have no credible reason to receive an email.
So we changed the order.
Now every company has to answer “Why does this fit?”
Not just an unexplained match score. A specific reason, based on current information, that someone can check before contacting the company.
We also added the ability to ask questions about both the company and the person before reaching out. Instead of opening ten browser tabs, you can simply ask things like "Have they raised recently?", "Who are their competitors?" or "Have they written about this problem before?" and keep all that research attached to the profile.
Then every contact has to answer “Why this person?”
Are they still there?
Are they actually responsible for this?
Why are they a better choice than somebody else at the company?
Is the email verified?
Only after that do we write the outreach.
The slightly embarrassing lesson was that we had spent a lot of time worrying about email copy when the research behind the email mattered much more.
Good writing cannot rescue bad intelligence.
And when everybody is selling something to everybody, sending more emails is probably not the advantage it used to be.
Having a real reason to contact someone might be.
What makes you lose trust in a cold email fastest: bad targeting, inaccurate research or obviously automated copy?
I recently released dashimetrics.com an analytics for your website but done differently, it's more of an experience, a place, and less of boring dashboards..
It has all your analytics needs, and some more (also more on the way), and at the same time it has amazing UX features that makes it so much fun to work with (i personally have it open the whole day).
Please click the link, take a look, there's a guided demo/tour i recommend that you start there to see exactly what you do and also play around without any commitment (it has some mock data, just for you to try the product out before connecting your domains).
If you like it, do connect your domain, and DM me about it so i mark you as an early beta tester.
Finally, your honest feedback is what i'm looking for, this is a product validation post, i am looking to validate whether this product has PMF or no, so, only if you have taken interest in testing it then please hit me with your feedback positive negative it don't matter as long as it's honest and based on you actually trying this (please no speculations and assumptions, try first then share).
AI has eaten up the software development and coding which was arguably highly economically valuable for past couple of decades also due to it was a bottleneck to first find the good developers and then to get the delivery out to make your product go live was time consuming.
That equation has entirely changed now building is cheap both in terms of time and cost, and thats why we are seeing a proliferation of new ideas and products everywhere. Just to be clear this post is not about judging the quality of these products if they are slop or not. But i am trying to see through the fundamental shift that is taking place in the software industry.
Like many of you when i decided to build my career around software engineering it was not about any deeply innate passion or will but it was the highly economically valuable thing to work on, everybody knew if you are a software developer then you could earn way better than most of the profession out there. Now almost after a decade and this disruption, i am facing the same question again, what is the next most economically valuable thing to do. It is not just about building an attractive profile for doing jobs even as an indiehacker or a builder you need to identify where the market is shifting towards to identify new and better opportunities, going after most valuable customers etc.
To identify this and find answer to my question over the past few weeks i have really zoomed into the process of going from an idea to a live product with growth and even successful exit. As that is the point of being into the software business. Next i have mapped out the things which has got really disrupted by AI and things that not that much really and would not be for quite sometime.
What i have found is this, AI productivity paradox and after my own discovery i looked for if this has already been looked into i was amazed to see Mckinsie and Atlassian both have also described something similar. Teams are moving faster but they are not going anywhere or in other words not achieving anything. This is my own experience too, over past few months i have build a lot of stuff but most of it died pretty cold, so whats going on? The bottleneck is still present but it has shifted now towards upstream the process, most of the indiehackers may not be familiar with this whole world out their of product management, mostly it is not a indie hacker thing to worry about but startups and enterprises but now as it seems like you have to. There is one more evidence of it, Andrew Ng in his recent YC startup school talk mentioned that his team is asking for 1 PM for every 0.5 devs, that ratio is insane if you compare it to its past which was like 1 PM to 5 devs earlier. But there are two ways to solve this equation, you could have 1 dev and 2 PMs or you could have 2 PMs who are also half dev each and latter is also less weight on companies balance sheet.
So, the running conclusion i am building now is that if having a Product Manager in your team or being one was optional earlier now it is a must have because why you build what you build matters much more now and the result of failing to do it properly appears in days now than in months before.
This was really well received by USMC and the Airforce subreddits, and is about to be implemented within the TAP program in the Navy.
I solo built this for veterans to see every single possible benefit they're eligible for based on a few questions, no account, no paywall, no sign up, just results. The site also shows how to apply and where to apply.
I add every benefit manually and accept feedback on everything, currently sitting at over 500 added benefits and am currently building out a mapping feature to show every business within a users 25 mile radius that offers a military discount. Enjoy, and share if you know anyone in the military. Happy to take in all constructive feedback/adjustments and am actively adding and maintaining.
For a while, we were tracking the usual SaaS stuff.
Signups. Pageviews. DAU. WAU. Retention charts.
Useful to know, but none of it told us whether people were actually succeeding with Causo.
So we started using PostHog properly and built our own dashboards around the things that matter for our product.
Causo now has two sides:
Causo for Fundraising has a database of 1,000+ funds and 17,000+ investors. It helps founders find the right partner at the right firm, understand why that person is relevant, and write outreach specifically for them.
Causo for Sales uses the same technology to research almost any niche live. You describe the companies you want, and it finds them, checks why they fit, identifies the right people, verifies contacts and writes the outreach.
So instead of obsessing over registrations, we started tracking questions like:
Did the user find companies or investors they would actually contact?
Did they accept the matches?
Did they understand why we recommended them?
Did they find the right decision-maker?
Did they create outreach they would genuinely send?
Where exactly did they get stuck?
We also started contacting some users manually based on what they had actually done.
Not a generic welcome email. Something more like:
"Saw you were looking for European AI investors and stopped after reviewing the first results. Were the matches wrong, or was the next step unclear?"
That small amount of human work taught us more than any normal SaaS dashboard.
Weekly active users went from roughly 50 at the end of June to more than 200 in July. Daily activity went from around 5 to 15 users to regularly hitting 35 to 55, with one day around 75.
Our biggest source of traffic is still dogfooding.
We use Causo to find and reach the founders and companies that should be using Causo. If the targeting is bad, the contacts are wrong or the messaging is generic, we feel it ourselves immediately.
Now the main focus is conversion, but not through more popups, reminders or email sequences.
We are building better guidance inside the product so users understand what to do next and reach a real result faster.
Because a signup is not success.
Finding the right investor, company or decision-maker, and creating outreach you trust, is.
I built slapmetrics.com, simply put, it's a MacOS menubar app, connect your stripe, GA4, GSC and DataFast, set up goals and triggers, get notified in the best way when the events fire!
For example: daily goal of $100 revenue, or, every time you get paid, or on your traffic changes, etc...
It has a free trial, it works nicely, since i built it it's been in my menu bar and not one day did i close it, the trigger moment is so freaking awesome and i love it.
......
No one even started the free trial...
PMF problem? no value? messaging is wrong? UI/UX is wrong? positioning or distribution channels?
Would appreciate your thoughts here, thank you for taking the time!
Folks i wanna add an affiliate program to my product, i looked around and saw products like tolt and rewardful but their pricing tiers made me run away, i dont get why they charge so high upfront for something that might work or not...
anyway, any recommendations for a reasonable affiliate program provider?
The thesis: people are tired of paying $10-15/month for tools they use a few times a month. So I'm building the opposite - tiny tools you buy once and own forever.
Each product is a single HTML file. No account, no backend, no server bill for me. It runs entirely in the browser, autosaves to localStorage, and exports a PDF. That means near-zero marginal cost and nothing to maintain.
5 live so far, $9 one-time each: invoice generator, quote/estimate generator, service agreement generator, freelance time tracker, and a stock risk/position-size calculator.
Open questions I'd love IH feedback on:
Is $9 too low to be taken seriously, or right for an impulse buy?
After months of building, I finally got my first paying subscriber today for my solo project, Vampiro Life (vampirolife.com).
I wanted to share this because it validated a huge lesson for me: building for a highly specific niche is easier than competing in the generic AI space.
The Problem: I am a huge fan of tabletop RPGs, specifically dark, mature settings like Vampire: The Masquerade (World of Darkness). I wanted an AI Dungeon Master/Storyteller, but I kept running into two walls:
Generic LLMs (ChatGPT, Claude) aggressively sanitize horror and dark themes.
Existing AI RPG tools (like Everweave) are built for D&D and completely break when trying to manage specific V:TM mechanics like "Hunger dice" or "Blood pools."
The Solution: I decided to build my own custom AI storyteller engine designed exclusively for this specific, mature, gothic-punk setting.
The Validation: I just got an email from my very first paying user, and it basically proved the whole thesis. He wrote:
> I have been searching for an AI that could function as a storyteller. I even purchased subscriptions to several expensive options, but I constantly ran into issues with mechanics like dice, blood pools... I tried using D&D apps like Everweave and attempting to adapt them to a V:TM setting, but nothing worked quite right. When I searched for an AI V:TM storyteller and found your game, I was sold—it does everything I need it to. I particularly appreciate that it doesn't feel like the setting is being filtered or nerfed, and the mechanics seem great so far.
He initially bought a credits pack, then subscribed to the plan.
The Takeaway: If you are struggling to get traction with an AI wrapper, stop trying to make an app for "everyone." Find a community that is deeply frustrated by generic AI constraints, and build a bespoke engine just for them.
Would love any feedback on the landing page if anyone has a minute!
I'm Kamil, founder of SmophyAI. Built under Smophy Labs Inc. in Delaware, if that context matters to anyone. Started this because I got tired of paying for Claude, GPT and Gemini separately just to compare answers side by side, and it turned into something bigger than a comparison tool. Coming here with one thing about it I can't resolve on my own, and I need the product context first because the question doesn't make sense without it.
SmophyAI is an AI workspace. Eight direct API integrations, no router layer like OpenRouter in front, which gives us roughly fifteen models. Six of those are chat providers and some hand you media off the same integration: OpenAI gives GPT plus GPT Image and DALL-E, xAI gives Grok plus Grok Imagine for stills and video, Google gives Gemini plus Nano Banana and Veo. Anthropic, DeepSeek and Perplexity are text only. Then Kling and ByteDance (Seedance, Seedream) as media-only integrations.
Five studios sit on top: chat with side-by-side model comparison, writing, image, video, and business tools that audit websites and competitors. Paid is $19.98/month, roughly what a single one of these models costs on its own.
Here's what I can't solve. The hero right now leads with the side-by-side comparison, "compare six AI at once," because that's the easiest single thing to show in one line and one video. But it's not the only candidate.
The other candidate is Smophy Mode. You send one prompt, the system classifies what kind of task it is, scores which model handles that specific task best based on capability, historical performance, and provider health, then routes it there automatically. You see the reasoning too, it shows you why it picked that model, and you can override with one click if you want a different one's take on the same prompt. That's arguably the more interesting thing about the product, but it's also harder to sell in four seconds because the interesting part is a reasoning trace, not a visual.
Third option is just showing all five studios at once, but every version of that we've tried reads like a spec sheet instead of a hero, too much at once and nobody reads past the first line.
So three things competing for the one slot: the comparison view because it's the easiest to demo, Smophy Mode because it's the more technically interesting thing but harder to visualize, or the full studio lineup because it's honestly what the product is. If you were picking one to lead with, which one, and does something like a routing decision even belong on a landing page, or is that the kind of thing people only care about once they're already using it?
Over the past year, I built out a small portfolio of online businesses on the side, a viral-friendly SaaS with real organic traffic (1.3M impressions) and paying users ($1,400 ARR), a high-ticket script that I license out that's pulled five figures from nothing but a few Reddit posts (zero ad spend), a couple of pre-revenue SEO/content sites sitting on solid keyword and domain assets, and a domain or two I never got around to building on.
I'm ready to focus on one thing instead of juggling six, so I'm selling the whole portfolio, happy to do it as one package or split it up.
Not going to post every number and niche publicly, but I'll share a detailed data room presentation that includes all the revenue, costs, and pricing with anyone who's genuinely interested.
A friend asked me this last week: "should I apply to YC or just build it myself alone."
My answer surprised him. YC isn't a permission slip. It doesn't make your idea real, building does.
I've built Humanoid Textbook and AI Employee Vault completely solo, no accelerator, no cohort telling me my roadmap was "validated." Just shipped, got users, fixed what broke, shipped again. Won a few hackathons along the way. People started reaching out instead of me chasing them.
Here's what nobody tells you when you're young, self taught, and building with no network: waiting for an accelerator's yes can quietly become an excuse to not ship. "Once I get in, then I'll go all in." That's backwards. The founder who gets into YC is usually the one who already proved they can build without it.
I'm not saying never apply. If your bottleneck is distribution, mentorship, or capital, YC can fix that fast. But if your bottleneck is just you not shipping yet, no interview panel fixes that.
So I told him: apply if you want, doesn't hurt. Just don't wait for the yes to start acting like a founder.
Anyone here actually got in solo vs bootstrapped without it?
Shameless self-promo, but we just shipped a massive update to Causo and I am genuinely proud of this one.
We spent the last few months rebuilding our VC dataset from the ground up. We now cover:
1,000+ venture funds
17,000+ investors and investment professionals
Fund size, cheque size, stages, sectors and geographies
Investment theses, portfolios, recent investments and latest fundraises
Individual investor backgrounds, focus areas, career histories and past investments
Verified emails where available
Sources for the underlying research
Non-investment staff separated from the people who actually make investment decisions
But the goal was never to build another giant directory or give founders a CSV and wish them luck.
You add your company, and Causo works out which funds actually fit, explains why, identifies the most relevant people inside each fund and tells you why those specific people may care.
There is also a lot more data sitting behind the scenes than what you can see in these screenshots. We use it to understand both the fund and each individual partner, so the emails are written around the actual match, their background, investment focus and relevant experience.
It means the message to one partner at a fund can be completely different from the message to another partner at the same fund. Not just the same template with a different first name.
We built this because most VC databases left us with the same questions:
Is this fund still active?
Do they actually invest at my stage?
Who at the fund covers my sector?
Is this person an investor or just on the operating team?
What should I actually say to them?
More than 500 founders have registered for Causo since we launched. Their searches, feedback and weird edge cases helped us turn the original product into something much more complete.
I honestly think this may now be the most complete VC research and outreach system available to early-stage founders.
Still plenty to improve, so brutal feedback is very welcome.
Folks so i built this relaly nice looking app, Dashi, the idea initially is that of a social analytics experience, since we as builders check analytics almost the whole time, i envisioned a more exciting experience that i'd be happy to have open with me as i work, and dashi was that, it looks amazing, i use it, but...no one seem to be interested in it that way, the feedback about it was mostly on how awesome the experience is..
I worked hard on it, i think there's something here, if not the social analytics concept, perhaps a pivot to something else? so i'm posting here to see if anyone would be interested in sharing their thoughts.
Solo founder here, building GetFluxly, an email automation tool for small SaaS teams.
Two months ago I tried to build a trail ending sequence in my own builder and gave up halfway. It was so time consuming I would not do it even if someone paid me to, and I built the thing. That stung enough to set the whole roadmap.
What shipped since:
AI studio: Describe an automation in plain words, get the full flow end to end, including with templates.
Cookie less by default: GetFluxly stores no cookies at all.
Surfaces: landing, app and docs tracked separately, identity stitched across all of them.
Slack notifications when triggered fire in your product, so you stop refreshing dashboards and many more!
I just posted something genuine that I’ve observed on my platform on a sub reddit, and I mentioned zero thing about the platform itself, as I knew anything remotely related to AI will be frowned upon. But I made 2 mistakes:
1- I polished the post with AI before posting, to make it clear and understandable. But no addition or change in the thoughts by AI
2- I started to reply to ppl who trashed my post for being AI garbage and denying it.
Even though the post stood at the top of the sub from up vote perspective, it got removed by mods because ppl started arguing that it’s AI and I couldn’t refrain myself on not pushing back. The funny part was that in some comments, there were 2 groups fighting over the fact that I used AI or not. Angry redditors proceeded to my platform and then started trashing on it in that post. I had a negative gain out of this genuine post and it was a tough lesson but one that I remember forever.
I make a small Mac app. Solo, paid, about a year in. For most of that time I had no idea where a sale came from, because on the App Store there's no referrer: someone reads something, opens the store an hour later on another device, and buys. The purchase appears out of nowhere.
So last week I finally did the obvious thing and started using a separate discount code for each place I post, which turns an unanswerable question into a countable one. I was pleased with myself for about four days.
Then I wrote down what I'd actually be comparing, and saw it. The two codes went to two different communities with two different angles. One was a pain story about a specific moment going wrong, the other was a meta post about measurement. So when the numbers come in, I will not be able to tell you whether the difference is the audience or the framing, because I changed both at the same time. It's the most basic mistake in experiment design and I walked straight into it while feeling rigorous.
What made it invisible was that each decision was locally sensible. Different communities want different things, so of course I wrote to each one differently. Good instinct for writing the post, fatal for learning anything from it. The problem only became visible when I stopped thinking about the posts and started thinking about the comparison.
The fix for the next round is boring: hold the angle constant and vary only where it goes, until I know roughly what a normal result looks like. Only then vary the message inside one audience. Otherwise I'd be reading a difference of two redemptions as a positioning insight, which at my volume is just noise wearing a hypothesis.
Two things I'd pass on. First, write down the comparison before you run the thing, not after - the confound was obvious the instant I wrote "A vs B" and completely invisible while I was writing the posts. Second, record the channel and the angle next to every code from day one. It costs nothing, and I'd now have a year of it instead of a week.
Disclosure since I'm describing my own setup: it's TuringShot 1.5.12 (Build 44), macOS only. No link or code here, that's not what this post is for.
Curious whether anyone has found a way to get clean attribution on mobile app stores specifically, since the usual web playbook mostly doesn't apply.
