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When Making a Change Requires Knowing Everything

Modern coding tools, like language models (LMs), are becoming essential for developers. These tools can assist in navigating complex systems and help with writing, debugging, or improving code. However, there’s a major red flag to watch out for: if making a change to your code base requires providing the LM with the entire system, you might have a serious problem on your hands.

This scenario arises when the code base is so interdependent and tangled that every part relies on something else to function or adapt. If you need to load the entire system just to adjust one component, it’s a sign that your code base has turned into what many developers refer to as a “spaghetti monster.” Overly coupled components, excessive dependencies, and poor modularity can all lead to this situation. The result is a system where even minor updates become an overwhelming task.

A spaghetti monster code base leads to inefficiency and frustration. Code becomes harder to navigate, changes take longer to implement, and new bugs surface more easily. Even advanced tools like LMs will struggle to provide meaningful support if they’re required to understand the entire system instead of focusing on a specific area. This doesn’t just waste tool capabilities—it consumes valuable development time.

The solution lies in embracing modular design. By structuring code into smaller, independent pieces, you simplify development for both humans and LMs. Modular systems reduce unnecessary dependencies and make it easier to isolate, update, and test individual components. Beyond modular design, refactoring the code, reducing entangled logic, improving documentation, and conducting regular code reviews can all help untangle the spaghetti monster.

If parts of your code base feel overwhelming or hard to navigate, take that as a sign to reassess the structure. A cleaner, more maintainable system will not only improve your workflow but will also make tools like LMs far more effective. Don’t let a tangled code base hold you back—it’s worth the effort to untangle the mess.

Levels of Human Understanding and Use of Computers

Human interaction with computers can be understood as progressing through distinct stages that reflect both the user’s capability and level of understanding. At the first stage, a person may neither understand nor use computers, relying entirely on analog methods and systems. This is the baseline starting point before any engagement with digital tools begins.

The second stage involves using computers to perform predefined tasks. People at this level can navigate standard systems but do so within the limits of what has been designed for general use. Whether it’s using email, browsing the web, or accessing basic software tools, the focus remains on following established patterns without modification.

At the third stage, individuals understand how to adapt or program computers to perform tasks for themselves. This often involves basic customization, scripting, or programming to automate processes or streamline personal workflows. For example, writing a script to organize files or designing formulas in a spreadsheet to meet specific needs.

The fourth stage builds on this by programming computers to perform tasks for other people. Instead of focusing on personal needs, users at this level create solutions tailored to external audiences—friends, coworkers, or customers. This might involve developing an app, designing software, or building workflows that address challenges others face.

The fifth and final stage is about understanding how other people program or adapt computers, enabling collaboration or further adaptation. This requires the ability to work within systems others have designed, refining or scaling solutions to integrate them into larger environments. Using open-source code, building integrations between platforms, or collaborating with other developers are all part of this level, where innovation often relies on shared creativity and cooperation.

Let the Problem Tell You What It Needs

When working on any build or project, it’s important to let the problems themselves guide the solutions. Avoid introducing fixes for hypothetical issues that don’t actually exist yet, even if they theoretically could in the future. Every solution you implement comes with a cost, whether in complexity, resources, or trade-offs. Adding unnecessary solutions risks creating complications rather than addressing real needs. Focus instead on tackling the issues that are present and tangible.

This doesn’t mean ignoring the potential for future challenges—it means balancing practicality with foresight. Previous experience from similar builds can be invaluable here. If you’ve encountered recurring issues in the past, there’s no harm in incorporating proven solutions to avoid them. However, this should only be done if those solutions don’t come at the expense of anything critical in the current project. Just because it worked once doesn’t mean it fits every situation.

Building something successfully requires staying adaptable while maintaining focus. New challenges may arise as the project evolves, and you should remain open to addressing them as they come. That flexibility is key. However, trying to engineer solutions for every possible scenario in advance is a trap. Over-preparation often results in bloated designs and wasted resources, leaving you with a project overly complicated for its intended purpose.

Allow the problems to dictate the solutions rather than the other way around. Focus on what’s actually in front of you, informed by lessons from the past. This approach creates results that are practical, intentional, and equipped to handle challenges without becoming weighed down by unnecessary work.

Feedback Loops in Intelligent Agents

Feedback loops are at the core of how systems learn and improve. They allow agents to evaluate their actions and adjust based on observed results. Most agents, however, operate almost exclusively on instant feedback and short-term evaluation. While this works well for immediate tasks, not all actions reveal their consequences immediately. Some have effects that become apparent in the medium- or long-term. For agents to handle these situations effectively, they need to incorporate longer feedback cycles into their decision-making processes.

Short-term feedback loops are the most straightforward. For example, when baking bread, the process involves continual short-term adjustments. Mixing the ingredients provides instant feedback in terms of dough texture. Similarly, baking in the oven involves short-term checks to ensure the bread is baking properly without being undercooked or overcooked. These short loops happen within minutes or hours and provide the agent or individual with immediate insights to improve the outcome.

Medium- and long-term feedback loops are more complex. Farming grain is a good example. In a medium-term feedback loop, a farmer plants, grows, and harvests crops in a single season. The results of this process—the size and quality of the harvest—can be evaluated to guide decisions for the next season. Long-term feedback in farming, however, involves managing soil health and fertility. Decisions about fertilizer use, crop rotation, and soil management accumulate over many years, affecting the sustainability and productivity of the farmland in the future.

Currently, most agents cannot handle these longer-term cycles because they primarily learn from what is happening “right now.” They focus on instant feedback rather than considering the broader impact of their actions. This limits their capacity to understand the full consequences of their decisions, particularly those that only become evident much later.

It is critical to recognize that true learning and effective decision-making require balancing the short-term results with medium- and long-term outcomes. Long-term feedback loops are essential for achieving sustainable and meaningful progress. Future developments in agent design must account for these extended timelines to allow for smarter and more responsible decision-making in complex and dynamic environments.

Two Ways to Use Language Models for Writing

Language models have become powerful tools for writers, offering opportunities to enhance both the ideation and execution phases of writing. There are two main ways to use these tools when creating a text.

The first approach involves using the language model as a brainstorming partner. It acts as a sparring partner to help you come up with ideas, content, or themes. In this case, the model supports your creative process, but you write the final version of the text yourself.

The second approach is different. Here, you take the role of the idea generator. You think of the key themes, solutions, and content, then ask the language model to craft the final text based on your input. It assists with the actual production of the polished version.

Interestingly, there’s something of a divide in how these two approaches are viewed. One of these methods tends to face criticism, while the other is widely accepted. The brainstorming method, where the writer maintains control over the final output, is often seen as the “right” way to use such tools. In contrast, letting the model write the finished text tends to draw questions about creativity, originality, and over-reliance on technology. It’s an interesting cultural reflection: does the process of writing matter more than the result, or is the content itself what truly counts?

At the heart of this conversation lies that very question. What is most important in writing—what is written or how it’s created? Should the process define its value, or is it the final message that matters most to the reader? For example, is originality tied to the way the text is shaped, or is it about the ideas and substance behind it, no matter how it’s written?

Ultimately, the answer might depend on the context. Perhaps the method of collaboration isn’t as important as the intention behind the work and the quality of the message. Whether you use a language model as a brainstorming partner or a full-fledged writing assistant, the value of your writing will always lie in its ability to connect with the reader.

AI-Assisted Gaming: A New Dimension in Gameplay

The concept of automated agents in games has been around since the beginning of video gaming. From chess bots to difficulty settings and computer-controlled opponents, these systems have always been part of how games are designed and played. Traditionally, these agents served robotic, automated purposes, following pre-programmed rules to either challenge the player or add complexity to the game environment.

AI-assisted gaming, however, goes one step further. This emerging genre shifts these systems away from being simple opponents or automated mechanics and transforms them into collaborative partners. In these games, the agent acts as a teammate, sidekick, or co-player—creating the sensation of gaming alongside another real person.

In action RPGs, for example, you might have an agent playing alongside you as though it were another player. You can build the agent’s character just like you would your own, providing instructions and feedback on how it plays. Over time, it learns and adapts based on playing with you, evolving into a personalized companion that complements your strengths and supports your strategies.

This fundamentally changes gaming experiences, especially in single-player games. AI-assisted games introduce tactics, builds, and strategies that were previously only possible in multiplayer settings, bringing new approaches to mastering games and exploring creative playstyles. It opens up exciting new dimensions for single-player games, making them feel less solitary and more dynamic.

What’s even more exciting is that this concept can be adapted to nearly any type of game. Whether it’s action, puzzle, RPG, or strategy, the specific approach will depend on the genre, but the application of AI-assisted features can enhance gameplay across the spectrum. For example, agents could act as co-strategists in a tactical game or assist with solving puzzles in a cerebral adventure.

AI-assisted gaming represents a significant leap forward for the medium. By transforming computer-driven agents into collaborative, learning companions, developers are creating more immersive and innovative gaming experiences that expand what players can achieve in both solo and cooperative play.

Text Generation and the Illusion of Process

Language models are remarkably good at generating text that fits specific patterns. These patterns can appear to be the result of a process, such as an analysis, a judgment, or critical thinking. When given a prompt, the model can produce outputs that mimic the form and structure of content created through such processes.

While the text generated by a language model may resemble the results of processes like analysis or evaluation, it’s important to understand that the model itself does not carry out these processes. Language models don’t analyze, think critically, or make judgments. Instead, they are trained to predict and construct text based on patterns observed in the vast amounts of data they have been exposed to.

The illusion of process is not inherent to the model; it comes from how users interpret the generated text. When presented with plausible results, it is tempting to believe that the model has actually performed an analysis or engaged in reasoning. In reality, it merely pretends—its output mimics the form but does not reflect authentic engagement with the process. Essentially, the appearance of the process is created by the user interacting with the model in a way that leads it to produce text aligned with their expectations.

Understanding this distinction is important for practical use. Users should be mindful of the limits of what language models can do. While they provide useful outputs and serve as powerful tools, their results should not be seen as the outcome of critical thinking or detailed analysis. By staying aware of this, users can take advantage of language models while avoiding misconceptions about their capabilities.

Future Prediction Model

Predicting the future may seem challenging, but with a structured approach, it becomes a manageable task. The foundation of this process lies in building a model based on the past. By examining historical data, patterns, and trends, you create a tool that can capture the way things have unfolded before. The primary purpose of this model is to replicate past outcomes, creating a basis for making predictions.

Once your initial model is developed, the next step is to see if it can describe the present. Testing the model against current conditions is essential to evaluate its accuracy. If it can successfully reflect the present, it gains credibility as a reliable tool. This is where adjustments and calibration come into play. If the predictions don’t align with real-world outcomes, refine the model until it does. Calibration makes the model adaptable and helps it reflect reality more closely.

Once the model is fine-tuned, it can then be used to predict the future. At this stage, its ability to anticipate trends, behaviors, or events becomes a valuable asset. A well-calibrated model allows for more informed decisions, whether you’re forecasting market changes, preparing for challenges, or exploring opportunities. Testing these future predictions against actual outcomes over time will further validate the model and keep it robust.

Prediction models are never finished. They require iteration as new data and insights emerge. Building, testing, and refining the model is a continuous process, and its strength lies in the accuracy with which it is updated to keep pace with changing conditions. By following this method, you’ll not only gain a clearer view of what’s to come but also develop a model that evolves alongside the future.

Object-Oriented Programming a Failed Abstraction

Object-oriented programming (OOP) is built on abstraction, encapsulating both data and behavior into “objects.” While this approach has dominated software development for years, it doesn’t always align with how programs are created and executed. Often, the abstractions OOP introduces feel mismatched with the dynamic nature of software design and operation.

A key problem lies in how OOP connects data and operations. Take the example of a “person” object in a system. Attempting to define everything a person can do or be involved in within a single object quickly becomes impractical. People interact with systems in countless ways, such as being members of different groups, triggering workflows, or being part of external processes. Trying to encapsulate all these interactions within one abstraction leads to unnecessary complexity and rigidity. Software is not inherently about “things” or “objects”—it’s about tasks, processes, and services.

Much of software development is process-oriented. Applications often center around actions that need to be performed, such as validating business logic, fetching data, or completing a workflow. Similarly, functional programming approaches emphasize operations acting on data rather than tightly coupling both into objects. These paradigms reflect an underlying truth: software is primarily about what happens, not static representations of entities.

Thinking about software as “something that happens” can lead to cleaner, more practical designs. Programs are dynamic systems where tasks unfold over time based on input, rules, and workflows. By focusing on what needs to be done rather than forcing abstractions into objects, developers can design systems that align more closely with how software actually operates.

This doesn’t mean abandoning object-oriented programming entirely. OOP can be useful when modeling concepts with clear boundaries and well-defined behaviors, but it’s important to recognize that abstractions based on static objects are not always the best fit. Often, process-oriented thinking offers a simpler and more scalable solution, especially when software revolves around actions rather than entities.

Developers don’t need to follow a rigid paradigm, but rethinking abstractions can lead to better decisions. Software is dynamic, and understanding it as tasks, processes, and workflows rather than a collection of objects can help developers create more practical and adaptable solutions.

Problem Solving with Thought Forking

Thought Forking is an approach to solving problems that emphasizes iteration, creativity, and refinement. The idea is to loop around a problem to understand it fully, then split into various ideas and solutions, evaluate them critically, and merge the best parts into a cohesive result. Once you have refined your ideas into a main thought or solution, the process can then be repeated as needed to further improve it or address new developments.

This method starts with looping around the problem. This means carefully examining the nature of the issue, considering its different aspects, and ensuring you understand it properly. By exploring the problem, you set the foundation for generating meaningful solutions.

Next, you fork—breaking out into different ideas and solutions. This is the creative stage where you brainstorm and welcome diverse, even unconventional ideas. The goal is not perfection at this point but exploration.

Once you have a set of ideas, you evaluate them. Take the time to analyze each solution for its strengths and weaknesses. Consider what’s actionable, sustainable, and aligns with your goals. From these evaluations, you identify the most promising elements and merge them into a single, solid solution that leverages the best aspects of your previous ideas.

After merging, you choose the result—the main thought. This represents the current solution, which can then move forward as the working plan. However, Thought Forking is iterative, meaning you can revisit the process whenever necessary. If the situation changes or new insights become available, you go back to the loop, repeating the steps to refine the solution further.