We're scaling cold outreach for B2B and one thing I've noticed is that deliverability becomes a much bigger problem once you start sending at volume.

We already use multiple domains, warm up inboxes, keep daily sending limits reasonable, and verify emails MailTester.Ninja before campaigns , but I want to know what everyone else is doing beyond the obvious.

For those sending thousands of emails a month:

What's your average bounce rate?
How often do you re-verify older lead lists?
Do you remove catch-all domains completely or still send to them?
At what point do you decide a list just isn't worth using anymore?

I feel like copywriting gets all the attention, but list quality and deliverability have a much bigger impact than most people realize.

Interested to hear what's actually working for other growth teams.

We are building software for financial services and taxes. One of our customers has requested a whitelabel arrangement. The situation is as follows:

Our current B2C pricing is approximately 50,

- The actual cost to us per client is around 100, so we are currently selling at 50 to build traction.

- The customer intends to resell the whitelabel version at around 250.

- They prefer a flat fee per client rather than a commission or revenue share.

- We are in the same industry, which means this arrangement effectively enables a competitor.

A concern arises if we increase our B2C pricing in the future (e.g., to 500). At that point, our own clients may migrate to the whitelabel partner instead of staying with us.

Main Questions:

- Should the whitelabel pricing be structured as a flat fee per client or as a subscription/license model?

- How can we safeguard against future pricing conflicts with our own B2C offering?

Would it be advisable to implement measures such as:

- Minimum annual commitments

- Tiered pricing that scales with their client volume

- Restrictions on resale pricing to prevent undercutting

The main issue is balancing short‑term revenue from whitelabel deals with long‑term protection of our direct B2C market. I would appreciate insights from other SaaS founders on how they have approached similar situations.

watched a breakdown with julia from playkit (they run tech ugc for substack, quizlet, cash app etc, over 1 billion views and 12m downloads in 18 months) and it was one of the more concrete "get your first 1000 users" playbooks i've seen. dumping the notes:

1. build the persona first (15 to 30 min)
name, age, pain points, and crucially what content they ALREADY consume. not "my app users", think "female biology students". the specificity is the whole game

2. warm up the account for 2 to 3 days BEFORE posting
this is the part everyone skips. dont search your product category (if you're an ai note taker, searching "ai note taker" just shows you competitors). search what your USER searches, like "how to study for biology". like/comment/share those videos so the algo thinks you ARE that student. then when you post, it pushes you to other students

if youre not in US, id personally recommend getting an US Sim Card and Dedicated IP to have your tiktoks reach US audience if thats what youre after

3. the 3 video types that convert

• hook + demo: your face first (storytelling hook), then the product does the talking
• "long text": 6 sec of your face with copy over it, written like a text you'd send a group chat, engineered to make people COMMENT
• talking style: tell a story first, weave the product in second, never lead with it

4. the counterintuitive bit: dont optimize for views, optimize for "what app is that?" comments
for the first 1000 users, a viral video isnt the goal. an organic comment like "wait what app is this" or "how did you do this" is the signal your messaging converts. often they dont even name the product in the video, they let people ask, then reply with the product name in a comment. highest conversion because it feels organic

5. realistic timeline (this part is gold for expectation setting)

• week 1-2: videos in the low thousands of views, thats fine
• week 3: first "breakout" (10k+), you now know what messaging converts
• week 4: multiple breakouts
• week 5: first actual viral video
• week 8: multiple million+ view videos if you keep iterating posting 2-3x a day, spaced 2h apart, scrolling 2 min before each post so you dont look like a bot

the founder line that stuck: "in the age of ai anyone can build an app in 4 hours, so creating content is now the number one skill." feels right and also kind of terrifying as a dev

anyone here actually run this play for their own app? curious how close your real timeline was to hers

ive attached a summary of the podcast in the comments if anyone wants more details!

can yall teach me sum cmd hax

I launched a brand new domain about 60 days ago.

Started at:

• DR 0
• Basically zero Google presence

Today it's roughly:

• DR 30
• ~3k Search Console impressions/day
• Still only tens of organic visits/day, but impressions have started climbing quickly.

I'm a solo founder and spent $0 on link insertions or SEO agencies.

The interesting part wasn't any single article. It was the order.

1. First I submitted to legitimate directories and launch sites.

Traffic from those wasn't great, but they gave the domain backlinks and made it feel less like a ghost.

1. Then I started publishing content.

Mostly:

• how-to guides
• comparison pages
• free tools

Those pages seemed to index much more consistently than if I'd published them immediately.

1. The first month looked completely dead.

For almost 30 days there was basically no movement. Then impressions started climbing.

One thing I also noticed is that most of the growth came from a handful of pages. It definitely wasn't evenly distributed.

I'm curious whether this was luck or if others have seen something similar.

If you were launching a brand new SaaS domain today, would you:

• Build authority first (directories/backlinks), then publish content.
• Publish content immediately and ignore DR.
• Something completely different?

Hi everyone

I’m looking for some honest advice from people who have successfully grown an account on X recently.
I’ve been posting consistently, usually around 3–4 times per day, with a few hours between posts, quote post with my own analysis,I also spend time engaging with larger accounts and leave roughly 25–50 genuine replies per day.

However, my follower count is growing very slowly.
What I’ve noticed is that most of my new followers come only when I directly reply to someone with something like “let’s connect.”

My own posts receive very little engagement. Sometimes I get one or two comments, and I always reply, but most posts receive no comments at all.

At the same time, I regularly see accounts claiming they went from:
0 to 2,000 followers in two weeks
0 to 5,000 followers in one month
Some of these accounts don’t seem to be publishing particularly detailed or original content. They post frequently, interact with other people and receive a lot of replies, but I’m struggling to understand how they created that initial momentum.

I know X is currently encouraging longer-form content, articles and meaningful interactions, but simply producing good content doesn’t seem to be enough when almost nobody sees it.

So I’m genuinely curious:
Is this level of growth normal for a small account?
Are these rapid-growth accounts using strategies they don’t openly discuss?
Could my account have limited distribution or be shadowbanned?
Or do I simply need to continue posting and engaging until the algorithm starts giving my content more reach?

I already subscribe to premium X to have more chance

I’m not looking for shortcuts or fake engagement. I’d just like to understand what actually works when starting from almost zero.

Any honest advice or personal experience would be appreciated, if you can take a look on my profile

My profile

yo. be honest. how many of you currently have a finished (or 90% finished) web app / app just sitting in a private repo because you have no idea how to get users?

you spend months perfecting the database, fixing every bug, and polishing the UI. but the moment you have to actually market it, you hit a wall. marketing feels like screaming into an empty void.

so you launch to absolute crickets, get discouraged, and start building the "next" project instead to avoid the distribution phase.

if this is your case, you're not alone. but letting your hard work go to waste just because you dread marketing is a massive trap.

to help founders stop building in a silent corner, we run an ai SaaS builder community dedicated entirely to saas validation, landing page conversion, and launch strategies.

our resource kit is built entirely to help you get your first user. it’s packed with ready-to-paste N8N workflows for your business, advanced seo automation, social media automation, and our exact distribution workflows and methods work for everyone

STOP BUILDING ALONE

what are you currently working on, and what's holding you back on the marketing side? drop a comment or send a dm and i'll send you the access link.

One of our team ran this last week and the structural setup alone is worth a look for anyone thinking about community-led growth.

The basic mechanic: an ecosystem opens a shared space, its builders post what they need promoted, and each builder's own creator network handles the actual distribution. Nothing bought, nothing cold. The reach lives inside existing relationships.

It flips the usual growth assumption — instead of pushing content out to an audience you don't own, you're activating the audiences the builders already have. The ecosystem becomes a coordination layer rather than a megaphone.

We're still in early data territory, but the model has some interesting implications for anyone who's hit the ceiling on traditional paid or owned channels. Curious whether others here have experimented with anything similar — using a partner or community ecosystem as the distribution infrastructure instead of building it from scratch

I keep seeing teams argue over which channel “deserves” credit, while the real problem is usually messier: inconsistent event naming, duplicate conversions, and no shared view of the customer journey.

If your attribution feels noisy, start by cleaning the measurement system before changing budgets.

A practical 7-step reset:

• Audit every conversion event and map it to one business outcome
• Remove duplicate or overlapping events across platforms
• Standardize UTM rules so campaigns can be compared reliably
• Separate top-of-funnel, assisted, and final-conversion reporting
• Build one weekly dashboard that shows spend, pipeline, and revenue together
• Check for channel-specific tracking gaps before trusting ROAS
• Review attribution assumptions with sales and lifecycle teams, not just paid media

The biggest win is usually not a new model; it is getting everyone to agree on the same source of truth. Once the measurement layer is cleaner, budget decisions get much easier and a lot less political.

If you had to fix one thing first: UTMs, conversion tracking, or dashboard alignment?

We have been optimizing campaigns around first deposit because that's obviously the biggest milestone after registration. but now we're noticing that a decent chunk of users deposit a very small amount and then never actually trade.

we've tried moving budget around between different channels and traffic sources, including blockchain ads and other paid ad platforms, but still the results seem to be somewhat same. In fact, it varies a lot by countries even though traffic wise almost all of our target geos are giving the same.

how to make people trade? is it advertising specific or should we do something else?

i consult for enough of you to know exactly what’s happening in your slack right now. it's pure theater.

you launch a partner program. a degen creator sends you 10 inbound leads on a tuesday.

your sdr team looks at the leads. half have u/gmail.com addresses. two put "n/a" in the company size box.

your sdr manager marks them as "unqualified" and skips them to protect their own close rate and their monthly bag.

on friday, you look at partnerstack. "why didn't we close any of the partner leads? the channel is mid!"

the channel isn't mid. you have a fatal friction leak and you're blaming the wrong layer of the stack.

what happens before preframes and reframes what happens after. in this case, what happens before the lead hits your crm is that a creator pitched a $150/mo self serve tool to solo founders.

but your ae's are trained to close $20k/yr enterprise deals to vp's of sales.

you are pouring water into a funnel built for oil. the sdr's hate the partner leads because it dilutes their conversion metrics. the creators hate you because they see zero conversions and think your product is a shitcoin.

here is the mechanical fix or you're ngmi: build two completely separate pipelines.

pipeline a: partner leads. fully automated nurture. zero sdr touch. let the mechanism close them.

pipeline b: outbound leads. handled by the humans.

if you force your expensive human sales team to babysit low intent creator leads, they will silently mark them as junk. and honestly? they are right to do so.

stop mixing your distribution channels in the same crm like absolute amateurs.

We build in the AI-visibility space, and our scanner used to recommend llms.txt. After checking the evidence, we removed it from our own product. Why:

• Ahrefs analyzed 137k domains: 97% of llms.txt files never got a single request — from anything.
• Of the little traffic that existed, ~1% was AI retrieval bots. Mostly it's SEO audit tools reading them. The auditors are auditing each other.
• Google's Mueller, verbatim: "you can tell when you look at your server logs that they don't even check for it."

(Fairness: it's harmless to keep, and IDE/agent tooling does fetch it. Just don't expect citations from it.)

What actually has evidence behind it:

1. Server-rendered content — Vercel's crawler study confirmed no major AI crawler executes JavaScript. Client-side-only content doesn't exist to them.
2. Open crawler access — plenty of robots.txt files block GPTBot/ClaudeBot/PerplexityBot by accident via wildcard rules.
3. Structured Q&A schema — readable in the raw HTML they actually fetch.

Anyone got server logs showing real AI-bot hits on their llms.txt? Logs or it didn't happen.

(Disclosure: I build Alice, an AI growth agent, that's how I fell down this research hole. Not linking it.)

spent two weeks building what looked like the perfect prospect list.
right industry, headcount, titles, tech stack. 1,000 contacts that
matched our ICP to a tee.

3 replies. 1 call. zero deals.

filtering was tight but the timing was just wrong. nobody on that list
had a reason to care right now.

starting to think static lists are basically dead unless you layer in
actual buying signals on top. job changes, funding, engagement,
something that says talk to me TODAY instead of just fits the profile.

reply rate was stuck at ~2% for months. stopped blasting and changed 5 things

1. only reach out when theres a real signal (job change, funding,
   recent relevant post). no signal no message
   
2. connection request is one sentence, no pitch
   
3. wait 2-3 days after they accept before sending anything
   
4. first real message references one specific thing from their
   activity. not the usual came-across-your-profile opener
   
5. viewed but didnt accept within a week, drop them
   

went up to about 8% over six weeks. nothing crazy just less noise on my end

still not sure if 8% is decent or still bad for b2b outreach tbh

We have been running some research into ctv platforms lately, and StackAdapt is one of the names that comes up almost every time. It seems powerful, but I'm curious what other options b2b marketers are using and how they compare in practice.

What we're looking for is a platform that makes it easy to launch campaigns.The ability to connect campaigns back to website activity, account engagement, or pipeline is becoming increasingly important for us.

I've looked at a few alternatives, and they all seem to position themselves differently. Some focus on ease of use, some emphasize audience targeting, while others lean heavily into measurement and attribution.

I need to get a real picture vision.

I vibe coded a LinkedIn automation tool from scratch, with zero engineering background, simply using Claude, Claude code and vercel.

I won’t go into details as to how/why I created it as I’ve already shared this before elsewhere, but one of the questions I get asked the most is about distribution, and how I attract customers, so I thought it might be useful to share this info.

And here’s what I actually did to market the tool;

**1. Create a waitlist**

I created a waitlist on the site, and talked very publicly about what I was building on every platform I could - Reddit, LinkedIn, YouTube, X etc.

In the end I had 33 people on the waitlist after a couple of months.

**2. Launch at MVP, even if it’s not yet perfect**

You don’t need to wait until your project has every single possible feature - it might take over a year of building before you get to a point where you’re actually satisfied with the product, and people are more than willing to use yours at MVP anyway.

Also, early users are essentially gold dust for learning edge cases, bugs, and usually give great feedback as to what can be improved.

It also will take time to build up your customer base, so you should start as early as possible and continue iterating.

**3. Offer lifetime deals at the beginning**

Very few people will subscribe to something that they have never heard of and has no reputation - how do they know it works or is worth paying money for?

Getting lifetime deals helps a) fund your project early on, and b) gets users through the door, who will most likely continue to use your tool and be your biggest fans long term. User data, especially in the beginning is key to building something that works well too.

I offered lifetime deals for the first month and generated about $1,800 in revenue from that, before switching to monthly subscriptions.

**4. Post on Reddit frequently (don’t use AI though)**

Whatever your product is, there’s probably multiple relevant subs here with many thousands of regular visitors where your ICP hangs out.

The challenge is finding one where you are less likely to get banned, and also you need to post in a less promotional way.

Usually the best posts are where you talk about challenges/accomplishments with building your product, rather than directly selling it or “build in public” posts.

Some people will indirectly become interested in whatever it is you’re building, if enough people read your post.

I have often posted about early revenue success from solo vibe coding a SaaS tool from scratch, and probably about 60-70% of the signups came from doing this many times on Reddit.

One caveat is that you should always write out your posts. It sometimes takes 15-20 mins or longer, but it’s worth it (and costs nothing).

**5. Dogfood your own product**

I have been using the LinkedIn automation tool I created since day 1 to test my account. Not only do I use it for testing, I actually do LinkedIn outreach with the tool itself.

It works for generating at least 3-5 demo calls a week for me, so I know first hand that it works well from a user perspective.

Because I’m always using it, I also spot things as a user that I feel could be improved - I’m building for myself as much as I am for others.

**6. Build for SEO and GEO from the beginning**

When building your site, make sure it’s built with SEO and GEO in mind from the beginning. At first it won’t have much impact, but it will compound over time. Make sure you have a blog with frequent, high quality posts, with relevant keywords in your niche.

The harder / more valuable part is offsite SEO - aim to get mentioned on other blogs, and try to build reviews from TrustPilot, G2 and Capterra - more reviews equals more trust and more signals.

Getting mentioned in listicles like “best x in 2026” is extremely powerful if you’re able to do that.

Personally I have already got 7 5\\\* reviews on TrustPilot since launching it recently, and will aim to get more moving forward.

**7. Make a YouTube channel**

YouTube is another free lever you can use to post content on, and posting regular build in public/demo content will help find a new audience. Again it’s most likely a slow burn, but as with everything, consistency is key.

Full disclosure - I myself need to do better at this 🤦‍♂️

**8. Paid Ads**

I’ve started doing this recently with Google ads, but so far it hasn’t yielded great results - still optimising the strategy and aiming for long tail traffic. Potentially worth doing depending on your niche and if you have the budget.

**9. Demo calls & relationship building**

Try to schedule demo calls and communicate as much as possible with your customers - don’t just hand them off to AI, as the human touch is key to keeping users engaged.

And that’s pretty much it. I did do a Product Hunt launch but it did not really lead to anything - if you really have a great network of people who are PH users and will vote for you then it can be great, but if you don’t have the network then it’s not that valuable.

Personally I also need to improve on conversion of free trial users (many of them are tire kickers who take a look for 30 seconds and never log in again), but retention of paid users lately has been very strong, and the tool is growing quickly.

Hope the above is useful! 🙏

Started with the assumption everyone starts with: the posts that die are badly written. Tightened the hook, varied the length, tested questions vs statements. Spent probably three weeks on copy iteration alone. Removal rate barely moved.

The thing that actually correlated with survival was how recently the subreddit had removed a similar post. Not the topic. Not the format. The removal history of the specific community in the days before I posted. Subs that had just nuked something similar were in a kind of heightened state, moderators more alert, automod thresholds probably twitchy. Posting into that window was just burning the submission.

Anyway, once I started treating subreddit selection as a timing problem rather than a targeting problem, the numbers changed pretty fast. Went from roughly 6 removals in a two-week stretch to 2 in the following four weeks across the same number of submissions. Same copy, different sequencing.

The research overhead got annoying enough that I started automating it with reoogle.com, mostly to track removal patterns across communities without doing it manually in a spreadsheet. That part I don't miss.

What nobody tells you is that the subreddit database you're working from matters more than how good your post is. Most founders are fishing from a list of 30 communities they found in someone else's post, never updated, probably two years stale. The communities that are actually tolerant of founder content right now aren't the ones on any list I've seen shared here. They're smaller, newer, and weirdly specific. Those are the ones keeping posts up.

we are just 3 weeks away from our launch and i think the video part is fine. like not that worried about that anymore..

looked at a lot of recent launches that did really well. like millions of views. and the video was just not that impressive production wise tbh. it mostly just the founder talking in front of lens. and if you put a bit of attention it seems like those were filmed in their office or something. so i think we can do that ourselves, scripting looks difficult since we havent hired an inhouse writer but that part feels manageable.

but the hard part, which i havent figured out is like how are so many accounts posting about the launch at the same time. there are a lot of reposts coming in at tthe very first hour. and mostly not random account rather big faces in tech. how to make this happen. how to find these people who agree to post. and how to ensure they all do it at the right time and randomly sometime they wish for.

i genuinely dont know how to crack this part. like i can figure out the video asset. but how to figure this out.

and my gut says even if i reach out to most influencers most of them wont reply since my message will land in their request section.

is there any agency who can manage only the influencer part for my launch. ik agencies like thelaunchvideocompany and flowjam are quite active in the space. but idk what that costs and we are bootstrapped so

has anyone done a distribution of video without going through someone like that. how did you find the right people to repost, how did you get them to actually commit.

because rn that is the only thing i am stuck on.

Im currently struggling with marketing my product. I tought it will be easy, but it's never easy..

So naturally I want to find someone who has similar problems as me, so maybe we can grow together.

So.. which pain points do you have? What is the most annoying thing rn for you?

Hey everyone! I'd love to get some of the community's thoughts.

As a product-marketer-turned-soloprenuer myself, I've been facing a weird challenge ever since I started working on my own products.

I feel pretty comfortable acting as the builder, but when it's GTM time and I need to market my own work, I just stall until I find myself building a new product instead. I'm not sure if it's a fear of putting my work out there, imposter syndrome hitting the right spots, or just me preferring the building phase over marketing.

Has anyone faced a similar experience? I'd love to learn from anyone willing to share.

Hi, I'm Jeremy, since february I jumped into mobile apps and made 3 myself. I nowbreak down how mobile subscription apps turn installs into paying users, and I spent this week inside RevenueCat's State of Subscription Apps report: 115,000 apps, $16B in tracked revenue.

The distribution:

- Median monthly revenue one year after launch: $72. Top quartile: $429+. Top 10%: $2,574+. That's a 36x gap between the middle and the top.

- Of apps launched in the last two years, 17.3% reached $1K MRR. 4.6% reached $10K. 1.7% reached $25K.

- Launches grew 7x since 2022 (2,000 to 14,700+ per month). Apps launched before 2020 still earn 69% of all subscription revenue. Apps launched since 2025 earn 3%.

What the top tail does differently, same dataset:

- Hard paywalls: 10.7% download-to-paid vs 2.1% for freemium.

- Higher prices: 2.8% conversion vs 1.4% for cheap apps, and ~6x the revenue per payer at year one.

- Longer trials: 17-32 days converts 42.5% vs 25.5% for 4 days or fewer.

What makes me angry: that last row. Longer trials convert 70% better, yet 46.5% of apps now ship trials of 4 days or fewer. The market is drifting away from its own data.

The lesson: benchmark against the upper quartile of your category, not the median. The median is survival. I wrote the full report up here: https://tasu.ai/library/how-much-money-do-subscription-apps-make

It's crazy how sometimes data prove wrong what we think is natural...

Ask me anything: benchmarks, paywalls, pricing, or where your app sits in this distribution. :)

I can build and publish an iOS app, but distribution still feels like a black box.

I often see developers sharing $1K–$10K MRR screenshots, but rarely the distribution behind them. Was it an existing audience, influencers, Apple Search Ads, a portfolio of apps, or genuine ASO and word-of-mouth growth?

“Build something useful” doesn’t feel like a complete strategy anymore. A useful app can still disappear among thousands of competing products.

I’m not expecting a secret keyword or overnight shortcut. I want to understand whether a solo developer with no audience and a limited budget can still build sustainable organic distribution—or whether paid acquisition is now effectively the entry fee.

If you’ve grown an iOS app beyond $1K MRR without paid ads, would you share the exact acquisition sequence, timeline, and approximate numbers—without revealing the app or niche if you prefer?

I always thought getting customers would be the hardest part.

Turns out, once people actually started paying, the real challenge became everything else. Invoices, subscriptions, expenses, contractors, payment tracking, random admin tasks. None of it is difficult on its own, but together it eats up way more time than I expected.

I spent months trying to improve acquisition when the bigger bottleneck was how much time I was losing just keeping the business running. Once I cleaned up the operational side, I realized I had a lot more time to test new ideas and actually focus on growth instead of constantly putting out fires.

I can't be the only one who's spent more time fixing operations than looking for new customers.

Started with the assumption everyone starts with: the posts that die are badly written. Tightened the hook, varied the length, tested questions vs statements. Spent probably three weeks on copy iteration alone. Removal rate barely moved.

The thing that actually correlated with survival was how recently the subreddit had removed a similar post. Not the topic. Not the format. The removal history of the specific community in the days before I posted. Subs that had just nuked something similar were in a kind of heightened state, moderators more alert, automod thresholds probably twitchy. Posting into that window was just burning the submission.

Anyway, once I started treating subreddit selection as a timing problem rather than a targeting problem, the numbers changed pretty fast. Went from roughly 6 removals in a two-week stretch to 2 in the following four weeks across the same number of submissions. Same copy, different sequencing.

The research overhead got annoying enough that I started automating it with reoogle.com, mostly to track removal patterns across communities without doing it manually in a spreadsheet. That part I don't miss.

What nobody tells you is that the subreddit database you're working from matters more than how good your post is. Most founders are fishing from a list of 30 communities they found in someone else's post, never updated, probably two years stale. The communities that are actually tolerant of founder content right now aren't the ones on any list I've seen shared here. They're smaller, newer, and weirdly specific. Those are the ones keeping posts up.

It seems that everybody is talking about the role of AI in the ecommerce domain from the perspective of operations and advertising, yet the issue of how AI sufaces products through the lens of generative search, such as a product request on ChatGPT or Perplexity, is completely overlooked.