u/ChrisHarpon2

Quick recap for anyone who missed the first one: ~70% of my churned users told me some version of « cool product, but i’ll build this myself with Claude in a weekend » i spent a solid month being annoyed about it.

My instinct was to hide the how.

i did the opposite.

what i changed:

• rewrote the entire site to be GEO/SEO readable. plain words, one page per problem it solves, no clever positioning nobody parses. LLMs quote you when you’re quotable.

• started publishing articles and videos explaining exactly how the thing works. rate limits, warming windows, why 80 connection requests in a day gets your account restricted, the actual mechanics.

• sliced the same content into LinkedIn/reddit/X posts. not « check out my tool » just how the problem works.

• outbound to entrepreneurs, indie hackers and small agencies. using my own product to do it, which is either dogfooding or a cry for help.

MRR went $606 to $2,042

the counterintuitive bit: publishing how it works didn’t cannibalise anything, it converted. « i’ll vibe code it myself » mostly means « I don’t understand what you’re doing so it looks trivial » once someone reads 2000 words on linkedin’s restriction behaviour, a decent share of them decide their weekend is worth more than $39/mo.

the rest still build it themselves. fine they were never customers, they were a support queue.

what i’d do differently: start the content 6 months earlier. and the GEO rewrite mattered more than i expected. people now ask an LLM « how do i automate linkedin outreach without getting banned » and i’d rather be in that answer than on page 2 of google.

still small numbers, still one person. ask me anything.

Your LinkedIn network is the one asset you can’t restore from backup.

Most advice on this stops at « stay under X invites a day » which misses the part that actually gets people restricted. I’ve spent a while building browser automation against LinkedIn’s UI, so I’ve watched this from the implementation side. Sharing what I’m fairly confident about, and what I’m not.

Volume is the third-most important thing

From what I can tell, restriction risk stacks roughly like this:

1. Where the activity comes from Datacenter IP, account live from two places at once, session cookie replayed outside a browser. This outweighs everything below it.
2. How much you do Volume beyond what the account history supports, especially in bursts. Acceptance rate under \~30% surfaces the « you’ve sent many invitations » notice well before any hard cap.
3. How you do it Click timings no hand produces, 24/7 activity, calls to endpoints a browser would never hit, interaction with elements that aren’t visible.

People obsess over #2 because it’s the only one with a number attached.

Proxies usually make it worse, not better

A rented « dedicated residential » IP isn’t the address your account has always used, it’s frequently been in a pool serving other automated accounts, and a sudden ISP or country change is it

Verification page, passkey prompt, security wall, limit banner on the invite screen. Whatever produces those, retrying through them is what converts a soft warning into a hard restriction. If a tool keeps hammering after LinkedIn asks a question, that’s the actual problem.

Honeypots and the shadow DOM

Anti-bot systems plant elements a human can’t interact with: off-screen links, buttons in zero-size containers. Anything walking raw HTML and clicking every match will hit them, and clicking something invisible is an unambiguous « no human here » signal.

Related: chunks of LinkedIn’s UI live in the shadow DOM, encapsulated and invisible to naive HTML parsing. Resolving elements through the rendered tree instead is more work, and it’s also the only way to act on what a person would actually see and click.

The private API is the giveaway

The endpoints their own front end calls aren’t open to third parties. Hitting them directly is cheap to build and trivially identifiable from the server side: the call pattern, the headers, and the total absence of the page loads a browser would have made. No user agent string fixes that.

What I’m less sure about

Whether acceptance rate is weighted directly or is just correlated with accounts that behave well generally.
How much account age actually buys you. Older accounts clearly ramp faster, but I can’t put a number on it.
Whether the recent checkpoint behaviour is a real policy change or just variance. Curious if anyone else has seen a shift.

Disclosure: I build a desktop tool in this space. Not naming or linking it, and happy to answer implementation questions either way.

I've written software since 1983 and I'm an executive at a big tech company. I also run a small software product on the side that has thousands of users. It plateaued because I never learned to market properly, which is a fixable skill problem, but it made me think harder about what I want the next fifteen years to look like.

The trade business appeal is straightforward. Customers who need you in an emergency, cash flow that doesn't depend on an algorithm, and a service that can't be automated away by whatever ships next year.

What gives me pause is that the data says I'd be late. PE went from 8% of HVAC deals in 2023 to 23% in 2024, add-on acquisitions were up 88% year over year through the middle of 2025, and there are something like 3,000 investors actively looking. Small shops go for 6 to 9 times EBITDA while scaled platforms fetch 17 to 20, and that spread is the whole reason the roll-ups exist. Most analyses describe the sector as midway through its consolidation cycle, which usually means the clean books and clear succession stories are already gone.


The part I can't judge from the outside:
- If PE is a quarter of the deal flow, what's actually left for an individual buyer? Am I looking at the businesses that got passed over, and is that always for a reason?
- How much of the value walks out the door with the retiring owner and their techs?
- The technician shortage is put at around 110,000 nationally. In practice does that cap growth no matter how much capital you put in?
- Is an absentee or semi-absentee model actually possible in year one, or is that a fantasy sold by people who make money selling courses?


I'd rather hear the unglamorous version from operators than the acquisition-podcast version.

ontext: I've been a software person my whole life, currently an exec at a large tech company, with a side SaaS that has thousands of users and good reviews. It grew, then it plateaued, entirely because I stopped marketing it. The product still works and people still use it daily. The problem is me, which is at least a fixable category of problem.


So I have a decision that isn't really about the product.


**Option A:** rebuild the audience and the distribution, which is the part I've always been bad at, and treat it as the actual work rather than something I do when I have time.


**Option B:** buy a small heating and cooling business. Real cash flow, work that involves a van, and something that doesn't get commoditized by whatever ships next year. Though the data says I'd be late: in the US, PE went from 8% of these deals in 2023 to 23% in 2024, and roughly 3,000 investors are actively hunting them.


**Option C:** turn a large property I own in the Alps into short retreats for executives and founders instead of renting it to holidaymakers.


I can fund one of these properly. What I don't have is a sense of the day-to-day reality of B or C. If you've bought a trade business after a desk career, I'd like to know what the first year actually looked like, especially whether you ended up owning it or working in it.

Last year I called a « verified » lead from our database. Guy had left the company 6 months earlier. The receptionist knew him. My CRM didn’t.

That was the moment I started doing the math, and the math is ugly:
~30% of B2B contact data decays per year. People change jobs, switch numbers, companies reorg. So that beautiful lead database you’re paying to build, enrich, verify, and store? It’s a depreciating asset.

You’re paying storage fees on melting ice.
And the whole stack exists to fight the melt:
- one tool to extract the data
- one tool to enrich it
- one tool to re-verify it (because it decayed since step 1)
- one tool to send from it
- your CRM to hold the corpse

Meanwhile the platform you extracted it from… already has the current version. LinkedIn knows the guy changed jobs. Your export from March doesn’t.
So I flipped the model. Instead of extract then store then watch it rot then pay to re-verify, I work off the live signal and reach out when it fires. Someone changes roles, posts about a pain point, starts hiring… that’s the moment, and it’s fresh by definition because I never cached it.

Full disclosure, I got so annoyed by this that I’m building in the space, so grain of salt: Reachy.ai, a local-first desktop agent, runs LinkedIn outreach with your own keys, everything stays on your machine, no export-into-yet-another-DB step. It let me cancel a scraper + enrichment + sender. Not linking a demo or anything, just context for why I have strong opinions here.

Genuinely curious though, anyone here gone the opposite way and made the owned-database approach actually work? What’s your refresh cost per month, honestly?

Saw a LinkedIn post today, guy offering “outreach templates that convert” like this post, comment DM, send a connection request. 719 comments.

719 people are about to send the same message to the same TAM.

I’ve been doing outbound for a while and honestly I don’t understand who these are for anymore. The template was maybe good the first month the guy used it. By the time it’s been distributed to 700 SDRs it’s just spam with extra steps. Buyers can smell a template from the first line, especially the “quick question” ones.

And the thing that gets me is that copy was never really the bottleneck. Every time my reply rate sucked it was because my list sucked, not my message. Decent message to the right 50 people always beat a “proven” sequence blasted to 500.

Genuinely asking for those who still grab these template packs, do they work for you? Or is it just something that feels productive?

When I started on Reddit, I made all what was forbiden including promoting my SaaS and now I am banned from the most important subs for me.
Now that I understand the codes of redit, is there a way to get my access back or should I just cry?

I was wondering if this sttategy is really worth spending my time to get customers for my Product?
It looks like people are ore looking for valuable tips rather than lther up and down which is most of the time story telling so pish their product.
Interested in your thoughts ..

has anyone ever gotten a single paying customer from a “drop your startup link” thread? They get hundreds of replies, but who’s actually clicking? Are we marketing to customers or just to each other? What am I missing? Interested in your thoughts ...

A business should at least make enough money for you to live on without needing a second job. we treat it as totally normal for someone to pay for the thing we built so why are so many of us thrilled about a single first payment? are we actually convinced our product is good?

A business should at least make enough money for you to live on without needing a second job. we treat it as totally normal for someone to pay for the thing we built so why are so many of us thrilled about a single first payment? are we actually convinced our product is good?

That’s the line I get from around 70% of my recent churn. I built a desktop LinkedIn outreach tool (runs locally, bring your own AI key). People sign up, poke around, and cancel saying some version of: « this is cool but I’ll just vibe-code it myself with Claude »

For a while that stung. Then I realized they are underestimating the exact thing that is hard and the prompt-to-message part IS easy to vibe-code. The part that isn’t: not getting your LinkedIn account flagged or banned. Rate limits, account warming, human-like pacing, staying inside ToS. That’s the 90% that takes real work, and it’s invisible until your test account gets restricted.

So my churn is not really « product too hard » but it’s « customers can’t see the hard part until they’ve been burned by it ». Which is a brutal positioning problem: the value is the stuff you can’t demo.

How would you sell the invisible 90%? Do I lean into fear (show them the ban risk up front), or just let the vibe-coders leave and target people who have already been burned?

I’ve been building a LinkedIn outreach tool for a while, and I’ve hit a fork I genuinely can’t decide on. Posting here because you’re the people who actually live this problem.

Quick context on what I’ve learned the hard way: I got my own account restricted while testing automation. That taught me more than any blog post, the bans rarely come from volume alone, they come from bad targeting (low acceptance rate, people marking « I don’t know this person »). The « how you send » matters less than « who you send to »

That experience pushed the product toward two possible directions, and I keep going back and forth:

Direction A - keep it dead simple. You paste a URL (your product site), it figures out who your ideal prospects are on LinkedIn, scan on a regular basis to detect signals and reaches out only the warmed leads , with strict safety limits. Zero setup, the tool does the targeting.

Direction B - put the user in control of targeting. Same flow, but you validate/adjust the ideal-customer profile before anything goes out, and you tune how strict the targeting is (precision vs. volume).

The tension: A is frictionless but bets everything on the auto-targeting being right (and bad targeting is exactly what gets accounts flagged). B is safer on targeting but adds a step that might kill the « it just works » appeal.

So my honest question to people who do outreach: how much do you actually want to control targeting vs. let a tool handle it? Have you been burned by a tool that targeted the wrong people for you? Would a validation step feel reassuring or annoying?

Not trying to pitch anything but genuinely stuck and your real-world experience would help me more than another framework. Happy to share what I’ve learned about safe limits in the comments if useful.