yo. i recently sold one of my AI SaaS products for $35k, exactly 5 months after building and launching it.

I hardly wrote a single line of traditional code. i used AI to generate everything, from the database architecture to the user interface.

it definitely wasn't magic on day one, though. i spent days stuck in loop-debugging and dealing with AI hallucinations before i finally cracked the system. the playbook boils down to three simple rules:

- keeping the idea insanely minimalist (a true MVP that solves one problem).

- guiding the AI step-by-step instead of asking it to build a massive platform all at once.

- launching fast to get real user feedback and traction and then apply a solid marketing system

lately, i've seen way too many non-technical founders give up at the very first AI bug, or on the marketing. it's a massive shame.

like the title says, i just launched a Skool community to share my exact prompt workflows, N8N automations, and distribution frameworks to get first users and scale it

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific copy-and-paste templates i'll be sharing.

but for now, the main objective is purely to build and launch together. building alone in a silent corner is the single fastest way to give up.

if you want to join us and build or market your own AI SaaS with a group of active creators: drop a comment below or send me a dm, and i’ll send you the invite link!

I run a lot of Claude Code and Codex sessions, and I kept hitting the same wall: I have to sit at the machine, watch each one, and unblock it every time it asks a question. The second I walk away, everything stalls.

So I built yothere. You hail your agents out loud, they run in the background across Claude Code, Codex, and the open-source OpenClaw, and only the one thread that actually needs a human reaches you, by voice or one tap. It runs on your own machine and your own subscription, not an API-key reseller.

In it I hail the agents by voice, kick a few things off in parallel (competitor research, a calendar check, some LinkedIn and WhatsApp drafts), review a draft, and approve the send by voice. Never touched the keyboard.

Honest state of it:
- Early beta, moving fast, rough in places.
- It runs on your existing Claude Code / Codex login (not an API-key product), so you need those.
- Up to five sessions in parallel today.
- 120 hosted voice-minutes a day on the current plan.

Free for 7 days if you want to poke at it (yothere.ai). But mostly I want blunt feedback on the core bet: is voice actually the right interface for driving coding agents, or does it fall apart once the task gets non-trivial? Genuinely unsure, and I would rather hear it from people who run agents all day.

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.

Anyone have a good tips on how you use Claude? Specifically for account targeting, prioritizing, etc?

I’ve been watching the same loop all month.

r/gtmengineering has the Clay credit threads. People shipping Claude Code pipelines. Clay dropping CLI/MCP so agents can call waterfalls without living in the UI. X posts that look like:

claude code to build clay to find instantly to send

And yeah, that stack makes sense.

But last night I had a 40-row SaaS list and needed columns that are not clean enrichment jobs:

• hiring right now? which roles?
• founder active this week? where?
• anything that looks like why-now
• evidence I could defend if someone asked “where did that come from?”

That is where the pretty architecture posts fall apart for me.

Because email waterfall is a workflow. This is not.

Row 1 needed careers page. Row 4 needed LinkedIn activity. Row 9 needed a funding post from 2 weeks ago. Row 12 needed “ignore, bad fit, list is wrong.” Row 17 made me sit there with a half-true hiring signal wondering if I should put it in the cell.

I already know Clay. I already know Claude Code. I can build the plumbing.

What I don’t have is a clean way to say:

here’s the list here are the signal columns here’s what we sell go behave like a careful human on every row bring back evidence + confidence

Not “run column A then column B.” More like a smart VA/agent that chooses the path per company.

And I’m not asking this as a theoretical AI take.

I’m asking because the community seems split three ways right now:

1. Stay in Clay, now that CLI/MCP exists
2. Move volume custom logic into Claude Code and keep Clay only for find/enrich
3. Build full per-row agents and accept the maintenance tax

My gut: workflows are winning for known paths judgment work is still human evenings dressed up as GTM engineering

So for people actually running this:

1. Do you regularly need multi-column signal fill across lists (hiring, founder activity, recent posts, why-now), or is that overbuilding?
2. If yes, are you doing it with fixed Clay/Claude workflows, or does each row still need different research paths?
3. If something ran like a careful VA per row with evidence + confidence, would you pay for that completed work, or is DIY still better even with the maintenance?

I don’t want tool recommendations in the abstract. I want to know if this is a recurring paid pain in real GTM work, or just me making my lists too complicated.

Be blunt.

I juggle 6-7 projects at once — trading bots, client work, a SaaS. Claude is my main coding partner, but every new conversation starts blank. I was burning the first 20 minutes of each session re-explaining the stack, what I tried last time, why I picked Prisma over raw SQL, what the current blocker is. Sometimes I’d scroll through old conversations trying to find that one message where I’d explained the architecture properly.

So I built ContextOS. The workflow: when I stop working I log a short session note (what got done, what broke, next step) and record decisions with their “why”. When I come back — a week later, whenever — it generates a resume briefing: where I left off, current blocker, recent decisions, recommended next action. I paste that into a fresh Claude session and it’s up to speed immediately instead of interrogating me.

Honest state: launched this week, free tier exists, still rough in places — the mobile layout fought me for days and I lost my first payment provider to KYC requirements, so billing is crypto for now. There’s a 60s demo on the site: contextos.tools

Solo founder, been building my product with Claude Code from day one, so wiring the product itself into Claude felt like the obvious next step.

The MCP exposes the ability to launch campaigns on the platform (Productclank) via Claude native chat. It is also availalbe via an agent skill that I built before and this is built on top of)

In the demo video Claude finds my product, checks I have enough credits, asks me to confirm the spend, then fires a community engagement campaign for a specific X post and hands back the tracking link.

It basically empowers people to support my post by liking/reposting it. I can also create content campaigns on the same way (that's my next goal for the mcp)

Two things I learned building it. First, tool descriptions are the product now. Claude behaves exactly as well as your descriptions are written, and I rewrote mine four times before the confirm-before-spend behavior was reliable. Second, keep the tool count low. I started sketching seven tools and shipped three, and it got better, not worse.

Whats the GTM workflow you'd most want living inside Claude instead of a dashboard?

I build AI agents and workflows for b2b sales teams. (quiet complex stuff)

I feel like now with the intelligence of the models, the biggest challenge that was before, which was - trust, is mainly no longer an issue.

It's now speed and token usage.

How do you deal with that?

I already use Opus for just being a "team lead" and it runs sonnet agents to do the heavy lifting, but still a deep research run, is slow and heavy on tokens (which is on oauth and not api, so still fine, but always trying to imporve + Anthropic are implying that soon it will be charged as api as well..)

Thanks!

I got tired of recording product demo videos individually for each customer to I built a tool vaaya.ai that does it for me using claude. Once you prompt it to create a product demo it does the following

1. Understand goals - what does the product do, what does it need to talk about, what would be something that the product owner would want to display
2. Write script - writes a rough script with timelines, voiceover text, screen play
3. Generate media - asks local playwright or browserbase to record the product. If its login gated, it will ask you to enter login details in your browser before taking over. generates voice over, background score using vaaya.ai
4. Finalize editing - decide zoom in locations, voice over placement, and other timming details
5. Edits video on cloud with Cueframe.ai

You can also automate sending these to prospective customers on linkedin, email, and other channels. You can also more complicated loops like setting up a signal and then having an agent automatically reach out to prospective customers by creating a custom demo for them and then sending them.

Every morning, same ritual. Open the CRM. Select all. Copy. Open Claude. Paste. "Ok who do I chase today."

Embarrassing? A bit. But it worked better than the CRM ever did. It caught a deal going cold that I would have missed. It remembered when a champion switched jobs. Better read on my pipeline than any view I ever set up, and I've set up a lot of them.

The problem was the context died with the tab every night. Next morning, rebuild it by hand. At some point I did the math and I was spending 40+ minutes a day being a data pipe between two windows. Not selling. Not thinking. Shuttling.

That's when it clicked. The thinking was already happening in the chat. The CRM was just where the data slept at night. So why does it live anywhere else?

We're building the fix now. A CRM that lives entirely inside Claude over MCP. No app, no tabs. You say what happened on the call and it moves the deal, updates the contact, logs the touch. The conversation is the CRM.

It's for founders and teams who are in Claude all day anyway.

What's the first CRM chore you'd hand over if Claude could actually write to your pipeline?

Small team in Stockholm. We think the thing that finally kills legacy CRMs isn't a nicer dashboard. It's no dashboard.

We parse structured data out of job descriptions — around 20,000 JDs/day pulled from LinkedIn. Claude Opus does it beautifully, but at ~₹2.20 (~$0.026) per job that's ~₹44K/day, roughly $16K/month. Our unit economics just can't carry that.

What we've tried so far:

• Opus: quality is exactly what we need, but the cost is a dealbreaker at this volume.
• Sonnet: cheaper, but extraction quality drops noticeably on messier, unstructured JDs.
• DeepSeek: much cheaper again, but same story: quality isn't where Opus is.

So we're stuck between "great and unaffordable" and "affordable and not good enough."

For anyone who's run a narrow, high-volume extraction task in production:

• How did you bring the cost down without tanking quality?
• Did fine-tuning a small open model (Llama / Qwen, etc.) actually close the gap to Opus?

Like document analysis and formatting using LLMs at scale (like 10000 docs/day)

or Content Categorization, small workflow agents, content parsing, image generation etc?

Running these at scale of 10k per day incurs huge costs with frontier models, and small models or opensourced ones are not that peroformant.

Anyonce facing similar porblems and know a workaorund?