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Learning the wrong lesson from trying and failing

Failure is often misunderstood. We try something challenging, it doesn’t work out, and we feel the sting of disappointment. But failure isn’t the villain—it’s a way to learn. Trying and failing isn’t just about facing setbacks; it’s an opportunity to gain insights and adjust our approach. With each attempt, we gather lessons that make success more likely the next time we try.

The problem arises when we misinterpret what failure is telling us. Instead of seeing failure as an opportunity for growth, we often learn the wrong lesson: that trying leads to failure, and failure should be avoided. This mindset can lead to giving up entirely, shutting the door on future attempts. Because failure feels uncomfortable, many avoid trying again, believing it’s safer not to take risks or face setbacks.

Avoiding failure comes at a cost. Without the willingness to try, there is no room for growth. Stagnation replaces progress, and opportunities slip away before they’re fully explored. While failure feels like a stopping point, it’s actually an invitation to refine your approach and try again.

To make the most of failure, our perspective needs to shift. Instead of treating failure as proof of inadequacy, it should be treated as feedback. Failure shows us what didn’t work, points to areas for improvement, and helps us better prepare for future chances. Seeing failure this way takes the pressure off, making it less personal and more practical.

The key to real growth is persistence. If you try, fail, and stop, you’ve missed the lesson. But if you try, fail, reflect, and try again, you’ve started to build a pathway to success. Every failure can help you learn something new and refine your skill, mindset, or strategy. It’s not about avoiding failure; it’s about making progress through it.

The next time you face failure, remember: it’s not the end of the journey. Treat it as a chance to learn, adjust, and try again. Failure doesn’t tell the whole story—it’s just one chapter in the journey toward success.

Short-term planning loop

Short-term planning loop is the most basic decision-making and thought process for a language model or agent. It is based on iterative thinking, where the agent continuously evaluates its progress and adjusts its actions as needed to move closer to achieving its goal. This looping pattern is simple yet highly effective for short-term problem-solving.

At the core of the loop is the agent’s continuous cycling of actions, evaluations, and adjustments—commonly referred to as agent loops. After an initial action is taken, the agent performs a review to assess the current state or outcome of the actions. This review involves analyzing what worked, what didn’t, and identifying areas for improvement. Reflection is critical during this stage, as it reveals valuable insights that inform the next steps.

The next step in the loop is to evaluate how far the agent is from the goal. This requires examining the gap between the current state and the desired result. It’s about identifying how much progress has been made and where effort still needs to be directed. By understanding this distance, the agent can focus its attention on the most impactful areas for change.

Based on the review and evaluation, the agent adapts its approach, refining actions as needed. From here, the cycle begins anew, with fresh adjustments driving each new iteration. This ability to consistently assess and modify actions allows the agent to respond effectively to challenges while steadily moving toward the goal. This process of adjust and repeat is a core part of the loop and ensures continual progress.

The short-term planning loop is useful not only for advancing the functionality of agents but also as a practical tool for everyday decision-making. Whether it’s managing personal tasks, solving problems, or completing a project, this loop can help achieve better results through repeated cycles of evaluation and improvement.

The benefits of this framework are clear. It provides a simple way to track progress, stay focused on short-term goals, and adapt as needed to changing circumstances. By emphasizing iterative action and measured adjustments, the short-term planning loop brings clarity and structure to the decision-making process. Its straightforward nature makes it accessible for both digital agents and individuals who want a practical strategy for tackling their objectives.

Your Personalized Universal Remote

Imagine an app that could handle most of your day-to-day tasks, a single tool acting as your main access point to the digital world. Instead of jumping between apps, systems, and platforms, you would have one customizable interface that brings everything together. This concept—the universal remote app—promises to simplify digital interaction in a way that works perfectly for you.

A universal remote app acts as a central hub for all your digital activities. All interactions go through this agent app, eliminating the need to manage separate applications and systems. It’s tailored to each individual’s needs, offering a personalized interface that reflects how you work, communicate, and organize your life.

What makes the universal remote unique is its ability to connect to and utilize virtually any app or system. Whether it’s handling your emails, coordinating your schedule, or managing tasks across multiple platforms, this app could seamlessly integrate the tools you use every day. Rather than trying to adapt your routine to match various apps, this app would adapt to you, giving you your own personal gateway to the digital world.

The benefits of this platform are clear. It simplifies your interactions, reduces friction, and helps save time. With fewer distractions and less need to switch between apps, you can focus on what matters most. A customized user interface ensures that using digital tools becomes smoother, enjoyable, and stress-free.

This concept also paves the way for exciting possibilities in the future. Imagine a tool that learns from you over time, anticipating your needs and automating repetitive tasks. The universal remote app isn’t just about making life easier today—it represents a vision for how human interaction with technology can evolve to become more intuitive, productive, and personalized.

The universal remote app puts you at the center of the digital world, empowering you to create a space where technology supports you rather than complicates your life. This is your own personalized remote to the digital universe—an idea that could finally bring clarity to the complex landscape of tools and systems we use every day.

How to Make the Right Decisions

What does it really mean to make the right decision? Often, it’s more about identifying which decisions truly need to be made than focusing on the individual decision itself. Not all problems or situations require a decision, yet many organizations spend significant time and energy debating matters that don’t truly require action.

This tendency to make unnecessary decisions drains an organization’s valuable decision-making capacity. Time and focus that could be spent on high-impact, meaningful decisions are instead used on trivial matters. Over time, this leads to larger, more important choices being neglected or deferred entirely, leaving the organization’s direction unclear. In these cases, no decision is consciously made—it “just happens.”

To avoid this, it’s critical to step back and recognize which decisions truly matter. The right decisions are those that align with the organization’s goals and create meaningful impact. By avoiding unnecessary deliberation and focusing on what matters most, organizations can make better use of their decision-making capacity and direct their efforts where they matter the most.

User Agent = AI

Web applications are evolving to cater to two distinct audiences: humans and language models (LMs) or agents. Developers now have the capability to present different versions of a website depending on whether the user is a human or an agent. This distinction can be made using approaches like media="AI" or User Agent = AI within HTML and HTTP protocols.

When creating a version for agents, the focus shifts away from visual design and toward simplicity and semantics. Agents don’t need to see what humans see. They don’t rely on visual elements like colors, animations, or layouts in the same way a human does. What’s important for them are the semantics: the meaning and functionality behind each element. This enables a more direct and efficient processing of the web content.

Text-based content is preferred for agents because it speeds up processing and provides immediate clarity. While images require additional time and computational effort to analyze, text allows language models to extract meaning instantly. Visual elements, like product images, can still be included but are largely unnecessary unless referenced for human-centered decisions or interactions.

Creating dual versions of a web application benefits both humans and agents. For agents, it enhances their efficiency and ability to perform tasks effectively. For humans, it ensures that the web app retains its visual appeal and usability without compromising performance. This approach doesn’t just optimize how web applications function but also sets the stage for smarter interactions between humans, agents, and digital platforms.

Reading Others’ Code

As developers, one of the most effective ways to grow and improve is by reading code written by others. This practice is often overlooked, but it is a powerful method for learning what makes code readable, understandable, and well-structured.

Start by picking small and manageable examples of code. Focus on reviewing code created by others, whether it’s from open-source projects, libraries, or colleagues. Work through the code, line by line, and aim to understand its functionality. Don’t worry if you don’t grasp everything right away—this process is about learning to decode unfamiliar approaches and picking up insights along the way.

Pay attention to the moments when you understand the code easily. What about the names, comments, structure, or logic made it so? Similarly, notice what feels difficult or unclear. This reflection is key to identifying which practices make the code accessible and which may hinder comprehension.

Reading and reflecting on others’ code allows you to develop a deeper awareness of what makes code readable and maintainable. Learn from the clarity you encounter and use those lessons to guide your own coding style. Strive to write code that is easy to understand—not only for your colleagues but for yourself when revisiting your work weeks or months later.

Finally, make code reading a regular habit. The more you expose yourself to different styles and approaches, the better you’ll become at adapting and improving your own practices. Learning from others is an ongoing process, one that can help you grow immensely over time.

Understanding the Strengths and Risks of Language Models

Language models have proven themselves as masters in creating text that seems polished and professional. They are extraordinarily good at appearing competent in their outputs, crafting persuasive responses, and even taking on roles within creative or professional scenarios like actors in a play. These qualities make them incredibly versatile tools for tasks such as storytelling, content creation, and communication.

However, much of their strength lies in their ability to imitate. Language models excel at mimicking reality, generating responses that can feel authentic and convincing. Yet, this skill of “pretending” can be deceptive. While their outputs can seem well-informed, it’s essential to remember that they lack genuine understanding. They generate text based on patterns learned from vast datasets, and their confidence can mask a lack of true comprehension.

This ability to imitate creates a potential pitfall. When relying too much on outputs from these tools, it’s easy to let personal beliefs, hopes, and expectations interfere with judgment. If users treat these suggestions as truths or infallible answers, there’s a risk of misusing them, whether in critical decision-making or emotional reasoning. Humans may inadvertently project their own intentions onto the tool and end up in a trap of unwarranted trust.

The key to avoiding these risks is approaching language models with a critical mindset. What they produce should be seen as helpful hints, not unquestionable facts. Fact-check their results, consult other resources, and always pair their outputs with human expertise. Their value lies in how we choose to use them—when complemented with careful analysis and application, they can be truly transformative tools.

Language models are powerful but are not substitutes for human understanding. Their role is best appreciated when we recognize their strengths as well as their inherent limitations, ensuring that our use of them remains purposeful, thoughtful, and effective.