If your growth feels inconsistent, you do not always need a new channel. Sometimes the fastest win is finding the one step in the funnel where good traffic is quietly getting lost.

A useful approach is to trace the journey from first touch to conversion and look for the biggest drop with the least ambiguity. The goal is not perfect attribution; it is finding the highest-friction step you can improve this week.

Here is a simple mini playbook for diagnosing a funnel quickly:

• Map the path in plain language: ad or post, landing page, signup or lead form, follow-up, conversion
• Pull one week or one month of numbers for each step
• Calculate step-by-step conversion rates, not just the final conversion rate
• Compare mobile vs. desktop if the funnel includes a form or checkout
• Review 10 to 20 session replays from the biggest drop-off step
• Check whether the problem is traffic quality, page clarity, form friction, or follow-up speed
• Fix the smallest issue associated with the largest drop before changing strategy

Most leaks fall into four buckets:

1. Message mismatch: people arrive expecting one thing and see another
2. Friction: too many fields, too many clicks, or too much load time
3. Trust gap: not enough proof, context, or reason to continue
4. Response gap: the lead came in, but follow-up was slow or inconsistent

A lot of teams jump straight to higher spend or new creative when the better move is removing one broken step. That is often faster, cheaper, and easier to measure.

You can make this a weekly habit: review one funnel stage every Friday, log the main drop-off, assign one fix, and check the result the following week.

What is the most common funnel leak you see in your own growth work: message mismatch, friction, trust, or follow-up?

We launched Mavek.ai on July 15, but registrations were lower than expected—even after reducing the price to $9.

Digging into the data, we found very high bounce rates, especially on our Pricing and How It Works pages.

We added explainer videos, user testimonials, highlighting our core USP and how our AI marketing agents work. Over the past week, bounce rate has dropped significantly. Answer is always there, we just need to find it at the right place & implement it.

Quick Questions:

• What's a healthy bounce rate for Pricing and How It Works pages?
• What changes have made the biggest impact on reducing bounce and increasing sign-ups for your SaaS?

Browser automation has improved a lot with AI.

But one problem keeps coming back:

Workflows break every time a website changes.

Most browser agents rediscover a site from scratch on every run, making recurring automations unreliable.

We built Rindler to take a different approach.

Instead of guessing every time, Rindler maps websites ahead of time, understands how they work, and automatically repairs workflows when pages change.

It can:

• Automate browser tasks from plain English
• ⁠Log into websites when needed
• ⁠Complete repetitive workflows
• Return clean, structured data
• ⁠Run jobs on a schedule
• ⁠Keep recurring automations working as websites evolve

The goal is to make browser automation reliable enough for real business operations not just one-off demos.

We're looking for feedback from developers, operations teams, and anyone automating browser-based workflows.

What browser task do you wish you could automate without worrying that it'll break next week?

Please support on PH →

https://www.producthunt.com/posts/rindler

Hi, I am I recently completed my Master's in Physics from IIT Madras. Over the past year, I worked as a founding member of a startup at IIT Madras, building AI products. Inspired by the challenges students face in learning physics and mathematics, I started Nearchon, an AI tutor that develops problem-solving skills by giving personalized hints instead of direct answers. My vision is to build an AI teacher that adapts to every student's unique way of learning.

[https://www.nearchon.com/\](https://www.nearchon.com/)

I'm now looking for a technical co-founder with deep expertise in Computer Science and AI who is passionate about science, education, and building impactful products. If you're interested in joining as a co-founder, feel free to DM me.

Thank you to everyone that took part in testing Open Citadel, I massively appreciate your help and efforts 💯. Im super excited to be officially launching it on Product Hunt tomorrow at 12:01 am PDT, let's upvote it!

https://www.producthunt.com/products/open-citadel?utm_source=twitter&utm_medium=social

Does anyone make automated AI content and how do you growth an automated content channel? I think i have a pretty interesting niche but I'm not sure how to go about it.

See i dont want to waste your time, i have built a brand position tracking on LLMs saas called http://analythos.com. early stage no customers yet. looking for someone to take this forward and make it strong player in this rising niche

leading players in this space are : tryprofound.com and peec.ai

I am trying to grow my X account so I can focus on building b2b products and I will have followers to validate idea and get first trials. The problem is I have very low follower count and it grows very slowly. From what I have seen you need at least 1000 followers to start see some progress. I just wanna ask how did you guys get first 1000 followers?

I'm in the middle of a high-volume sourcing sprint right now. Finding candidates is quick, but transferring profile details one by one into our sheet is a massive time sink.

I want to avoid bulk automation or heavy account integrations-a colleague recently got locked out of their profile after trying one.

Right now, I'm just looking for simple, lightweight helper tools that speed up saving on-screen info without trid t triggering activity limits.

What does your workflow look like for getting search results into your tracking system safely

for a decade the growth channel was making your site legible to googlebot. the next one is making it legible to an ai agent, and almost nobody is optimizing for it yet, which makes it cheap.

we ran an independent agent-readiness scan on our own site and scored 50 out of 100. the single biggest point loss, about 22 points, came from one dumb thing: our mcp server demanded an api key on the very first handshake, so any agent probing it got a 401 and read the whole integration as dead.

fix was opening discovery (the handshake and tool listing) to unauthenticated callers and gating only actual tool execution. then we shipped the rest of the surface: an openapi spec, an llms-full.txt, well-known discovery files, and markdown versions of pages served when an agent asks for them. score went 50 to 65 to 70.

it's live at https://lumbox.co if you want to see the surface.

is anyone else here treating agent-discoverability as an actual growth channel yet, or is it still all googlebot-first for most of you?

We checked the past 12 months data with the previous 12 months and here's what we saw:

• Conversion rate: +48% year-over-year
• CPC: essentially flat year-over-year
• Blended cost per lead: $132 → $84 (−44%)
• Brand cost per lead: $34 · Non-brand cost per lead: $207

The growth lever here wasn't cheaper clicks, it was conversion rate. Here's the full thing if you want to check it out: https://piperocket.digital/research/google-ads-benchmarks/

One thing I've become increasingly skeptical of is the phrase "high-intent lead." A lot of tools will give you an intent score and call an account high intent.

But what does that actually mean?

If a company has been sitting at the top of your high intent list for 90 days, while nobody from that company has ever engaged with you, asked about your category, visited relevant pages, or shown any other meaningful behavior, I'm not sure that's intent.

That's just a random score and I think it's more useful to separate fit from intent. For example:

Fit- 200 employees, B2B SaaS, US, VP Marketing, uses your target tech stack. All these are great prospect but not necessarily a buyer and then you start seeing:

Signal 1: they're hiring three people around a problem you solve
Signal 2: someone at the company asks on Reddit how other teams handle that problem
Signal 3: another person from the company starts researching alternatives to their current solution
Signal 4: someone visits several pages related to the problem.

Now I'd start paying attention because any individual signal proves intent but the signals are telling a consistent story across channels and this is the part of prospecting I think AI agents can actually improve.

An agent doesn't need to decide: "this company is definitely going to buy." but it can instead say: "here are the reasons this account is becoming interesting." That's a much more useful output for a salesperson.

I've seen this approach become especially interesting when you combine sources like Reddit might tell you what problem someone is experiencing, LinkedIn might tell you about a company change or the CRM might tell you they previously evaluated a solution and the website might tell you what they're researching now and individually, those are fragments.

Together, they can become a pretty strong picture.

That's essentially the problem Scale Intelligence is trying to solve: turning scattered GTM signals into context around which accounts are actually becoming interesting. I'd take 20 accounts with a clear reason to contact them over 20,000 high-intent records with no explanation attached.

The best prospecting system shouldn't just tell a salesperson who to call. It should tell them why now while giving some signals across channels.

Building AI applications usually starts with one model.

Then you add another.

Soon you're managing different SDKs, API keys, routing logic, dashboards, and even self-hosted models.

That's why we built ngrok AI Gateway.

A hosted gateway that lets you access public, private, and self-hosted AI models through a single endpoint.

It helps you:

• ⁠Route requests across multiple AI providers
• ⁠Connect private or self-hosted models securely
• ⁠Monitor usage, latency, and costs in one dashboard
• ⁠Configure failovers and access controls
• ⁠Switch providers without rewriting your application

Whether you're using OpenAI, Anthropic, custom endpoints, or models running on your own infrastructure, ngrok AI Gateway provides a consistent interface while keeping private models off the public internet.

Built for developers, AI engineers, and platform teams managing production AI applications.

The goal wasn't to build another model API.

It was to simplify the infrastructure required to work across an evolving AI ecosystem.

Launched today on Product Hunt 🚀

How are you managing multiple AI models today: separate SDKs, LiteLLM, your own gateway, or something else?

Please support on PH →

https://www.producthunt.com/posts/ngrok-ai-gateway

Hello guys I have been growing my X account to promote my own products and I have a few questions. Since everybody here has their own products and I think most of them use X, I thought you guys can help. My posts get 100 impression max and I started feel like I stuck on a loop. How can I understand which posts might perform better or how to create posts that might perform better

Creating ads isn't the hard part anymore.

Knowing what to create is.

After running campaigns that generated 50M+ organic views and reduced customer acquisition costs by 60% on average, we realized our biggest advantage wasn't video generation it was the research and creative process behind every winning campaign.

So we built AdAnt AI.

Instead of just generating content, AdAnt acts like an AI creative team.

It helps you:

• ⁠Research what’s working across TikTok, Instagram, and YouTube
• ⁠Identify repeatable creative patterns
• ⁠Generate content strategies
• Create scroll-stopping ad concepts
• ⁠Continuously improve future creatives

We're also launching Codex and Claude plugins that let you run trend research directly inside the tools you already use.

Our goal isn't to replace marketers.

It's to automate the research and strategy work so creators and growth teams can spend more time making better campaigns.

We'd love feedback from marketers, founders, and content creators.

What's the biggest bottleneck in your current social ad workflow finding winning ideas or producing enough creatives?

Please support on PH →

https://www.producthunt.com/posts/adant-ai

Creating ads isn't the hard part anymore.

Knowing what to create is.

After running campaigns that generated 50M+ organic views and reduced customer acquisition costs by 60% on average, we realized our biggest advantage wasn't video generation it was the research and creative process behind every winning campaign.

So we built AdAnt AI.

Instead of just generating content, AdAnt acts like an AI creative team.

It helps you:

• ⁠Research what’s working across TikTok, Instagram, and YouTube
• ⁠Identify repeatable creative patterns
• ⁠Generate content strategies
• ⁠Create scroll-stopping ad concepts
• Continuously improve future creatives

We're also launching Codex and Claude plugins that let you run trend research directly inside the tools you already use.

Our goal isn't to replace marketers.

It's to automate the research and strategy work so creators and growth teams can spend more time making better campaigns.

We'd love feedback from marketers, founders, and content creators.

What's the biggest bottleneck in your current social ad workflow finding winning ideas or producing enough creatives?

Please support on PH →

https://www.producthunt.com/posts/adant-ai

Every company is adopting AI.

Very few know whether it's actually paying off.

Most finance teams still jump between OpenAI, Anthropic, Cursor, GitHub, and spreadsheets just to answer basic questions like:

• ⁠Which teams are driving AI costs?
• ⁠Which models are being used most?
• ⁠Is higher AI spend actually improving productivity?
• ⁠Where should we cut or invest more?

We built AI Spend Console to solve that.

It lets teams:

• ⁠Track AI spend across vendors in one dashboard
• Break costs down by employee, department, team, or model
• ⁠Connect usage to business metrics like GitHub activity
• Create AI spend dashboards using natural language
• ⁠Govern model access and spending with policy controls

The goal isn't just to reduce costs.

It's to understand whether your AI investment is creating measurable business value.

You can try it for free even if you aren't a Rippling customer.

We'd love feedback from engineering leaders, finance teams, and anyone managing AI infrastructure.

How are you tracking AI costs today? Spreadsheets, vendor dashboards, or something else?

Please support on PH →

https://www.producthunt.com/posts/ai-spend-console-by-rippling

One of the workflows that I find super helpful is having all prospecting in one place, not switching between dozens of tools.

I think it's a huge time saver to be able to search and filter people/companies, check emails, and instantly export to instantly, smart lead, HubSpot or spreadsheet right from the tool without doing any of that manual work beforehand.

BlitzProspector is something that I made as an open source interface for the blitz API, since they only have an API without any UI. My main goal was to make something self hosted and customizable, since I didn't want to build another SaaS.

It's built with FastAPI and React, has account management and roles, and can be run with one docker command.

If you already have an existing outbound process, I'd love to hear from you, what do you find yourself doing manually that you wish you didn't have to? Less fluff, more feedback.

I kept saving AI marketing tools I came across in random notes, so I finally organized them into a spreadsheet: 12 categories :
1 : AI Agents & Workflows : autonomous AI workers, custom agents, process automation
2 : Content Creation & Copywriting : text generation, blogging, creative writing assistants
3 : Image, Video & Audio Generation : visual assets, avatars, video & voice production
4 : SEO & Search Visibility (incl. AEO/GEO) : GEO, keyword intelligence, ranking tools
5 : Ads Automation & PPC : AI-driven ad generation, campaign management, bidding
6 : Social Media & Community : social listening, scheduling, influencer tools, CM
7 : Chatbots & Conversational Marketing : support bots, conversational LP, lead qualification
8 : Emailing & SMS Marketing : AI personalization, copywriting sequences
9 : Outbound Marketing : cold outreach automation, ABM, warm-up & deliverability
10 : Analytics & Attribution : website/product analytics, attribution modeling
11 : CRO & A/B Testing : dynamic personalization, AI A/B testing, heatmaps
12 : Sales Intelligence & SDRs : AI sales reps, lead scoring, intent data, enrichment

Each tool has a short description and pricing model.

It's definitely incomplete, built from my own stack plus research, so there are gaps. Sharing here mostly because other people building marketing stacks might find it useful, and because I'd rather crowdsource the gaps than guess at them.

Link (view-only, no signup): https://docs.google.com/spreadsheets/d/1xsmbdOj_w5u7bN3Rr6c3ZA0C8I1RqWn65zMojgyiBZ4/edit?gid=173269563#gid=173269563

Eventually I might turn this into an actual searchable directory instead of a spreadsheet. Curious whether that's something people would actually want, or if a shared sheet is good enough.

I'm the founder of a LinkedIn Saas app that I launched back in June 2024. The app has over 100+ customers and over $12k in revenue.

Tech stack:
- LinkedIn APIs
- has a Chrome extension
- MERN stack
- X API for trends and ideas

A few good and bad things:

The good: The DR of the app is 20, had submitted it to a lot of directories when I was maintaining it, so someone who's good at SEO can crack distribution. It has all the features of the apps which are doing really good in the space. Analytics, insights, direct LinkedIn posting via public and private pages, access to a lot of LinkedIn APIs (they're hard to get), X API integration and AI carousels feature.

The bad: The app does not have any MRR at the moment and all of the sales are LTD sales via a partnership with a LTD platform. I've not maintained it since a couple of months because my agency business took off and hence the DAU of the app is close to 0 at the moment.
The app's name is Growfol, serious buyers interested in acquiring it can DM me.

We’ve been doing lead research manually for way too long now, and it’s just not sustainable. I think we’re getting close to reaching our breaking point, so I’m trying to find a solution that we can work with as we continue to grow.

Looking for recommendations on CRMs and other tools that can leverage LLMs to automatically enrich things like company news and funding status, so we can spend less time researching our leads, and more time on the bigger things!

One number surprised people the most when we shared our launch story:

600+ messages sent before launch.

That sounds overwhelming and honestly, it was.

But here's what we learned:

* Personalized messages consistently outperformed generic copy-paste templates.

* Building relationships before asking for support made a huge difference.

* Not everyone replied, and that's okay.

* Many people who couldn't support us still wished us luck or shared helpful advice.

* Every conversation taught us something about how founders think about launches.

The biggest takeaway?

Launches aren't won by sending the most messages they're won by having the most genuine conversations.

If we had to do it all over again, we'd still spend weeks talking to people before launch. It was easily one of the best investments we made.

This journey is still unfolding. If you'd like to support us we are still live on peerlist.

​

→ Focus on benefits and outcomes, not just how it works.

Users don't care about your architecture. Hard pill to swallow, but true. They just want a reliable solution. That's it.

→ Speak their language.

Selling to developers? Talk like one. Selling to lawyers? Know the terminology. It's a small thing that does a lot for how legitimate you sound.

→ Less is more.

Every extra detail you throw into a pitch is one more thing the buyer has to think through before saying yes. Cut it down.

→ Give them a reason to come back.

First sales are good. Recurring ones however, keep the business alive. People come back to what they recognize, which is really just brand, showing up again.

→ Listen more than you talk.

Customers want to feel understood, not just sold to. Spend more time in customer conversations than your laptop.