RPPR

Long-term investor. Deep dives into businesses, innovation & asymmetric ideas. Following evidence, not narratives.

[RPPR @RPP_Research]

@Dr_Crossroads Who wouldn’t be? He’s one of the most legendary Chess players, and even more in short formats!
What a thrill! Hope you enjoyed a bit. :)

[RPPR @RPP_Research]

@TheSophRetard @tonileuften Why are you everywhere spreading negative thoughts of yours?
Just find your own passion, go touch some grass.
If you REALLY cared about the people “wrongly buying into $TE ‘s thesis”, you would bring the whole thesis down with your logic.
All I read is “yap yap yap”

[RPPR @RPP_Research]

@makingmoneynow1 @TheLongInvest @TheValueTrade @dmeinvestor @The_PennyQueen @ClassicValueInv @StockResearchWk @Amateur_Inv 12 days for them to cover though looking at the pace we’re moving.

[RPPR @RPP_Research]

Not your average Joe hire…

=>

Brian Zarchin — quick pedigree

* 🇺🇸 21+ years U.S. Army Aviation — retired Colonel; military rotary-wing aviator with operational/combat experience.
* 🚁 Army aviation commander — commanded units and served in senior Army aviation roles; deployed to Bosnia and Iraq.
* ✈️ Insitu / Boeing — ~5 years working on U.S. Army programs for Boeing-owned drone specialist Insitu.
* 🛩️ Airbus U.S. Space & Defense — VP of U.S. Army Systems, then VP of Uncrewed Aircraft Systems.
* 🏗️ Built Airbus’s new U.S. military-drone business — Airbus put him in charge when it established the UAS business line aimed at DoD requirements.
* 🔥 AeroVironment — VP / Head of Growth at MacCready Works, AV’s advanced-technology organization.
* 🎯 Now Merlin — General Manager, Tactical Autonomy, according to the announcement in your screenshot.

In one line: Army aviator → Boeing/Insitu drones → Airbus defense/UAS → AeroVironment advanced systems → Merlin Tactical Autonomy.

That’s a remarkably specific résumé for what Merlin appears to be trying to build.

[RPPR @RPP_Research]

@ReeceCards THOSE are the posts I’m longing for.
Keep it up Young Man. 👏

[RPPR @RPP_Research]

$ADUR — This is what technical de-risking actually looks like.

Aduro just released another NGP Pilot Plant update, and I think the progression matters much more than the headline yield.

A few months ago:

→ 47 hours continuous operation
→ controlled waste-derived PP feedstock
→ 86% liquid hydrocarbon recovery
→ demonstrated steady-state operation

Today:

→ 75-hour campaign
→ ~758 kg processed
→ progressively changing PP/PE mixtures
→ 88.8% yield on 100% PP
→ 90.8% on 80/20 PP/PE
→ 89.2% even at 50/50 PP/PE
→ product-quality indicators remained consistent
→ system returned to target conditions ~2 hours faster than in the comparable previous run

That last point shouldn’t be overlooked.

The story isn’t simply:

“$ADUR can turn plastic into hydrocarbons.”

We already knew the chemistry worked.

The question is increasingly:

Can they turn HCT into a repeatable, controllable industrial PROCESS?

And campaign after campaign, we are getting more evidence.

Longer runtime.
More material.
More complicated feed composition.
Stable product characteristics.
Better process control.
Faster recovery after operating changes.

And perhaps most importantly:

NGP → operating data → $SPM / Saipem engineering → Chemelot FOAK.

Saipem is already receiving these operating results and using them for the Process Design Package, equipment optimization, utilities and CAPEX work for the planned FOAK plant.

In other words, the pilot isn’t running experiments in isolation anymore.

The data being generated TODAY are helping design the industrial plant of TOMORROW.

And now we get to the really interesting part.

Aduro’s next step is longer campaigns using increasingly complex real-world waste plastics supplied by prospective FOAK feedstock partners.

THAT is the next major test.

Controlled PP/PE mixtures are one thing.

Dirty, inconsistent, contaminated real-world plastic waste is another.

If HCT can maintain strong yields, product quality and operational stability there, the conversation around $ADUR changes considerably.

And the timing isn’t exactly boring either…

Today, Aduro is presenting HCT to $PEP PepsiCo teams specifically as a potential solution for difficult flexible and multilayer polyolefin packaging.

So zoom out:

Lab → R2 → NGP → longer campaigns → mixed PP/PE → real waste → FOAK → commercial deployment.

We aren’t at the end of that road.

Not even close.

Scale-up risk remains very real.

But $ADUR keeps doing something I want to see from every pre-commercial technology company I own:

turning unknowns into data.

One campaign at a time.

[RPPR @RPP_Research]

I am extremely high conviction long term for this company.
Too many patents, too many industries, pretty high margin, explosion in data centers and the numbers in robotics aren’t showing yet.
The only thing that stops me from “betting the house” on it is the feeling it may NOT move as fast as many other stocks due to its location in Europe rather than the States.
Thoughts? @HunterAllen4 @Versatileincome

[RPPR @RPP_Research]

Thank you for your hard work.
You definitely got me interested in the stock and I finally pulled the trigger in the last few days.
I might have bought too big of a position relative of my portfolio but it seems like a great opportunity.
Of course I understand I am the one who chose to buy. ☺️

[RPPR @RPP_Research]

@DraupnirAlpha @IvanHri91235556 There is no way you are not a big short or absolutely hate the guys of someone related to the company. You are spending your time commenting on everything $MRLN
If only you had something interesting to say. I’m blocking you and hope I won’t be the only one. Seriously pathetic.

[Aduro Clean Technologies @AduroCleanTech]

This weekend, communities worldwide take part in cleanup events for World Cleanup Day, observed globally on September 20. @LetsDoItWorld is one of the largest volunteer environmental actions on the calendar.

Cleanup gets waste out of the environment, but what happens to it next matters too. Hydrochemolytic™ Technology is built to give polyolefin plastics – HDPE, LDPE, PP, PS – a pathway back into usable material, rather than landfill or incineration.

Join in: https://t.co/js1VyQpXIt

#WorldCleanupDay #CleanTech #EcoInnovation #ChemicalRecycling #PlasticRecycling

[RPPR @RPP_Research]

@ShawarmaCapital Are we still playing?…

[RPPR @RPP_Research]

@Dailyhodlpoll As long as it runs up the same way, I am fine with that ^^.

[RPPR @RPP_Research]

@chadwahl @amitisinvesting 3 mics 2 blocs 1 Love

[RPPR @RPP_Research]

@TonySeruga Technically That’s Three but thank you for sharing.

[RPPR @RPP_Research]

@stan100x Congrats!
What do you consider being your RKLB story of 2026/28 on your way to 10M?
Hope that is not an everyday diet though, gotta go for those gains in both body & wallet ;).

[RPPR @RPP_Research]

1. “What would have to be true?”
Instead of asking “Is this a good idea?”, ask:
“What would have to be true for this to be an exceptional investment/business/decision?”
It forces the analysis toward assumptions, dependencies and falsifiable conditions.

2. “What am I missing?”
“Assume I’ve already understood the obvious bull and bear cases. What important factor am I probably overlooking?”
This is excellent after you’ve already spent hours researching something because another generic summary adds almost nothing.

3. “Try to kill the thesis.”
“Assume my thesis is wrong. Build the strongest evidence-based case for why it fails. Don’t manufacture risks just to be contrarian.”
Probably one of my favorites for investing. The last sentence matters: otherwise AI sometimes invents weak objections to satisfy the prompt.

4. “Separate facts, assumptions and speculation.”
“For every important conclusion, separate what we know, what we’re reasonably inferring, and what we’re speculating about.”
This dramatically improves research quality. It’s especially useful with early-stage companies where a compelling narrative can quietly become treated as fact.

5. “What changes the answer?”
“Identify the 3–5 variables that actually determine the answer. Ignore factors that sound important but wouldn’t materially change the conclusion.”
Great antidote to 5,000-word AI reports containing 4,500 words of noise.

6. “Update, don’t restart.”
“Here is my existing thesis. Analyze only what has changed since then and tell me whether each development strengthens, weakens, or leaves the underlying thesis unchanged.”
For ongoing company research, this is much better than repeatedly asking for a new deep dive.

7. “Give me the decision tree.”
“Don’t tell me what happens. Give me the major possible outcomes, what would cause each one, and what evidence would tell us which path we’re entering.”
This is particularly powerful because the future isn’t one forecast.

8. “Find the bottleneck.”
“If this industry grows as expected, what becomes scarce? Which part of the value chain becomes the bottleneck, and who captures the economics?”
One of my favorites for discovering investment ideas. It shifts the question from “AI will grow, what AI stock?” to “What does AI growth make unavoidable?”

9. “What is the market implicitly assuming?”
“Reverse-engineer what today’s valuation appears to require regarding revenue growth, margins, market share and terminal economics. Then assess which assumptions are doing most of the work.”
Much more useful than simply asking whether something is “expensive.”

10. “What evidence would make you change your mind?”

“Before reaching a conclusion, specify what future evidence would materially strengthen it and what evidence would invalidate it.”

This prevents a thesis from becoming unfalsifiable.

And there’s one meta-prompt I particularly like:

“Before answering, determine what question I should actually be asking.”

Not because AI should override your question, but because sometimes the question itself contains the mistake.

For example, “Which stock should I sell to buy X?” assumes X should be bought. A better analysis might first ask whether X improves the portfolio enough to justify doing anything at all.

For the kind of research you often do, I’d combine several into one:

“Investigate this from first principles. Establish the facts first. Separate facts from inference and speculation. Identify what would have to be true for the thesis to work, then try to kill the thesis using the strongest available evidence. Identify the 3–5 variables that actually matter, what the market appears to be assuming already, and what future evidence would confirm or invalidate the thesis. Finally, tell me what I am most likely overlooking.”

That prompt tends to produce research rather than affirmation—which is where AI becomes much more valuable.

Answered by Chat-GPT & ME

[RPPR @RPP_Research]

@HunterAllen4 Thanks for taking the time to reply. I might have to check what you do from a little bit closer ;).

A lot of winners in that list 👍 👏

[RPPR @RPP_Research]

All I am saying is:

He posted his thoughts about 2 hours ago.

You replied about 2 hours ago.

You have had your shares for at least 24hrs+ since market has not been open.

Therefore do NOT say the reason why you bought back into it is what you say it is.

Instead, just tell everyone @L4gcy has a theory you had not thought of and it makes a lot of sense! And add your thoughts into it.

Anyway.. I’m out ✌️

[RPPR @RPP_Research]

@ReeceCards My bad, I posted the wrong attachment.

(Facing my own mirror for a second)

[RPPR @RPP_Research]

Your dishonesty does not lie in holding the stocks you say you do but the way you communicate it..

“Why? Because I …”

No YOU didn’t… or at least NoT for this reason, hence my comment. 🙏

Do what you want and be whoever you want, but make sure it aligns with a man of principles, because the opposite will always catch you when you decide to truly face the mirror.

All the best.