Author: Nicolai Friis

How Language Models Handle Knowledge, Instructions, and Examples

Most text that language models learn from is narrative or result-based, rather than structured around instructions or chat interactions. Text often tells a story or represents the outcome of a process, but the steps that led to that process are rarely documented in the source material. As a result, models primarily learn from the “surface” representation of text, which shapes the way they process information and form responses.

This can make it harder for a model to match patterns when the input comes in a different format or genre, such as direct instructions or step-by-step guides. Examples often work better than instructions because they bypass the need to interpret procedural steps explicitly; instead, examples implicitly embed the process within a narrative or demonstration. This alignment with the model’s training patterns helps produce clearer outputs.

A key question is whether a language model can separate raw knowledge and information from the instructions used to create that information. If models could fully distinguish between “what needs to be done” and “how to do it,” it would open new possibilities for designing interactions that take advantage of their strengths while minimizing limitations. For now, examples remain a powerful tool to ensure alignment with how models process text, often outperforming direct instructions in clarity and effectiveness.

A Platform for Sharing Ideas

Everyone has thoughts and ideas. Some of them might be completely unique, worth sharing with the world. Yet not everyone has the ability, confidence, or tools to express their ideas clearly and communicate them to others.

Imagine a blog platform where anyone can find support to articulate their ideas and share them with an audience. A space designed to help individuals put words to their thoughts, however big or small, and make their voices heard.

This platform wouldn’t just be about one-way communication. It would also be a place to gather ideas and insights from others, building upon shared creativity. People could use it to collaborate, refine their ideas, and then share them back with the community to continue the cycle of growth and inspiration.

The goal is simple: to empower everyone to express themselves, share their ideas, and contribute to a flow of thoughts and innovation. A platform like this could connect people and foster meaningful conversations, helping great ideas grow into something even greater.

Let’s create a space where everyone’s ideas can find their place, and every voice can add to the richness of collective inspiration.

Find someone better than you

Personal growth can sometimes stagnate. What once felt like steady improvement may suddenly plateau, leaving progress at a standstill. To ensure this doesn’t happen, it’s important to keep expanding your knowledge and experience. Growth should be a continuous process, but maintaining it requires intentional effort.

One way to push through stagnation is to seek out people who are better than you at what you do. Look for individuals who excel in the same area, observe their methods, and learn from their successes as well as their failures. It’s not about copying them but about finding inspiration and discovering ways to refine your own approach.

In addition to learning from those in your field, look for people who excel in completely different areas. This opens the door to new perspectives and innovative strategies. Someone accomplished in a different domain might offer ideas or approaches you never would have considered on your own. Learning across disciplines often sparks creativity and expands your ability to think outside the box.

Studying and learning from others is an active process. Take time to identify what you can take away from their experiences. What challenges have they overcome? What strategies led them to their success? Dive deeper than just their results and focus on the mindset and decisions behind those outcomes.

Finally, make lifelong learning a priority. Personal growth doesn’t have a set destination. To keep progressing, make it a habit to regularly seek new knowledge, revisit your goals, and embrace curiosity. Growth is about more than just becoming better—it’s about expanding your perspective and evolving in ways that make you more adaptable and resilient.

By learning from others, both in your field and beyond, you can avoid stagnation and ensure that your personal development remains dynamic and full of possibility. Keep challenging yourself to grow, and you’ll find endless opportunities to refine your knowledge and skills.

Understanding the Agent Context Protocol (ACP)

The Agent Context Protocol (ACP) represents a shift in how digital systems operate, moving beyond traditional methods that rely solely on models. ACP introduces a framework for interaction between agents, which can be either user agents, representing human users, or machine agents, autonomous processes acting independently. These agents work together in a network of clients, enabling dynamic and coordinated communication.

Rather than focusing on isolated models, ACP emphasizes collaboration. Agents within the network interact to complement each other’s roles, creating a system that is responsive and adaptable to a variety of contexts. This allows for both users and machines to engage more effectively in tasks and problem-solving.

ACP has practical applications across industries. In automated systems, machine agents can manage processes while staying synchronized with user agents for oversight and decision-making. In customer support, ACP can enable human representatives to work alongside automated systems for faster and more personalized responses. It also holds potential for scenarios where distributed networks of agents tackle complex tasks, such as logistics or resource management.

One of ACP’s strengths lies in its ability to facilitate communication and coordination, making networks of agents not only efficient but scalable. By enabling agents to operate collectively, ACP supports systems that can adapt to changing conditions and expand without compromising reliability.

While ACP is promising, there are challenges to address. As networks grow larger, effective coordination between increasing numbers of agents can become complex. Security also plays a critical role, as data integrity and privacy must be safeguarded during communication. Additionally, establishing universal protocols to ensure smooth interaction between agents from different systems will be essential.

ACP is an early step toward building highly connected systems that integrate human oversight with machine autonomy. As the framework evolves, it could support environments where agents continually learn and improve, creating increasingly adaptive networks.

The Agent Context Protocol is an exciting development, opening new possibilities for how systems interact and collaborate. With ACP, networks of user and machine agents can move beyond isolated functionality to create dynamic, scalable, and efficient solutions. Exploring ACP’s applications could unlock transformative opportunities for businesses, developers, and industries alike.

Owning Your Digital Identity

Who owns your digital identity? It’s a question many of us haven’t paused to consider, even though our digital identities—email accounts, social media profiles, banking logins—are integral to how we navigate modern life. Yet, despite their importance, there’s an unsettling truth: someone else owns them. Companies, platforms, and institutions control our digital selves, and at any moment, they could alter, restrict, or even take them away.

Fundamentally, your digital identity should be yours. Owning it should be a basic human right. The frustration many of us feel with the endless chaos of managing passwords and accounts stems from an underlying issue: the lack of control. Passwords are just patches on a deeper problem. The fact is, most of us don’t have any real say in what happens to our digital identities. If a company wants to deactivate or ban your account, there’s often little you can do.

Creating a system where you truly own your digital identity is technically possible, yet no one has delivered it. Why? Perhaps it’s because organizations would need to give up their ownership over your digital data—and that’s not something they’re eager to do. This raises troubling questions about power and control. Why should external entities govern something as personal as your digital identity? These systems aren’t built for your autonomy; they’re designed to benefit the organizations that manage them.

The lack of self-ownership is more than an inconvenience—it’s a vulnerability. Knowing that someone else controls your digital identity is unsettling. At any moment, it could be revoked, hacked, or manipulated, leaving you without recourse. This sense of powerlessness is at the root of why digital identity ownership matters so much.

A future where we own our digital identities isn’t just wishful thinking—it’s achievable. Technologies like decentralized systems, cryptography, and blockchain offer pathways to building identities that belong to individuals, not institutions. But the challenge isn’t just technical; it’s about shifting mindsets and power dynamics. Solutions must prioritize autonomy, privacy, and security, ensuring individuals control their own data.

Owning your digital identity isn’t just a convenience—it would fundamentally change how we interact with the digital world. It would mean freedom from the fear of losing access, reduced reliance on passwords, and greater protection for your personal data. Most importantly, it would restore a sense of self-possession and control.

As individuals, we must push for this future and demand systems that work for us—not against us. If digital identities are as essential as they seem, should we accept anything less than full ownership?

It should be a basic human right.

What is an agent?

An agent is something that acts, interacts, and reacts. It is not an abstract concept but an actual instance—a real and functioning entity. An agent plays a role in the world, carrying out actions, engaging with other entities, and responding to changes. This dynamic nature is what distinguishes an agent from other forms of systems or ideas.

Agents are characterized by having a local state, which means they maintain their own specific conditions. This state allows them to operate independently and adapt to their context. For example, a program running on a computer might have its own setup and configurations, while a person may act based on their own understanding of a situation. This local state is crucial for how agents interact with the environment and make decisions.

In addition to their state, agents rely on data and knowledge to function effectively. They gather and store information, using it to guide their actions and interactions. A navigation app, for instance, uses map data to help users find directions, while a human draws on their experience and knowledge to solve problems or adapt to challenges. Data and knowledge are the foundation of an agent’s ability to act with purpose.

Agents can take many forms. They might be programs performing tasks, humans acting with intent, apps that assist users, or even companies operating collectively to achieve goals. For example, a company delivering products or services can be seen as an agent—working as a unit with state, data, and the ability to act, interact, and react. Ultimately, anything that operates autonomously or semi-autonomously within a system can be considered an agent.

Understanding what an agent is helps us appreciate its role in practical systems. Whether it’s software performing tasks, a person making decisions, or an organization navigating complex goals, agents are all around us. They are essential entities that shape how actions are carried out, interactions occur, and reactions drive progress.

Building a Platform for Automating Control Processes

Control processes are essential for ensuring consistency, compliance, and reliability in business operations. Automating these procedures can save significant time, reduce errors, and improve overall efficiency. This guide outlines the key elements needed to design a platform that supports the creation, execution, and testing of control processes.

The platform should allow users to log in securely and create new control processes. A straightforward setup makes it easier for users to begin their workflows. Once logged in, users can start by selecting the subject of the control process. Subjects might include physical or digital objects, individuals, documents, data, or even larger business processes. Providing flexibility in subject selection ensures broad applicability across different use cases.

From there, the platform should help users identify relevant rules, laws, and regulations connected to the selected subject. This step is crucial for ensuring compliance. Whether referencing internal rules or external regulations, users need tools to locate these frameworks easily.

Defining control outcomes is an essential step. Outcomes represent the end goals of the control process and ensure the work done has measurable and actionable results. Success might be defined as confirming compliance, identifying gaps, or achieving specific metrics like reduced error rates or improved processing times.

The workflow design, or control flow, forms the core of the control process. It maps out how tasks are executed—from sequential steps to branching pathways that account for conditional outcomes. This structure needs to be intuitive but flexible, allowing users to adapt workflows as requirements evolve. Supporting templates and visual design tools can simplify the creation process further.

Before deployment, control processes should be tested and validated within a simulation environment. Simulation helps identify weaknesses or inaccuracies while ensuring the process handles typical use cases and edge cases effectively. Users can iterate on their workflows based on test results, reducing the risk of issues when processes go live.

Building and refining automation for control processes is an ongoing effort. A well-constructed platform empowers users to create robust workflows while maintaining compliance and improving efficiency. Following these steps lays the groundwork for a system that evolves with organizational needs while consistently delivering value.

Automating Control Processes Using Language Models

Many industries rely on control processes to ensure operational accuracy, maintain quality, and comply with regulations. Common examples of these processes include deviation control, quality control, compliance checks, fraud detection, and documentation control. These checks often happen at different stages, such as pre-controls, post-controls, or through mapping workflows. Traditionally, these processes have been done manually, which can be time-consuming and prone to errors.

Language models offer a new way to automate control processes without needing to specify or code every detail explicitly. Instead of relying on predefined rules, language models work by identifying patterns. This makes them effective at detecting deviations or irregularities on their own. Specialized versions of these models can be fine-tuned to focus on specific tasks, such as fraud detection or anomaly identification, making them powerful tools for modern automation.

To automate control processes using language models, it’s helpful to take a step-by-step approach. First, identify what needs to be controlled, what data is required, and where this data resides in systems and processes. This involves close collaboration with domain experts such as lawyers, engineers, or healthcare professionals, depending on the field. It’s important to focus on areas with high potential for improvement, where automation can have the greatest impact.

Next, determine which control steps and processes are suitable for automation. Processes where there are large data volumes, significant manual effort, or readily available data are often good candidates. Once areas for automation are identified, the next step is to test with a proof of concept. Starting with simple examples in a secure sandbox environment helps validate the model’s capabilities. Testing different language models is essential to finding the best fit for specific needs.

If the proof of concept shows promise, the next step is to run a limited pilot program. A subset of real-world data can be used to experiment with automated controls while comparing different approaches. The results should be carefully analyzed to assess whether automation delivers measurable improvements. Pilots should function as separate processes to avoid disrupting ongoing workflows while testing scalability and reliability.

When automated controls prove valuable in pilot testing, the final step is scaling up for full production. Successful solutions can be integrated into live systems to streamline workflows and handle larger data volumes. Monitoring and refinement are critical during this stage to ensure continued effectiveness and adaptability.

While automating control processes offers significant advantages, practical challenges need to be addressed. Collaboration with subject matter experts ensures that automation captures all critical requirements. Reliable, accurate datasets are key to achieving good results. Additionally, building trust among stakeholders is crucial to gaining buy-in and ensuring that automated controls are accepted. Finally, successful implementation relies on starting small, testing thoroughly, and scaling gradually.

The potential for automating control processes with language models is immense. By reducing manual workload and improving accuracy, organizations can increase efficiency and build smarter workflows. Starting with smaller tests and scaling gradually provides a clear path to unlocking these benefits while maintaining quality and compliance.

Training a Language Model for Text Comparison

Text comparison represents a unique and challenging use case for language models. Unlike tasks such as question answering, searching for information, or generating content, text comparison focuses on analyzing and identifying subtle differences and patterns between two or more pieces of text. This process is geared towards detecting how one text deviates from another, whether in structure, tone, or meaning.

The model’s focus is not on answering questions but rather on recognizing patterns of deviation—an area that traditional models often overlook. These deviations can reveal meaningful insights and are particularly useful in contexts where precision and detail matter. For instance, a text comparison model can identify subtle linguistic shifts, rephrased sections, or even structural differences between similar documents.

This use case stands apart from typical applications like chat, search, and writing assistance. While those tasks focus on interaction, retrieval, or generation, text comparison prioritizes subtle analysis. Detecting nuances often requires a tailored approach, one that emphasizes detail over generalized functionality.

The training process involves equipping the model to capture and interpret these patterns effectively. This requires specialized datasets where textual pairs highlight similarities and differences. Examples might include rephrased paragraphs, altered clauses in contracts, or variations in translated content. Training the model to identify these deviations ensures it is uniquely suited for tasks like plagiarism detection, legal document review, or content consistency verification.

Applications for this type of specialized model are vast. In academia, it can help detect cases of paraphrased plagiarism. In the legal field, it ensures that slight shifts in agreement wording don’t go unnoticed. For content creators working across languages or platforms, the model can maintain consistency with the original material while catching deviations in tone or meaning.

By training a language model specifically for text comparison, we can address challenges that generalized systems struggle to handle. This tailored approach ensures accuracy, reliability, and meaningful insight for industries and tasks that rely on precision. The development of such focused use cases underscores the potential for innovation in language modeling and opens up exciting opportunities for problem-solving in critical domains.

Give me a problem

“What’s your biggest problem right now?” This question can be one of the most powerful tools for selling, whether it’s yourself, a technology, or a service. The idea is simple: by directly addressing a real and concrete issue the recipient is struggling with, you immediately demonstrate your value, engage them emotionally, and create a productive dialogue.

The key is to work with an actual, specific problem the recipient cares about. This isn’t about vague concepts or abstract ambitions—it’s about something tangible and relatable. A real problem resonates because it’s something the audience can logically understand and emotionally feel. It should be specific enough that it can be broken down into actionable details, allowing you to dive into solutions that matter.

Here’s where choosing the right level of detail is crucial. For example, saying, “I want to cure cancer” is too broad and lofty to be actionable. However, if you refine it to, “I want to cure cancer, but I’m struggling with researchers spending too much time writing reports to secure funding,” it becomes a problem you can tackle. Similarly, “The company needs to increase revenue” is too generic, whereas “We need to boost revenue, and several consultants currently don’t have ongoing projects” is grounded and solvable. The same applies in tech: “The development team isn’t delivering all the functionality users want” is abstract, but “The team spends most of their time on non-functional tasks and can’t deliver new features quickly enough” identifies specific challenges and barriers.

Starting with the recipient’s biggest problem allows you to demonstrate your skills in problem-solving, analysis, and creativity. By breaking the problem into smaller components, you can sketch alternatives, evaluate solutions “on paper,” and engage them in a focused conversation about possibilities. It’s not just about providing answers—it’s about encouraging clear thinking and collaboration.

This method has wide-ranging applications. In a job interview, you can show your ability and expertise by addressing the company’s key challenges. In sales, you can frame your product or service as the solution to their major pain points. During demonstrations, you can showcase your capabilities by working with relatable, real-world examples.

At its core, this approach builds credibility and trust. It’s a way to prove your ability to navigate and tackle tough challenges while focusing on what truly matters to the recipient. By connecting with someone’s most pressing issue, you encourage collaboration, deepen the dialogue, and position yourself as someone who delivers results.

So next time you’re pitching yourself, a product, or an idea, try starting with this question: “What’s your biggest problem right now?” It’s a way to cut through distractions and focus on creating real value. Sometimes, the simplest approach is the most impactful.