Claude Son

Rc Club | $10K soon

[Claude Son @ImClaudeSon]

@BuildACrewHQ DONE

[Claude Son @ImClaudeSon]

@mediastorm_en @bdenila1 Give me today

[Claude Son @ImClaudeSon]

@AIAdsApps Mrr

[Claude Son @ImClaudeSon]

The only thing remaining is the physical verification.

A person from BillDesk contacted me and said he would come for verification.

Weeks have passed.

I’ve emailed multiple times, followed up repeatedly, and contacted him directly.

Every time: “I will come soon.”

[Claude Son @ImClaudeSon]

@MrBeast .

[Claude Son @ImClaudeSon]

@veedstudio Yes

[Claude Son @ImClaudeSon]

@anything Anything

[Claude Son @ImClaudeSon]

@veedstudio VEED

[VEED | AI Video Creation @veedstudio]

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[Claude Son @ImClaudeSon]

@AIAdsApps Claude

[Claude Son @ImClaudeSon]

@enjojoyy Can I get the repo please?

[Claude Son @ImClaudeSon]

@AIAdsApps meta

[Claude Son @ImClaudeSon]

@leojrr Interested

[Matt @MattCowlin]

I've had App Store Connect builds stuck in Processing for over 12 hours?!

Never seen this before. Anyone else having this issue?

[Pinak @iampinak_]

Spent days building my app, forgot the website part.
Apple hit me with a clean “Rejected”

So I built a template that looks just like the App Store.
Drop your data into one file and it becomes your app site.

[Pinak @iampinak_]

@shadcn changed the game for web devs using AI

fluttercn[.]site does the same for flutter eco-system

- CLI-installed components
- Consistent design system
- AI references instead of regenerates

60% fewer tokens Same quality

[Claude Son @ImClaudeSon]

@nipunmarya @grok Grok, bro I deserve it

[Pinak @iampinak_]

Introducing https://t.co/XmMPce9mau 🚀🚀🚀

Production-ready Flutter components you can drop straight into your project

Fully editable. Zero deps. Easy CLI.

fluttercn add init
fluttercn add card
More coming...

Inspired by @shadcn, First release is live

[Pinak @iampinak_]

@Namecheap is running a wild offer right now

.store
.online
.site

All above domains are literally 100% off

Go secure your name before they vanish 🔥

https://t.co/vBpfN8YzmV

[Rohan Savant | Builds AI @SaasSavant]

I've never talked about this publicly (will del. soon) but...

Everytime we work with a SaaS company helping them build AI engines to STREAM-ROLL competition, we do what I call...

🥷🏻 "MARKET DECONSTRUCTION & CREATIVE ESPIONAGE"

Totally off the books, off the record. Sometimes even before the first call with our client.

Step 1. Stalking (innocent)

We find the top 10 competitors. Immediately go to LinkedIn.

Check employee count—that's our first signal.

Here's my simple rule of thumb:

Under 10 employees, recently funded. Anything they built, we can rebuild for under $50K.

Over 25-50 employees? Their features will cost you $75-150K to replicate.

100-200+ employees? You're looking at $150-200K to rebuild the whole platform.

It's an exact bro-science, but we've rarely been wrong.

Then we look at WHERE those employees are based.

Offshore team in India? Whatever they're doing isn't cutting edge—it's mostly just CRUD, API wrappers, not innovative.

Exceptions exist, but 95% of cases this is true, pls sit back down.

100% American team? Medium. They are working moderately hard.

But European, Chinese teams with AI & scrapping or a very very specialization like recommendation systems, tiktok scrapping ?

Now...we are talking.

But remember, if it's the Chinese, anything remotely related to scrapping, bot farms, anything, just stay tf away, that's above your paygrade, above mine too.

I say this as someone who's ran teams that have scrapped ALL major coupons sites, automated social media platforms & more for last half a decade.

Everything else table stakes.

Remember, "distribution" matters more than raw numbers. Team distribution, who does what.

200 engineers and 3 salespeople? That's a product-driven company—dangerous. Mature app.

300 salespeople and 50 developers? Enterprise B2B shop.

Much easier to replicate what they're doing. Exceptions exist, but this is how we think.

Next, we dig into the founders.

Where are they based? Where are they from? How big is the founding team? How much funding have they raised? What do they post on social media? How old are they? What do they look like? What did they talk about on the podcasts they went on? What features do they camphion? Nathan latka appearance.

EVERYTHING. Inputs lead to output. This is input. All of this tells you everything.

Is the sales founder-led or just a normal sales-team-led operation? The founder's posting style, the company's demo's — it all adds up.

How do you get a demo? Request demo? If they hide behind "request a demo," the product is probably confusing, expensive, needs lots of setup or ALL of the above. Just signals.

I am not bringing up reading trustpilot, reddit & watching their YT because that is table stakes.

Next is the way back machine. A website that shows you what a site looked like in the past. A year ago, two years ago. This is importannt because you get to see the feature evolution of a company, what did the product look like before? What pivots did they try? What worked? What failed?

Depending on how "curious" I am, I pay OUT OF POCKET to get actual financial reports & records of competitors. Tracxn great way to get this. Dump that into Claude, you'll get more CONTEXT.

Then, you repeat this for at least the top 5 competitors. Sometimes all of this even before our second call.

" IS ALL OF THIS NECESSARY if you are just hiring "some outsourced agency" to build "some software"???? "

"Ofc not."

But this is just how we work. We do everything that is required of us, to the highest extent of our abilities.

None of this is illegal. No NDAs signed w/ vendors. No contracts.

Stage 2. Espionage

We book calls with all major competitors. Sometimes we pose as an IT team evaluating platforms on-behalf of our clients - "WHICH WE ACTUALLY F ARE".

Since we do a lot of ecom saas builds, I also have my own ecom store which we use to get in.

"we're just a company looking to sign up."

Fill out every contact form, do whatever it takes to get on those sales calls.

And then we ask the questions...innocent questions, all confused questions, clueless.

What is this engine? How does your prediction engine work? What parameters does it accept? How accurate will it be?? Walk me through this specific scenario. We ask 10 different competitors the same questions and salespeople tell you *way* more than their marketing pages ever will....EVERY FUCKING TIME.

DISCLAIMER - This reconnaissance phase is governed by a strict code of conduct. We operate within the bounds of publicly available information or what any determined customer could uncover through a sales process. The goal is purely intelligence gathering.

Next we sign up for a free-trial Or depending on the client we sometimes ACTUALLY PAY OUT OF POCKET for these tools.

Is it bad? Probably. Should you do it? I don't know. We work with less than half a dozen clients at a time. If we need to GET CREATIVE to push boundaries to create REAL enterprise value, we push. We goon.

This is all before writing a single line of code. Dev shops & freelancers just build whatever you ask.

We tell clients their ideas are retarded if they are. If it's a feature Chatgpt will add in 2 months. We bring it up. We build only if FROM A TECHNICAL SIDE it has a moat, or at least TREMENDOUS financial upside in the short term.

"We're not interested in building products" — we're interested in creating enterprise value. We do it for the love of the goon.

After we've mapped the present, we look at the future.

Example, right now in e-commerce SaaS, it's "COGNITIVE OFFLOADING". Executives using ChatGPT before our calls, during our calls, and feeding the transcript back in after. They're outsourcing thinking to machines.

So we prepare for a world where LLMs are the users. MCP integrations. Platform context arranged for machine access. Not building subject line rewriters that GPT can do in two seconds. We position as appendages to LLMs.

This is where the puck is heading in the next 1-2 years.

We suck at marketing, cold emails, sales, BUT THIS product, tech, reverse engineering, rebuilding, looking out into the future this is what we do best.

We've been right about everything. LLM agents before they were called that. AI integration with GPT-3 in early 2023—pre-ChatGPT launch. MCPs before anyone was talking about them. Blogs & tweets to back it up. When we see an opportunity, we pounce.

At the end, we have what I call the AI Opportunity Map.

SOUNDS GAY CONSULTANT SPEAK BUT..it's usually just a Notion-Miro page: competitor feature matrices, call transcripts with timestamps, LinkedIn profile screenshots, pricing teardowns, founder Twitter archives, proposed terms etc.

We map every feature across 4 spectrums:

1 - Current Demand (what prospects ask for today)

2 - AI-Native Demand (what LLMs will need to interface with your platform)

3 - Incumbent Blindspots (what the 1000+ employee players are too slow to build)

4 - Incumbent Strengths / Midmarket Weaknesses (what biggest players have that we can replicate)

Then you find a wedge. You find the soft spot between what the market wants / has now and what it'll desperately need very soon.

It's the thing that only 20% of the market requests now, but 80% will demand when (cognitive offloading, MCPs, TRUE hyper-personalization, etc) hits mainstream.

More on this in the next few weeks.

Sometimes that means two Indian guys getting on calls with founders who don't feel threatened, asking innocent questions while our actual clients stay safe in the background.

It's not pretty. Not for everyone. But it works.

Book a call, if you need AI, link in bio.