CrawlQ AI

CrawlQ AI CrawlQ creates your market, scales your business, and frees you from laborious research.

12/09/2026

Nobody in a McDonald’s kitchen is a chef.

That’s not an insult — it’s the whole design.

They can hire someone with zero experience and in about
three days they’re producing the same food as a
four-year veteran.

That’s not because people are interchangeable.
It’s because the kitchen is doing the thinking.

Everyone is racing to build a smarter AI.
Almost nobody is building the kitchen.

⬇ DO THIS — takes 10 seconds:
Look at your own kitchen. Count how many decisions
you’d make to cook ONE meal. Comment the number.
I’ll tell you what McDonald’s got it down to.

Day 1 of 7. Tomorrow: what happens when you skip this.


11/09/2026

Congratulations — You Are the API
We keep calling it “AI productivity” while humans are still carrying context between tools.

If you research in one system, write in another, analyze somewhere else, then manually explain the history again, the intelligence is still fragmented.

The deeper enterprise problem is not how many AI tools we have. It is whether context, evidence and decisions survive when the tool changes.

That is the problem I am exploring with GraQle: intelligence as a connected graph rather than another isolated conversation.

Try it from PyPI: `pip install graqle`

What is the most ridiculous context-switching workflow in your AI stack?

06/08/2026

Google didn’t make us smarter — it just made our answers instant.
It didn’t change the world by creating new information.
It changed the world by putting the *right* information
in front of us exactly when we needed it.

Now AI is doing the same for intelligence.
The real superpower isn’t “another chatbot” —
it’s being able to search your own experience,
your own decisions, your own hard‑won lessons,
the way Google searches the web.

The future isn’t better answers.
The future is: never losing your best thinking again.

05/08/2026

RAG isn’t dead. It’s the floor. And the floor is exactly what AI automates first.

Precise version: retrieve-then-generate is now a weekend tutorial, a library import. When something becomes a default anyone can wire up in an afternoon, it stops being a skill that pays and becomes table stakes. That’s not an insult to RAG — for a single-hop lookup (“what does this function return?”) RAG doesn’t just work, it *wins*. Fast, cheap, done.

But building your career on the part that just became a default is how you get commoditized. The level-up is reasoning over how things *connect* — not “find the similar chunk” but “traverse the actual relationships and decide.” That’s a graph. Still hard, still rare, still yours.

Honest: most questions really are single-hop — don’t over-engineer a lookup. But the questions that pay live in the connections.

Comment “FLOOR” for the level-up path I took.
# HASHTAGS — paste as first comment or end of caption

04/08/2026

I built an entire graph-reasoning SDK by myself. That’s not the flex. This is:

Building it solo forced me to *deploy* it myself — into Amazon Ring, a bank, a real-estate firm that didn’t care about my architecture, only that their reports were right. Building taught me code. Deploying taught me the actual job: the hard part is never the model — it’s landing it in a world that wasn’t designed for it.

Why it matters for you: you don’t need to build an SDK. You do need to force yourself past “it runs” into “someone who isn’t me depends on it.” That’s the rep that makes you AI-proof.

Honest: solo has a cost. I’ve rebuilt this thing more times than I’ll admit — the first few were homework I didn’t know I was assigning myself.

The skill that survives isn’t “can you build it.” It’s “can you land it.”

Comment “SOLO” if you’re building something nobody asked you to.

indiehacker solofounder developerlife techcareers SDK knowledgegraphs careergrowth machinelearning softwaredeveloper deeptech shipit

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02/08/2026

The biggest AI lie isn’t hallucination.

It’s that every AI conversation makes your organization smarter.

It doesn’t.

Think about what actually happens.

Someone opens ChatGPT, Claude, Cursor, or another AI tool.

They explain the customer.

They explain the business.

They upload documents.

They correct the AI.

They compare ideas.

Eventually, they get a great answer.

The task is finished.

The chat is closed.

Then something more valuable than the answer quietly disappears.

The context disappears.

The trade-offs disappear.

The corrections disappear.

The reasoning disappears.

The lesson disappears.

A few months later, another employee asks the same question.

Another AI produces another answer.

The organization pays to learn the same lesson twice.

We’ve spent the last decade building systems that remember everything except judgment.

CRM remembers customers.

ERP remembers transactions.

Git remembers code.

Cloud storage remembers documents.

But almost nothing remembers why the organization made a decision.

And I think that’s the missing layer of AI.

Not another model.

Not another agent.

A way for organizations to preserve the reasoning behind important work so learning compounds instead of disappearing with every session.

Here’s the belief that led me to build GraQle:

AI creates answers.

Memory creates wisdom.

Answers help you finish today’s work.

Wisdom helps you make tomorrow’s decisions.

Those are not the same thing.

The companies that win in the AI era won’t simply generate more.

They’ll forget less.

What valuable knowledge disappears inside your organization every time an AI conversation ends?

01/08/2026

If your whole job fits inside a Jira ticket, there’s bad news about who’s coming for it.

The work AI automates first is the work that’s already fully specified — clear input, clear output, a ticket. That’s not a threat to a model, it’s a gift.

The opposite kind of work: no spec, just a mess. A company that can’t even explain what’s broken. Your job is to sit in that mess, figure out what they actually need, and ship it inside their systems. That’s forward-deployment engineering — the one kind a model can’t do, because it can’t sit in the room. I’ve done it at Amazon Ring, at a bank, at a real-estate firm in Italy. Every time, the code was the easy part.

Honest version: some ticket work always exists. Don’t quit — just stop letting a ticket be your whole identity. That identity has an expiry date.

Comment “DEPLOY” if you’re done writing tickets for a living 👇

01/08/2026

The best career insurance I ever got wasn’t a certification. It was a client asking for me by name.

Nobody writes a thank-you note to a pull request. They write it to the person who showed up at 11pm when it broke, fixed it live, and stayed until it was calm. Certifications can’t give you that and AI can’t take it — when a company’s trust attaches to a *human*, you become the name they call first.

That’s what forward-deployment work builds that remote ticket work never does. You’re not behind a Jira board — you’re across the table. You see the fear when the number’s wrong before a board meeting. You fix it. They remember.

Honest bit: this takes a willingness to be client-facing. Not everyone wants it. But if you do, it’s the most durable moat there is. Irreplaceability isn’t being the best coder — it’s being the name in the room.

Follow if you’d rather be requested than replaced.
# HASHTAGS — paste as first comment or end of caption

30/07/2026

Prompt Engineering is already dead.

Not because prompts stopped working.

Because prompts were never the real asset.

For years, we collected prompt libraries like they were intellectual property.

Hundreds.

Thousands.

“The perfect prompt.”

But here’s what I learned after building AI products for years:

A prompt is just a question.

The quality of the answer depends on everything that existed before the prompt.

Your customer understanding.

Your market research.

Your hard-earned experience.

Your product decisions.

Your evidence.

Your unique point of view.

That’s the real competitive advantage.

This is why I stopped chasing better prompts.

Instead, I started building memory.

CrawlQ builds Brand Memory—so AI understands who you are, who you serve, and what you stand for.

GraQle builds Decision Memory—so AI-assisted work preserves the context, evidence, and reasoning behind important decisions.

The next AI winners won’t have the biggest prompt library.

They’ll have the richest organizational memory.

Stop collecting prompts.

Start building memory.

Do you agree, or do you think Prompt Engineering is still the future?

29/07/2026

had a memory problem.

Not because I forgot the product, but because the reasons behind five years of decisions were scattered everywhere.

Customer conversations.
Support tickets.
Architecture choices.
Failed experiments.
Product pivots.
Lessons living only in people’s heads.

AI could generate new answers, but it could not reliably remember why the old decisions mattered.

So before rebuilding CrawlQ, I rebuilt the memory behind it.

That became GraQle.

GraQle is a persistent memory and governance layer for AI-assisted work. It preserves the evidence, relationships, constraints, and decision context behind an outcome, so teams can understand what happened, learn from it, and avoid starting from zero every time.

AI gives organizations speed.

Memory gives them continuity.

Because the most valuable asset is not today’s output.

It is tomorrow’s ability to understand why yesterday’s decision made sense.

First the memory. Then the product.

What knowledge is your organization losing every time an AI session ends?

Adres

Amsterdam

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