u/Due-Manager-6248

started with manual monitoring. checking subreddits where my target users posted, looking for threads with real buying signal. worked but did not scale. missing too much.

built a basic pipeline. Reddit API for data collection, keyword filtering for initial pass, embedding based scoring for intent classification. the output is a ranked feed of posts ordered by how likely someone is actually looking to buy something versus just talking about a problem.

the classification part is harder than it looks. short informal text is noisy. someone describing a frustration and someone actively evaluating tools can look similar on the surface. the model needs enough labeled examples to tell the difference reliably.

once the scoring was stable the quality of outreach changed. not more volume. better targeting. the conversations that came from high intent posts were different from cold outreach. context already existed, the person had described their situation, the reply could be specific.

for a solo founder the build time was reasonable. the ongoing infrastructure is lightweight. the harder part was getting the intent model precise enough to trust.

curious whether others are doing something similar or still doing this manually.

I was tracking the wrong thing for a long time.

Dashboard looked fine. Signups coming in. Activation rate acceptable. But the buyers who stayed were different from the ones who churned and I did not know why.

Started reading Reddit threads. Not my own. Subreddits where my target users actually talked. No prompt, no survey, no interview structure. Just people describing their situation when they thought nobody from a software company was watching.

The language was different. The problems they described were more specific than what came through support tickets. The frustration was clearer. And the posts that showed real buying intent, someone mid decision, asking what tool to use, those were easy to spot once I knew what to look for.

It changed how I wrote my landing page. Changed which features I prioritised. And a few of those threads turned into direct conversations with people who were ready to buy.

Simple process. No complex tooling required to start. Just reading the right places at the right time.

Curious whether other founders are using public community data this way or whether it stays informal.

The build was straightforward. Clear scope, known stack, no major surprises.

Distribution was different. Cold outreach response rates were low. SEO takes too long for an early product. Paid ads require budget and conversion data I did not have yet.

What worked was Reddit. Not posting and hoping. Monitoring specific communities for posts where people describe the exact problem the product solves. People write out their situation in detail. The intent is visible if you know where to look.

The problem is doing this manually does not scale. Too many subreddits, too much volume, too much noise.

I built Leadline to solve this. It monitors Reddit in real time, scores posts by buying intent, and surfaces the threads worth responding to. No manual scanning. The output is a list of people already in problem-aware mode.

First users came from this method. Conversion from Reddit outreach was higher than any other channel I tested. The leads already understood the problem.

For B2B SaaS with a narrow buyer profile it is efficient. The signal is there. The question is whether you can find it fast enough to act on it.

What distribution approach worked for you at the early stage?

The discovery is the problem. Not the product.

What I see repeatedly is this: sales rep gets a demo booked, prepares the standard walkthrough, shows the features. The prospect is polite. No next step.

The issue is the demo was built around the product, not the buyer's situation. The rep did not know enough going in. So the demo is generic. It covers everything and resonates with nothing.

The fix is straightforward. Before any demo, you need three things confirmed. What specific outcome is this person responsible for. What has already been tried. What happens internally if the problem is not solved.

Without that the demo is a feature tour. With it the demo becomes a direct response to a real situation the buyer is already thinking about.

Most SaaS demos are too long because they are compensating for weak discovery. A 20 minute demo with precise context outperforms a 45 minute walkthrough built on assumptions.

The product is usually not the issue. The preparation is.

What changed the quality of your demos more than anything else?

Find leads automatically on Reddit and reach out to those leads with sounding like a slop machine and actually get replies demos and sales. Leadline.dev

The standard outbound stack starts with a list. The list is static. It tells you company size, industry, title. It does not tell you if the person has the problem right now.

Reddit does. Someone posting about a broken process or asking for tool recommendations is in the problem actively. The timing is known. The pain is specific. The context is already there before the first message.

The conversion difference is significant. Cold list contact is interruption. Reddit intent contact is timing.

The manual version does not scale. Monitoring it automatically does.

Anyone here using Reddit as a serious lead input. What is the workflow.

Spent several months doing standard outreach. Cold email, LinkedIn, sequences. Tested copy, tested timing, tested everything.

Reply rates stayed low. Conversion to real conversations was worse.

The issue was not the message. The issue was the list. Most people on a cold list have no active problem right now. The message reaches them at the wrong moment. They ignore it.

What changed things was finding people who were already describing the problem publicly. Forums, Reddit, niche communities. Someone posting about a specific pain is a different contact than someone who fits the ICP on paper.

Same message to someone mid-problem converts differently. Not because the message is better. Because the timing is correct.

Volume outreach assumes you can compensate for bad timing with enough contacts. In my experience this does not work as well as finding fewer people at the right moment.

How are other founders handling lead quality versus lead volume? Is anyone filtering by intent signals before outreach?

Before, the first conversation was always about establishing that a problem exists. You spend the first call convincing someone they should care. Most of the time they are not ready and the timing is wrong.

With intent sourcing it is different. The person already wrote out their problem in public before we ever contacted them. They are in motion. The first conversation skips the problem qualification entirely and goes straight to fit.

I use Leadline to monitor relevant subreddits and surface posts with real buying intent. When something fits I engage honestly in the thread first. No cold pitch. Just useful.

The conversion from that kind of first touch is noticeably better. Not because the outreach is more clever. Because the timing is right and the intent is already there.

The pipeline is smaller in volume than a full outbound sequence. The close rate is not close.

Curious whether others here have found intent based sourcing worth the tradeoff in volume or whether most teams still default to sequence volume.

The rate limits and pricing shift forced a different architecture than what I originally planned. You cannot pull everything and filter later. The cost does not work. You have to be precise about what you actually need before you make the call.

That meant defining intent scoring logic before ingestion rather than after. Filter at the query level, not the processing level. Less flexible but significantly cheaper to run at scale.

The other constraint is that Reddit data is noisy in ways that are hard to anticipate until you are reading thousands of raw posts. Sarcasm, venting, hypotheticals. They pattern match on keywords but have no intent behind them. The classification layer took longer to get right than the infrastructure.

Leadline is what came out of that process. Real time Reddit monitoring scored by buying intent for B2B use cases. The architecture ended up cleaner because the API forced us to be specific early.

Curious whether others building on Reddit data found different ways around the rate limit problem or approached the filtering layer differently.

Scraping Reddit is straightforward. The API is messy but manageable. That part took a week.

The actual problem was scoring. A post that says I need a tool for X reads differently than a post that says we are evaluating options for X before Q2. Both mention a problem. Only one signals real intent.

I spent a lot of time reading raw posts manually to understand what patterns actually indicated someone was in motion versus just curious. Post length, specificity, urgency language, whether they mentioned budget or timeline, whether they were asking for recommendations versus venting.

That became the scoring model behind Leadline. Reddit intent monitoring for B2B founders. The output is a ranked list of posts worth engaging with rather than a raw feed you have to process yourself.

The technical layer is less interesting than the classification problem. Unstructured text with no consistent format and a lot of noise.

Curious how others here have approached intent or relevance scoring on unstructured data. What actually worked.

The problem with Reddit as a lead source is timing. A post about a specific problem has a short window. Maybe a few hours before the thread is too old to reply naturally. Manual searches do not solve this. By the time you run a search the window is usually closed.

The way it works is straightforward. You monitor subreddits in real time. Not keyword alerts. Continuous monitoring. Every new post gets evaluated for buying intent. The output is a filtered list of posts worth responding to, ranked by how close the person is to making a decision.

The scoring looks at a few things. Whether the person describes a specific problem or just asks a general question. Whether they mention what they have already tried. Whether they ask about alternatives or pricing. These signals are readable and consistent across categories.

The result is you spend time on conversations, not on searching. The posts that reach you are already pre-filtered. You reply, you help, you let the profile do the rest.

For micro SaaS this is useful because you usually do not have a big audience. You need to find buyers who are already in motion rather than building awareness from zero.

Curious how others here are handling early customer acquisition when you have no existing distribution. What is actually working right now.

[ Removed by Reddit on account of violating the content policy. ]

The scope was too large at the start. Monitoring, scoring, outreach, CRM sync, reporting. All relevant. All distracting from the part that had to work first.

The scoring layer is the product. Everything else is downstream of it. If the intent classification is not accurate the rest has no value.

So that became the only thing. Get the classification right. No outreach features. No integrations. No reporting dashboard. Just accurate intent scoring on live Reddit posts.

Leadline works because of that decision. The surface area is small. The mechanism is solid.

Most of the microsaas products I see fail because the core mechanism never gets enough focus. Too many features built around something that does not work well enough yet.

What did others cut to make the core thing actually function.

I would appreciate some honest feedback on the design of my landing page.
It is for a SaaS called Leadline.
The product helps B2B founders find Reddit posts where people are actively looking for a solution.
I am mainly looking for feedback on visual clarity, hierarchy, spacing, and whether the page feels polished or generic.
Site: https://leadline.dev
Specific questions:
Is it immediately clear what the product does?
Does the page feel trustworthy?
What looks weak, cluttered, or too generic?
What would you change first?

The more time I spend around small software businesses, the more I think the bottleneck shifted.

Building is faster now. Prototyping is faster. Shipping is faster. A solo founder can get surprisingly far without a team.

What still feels slow is figuring out where real demand already exists.

Not polite interest. Not vanity traffic. I mean people actively describing a problem, comparing options, or already looking for a fix.

That part still feels much more manual than most people admit.

Curious if others here feel the same. Is building still the hard part for you, or is demand discovery now the bigger bottleneck?

The build process is becoming more efficient.

You can prototype faster.

You can launch faster.

You can automate more of the work.

The difficult part still seems to be demand discovery.

Not attention.

Not traffic.

Real demand.

I mean people actively describing a problem, comparing options, or looking for a solution now.

That part still feels manual.

The more efficient building becomes, the more obvious this bottleneck gets.

Curious if others here see the same shift.

Is shipping still your main constraint, or is finding real demand now the slower part?

I have a straightforward question for business owners here.

What is your most reliable channel for finding people who are already looking for what you sell?

Not general traffic.

Not broad awareness.

I mean real demand.

The build side of business has become more efficient.

Websites are faster to launch.

Tools are easier to set up.

Campaigns are easier to run.

The difficult part still seems to be demand discovery.

Where do you find the highest intent prospects now?

Google search

Reddit

Facebook groups

LinkedIn

Cold outreach

Referrals

Something else

I have tracked this across several products. The thing you spent three weeks on gets ignored. Something you shipped in an afternoon becomes the reason people stay.

The pattern is consistent. Builders optimize for what feels important technically. Users optimize for what removes friction in their specific workflow. These are usually different things.

What actually works is shipping the smallest version of each feature and measuring what people actually do with it before building more. Not asking them. Watching them. Users say one thing and do another often enough that the survey data is not reliable.

The build time is not the problem. The decision about what to build next is where most time gets lost.

How do you decide what to build next when you have limited time?

They benchmark on demos. They read comparison posts. They pick the model that scored highest on some leaderboard from three months ago.

In production the model is rarely the problem. The prompt is the problem. The context structure is the problem. The output handling is the problem.

GPT-4 with a poorly structured prompt loses to a smaller model with a well defined task every time. This is not theoretical. It shows up consistently in production data.

The other mistake is using one model for everything. Routing matters. A fast cheap model handles simple classification. A stronger model handles ambiguous edge cases. Running everything through the most expensive option is not a strategy it is just expensive.

The model race gets coverage because it is easy to write about. Prompt architecture gets ignored because it is harder to explain and not tied to a product launch.

Most of the performance gap people attribute to model choice is actually prompt and context quality. Switching models feels productive. Fixing the prompt actually is.

Curious what others have found. Is model selection actually moving the needle for you or is it mostly prompt work underneath.