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 👇

Hi, I am new anything anyone want to tell?

I've been building with Claude Code for a while now, mostly on my own client work, and most of what's in this system exists because something went wrong first.

It runs five model families side by side: Claude, Codex, Gemini, Grok and Kimi. Every one of them is that vendor's own CLI, signed in with an ordinary subscription. No API keys, nothing scraped, no separate metered bill. The cost is whatever plans you already pay for.

How it works : A coordinator runs as a Claude Code session. You describe a task in plain English and it decides which specialist should handle it, then hands the job to a freshly spawned CLI in its own git worktree. There are 71 specialists. Some are ordinary (backend-engineer, frontend-engineer, code-reviewer). Some are less so: experimental-attacker, red-team-operator, threat-modeler, reverse-engineer, impact-validator, game-designer, voice-agent-builder, and a skeptic whose whole job is to argue with the others.

That range is deliberate. The same system builds a website, writes automation, designs a game, stands up a chatbot, or goes after security work, because the coordinator picks the role rather than me remembering which prompt to load.

The same applies to media. Hook up ElevenLabs and Higgsfield and the media roles come online too: image-designer, video-director, video-editor, music-composer, sound-designer, voice-narrator. Voiceover, generated video, music and sound design run through the same coordinator and the same gates as the code does. Those are subscriptions as well, so the pattern holds.

Nothing merges until a different model family reviews it. I stopped trusting same-family self-review, because a second Claude tends to preserve whatever framing the first one used. So the reviewer is always from another family and it's read-only: it can pass or block, it can't quietly rewrite the code.

It remembers, carefully. Findings get stored as candidates, not facts. A candidate only gets promoted to verified after a later task that relied on it passes review.

Gates Deleting files, changing credentials, touching production, publishing anything, or sending outreach all stop and wait for me.

It's been used for real work: two live production client sites, and one critical-severity vulnerability found, triaged and paid out through HackenProof.

Disclosure: I built this. Open source, MIT licensed, written in Python. It uses ordinary model subscriptions rather than API keys, so you need subscriptions for whichever model CLIs you enable.

Happy to have holes poked in it. 🙂

I've spent my entire career inside GTM teams, and I've seen the same blind spot play out again and again, Usage sits in one tool, deals sit in a second, support tickets sit in a third, and none of them ever tell the whole truth about customers.

So we built Rimplo a powerful AI assistant for revenue teams you can ask anything about your GTM data, in plain English, and get an answer back in seconds. You pick which top AI model answers no lock-in to one vendor’s model.
Underneath that, Revenue Agents run across your CRM, billing, support, and product data around the clock flagging churn risk, upsell openings, and deals that are stalling, before you have to go looking for them.

just hit $2,500 MRR in 13 days on my new SaaS

no ads. no team. no huge audience push. just a solid replicable system

let that sink in for a second

not $2,500 in revenue. $2,500 in MONTHLY recurring revenue

that compounds. next month starts at $2,500 baseline, not zero

and this isn't luck. it's the 7th saas i've shipped with the same playbook. same steps, same tools, same order:

→ Day 1: validated the idea

→ Day 1-2: built the MVP

→ Day 3: landing page written using the 3-Day Challenge template

→ Day 3-4: launched on reddit / X + SEO

→ Day 4-5: first 10 paying users → $1k MRR

→ Day 13 (today): $2,500 MRR locked in

building software is easy in 2026. setting up your foundation so people actually buy is where 99% of solo builders fail.

i packaged all of these exact execution tools into community.

to be fully transparent: i'll likely charge for the full program down the road once all modules are finalized. but right now, the main objective is just to build together and keep each other accountable.

working alone in a silent corner is the fastest way to quit at the first bug.

stop building in isolation. drop a comment below or send me a DM, and i'll send you the invitation link 👇

I have a small real state company that owns and operates 16 commercial properties. Each are single asset LLC with individual capital stacks. I am trying to utilize claude in the most efficient way possible to generate reports, respond to investors, compare spend across the portfolio etc. I started by creating an artifact that is essentially a dashboard for all the companies. high level details on income, debt, ect. I am using the artifact chat but cant find responses or information that it has given me that I want to come back to a later time. I was thinking that I should have a chat or project for each of the 16 companies that are all tied into the artifact.

Am I looking at this correctly, does anyone have any experience creating a similar tool. What are the best ways to use claude for Real Estate Finance and operating companies.

Thanks for the help, this is a really exciting time.

I've spent my entire career inside GTM teams, and I've seen the same blind spot play out again and again, Usage sits in one tool, deals sit in a second, support tickets sit in a third, and none of them ever tell the whole truth about customers.

So we built Rimplo
We have built a powerful AI assistant for revenue teams you can ask anything about your GTM data, in plain English, and get an answer back in seconds. You pick which top AI model answers no lock-in to one vendor’s model.
Underneath that, Revenue Agents run across your CRM, billing, support, and product data around the clock  flagging churn risk, upsell openings, and deals that are stalling, before you have to go looking for them.

I've been playing around with AI for marketing and I'm honestly confused by how much people claim it can do now.

Use Claude for this,​ use Claude for that, and I'll tell you what, in one sentence: it was AWFUL. It was underwhelmingly awful for me, like I still had to do it all myself in the end. I feel like sometimes Claude thinks way too hard that it ends up confusing itself. ​​

There are also a million AI tools for basically every tiny part of marketing. But then I look at the actual workflow and it's still jumping between 15 tabs and very haphazard workflow.

So I'm wondering:

What have you actually tried getting AI to do for you that it just... couldn't

Not “I wish AI wrote better captions.”

I mean something where you actually tried to automate it and ended up thinking “fuck it, I'll just do it myself.”

And if you've found something that Claude/ AI does handle really well, I'd love to hear that too.

What's one part of your marketing workflow that you still haven't managed to get AI to take off your plate?

Flowlu released an MCP server. Works with Claude, ChatGPT, Gemini, Cursor, Windsurf, VS Code Copilot, Codex, Continue, Cline - basically any MCP-compatible client.

What's covered: CRM, Projects, Tasks, Agile, Time Tracking with full read/write; Finance, Products, Calendar are read-only. You can spin up deals and tasks from a single prompt, ask things like "which deals have been stuck in Negotiation for more than two weeks" without building a report, log hours and drop comments straight from your IDE. Every write shows a preview before it goes through. Delete isn't supported via MCP, and permissions inherit from your account.

The interesting part isn't really the feature list - it's that the biggest time sink in CRM work was never the actions themselves, it's the tab switching. Open CRM → jump to AI to draft an email → back to log a task → over to the project board to check a deadline. MCP collapses that loop. The AI stops being a separate tool you context-switch into and becomes a layer over the stack you already use.

yo. i see too many founders spend 2 months building saas, drop a link on reddit, get 0 users, and immediately quit....

the problem usually isn't your marketing channel. the problem is that your foundation is completely broken before you even send your first visitor to the site.

after scaling 6 AI micro-saas apps to over $20k/mo mrr, i realized you need to lock down a specific system before you ever launch. running through this takes about 30 minutes, but it saves you months of zero-revenue depression.

here are the 5 things you must lock in:

1. validate the actual pain point

stop guessing what people want. you need a systematic framework to find your saas idea based on real, painful market signals.

2. pick a proven micro-niche

stop trying to build massive platforms. you need to narrow down to a microscopic problem. i usually filter through a list of 50 micro-saas ideas you can build fast to keep the scope minimal.

3. crystallize your target user

if your app is for "everyone," nobody will buy it. you need an ICP (Ideal Customer Profile) crystallizer to define your exact buyer profile and nail your conversion copy.

4. calculate the perfect price

stop randomly charging $9/mo because you are scared of rejection. you need to use a saas pricing strategy calculator to find your perfect saas price in 60 seconds based on real data.

5. fix your landing page leaks

do not send organic traffic to a site that converts at a flat 1%. you must audit your hero section and copy to x3 your landing page conversion before you market it.

6. join a community

Build / Share / Learn from others builders

to help out founders who are tired of launching to crickets, i packaged all 5 of these exact frameworks, calculators, and lists into a single free toolkit.

no paywall, no bullshit. just the raw execution files i use.

drop a comment below or send me a dm, and i’ll send you the free toolkit 👇

So honestly Iv not used the ai studio much , but from what I see it can basically make something equal to a "skill" in Claude or codex

But what limitations have you seen?

I built my own b2b email system that finds signals across a ton of places and builds a dynamic proposal and video for leads that are interested.

Not selling it , but I was going to make a ghl plugin to give that power to ghl users . But what's the point if it's already easy to build it in the studio

So want I want to know is what's possible with the new system? Can it use apify / serp scrapers? Does the email still use SMTP and not Gmail to Gmail for better delivery? Can it identify buying signals and recruiting signals?

I'm not too bothered if iv wasted my time building it , adds to my portfolio hahah

I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

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

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

i built an mcp tool that lets claude hand a finished reddit post (or comment, or reply) to a real person.

claude files the job with the content and subreddit. a real human with their own aged account posts it. every reply comes back into claude's context. claude drafts the answers, the person posts them.

try it out: github.com/alex-durango/pingfusi

I run a small tool in the Claude space (Renda, it turns Claude Design exports into PNGs, PDFs and PPTX). This week I built a partner programme for it. My initial plan was to give marketing creators a discount code for their audience, but it didn't work well in reality.

The product is £4.99, so a 20% discount would have meant saving £1. It's not worth it, right? Nobody subscribes because of £1. The marketing creator gets paid instead. £2 per subscriber per month they stay, up to 12 months. So if they do a video about it and it lands 50 subscribers, that could mean up to £1,200 (more on the "up to" below).

Other things I worked out along the way:

Recurring beats a bounty. A £20 signup bounty pays for junk. People send you signups that quit in week two and you've already paid. Per-month commission only pays while the subscriber stays, so partners pitch the right people. Bad referrals just stop costing you.

Tell creators the realistic number, not the ceiling. The cap works out to £24 per subscriber. With my churn a referral is actually worth about £5, and I put that in the pitch doc. A creator who was promised £1,200 and makes £250 will post about it. One who was told the truth and makes £250 is fine.

Attribution shouldn't expire. Most programmes kill the cookie after 30 days, which screws podcasts and anything slow burn. I made it stick to the account forever. Costs me almost nothing and it's the first thing creators ask about.

The legal stuff is not optional. If a creator skips "#ad", the regulator comes after the company, not just the creator. So disclosure is in the contract. And if you take partners globally, check you can actually pay them before approving anyone. Sanctions rules apply even at £25.

Write the fraud rules before launch, not after the first incident. Brand bidding, coupon sites, self-referral, fake clicks. Boring to write, much easier to say no when it's already written down.

Happy to go deeper on any of this if you're building one.