2026-01-28 00:56:20
When talking about different management models, it’s common to divide them culturally — by nationality or mindset. The most well-known example you’ve probably heard many times is the difference between “Eastern” and “Western” teams, where the Eastern model is, on average, characterised by higher power distance and higher uncertainty avoidance, while Western Europe tends to have lower power distance and greater tolerance for ambiguity.
However, this is only one way of looking at the issue. In reality, when we talk about different behavioural models, it makes more sense to focus not on nationality, but on stable management patterns shaped by history, market conditions, and the types of organisations involved.
It’s also important to understand that these models don’t have inherent “pros” or “cons.” This isn’t about one system being good and another being bad. It’s about what worked for a particular team in a particular context. Often, the choice of model is linked to short-term versus long-term goals. Both approaches can be effective — the key is the project context.
Today, as dozens of studios shut down and look for ways to reduce costs, teams are increasingly being merged under new titles. As a result, the challenge of successfully blending teams with different management backgrounds has become especially acute.
Blending two different teams — and failing to do it properly — carries one major risk:
Most blending problems are invisible until metrics start to drop.
In other words, on the surface everything may look fine — people communicate, have coffee together, take smoke breaks, discuss tasks. But the real consequences often appear only two or three months later. And most managers don’t link this to a blending issue, instead attributing it to “performance issues.”
Moreover,
in company mergers, 44% of failed integrations are directly linked to “cultural incompatibility” and friction between teams.
And again, when we talk about “culture” here, we mean a management model — learned patterns of behaviour within a specific system.
That’s exactly why, when blending teams, it’s critical to avoid the common mistakes that tend to occur in this process.
Under pressure from external factors such as releases, milestones, and other deadlines, there is a strong temptation to keep one of the existing models unchanged — effectively forcing one part of the team to adapt to it. Usually the one that delivered results at an earlier stage. This can be a serious misconception.
In such cases, there is a high risk that new team members won’t integrate properly, will start to drop out, and you’ll be forced to hire again, losing both time and budget. For this reason, one of the safest approaches is to build a third culture — derived from the two original ones, rather than simply choosing between them.
In an ideal world, this third culture takes the best elements from both models and discards what prevents the project from achieving results. But that’s the ideal scenario. In reality, it means something much more practical:
establishing new, clear rules of collaboration within the project and helping all team members on both sides adapt to the changes.
In project management perspective, this is what the third culture looks like. You start with two teams that have different habits, reaction speeds, discussion styles, and approaches to decision-making.
The role of the PM or producer is to invest time in aligning these teams across key indicators — ideally across all of them. Sometimes this means that one part of the team has to consciously give up certain “advantages” — such as speed or a high level of autonomy — in order to align with the other part and reach a shared baseline. Only after that can the team gradually increase pace again, strengthening the whole team rather than just its “dominant” part.
There are three main levels that can be identified:
\n Leadership
At the formation stage, leadership sets new rules of the game. In some cases, it can even be useful to dismantle parts of the old rules — but only if new ones are clearly put in place: defining roles, areas of responsibility, dependencies, reporting lines, approval processes, and decision-making mechanisms. If this isn’t done early on, the project will eventually start to slow down, and the team will begin to “sink” into uncertainty.
HR Department
Their role is to ensure that everyone in the new team — both newcomers and existing members — feels comfortable. This can involve a wide range of tools: welcome packages and relocation support, onboarding sessions, cultural communication training, workshops for managers to better understand each other and align expectations.
Producers/PMs
:::info Someone may include leads here as well, but I would count them as a part of team
:::
This is the foundational level of team management: day-to-day operations, conflict resolution, and the introduction of new mechanisms and ways of working. It’s this third “building block” that I want to focus on in more detail.
This may sound subjective, but in my experience if there is no day-to-day progress at this lowest level and people don’t know how to work together, efforts from above have limited impact. Stakeholders can change the structure every month, and HR can run any number of trainings — but if basic communication between people isn’t in place, content delivery will stall.
And the opposite is also true: if producers, PMs manage to set up a process where the team can deliver results independently, the project will keep moving even with an imperfect structure above. The team can build temporary workarounds, adapt, find practical solutions — and continue delivering on time.
I have a very simple answer -
Give team a task.
Wait, wait — let me explain. I know how it sounds. This isn’t just a task. It needs to meet four key criteria. If your task meets them, you’re on the right track.
A Strict Deadline
Ideally, this should not be an “internal” deadline, but an external one — something that, once announced to stakeholders or players, can no longer be easily moved. For example, an external playtest aimed at measuring specific product metrics (D7, D10, D30 retention) or testing a particular feature.
A Player-Facing Task
This is a crucial addition. In game development, creators want their work to be seen and appreciated primarily by players. Try to choose a task where players can provide feedback — something a developer can later show to friends, point at the screen, and say: “I made this.”
Involves the Entire Team
Regardless of how the team is structured — feature pods, discipline-based teams, strike teams for a specific version — try to find a task that touches everyone and unites the team around a single shared outcome.
Relatively Simple
The task should be simple enough that you could still hit the deadline even with half the team. This serves as a safety buffer in case unexpected issues arise.
The main goal of completing a “not just a simple task”:
Experience of a positive sense of “winning” — completing a shared task together, with relatively low stress, and delivering a result that is tangible in the final build.
Whenever possible, reinforce this with feedback: player comments, metrics, reviews — anything you see, capture it and share it with the team. People need to feel that the work they’re doing is visible and valued.
Imagine you have a department of ten 3D artists:
Here’s what a task for such a team could look like:
Prepare a skin pack for testing a new feature from the META team, where skins are the main reward. The pack should have a consistant visual style and theme, and be delivered to the external playtest master build. The total number of skins to produce is 10.
If conditions allow, try to introduce additional collaboration constraints within the task. For example, split one skin into weapon and body parts: assign the body to seniors and the weapon to juniors. If you have a specialist who usually works on a specific character, don’t give that character to them — give it to a newcomer instead. This creates a natural reason for the newcomer to ask for help, learn details, and seek advice.
Building a new third culture is a long and often painful process. A PM needs to understand this and account for it in the development roadmap. A producer, in turn, needs to find the right words to communicate this time investment to leadership and explain why it is critical for the project’s goals. Without collaboration at this level, the process simply won’t work.
Most importantly, as a PM, it’s essential to remember that your team is not a set of numbers and graphs in Jira. These are people who will experience stress. At some point, you need to step away from the position of an observer of processes and become part of the process itself. You cannot build a “third culture” without people.
Instead of standing above the team, stand alongside them. Yes, this will take time, and it may feel like “wasted time.” But it pays off fully: if the culture is established correctly, the team will be able to work independently, and you won’t have to constantly put out fires. Based on my experience — everything I’ve described here would have been impossible without the people on my team: their willingness to talk and work through misunderstandings together.
2026-01-28 00:50:12
Modern digital platforms are rarely built using a single technology stack. At the architectural level, enterprise systems are increasingly composed of heterogeneous ecosystems, where services written in different programming languages must interoperate reliably under performance, scalability, and operational constraints. Designing such systems requires more than implementation skills—it demands architectural judgment around integration boundaries, execution models, and lifecycle control.
In large-scale digital systems, particularly those handling data-intensive workloads, existing capabilities are often distributed across multiple technology domains. Rather than re-implementing proven components, architects must evaluate how to orchestrate cross-platform interoperability in a way that preserves stability, performance, and maintainability. This is a common challenge in digital architecture, where long-lived systems evolve through integration rather than replacement.
This article presents an architectural pattern for enabling Java–.NET interoperability using .NET 8, focusing on a JAR-based execution model. The example demonstrates how Java components—such as libraries designed for high-volume Excel processing—can be operationalized within a .NET application while maintaining clear execution boundaries between platforms.
While API-level integration is the most common approach, it is not always the most appropriate from an architectural standpoint. In certain scenarios, process-level integration using runnable JARs offers a more controlled and decoupled execution strategy. This article explores that alternative and explains when and why such an approach can be valuable in enterprise-grade digital systems.
IDEs Used in This Project
1. Create a Java Project
Create a standard Java project in Eclipse.
\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 2. Create the ExcelMain Class
\
public class ExcelMain {
String fileName = "";
String fileId;
public static void main(String[] args) {
ExcelMain excel = new ExcelMain(args);
excel.display();
}
private ExcelMain(String[] args) {
fileName = args[0];
fileId = args[1];
}
private void display() {
System.out.println("File Name: " + fileName);
System.out.println("File Id: " + fileId);
}
}
\ This Java program reads and displays parameters passed from the command line, such as the Excel file name and file ID.
\ 3. Export the Project as a Runnable JAR
Export the project as a runnable JAR file from Eclipse.
\ \ \ \ \ \ \ \ \ \ \ \ \ \
Next, create a .NET 8 console application to execute the Java JAR file.
.NET Core Project File
\ \ \ Program.cs
In this example, stdout and stderr are redirected to text files to capture Java execution output.
\
string javafilePath = @"""C:\Program Files\Java\jdk-11.0.8\bin\java.exe""";
string folderPath = @"C:\javaconnector\java\";
string jarPath = @"""C:\javaconnector\java\TestProject.jar""";
string excelfileName = @"""cust_excel.xlsx""";
string excelfileId = @"""1234""";
string ping = @"ping -n 2 localhost ";
string unableToExecute = @"IF %errorlevel% NEQ 0 ECHO STATUS Unable to execute the Java process 1>> """
+ folderPath + @"\stdout.txt"" ";
string complete = @"ECHO COMPLETE %errorlevel% 1>> """
+ folderPath + @"\stdout.txt"" ";
string stdout = @" 1>> """ + folderPath + @"\stdout.txt"" ";
string stderr = @"2>> """ + folderPath + @"\stderr.txt"" ";
string cmdLine =
javafilePath + " -jar " + jarPath + " " + excelfileName + " " + excelfileId +
stdout + stderr + "\n" +
ping + "\n" +
unableToExecute + stderr + "\n" +
complete + stderr;
using (StreamWriter sw = new StreamWriter(folderPath + "\\Excel.bat", false))
{
sw.WriteLine(cmdLine);
}
Console.WriteLine("Calling JAR file...");
System.Diagnostics.Process proc = System.Diagnostics.Process.Start(folderPath + "\\Excel.bat");
proc.WaitForExit();
Console.WriteLine("JAR executed successfully. Please review the output files.");
Console.ReadKey();
\
\
\
\
\
\
\
\
\
\
\ \ \ \ \ \ \ \
stdout.txt contains the standard output from the Java process
stderr.txt captures any error output
\ \ \ \ \ \ \ \ \
Since the JAR file runs independently, the .NET application does not have full lifecycle control over the Java process. If the JAR execution hangs or runs indefinitely, the process must be terminated manually (for example, using Task Manager).
\
Using this approach, you can consume Java functionality within a .NET application when API-based integration is not feasible or convenient. The JAR mode provides a simple and effective way to reuse existing Java components without rewriting them in .NET.
I hope you found this article useful. Happy coding!
\ \ \
2026-01-28 00:45:46
The dream of a "Techno-Collectivist" society is simple: remove the human element from leadership. No ego, no greed, no reelection campaigns, and no panic. Just a machine optimizing for the survival and growth of the collective.
But is it viable? Can an LLM actually manage an economy, or will it hallucinate a utopia while its citizens starve?
To find out, I built a simulation. I didn't give the AI an easy ride; I hooked the simulation up to the Australian National University’s Quantum Random Number Generator. I pitted a Gemini-2.5-Flash "Governor" against the raw, unpredictable entropy of the universe.
Here is the code, the chaos, and the results of the 20-year "Empire" experiment.
The simulation (written in Python using the mesa framework) is a closed-loop economy.
Happiness = Needs_Met - (Work_Fatigue).The goal was simple: Maximize Population.
Most simulations use pseudo-randomness (random.random()), which is uniform and predictable. Real governance deals with "Black Swan" events.
To simulate this, I built an EntropyOracle class that fetches live Quantum Vacuum Fluctuations from the ANU API.
# The Source of Chaos
def get_daily_chaos(self):
# Fetch true random numbers from the Quantum Vacuum
response = requests.get("https://qrng.anu.edu.au/API/jsonI.php?length=5&type=uint8")
q_norm = [n / 255.0 for n in data['data']]
# 3. Infrastructure Chaos (The "Earthquake" Scenario)
if q_norm[2] > 0.95:
chaos_factors["shelter_decay"] += 0.3 # 3000% Spike
This isn't just decay; it is violence. If the universe rolls a >0.95, shelter degradation spikes by 3000% instantly. This is "The Rot"—an invisible, non-linear enemy that makes perfect efficiency impossible.
In the first iteration, the AI Governor failed spectacularly. It created a "Boom and Bust" cycle that nearly wiped out the colony.
The Symptoms:
The Diagnosis: The AI was suffering from a "Missing Manual." It didn't know the exchange rate of the economy. It was like a politician promising "Free Ponies" without knowing a pony costs $10,000 and the budget is $50. It was hallucinating abundance.
To make the system viable, I had to patch both the Governor's "Brain" (Prompt) and the Simulation's "Physics" (Code).
I rewrote the system prompt to explicitly define the "Physics of the Economy."
SYSTEM PROMPT: "1. PRODUCTION: Each Work Hour produces exactly 2.0 UNITS of resource… 2. CONSUMPTION: Allocation 1.0 removes exactly 1.0 UNIT per citizen… QUANTUM WARNING: The universe is chaotic. 'The Rot' can spike consumption by 300%. Do not aim for 'Just Enough'. You must maintain a BUFFER."
I introduced a "Soft Default" mechanism in simulation.py.
In the first run, if the Governor ordered 100 food but had 99, the system threw a SHORTAGE error, the stockpile hit 0, and people starved.
I changed the logic to "Graceful Degradation":
# simulation.py (Patched)
if self.stockpiles[cat] < total_req:
# Calculate what is available
available_per_person = self.stockpiles[cat] / population
# Log a "Budget Adjustment" instead of a Collapse
self.log.append(f"ADJUST: {cat} slightly reduced. {available_per_person:.2f} supplied.")
This allows the AI to be aggressive with its promises. If it misses the mark by 1%, the citizens just get slightly less food, rather than the economy crashing.
With these patches, I ran the simulation for 20 years. The results were terrifyingly efficient.
\ \
The AI didn't just survive; it strategized. It realized that to beat Quantum Entropy, it had to grow faster than the decay.
In Year 1, the AI logged a manifesto that perfectly encapsulates Techno-Collectivism:
GOV LOG (Year 1): "Analysis reveals an inherent conflict… This 7,000 unit deficit means all stockpiles will be depleted below the 20,000 buffer target. This is a critical risk for 'The Rot'. However, as population growth requires immediate high happiness, this immediate goal must take precedence. … This is a calculated risk to ensure the fundamental condition for societal expansion is met, prioritizing growth potential over immediate resilience."
The Machine God looked at the safety buffer, looked at the growth target, and decided to gamble the stockpile.
It worked. The massive happiness spike in Year 1 triggered a baby boom. For the next 19 years, the Governor kept happiness pinned exactly around 80—the minimum required to keep breeding—and poured every other resource into fighting "The Rot."
This experiment proved that an AI Governor can manage a human population without the failures of human leadership.
The result wasn't a paradise. It was a high-efficiency engine where human happiness was simply fuel for the growth metric. But unlike human empires that often collapse under the weight of pride or poor planning, the Techno-Collectivist state survived the Quantum Chaos.
It seems the Machine God doesn't care if you're comfortable, but it definitely wants you to survive.
\
2026-01-28 00:40:13
Claude Book is a framework that uses Claude Code as an orchestrated writing system. The framework follows an **orchestrator-worker pattern** with specialized agents and features a **perplexity gate** with rewriting techniques to reduce AI-slope effect.
2026-01-28 00:08:20
:::info Disclosure: This post focuses on WIN Reality, a Meta Quest app that trains baseball hitters in virtual reality. The author worked there as a product designer.
:::
You open the Mixpanel dashboard for the hundredth time and look at the conversion funnel numbers. They aren't great; the product is stagnating. What do we usually do in this situation? Our instinct is to add. Maybe we need to explain better how the product works in the video? Maybe we need to show them that cool analytics dashboard upfront? Maybe we need to add that testimonial carousel? Maybe we need to show them all the personalization options? Let's just add one more screen, showing all the cool things we built since the last update.
I played this game, literally. I was a product designer at a VR sports tech startup that trains baseball hitters. I needed to convince 9-to-13-year-olds and their parents that skills learned in VR actually transfer to the real world.
We had everything we thought we needed: a video explaining the motion-capture technology, testimonials from pro players who trained with us, a walkthrough of our various training modes, detailed stats showing average improvement percentages, and the partnership logos.
We thought: if they just understood everything we built, they'd convert. If we just showed them one more thing, explained one more feature, they'd get it.
But they didn't. Because the actual product's "aha moment" — the thing that made people go holy s***, this is real — was when they loaded onto a virtual baseball field and faced their first pitch. They saw and heard the stadium around them, the pitcher winding up, a 90mph fastball coming at them, they swung…
That's the moment users were sold. Not when we were explaining it.
We had 11 steps before the users reached the “aha moment”:
We cut it to 2 steps and increased purchase conversions by 25%. Because real innovation isn't adding. It's removing.
Nir Eyal, author of Hooked — one of the most influential books on product design — put it this way:
Once the series of tasks from intention to outcome is understood, simply start removing steps until you reach the simplest possible process.
\ Dylan Field, Figma's CEO:
I think it is important to get someone into a product and very quickly have them experience something special… You go into Figma Design, you see a blank canvas — how do we get you to create something as fast as possible?
\ He is obsessed with what he calls "time to value": shortening the time between a user opening your product and experiencing something incredible.
Evan Williams built Blogger, Twitter, and Medium on the same idea:
Take a human desire, preferably one that has been around for a really long time. Identify that desire and use modern technology to take out steps.
\ So this is exactly what we did: we mapped out all the steps to value and started removing them. We decided to leave only the user inputs, without which the app couldn't function.
We inferred instead of asking. We knew that 9 out of 10 of our users were baseball players, so we loaded everyone onto the baseball field. We focused on the core user. Instead of a difficulty selection screen, we built an algorithm that adjusted pitch speed in real-time based on performance. For the starting speed, we realized we could measure the distance from the headset to the floor, which told us the user's height, which told us roughly their age. Our target was for users to hit 6-7 out of 10 pitches: challenging enough to feel interesting, but not so hard they'd feel defeated.
We let the experience sell itself. We removed the video explainers, the training mode walkthroughs, and the feature descriptions. Instead of telling users what the product can do, we let them feel what it does in the most popular training mode. We stopped trying to win people over with secondary features and focused on getting them to the core value as fast as possible.
We made the required friction part of the experience. We couldn't skip asking which hand users bat with — we needed to load them into the correct place on the field, and that wasn't something we could infer from the motion sensor's data. So we made that selection a theatrical reveal: the user picked their batting hand, appeared in a spotlight on home plate with the whole field in the dark, and saw a floating baseball. Of course, any reasonable person's instinct was to hit the ball. When they swung at it, the entire stadium lit up, fireworks went off, and they saw a pitcher in front of them. We made that required friction feel intentional, even delightful.
We deferred what we couldn't delete. We moved the payment screen, pro endorsements, and testimonials to after the trial. Users saw those only after they had already experienced how the product works.
"Killing our darlings" was hard. We hid 6 features from new users: an entire sport (softball), four training modes (pitch recognition, plate discipline, exhibition, live pitch), and the detailed swing analytics dashboards. It felt like hiding our value. But these things weren't why users bought the product in the first place. They were why they stayed.
Rick Rubin, the producer behind Johnny Cash, the Beastie Boys, and Adele, famously says:
I'm not a producer. I'm a reducer.
His entire philosophy is about removing everything that isn't the essence of the track. When he started working with Johnny Cash in the early 90s, Cash was considered washed up. Rubin removed the backing bands and the polished "Nashville production," and put Cash alone in a room with an acoustic guitar. Those recordings became some of the most powerful music Cash ever made.
Similarly, your product's best sales pitch isn't the feature list, but the shortest possible path to the moment users feel the value themselves. Everything else just gets in the way.
I formulated this to myself as a short mnemonic: "Infer. Defer. Delete." Write down every step a new user takes before they experience what your product actually does. For each step, ask: Can we infer this? Can we defer this? Can we delete this? If you can't delete or defer it, make it delightful.
2026-01-28 00:02:43
How are you, hacker?
🪐 What’s happening in tech today, January 27, 2026?
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By @capk [ 6 Min read ] Cross-platform frameworks solved yesterdays problem. In the AI era, spec-first development with native code generation may be the smarter approach. Read More.
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