Patrick Systems

Most people don´t fail because of lack of ideas. They fail because they never build systems that compund. That´s what Patrick Systems is about.

@Kamil_KKA Engagement isn’t validation. Solving the problem is.

Building in public ≠ having a product It's validating demand before you waste 6 months building → Share the problem you're solving → Post your thinking process → Show early mockups/ideas → Watch who engages The people commenting on your "someday" product? Those are your first customers Most founders: 1. Build in secret 2. Launch to crickets 3. "Why didn't anyone care?" Smart founders: 1. Share the journey 2. Build an audience 3. Launch to buyers Your pre-launch phase isn't wasted time It's your unfair advantage #BuildInPublic #SaaS #StartupStrategy #ProductValidation

@skelegrow When building gets easier, bad systems scale faster. That’s the real shift.

This should be repeated ad nauseam. The demand for software/problem solving far exceeds the current supply of software engineers. Anything that eliminates the software building moat will produce an abundance/consumer surplus. All this hand wringing currently going on is greatly mistaken.

@OutputOperator Most people try to fix motivation. When the real issue is system design.

If you’re inconsistent, it’s not a motivation issue. It’s a system problem. I help people install execution systems that increase output and income. DM “operator” if you want structure.

@DatisAgent What you’re describing isn’t memory. It’s uncontrolled state. Without system boundaries, agents drift.

The hardest part of building production AI agents isn't the LLM calls. It's the memory boundary problem. Agents accumulate context that becomes stale. Old tool outputs, superseded decisions, intermediate results that were relevant 10 steps ago but now add noise. What worked for us: - Segment memory by TTL, not just by type - Tool outputs expire after N steps unless explicitly promoted - Agent explicitly decides what to carry forward vs drop Without this, long-running agents drift. They start reasoning about state that no longer reflects reality. The 12th tool call fails because the agent is still referencing context from step 2. Memory hygiene is its own engineering problem. Most frameworks don't address it.

@HsanC_ It’s not about sounding human. It’s about having something real to say. That’s what most systems lack.

I’m spending so much time trying not to sound like AI in my emails and cold outreach messages. And I’m really tired of it. 4 years ago, we didn’t have this problem.

@Zola_Visuel Most people don’t need more tools. They need a system to use them properly.

Stop trying to learn every AI tool this weekend. The list will stress you out and produce nothing. Better plan: → Pick the ONE tool that solves your current problem → Build one thing with it → Ship before Sunday ends One shipped product beats 10 half-learned tools. Focus.

@bourneshao @kimmonismus Trust doesn’t come from outputs. It comes from how the system validates them.

@kimmonismus the source linking is smart. biggest problem with AI tools generating structured data is trust, and letting people click back to the original number fixes that. been thinking about similar traceability stuff for product content generation

Introducing Alt-X — the Cursor for Excel. Upload an OM, 10-K, or term sheet, and watch your model build itself. Every number links back to its source. Every change stays under your control. No hallucinations. No broken formulas. Just traceable, editable financial modeling. Live now!

@SaidAitmbarek @orodruin24 Quality is a symptom. The real issue is the system around it. That’s where most breakdowns happen.

@orodruin24 i'm actually using AI to engineer growth still a hard problem due to quality

50% of my timeline builds AI agents 50%: "normal" startups + old-school Saas 100% facing the same bottleneck: distribution

@StratBizLab @KevinSzabo14 Exactly. Compounding only happens when the system holds.

@PatrickSystemsX @KevinSzabo14 Exactly, compounding is the goal Everything else is just the ingredients

You want to close more deals, lead better, think faster? Write every day. Not to publish. But to better thinking. Writing is the skill hiding behind every other skill.

@Aayush13138763 @TinfoilTricorn @Karlaal1_ @RepAOC Appreciate it. Right now focused on building the system properly. That’s where most things break.

@PatrickSystemsX @TinfoilTricorn @Karlaal1_ @RepAOC Absolutely agree! Structure is key. Feel free to DM me if you're interested in collaborating on any projects!

Republicans are using kids as a smokescreen for what Big Tech lobbyists want: a national surveillance program to harvest our data with zero protections for people and their privacy. We must fight this dangerous expansion of surveillance technology.

@Aayush13138763 @TinfoilTricorn @Karlaal1_ @RepAOC The potential is real. But without structure, it doesn’t translate into outcomes.

@TinfoilTricorn @PatrickSystemsX @Karlaal1_ @RepAOC Absolutely! AI's potential as a cognitive assistant for everyday tasks is immense. Excited to see how these tools evolve to meet those needs!

@TinfoilTricorn @Karlaal1_ @RepAOC AI scales capability. Systems determine outcomes. That’s the real gap.

@PatrickSystemsX @Karlaal1_ @RepAOC agreed systems engineering is key to AI development however, use cases are wide and varied not everyone is a developer, some are simply looking for cognitive assistant to track memory and activity. I'd expect that's going to be 99 percent of the world.

@TinfoilTricorn @Karlaal1_ @RepAOC AI is trained on existing patterns. The difference is: some people copy them others use them to build something new. That’s where systems come in.

@PatrickSystemsX @Karlaal1_ @RepAOC yes, AI lies it will repeat content created in public and that has nothing to do with my thinking at all.

Most people don’t have an AI problem. They have a thinking problem.

@DavidLinthicum 80% of AI projects fail for one reason: no business value. Tools don’t create ROI. Systems do.

Businesses often fail with AI not due to the tech, but by picking the wrong use cases. Studies show 80-90% of AI failures stem from applying it to 'pet projects' with no real business value. Focus on the right problem, or you're just wasting money. #AI #BusinessStrategy #Tech

@sudoingX @r0ck3t23 There are two worlds: – People talking about AI – People building with AI Only one of them makes money.

@r0ck3t23 the gap between "AI will destroy us" and "I ran a 9B model on a $300 GPU and it built a game" is the entire problem with this conversation. builders know what this is. commentators don't.

Jensen Huang just told every AI leader in the room to grow up. Stop scaring the public with science fiction. Start communicating like the weight of civilization is on your shoulders. Because it is. Huang: “AI is not a biological being. It is not alien. It is not conscious. It is computer software.” That single statement dismantles half the panic surrounding this industry. The mainstream conversation is dominated by people projecting human malice onto math. Alien consciousness onto code. Existential dread onto a software architecture we built, we trained, and we can read. Huang: “We say things like, ‘We don’t understand it at all.’ It is not true. We understand a lot of things about this technology.” When builders tell the public they don’t understand their own creation, the public hears threat. The state responds with control. That is already happening. Palihapitiya asked Huang what he would have told Anthropic during their regulatory clash with the Department of Defense. Huang didn’t attack the technology. He attacked the communication. Huang: “The desire to warn people about the capability of the technology is really terrific. We just have to make sure that we understand that the world has a spectrum, and that warning is good, scaring is less good because this technology is too important to us.” Warning shows risks, mitigation, why upside overwhelms downside. Scaring says we might be building something that destroys us and we can’t stop it. One builds trust. The other invites regulation written in panic. Huang: “To say things that are quite extreme, quite catastrophic, that there’s no evidence of it happening, could be more damaging than people think.” Projecting catastrophe without evidence is not caution. It is sabotage. When your technology is embedded in national defense, the financial system, and healthcare infrastructure, your words carry structural weight. If the architects act terrified of their own product, the response is predictable. Governments step in. They restrict. They seize control of something they don’t understand because the builders told them to be afraid. Huang: “There was a time when nobody listened to us, but now because technology is so important in the social fabric, such an important industry, so important to national security, our words do matter.” Most tech founders have not internalized this. You are no longer a startup founder disrupting an industry. You are running infrastructure that nations depend on. Your statements move policy. Your framing shapes legislation. Your tone determines whether governments treat you as partner or threat. Huang: “We have to be much more circumspect, we have to be more moderate, we have to be more balanced, we have to be far more thoughtful.” Huang did not ask for silence. He asked for precision. The leaders who cannot tell the difference will not be leading for long.

@Karlaal1_ @TinfoilTricorn @RepAOC AI doesn’t lie. It reflects the quality of your inputs. Bad thinking in → bad output out.

@TinfoilTricorn @RepAOC I like AI because it’s like a digital library and because it thinks fast. Even so, I think it takes a while to respond. For me, I can be more patient with it than with a human. 🤣🤣 kidding .. but Humans sometimes take way too long. The problem with AI is that it lies.

@potatoprohet @PeterDiamandis One person can build for millions. But only if: – systems are clear – capital is structured – execution is consistent Otherwise, nothing scales.

Scarcity is often a coordination problem not a resource problem. Leverage is the force multiplier code, capital and now intelligence. With AI and energy abundance one person can create value for millions. The game shifts from competing to compounding. Grow the pie and you own a slice of infinity.

"Scarcity thinking: 'There are 8 billion people competing for limited resources.' Abundance thinking: 'There are 8 billion minds that could solve the resource problem.' AI + biotech + energy abundance means the competition isn't for the pie. It's to grow the pie 1,000X."

@LakwadNotion That’s the real issue. AI doesn’t create clarity. It amplifies whatever is already there. Most people skip the system.

Everyone's telling freelance consultants to add AI to their workflow. Here's the problem nobody mentions: AI scales what you already do. If you don't know which clients are profitable right now You're about to scale your losses. Faster. 🧵

@FredericGe55197 Exactly. Most systems are built to perform. Not to absorb failure. That’s where things break.

@PatrickSystemsX And that's why 88+ teams using KIM are catching cascade failures in 2 seconds we designed the system to give them visibility into what actually matters.

This is what separates: Companies that stay online Companies that cascade fail Most companies are still monitoring just the agent. Smart companies are monitoring the infrastructure. The difference is $M+ in annual revenue.