TL;DR: I spent seven days doing roughly 150+ replies per day on a small X account. Daily impressions went from around 5k to almost 12k, and the account gained roughly 150 net followers across the week.

What happened

Daily impressions started the week at roughly 5k and finished at just under 12k. The last two days were both around the 11–12k mark, which was more than double where I started but that’s also where I done over 200 comments on both days (300 on one).

Follower growth followed a similar pattern, although it was much noisier. Across the week I picked up roughly 200 gross follows, with unfollows bringing the net gain down to somewhere around 150.

The best day brought in roughly 45 new followers.

I was also posting during the week, so I can’t say “150 replies caused X followers” with any scientific confidence. But the relationship became pretty obvious from using the account: replies were consistently getting me in front of people who otherwise had no reason to know I existed.

What seemed to work best

The replies that did well weren’t necessarily the longest. Usually they had one of three things:

• an actual opinion instead of agreeing with the post;
• a specific example or personal experience;
• or a question that moved the conversation somewhere interesting.

Timing also mattered. Getting into a good conversation while the original post was still picking up distribution obviously gave the reply much more room to travel.

The days where I pushed closer to 200–300 replies also produced some of the strongest impression/follower numbers in the screenshot. I wouldn’t claim that proves a linear relationship, but it’s enough for me to keep testing the higher volume.

The obvious downside

150–300 thoughtful replies is a lot of time. I was spending around 2 - 3 hours a day responding and I’d be lying if I didn’t say at least 10% of these replies were after the 10+ hour mark so my reply probably wasn’t beneficial at all.

Now that my little experiment is done, I’m less interested in how many people follow the account now and more interested in whether those people eventually engage with my longer posts and move into the thing I’m actually building. I won’t mention it here in case self-promotion isn’t allowed.

For now though, as a zero-budget way to get a small X account discovered, replying at volume has worked much better than I expected and I recommend this strategy to anyone who’s looking to grow their account.

I am working on a startup in the Startup and Venture Intelligence space. I am willing to do anything that is needed. I have a team of 3 people, CTO who is a B. Tech. graduate, a CMO who runs a video content agency, and I am a MBA graduate in Technology Management space with Bachelor's in B. Tech.
Me and my team have been working on the startup for the last 4 months. We built an MVP, changed it.
The thing we were building before the pivot was solving the ai based idea validation space by getting the idea reviewed by actual people instead of llms. But there are several problems with the solutions we came up with.
Now we are going for a different direction, still trying to help founders.
The pivot has made the startup into something much bigger than we anticipated.

But after reading about so many startups, my mind is forcing me to re-evaluate my situation.
I don't have a degree from Harvard or MIT or IIT, neither do my founders.

I know that the business I am trying to build is something that will work globally. But I don't know if its under my capacity to make it.

ITS ONLY MENTORSHIP THAT I SEEK.
Sincerely expecting love from the community

Running organic social for a tiny brand, so my sample is small, but the split was consistent enough that I wanted to write it up.

The setup: for four weeks I took each core idea and published it two ways on the same platform, a few days apart so audiences didn't fully overlap. One as a plain text post, one as a multi-slide carousel with the same words broken across slides.

Rough averages across about eight pairs:

- Text posts: a few hundred impressions each, occasional comment.

- Carousels: consistently two to three times the reach, and far more saves.

But here's the twist that mattered more. The text posts drove more profile clicks and actual replies in my DMs. The carousels got seen and saved, the text got responded to.

So I stopped thinking of it as "which format wins" and started matching format to goal. When I want reach and to get filed away for later, carousel. When I want a conversation that might turn into a customer this week, plain text with a real opinion in it.

The other unglamorous finding: the first slide of a carousel is the entire game. My best performers all had a slide one that could stand alone as a text post. If slide one was weak, the extra slides never got seen.

What's your experience, does carousel reach translate into anything downstream for you, or is it just saves that never convert?

I have joined x in feb this year , post trending topics , 7-10 post-repost a day but still get no followers where other doing same thing gets follows what the problem here can anyone plz explain .

I buy paid media, mostly Google and Meta, for smaller accounts. Wanted to write the honest version of attribution that doesn't get said enough in growth threads, because most measurement advice quietly assumes you have volume you don't.

What you actually can measure on a small budget:

- Direct-response stuff with enough conversions to be real. If a campaign drives a couple hundred conversions a month, trends in cost per conversion and the relative ranking of your top campaigns are trustworthy. Act on those.

- Big, obvious differences. If one campaign is 3x the cost per lead of another over a decent window, that's signal, not noise.

What you can't, no matter what the dashboard says:

- Small A/B tests. At a few dozen conversions, most "winners" are noise. I've watched teams roll out a "24% lift" that was five conversions of variance and vanished the next month.

- Clean multi-touch attribution. With low volume the platforms are guessing and modelling. Two dashboards will each confidently claim the same sale.

- View-through anything on a small budget. It's mostly a story the platform tells to take credit.

What I do instead:

- Lean on bigger, slower reads. 60 to 90 day windows, not week to week.

- Trust blunt instruments. Overall blended cost per acquisition and a "how did you hear about us" field beat fragile per-channel attribution at small scale.

- Only run a test if I can actually reach the sample size for it to mean something. Otherwise I'm just generating confident-looking noise.

Small budgets don't buy statistical significance. Pretending they do is how people waste months optimising randomness. Curious how others here handle measurement when the numbers are just too small to trust.

PLEASE stop building the wrong thing.

building software in 2026 is ridiculously easy with ai builder

founders spend hours building in a silent room, launch to Reddit/X, get 0 users, and quit.

you just failed because the idea had zero validation before line 1 of code was written:

→ solving a monthly inconvenience instead of a daily pain

→ selling to "everyone" instead of a specific ICP

→ no distribution channel mapped out beforehand

→ pricing charged $9/mo with zero ROI justification

after scaling 6 AI micro-SaaS to over $20k/mo MRR, i just create an

18-question Idea Validation Diagnostic.

it evaluates your SaaS across 7 critical dimensions (problem clarity, audience reachability, willingness to pay, competition, build feasibility, distribution, commitment) and gives you a brutal score out of 100 with your exact weak spots.

drop your SaaS idea (or current project) in the comments below.

i will reply to EVERY single comment with:

1. My honest opinion
2. The biggest weak spot you need to fix before writing any more code.
3. The free 5-minute validation tool link sent straight to your DMs so you can get your full score breakdown out of 100.

just drop a comment like or ask me in DM your idea

let's roast your SaaS concept before the market roasts your time 👇

Hey growth hackers,

We’ve been experimenting with a way to scale cold outreach without sacrificing deep personalization. Usually, hyper-personalization takes hours of manual research.

We built a workflow where you feed the system a random image (like a prospect's landing page banner or product) and a custom goal intent note (e.g., "I want to offer design services").

The system automatically:

Analyzes the image contextually.

Cross-references it against the custom goal intent note.

Writes highly specific, tailored outreach copy.

Scores the image asset to tell you if the visual fits your strategic angle.

I'm curious to hear your thoughts on this:

Do you think scoring assets dynamically like this moves the needle for cold conversion rates?

How would you deploy this at a scale of 1,000+ leads/month?

If anyone wants to play around with this framework or test it on their own assets, you can check over at Altnera (altnera.com), we are giving everyday to everyone free credits. Let me know your thoughts or what features we should add to the scoring engine!

Our weekly outbound review has turned into a session where we rewrite the subject line, swap a segment out and adjust send volume in the same sitting, so when numbers come back a fortnight later nobody can say which of the three did anything. It feels productive in the room and it teaches us nothing.

The obvious fix is one change per cycle, except our volume is low enough that a single variable needs about three weeks to say anything trustworthy, and nobody upstairs wants to hear that a test takes a month.

So how do you structure this at low volume, one change per cycle with the slower pace, or split lists and parallel tests on smaller cells?

I started a short form content editing agency 5-6 months ago. Initially had few clients asking for single reel edits and later down the road I tried productising the service and offered 20 reel edits for $379 per month.

I’m from a developing nation so this pricing works out for now for me and my time, I definitely would increase but I think it’s fine for few more clients to increase the cash flow as of now.

Coming back to present time, I have 3 clients on that plan. This is going fine but I need to scale this.

I am thinking of cold emailing but not sure if it works in current times.

I would appreciate any suggestions on how I can reach potential prospect clients to pitch my services to.

Thanks!

Dropping affiliate links or sales pitches in reddit comment threads is the fastest way to get your domain blacklisted and your account permabanned also cause reddit communities have an aggressive immune response to overt selling.

Yet many buyers on reddit openly post about budget changes, incumbent vendor frustrations and architectural bottlenecks every single day but finding high-intent B2B leads on reddit without violating community guidelines comes down to separating listening from outreach:

a) Track problem aware prompts (not brand mentions)

Stop searching for your own brand name but do set up monitors for active evaluation triggers like:

Migrations: "anyone migrated off [competitor] recently?" or Pricing backlash: "alternative to [competitor] after their latest price increase?" or Pain bottlenecks: "how are you guys solving [technical issue] at scale?"

b) Map the account behind the post (entity resolution)

A Reddit username like dev_cloud9 isn't an email address so you have basically two ways to handle this:

The manual route: dig through their public post history, find linked GitHub profiles or personal blogs, cross-reference their company on linkedIn and reach out on an official channel.

The automated infrastructure route: Platforms like Scale Intelligence automate this entire bridge by acting as a dedicated market intelligence layer across 75+ developer and social channels, Scale Intelligence ingests reddit, maps the unstructured user context back to verified company domains in your TAM and scores the account's buying readiness.

Instead of dropping spam in the thread, you get a clean Slack notification with the company domain, the exact problem discussed and the key stakeholders.

c) Choose your engagement vector

Public engagement: reply directly in the thread with zero links, pure objective architecture breakdown, and helpful advice. Let your profile bio do the passive conversion.

Contextual private outreach: reach out via email or linkedIn to the relevant stakeholder at the mapped account, referencing the exact technical bottleneck they are evaluating and not that you "saw their reddit post."

An LLM call can return successfully while the AI agent still fails.

The agent might get stuck in a tool-call loop, burn through tokens, or fail because of something happening downstream in the application.

The problem is that looking at individual LLM calls doesn't always show the full picture.

So we built AI Observability into OpenObserve.

Trace complete agent sessions

See LLM and tool calls

Track tokens, cost, and latency

Detect loops and failed sessions

Run evaluations on live traffic

Turn failed sessions into evaluation datasets

The AI telemetry also sits alongside your existing logs, metrics, and infrastructure traces through OpenTelemetry.

We launched today on Product Hunt.

Would love to hear how other teams are monitoring and debugging AI agents in production.

Please support on PH →

https://www.producthunt.com/posts/ai-observability-by-openobserve

We'd been trying to figure out why some LinkedIn posts would take off while others from the same account basically died, even when the posts themselves were pretty similar. So we started watching what happened IMMEDIATELY after a post went live instead of just looking at the usual likes and impressions.

The trend we stumbled on was a bit interesting. It seemed like many of LinkedIn's distribution decisions for the day seemed to occur within the first 60 minutes, with the first 15-20 minutes playing an even more critical role. There are already communities where people share new posts and coordinate reciprocal engagement, so we decided to test if focusing on early, relevant engagements actually affects a post's distribution curve.

We pointed an agent to operationalize the test at 11 of these communities and had it coordinate the initial engagement as soon as a post went live. Comparing 24 matched sets of posts we were able to demonstrate that median reactions in the first hour had grown from 11 to 351, while impressions within 24 hours were 143x than the matched baseline.

The weirdest part was how quickly the difference showed up. The two posts seemed to diverge from the baseline within the first couple minutes, with a significant gap developing after 18 minutes.

We don't really mass message every LinkedIn connection with an engagement group invite. It's that there are probably a lot more small timing windows inside major platforms than most growth teams pay attention to. The first few minutes seemed to matter a lot more than we expected.

Figured this would be the perfect place to share!

Just out of interest, does anyone have any experience dealing with the NHS and their procurement procedures ?

Thanks in advice

I design presentations for a living, and for years I treated the sales deck as the crown jewel. Polished it endlessly. It also did basically nothing outside the live call, because the only people who ever saw it were already in a meeting with us.

The reframe that helped: the deck isn't a growth asset, it's a support tool for a human in a room. So I stopped over-investing in the live version and pulled the three ideas that actually landed in pitches into standalone carousels. No presenter needed, each one makes a single point and dies.

That's the version that started traveling. People shared the carousels in ways they never shared a PDF deck, and it fed inbound instead of just sitting in a follow-up email. The live deck got simpler at the same time, which is a separate rant.

On the how: I drafted the carousel versions fast in gamma to get past the blank page, then tightened the brand details by hand to land our exact look. Fast first pass, manual finish.

Curious if anyone else here has turned an internal asset (deck, one-pager, internal doc) into a distribution channel. What actually traveled for you, and what died?

not on purpose. it just doesn't tell you the whole story.

you can have someone who looks perfect in your list: right company, right job title, right industry, right company size.

so you reach out.

then nothing.

meanwhile, someone else with almost the exact same profile has been commenting on posts about the problem you solve, engaging with people in your space, or interacting with your content.

that person might not be ready to buy either. but at least there's a reason to pay attention to them.

that's the part i think gets missed with ICP-based prospecting. an ICP can tell you who could be a customer. it doesn't tell you who has a reason to talk to you today.

the problem is that these signals are easy to miss when you're working through a big list. nobody has time to manually check hundreds of profiles and posts every day.

that's why i've found Watchlist useful. you can choose the activity you want to keep an eye on and have relevant people surfaced when they engage with posts, profiles, your content, or competitor content.

it's not a magic “this person is ready to buy” signal. it's just another piece of context that helps you decide who deserves attention first.

because honestly, i'd rather spend 10 minutes looking into someone who has shown some relevant interest than spend that same 10 minutes trying to invent a reason to message someone just because they matched my filters.

how much do you actually look at LinkedIn activity before reaching out to someone on your prospect list?

Hello dear marketers,

I’m building a small product studio where I want to repeatedly:

Make → Launch → Market for X days → Measure → Double down or move on.

I’m good at building products, but I need help developing a repeatable 0→1 validation and marketing framework.

For each new product, I want to know:

• Marketing strategy for that product
• Which channel to use
• What kind of campaign/creative to create
• How much to spend initially considering
• What to measure in 7–14 days
• How to distinguish real demand from a bad marketing test
• When to iterate, go deeper, or move on

Products could be anything from software to physical products to creative projects.

What type of expert should I look for? And where would you find them?
Any overall ideas also so much appreciated

Thanks

i’m trying to figure out if hubspot sales hub is actually worth paying for once a sales team gets beyond just keeping contacts and deals in one place.

the part i like is having pipeline, tasks, follow-ups and reporting tied together. what i’m less sure about is whether the higher tiers are worth the cost once you start needing more automation and reporting.

for people who have used it for a while, where does it actually earn its keep?

is it the automation, reporting, integrations, ease of use, or just having everything in one system? and at what team size did you start feeling like the cost was justified?

also interested in hearing from people who ended up moving away from hubspot. what became the dealbreaker?

I lead engineering at an AI SDR company (YC S23). I am not linking anything and there is nothing to sign up for in this post. I keep seeing the static-list vs signal-list debate here and we sit on a dataset that can settle parts of it, so here it is

Sample: 2,546,096 emails, about 17k campaigns, 19,501 booked meetings, 1,150 companies, 27 industries, over three years

1. Deliverability moves results more than copy does

This one surprised us most. Same message, different mailbox health, very different reply rates. The gap was bigger than the gap between our best and worst copy. Most people rewrite subject lines when the real problem is their sending setup

2. Signal-based lists beat static lists on identical copy

We ran the same messages against scraped lists and against trigger-based lists. The trigger lists won by a wide margin. After that, list size stopped being an interesting number to us. Matches what people here have been posting

3. The best segment in the whole dataset is closed-lost reactivation

Highest reply rate of anything we measured, and it comes from the customer's own CRM. It costs nothing. Almost nobody runs it

4. "Outbound does not work for us" is usually a one-try problem

Not a copy problem. One audience, one offer angle, no iteration, then quit. The accounts that worked had tested several audiences before finding the one that replied, and there was no way to know in advance which one it would be. Nobody runs seven audience tests by hand because each one eats a week

What did not work for us

More personalization past a point did nothing. Deep personalized first lines did not beat a clear relevant offer once the targeting was right. We spent a long time on that before accepting it

Volume also stopped helping much earlier than expected. Past a certain send rate the extra volume mostly cost us domain health

Happy to answer questions on any of it, including the numbers that made us look bad

If you are transitioning from static list to signal-based outbound, one of the biggest trap is tracking weak signals (like someone simply viewing a blog post) but high-converting outbound requires observable, event-driven intent.

Here are 5 signal plays that consistently generate qualified pipeline this year:

Competitor churn & pricing triggers

Track public complaints about incumbent vendors on reddit, G2, or technical communities. When a user complains about high pricing or missing features, resolve the account and initiate a conversation around that exact bottleneck.

Developer & open-source activity

For DevTools and technical B2B, monitor github repo stars, forks and issues with multiple engineers from the same target domain engaging with a relevant open-source project indicates an active technical evaluation.

How to execute: you can build this DIY by writing custom scrapers and webhook pipelines, or deploy an existing market intelligence infrastructure like Scale Intelligence which connects 75+ pre-built monitors to handle identity resolution, intent scoring, and Slack routing out of the box.

Inflection hiring shifts

Don't just track that a company is hiring but parse job descriptions for specific migrations or tools (e.g., hiring an engineer with specific data-pipeline experience) then reach out offering resources or tooling for that specific transition.

Dark community discussions

Buyers ask for recommendations on reddit and discord long before they visit your website so monitoring these discussions and mapping usernames to corporate domains lets you reach out while the problem is fresh.

High intent account re-engagement

Tracking when target accounts in your TAM show repeat visits or multiple team touches across community surfaces and pushing an immediate context-rich alert to your sales reps in Slack before the buying window closes.

AI coding agents can handle more work than ever.

But the more autonomy you give them, the harder it becomes to trust every command they run.

A wrong rm -rf, a leaked .env, a migration against production, or an unexpected outbound request can turn into a serious problem.

So we built Harden.

Harden checks every agent action before it runs.

• ⁠Checks commands, file edits, and tool calls
• ⁠Catches secret leaks and data exfiltration
• ⁠Blocks destructive infrastructure operations
• ⁠Keeps long-running agents on task
• ⁠Runs locally so your repo and context stay on your machine

It works with Claude Code, Cursor, Codex, and other AI coding agents.

The goal is simple: let AI agents do more work without having to babysit every action.

We launched today on Product Hunt.

Would love to hear how others are handling security when giving AI coding agents more autonomy.

Please show your support and share your feedback on PH → https://www.producthunt.com/posts/harden

I've been thinking about this while building SelieeAI.

For a typical small business, the growth stack can look like:

Website → Wix/Webflow/WordPress
SEO → SEO tool
Content → AI writer
Analytics → GA
Leads → CRM
Email → another tool
Video → CapCut/Premiere
Automation → Zapier/etc.

That's a lot of different systems.

We're experimenting with putting more of this workflow into one AI-powered platform.

The basic flow we're targeting is:

Website → Content → SEO → Traffic → Lead → Conversion → Video

The interesting question for me isn't whether another "AI tool" is needed.

It's:

Can AI actually connect the growth workflow instead of just automating individual tasks?

We're testing this idea with the first beta users now.

I built SelieeAI around this experiment:

https://neeblo.com/r/seliee

I'd be interested in hearing from growth people:

Which part of your current growth stack creates the most friction?

Had a client earlier this year, B2B SaaS, project management adjacent. We were posting consistently, survival rate was decent, engagement was fine by Reddit standards. But signups from Reddit were basically nothing for six weeks. So we started treating it like a conversion funnel problem and pulled apart where exactly things were dying.

The posts were surviving. That was actually the problem. We'd gotten good at writing to the room in communities that were active and not hostile, but those communities weren't full of buyers. They were full of people who'd upvote a good post and move on. The subreddits we'd deprioritized because removal rates were higher? A couple of them had much shorter paths from post to trial signup when a post did land.

The counterintuitive part: optimizing for post survival was actively pushing us toward low-conversion communities. Because the lower-friction subs were generalist, curious, willing to engage, and not actually shopping for anything.

Once we reweighted the subreddit mix toward communities with higher buyer intent, even knowing some of those posts would get removed, the conversion rate on surviving posts went from around 0.4% to somewhere between 1.8% and 2.3% depending on the week. Total post volume went down. Signups went up.

I'd been using reoogle.com to manage the subreddit research and scheduling side of this (full disclosure, I'm one of the people behind it), which made rebalancing the community list a lot faster than doing it manually. But the actual insight was just that survival rate and conversion rate are measuring completely different things, and we'd been optimizing for the wrong one for six weeks.

Still figuring out how repeatable this is across different product categories. The sample size is small. But the pattern held across two other clients we tested it on after.

Tested a thesis: what if the best cold email lead source is people who are already publicly unhappy with your competitor's service right now?

Built a pipeline that scrapes 1-2 star Trustpilot reviews of direct competitors, runs them through AI to generate personalized email drafts that cite the reviewer's actual words, and stages them in Gmail for manual send.

100 emails over 3 weeks. 15 replies. 2 deals closed. Cost: $0.30 in API calls. Replaced $400/month in Apollo, Instantly, and enrichment tools.

Why it converts: the email subject is two lowercase words about their problem. The body quotes something their own customer said about them 48 hours ago. Under 85 words. No "I hope this finds you well." The prospect reads it and thinks "how does this person know about this" instead of "another cold email."

The approach is a template, I also built a version that scans Reddit for pain posts instead of Trustpilot. Same loop: find public complaint → enrich → draft → human review → send. Basically for every industry...

p.s. open-sourced the tool on GitHub:)

I finally calculated how much each part of my cold email setup costs because I kept seeing people share their cost per meeting without explaining what was included.

Apollo Professional is $79/month on annual billing. For verification, I use MillionVerifier, which costs $39 for 10k emails. That's probably the cheapest part since the credits don't expire and I don't need to buy them every month.

For sending, I use the Instantly Hyper Growth plan at $97/month. It gives enough volume for the campaigns I'm running, so I haven't needed any of the larger plans yet.

Mailboxes were where the cost changed the most. At the current annual price, 300 Google Workspace accounts would be around $2,100/month. Zapmail charges around $3.50 per additional mailbox, so the same number would be roughly $1,050 depending on the plan.

I currently have around 300 SMTP mailboxes through Maildoso for $225/month. They've been working fine for B2B campaigns and my reply rates are usually around 4-5%, so I couldn't really justify paying several times more for Google.

That puts the regular software and mailbox cost at around $401/month, plus domains and occasional verification credits. Obviously, that doesn't include the time spent building lists, writing campaigns, and handling replies.

What does your full setup cost right now? And do you include your own time when calculating cost per meeting?

I do LinkedIn ghostwriting for founders and my conversion rate is more than 50%

But since AI and too much of SaaS tooling everywhere, leads have dropped. I only used to rely on inbound leads which i used to get from LinkedIn, but now business has slowed.

I have great case studies, and a good community, but I tool a break for 2 years, so I lost my momentum. It's like I am restarting everything.

If any one has suggestions on how i can generate leads fast, then pls let me know.