Author: Nicolai Friis

Overcoming the Pitfalls of Generic Domain Models

Domain models are essential tools for structuring complex systems, ensuring they align with the needs of their domain and are usable for developers, users, and stakeholders. However, a frequent problem arises when these models become overly generic, favoring abstract, universal representations over specific, meaningful ones. While high abstraction might seem appealing for flexibility, it can introduce significant challenges in usability, implementation, and maintenance.

When the abstraction level is too high, everything starts to blur into vague concepts like “data” or “thing.” These overly generic representations fail to capture the unique aspects of the domain and offer little guidance for users or developers. The model becomes hard to learn, difficult to interpret, and impossible to understand without extensive documentation. The lack of clarity means that users cannot rely on the model to glean its intended use or functionality—they have to refer to external instructions instead. It sacrifices usability for flexibility in a way that ultimately benefits neither.

In implementation, generic domain models also create complexity. Developers struggle to map abstract concepts to concrete systems, which can lead to errors. Without clear distinctions and definitions, data often gets mixed up or incorrectly applied, increasing the risk of bugs and reducing the system’s reliability. These models also tend to be incompatible across implementations, as different teams interpret them in varying ways, resulting in inconsistencies in how they’re applied.

Striking the right balance between generalization and specificity is critical. If the abstraction is too high, the model becomes generic and error-prone. If it’s too low, it becomes overly tailored to a single use case, losing the flexibility necessary for broader applications. The goal is to find an abstraction level that captures the essence of the domain, while remaining intuitive and adaptable.

To find this optimal balance, clarity should always be prioritized. It’s important to identify the central concepts and relationships unique to the domain and avoid reducing them to overly vague terms. Collaboration with domain experts, users, and developers is also key. These stakeholders can help validate the model’s design, ensuring it meets the needs of its intended audience and aligns with real-world use cases.

Refining the model should be an iterative process. Feedback from users and testing in practical scenarios can reveal whether the abstraction level provides sufficient guidance while maintaining flexibility. Real-world validation ensures that the model works in practice, not just in theory.

Overgeneralized domain models undermine their own purpose. They create unnecessary complexity, increase the risk of errors, and fail to guide developers and users. A well-crafted domain model finds the middle ground—specific enough to provide meaningful structure, yet flexible enough to grow alongside evolving requirements. Thoughtful design and collaboration are the keys to making domain models both effective and intuitive, ensuring they act as strong foundations for long-lasting systems.

Exploring Model Context Protocol (MCP) and kontekst.cloud

Model Context Protocol (MCP) is an open protocol designed to standardize how applications connect with language models (LMs). Think of MCP as being similar to a USB-C port, not for hardware, but for AI-driven systems. It provides a structured way for applications to interact efficiently with data sources, workflows, and tools. The three main features of MCP are resources, prompts, and tools. Resources consist of context and data that the user or model can utilize. Prompts are templated messages and workflows that guide interactions. Tools are functions that a language model can execute to complete specific tasks. This standardized approach makes MCP useful for integrating applications in a clear and repeatable way.

The concepts in MCP have noticeable similarities with kontekst.cloud, a platform that organizes systems around the central concept of “context.” Most features in MCP align directly to kontekst.cloud’s terms. Resources in MCP correspond to content in kontekst.cloud. Tools translate to actions, and prompts could align with agents or actions. However, prompts are tricky to define in kontekst.cloud since they are used differently. One suggestion is to treat them purely as templated messages and separate workflows as their own distinct concept. Unlike MCP, kontekst.cloud introduces threads that capture logs and process information, extending beyond the limited technical logging seen in MCP. This ability to store execution histories helps define workflows and track processes in greater detail.

Some challenges exist with terms like “resources” and “data,” as they are too broad and often end up encompassing everything. Kontekst.cloud has made efforts to be more precise by splitting features into content, process data, and actions. The platform uses an endpoint called /data to store all information related to features, but alternatively, /resources could be used. However, the generic nature of these terms still poses some risk of overlap between concepts. Despite this, the flexibility built into kontekst.cloud allows substantial customization, which makes implementing MCP on the platform relatively straightforward.

Kontekst.cloud’s design also enables support for alternative protocols like SOLID or other semantic web technologies. By adding a compatible layer, the platform can easily integrate standards like MCP while retaining the ability to work with other options. This adaptability positions kontekst.cloud as a versatile tool for building interoperable systems. Whether working with structured standards like MCP or experimenting with decentralized architectures supported by protocols like SOLID, kontekst.cloud provides the foundation for highly flexible implementations.

An important distinction between MCP and kontekst.cloud lies in the concept of context itself. In kontekst.cloud, context operates as the central organizing principle and can be seen as the “server” that ties together content, actions, workflows, and threads. MCP lacks this central concept and instead ties resources and tools to individual servers. To bridge this gap, kontekst.cloud could represent each context as its own independent server, assigning a root URL to each. This modular approach enhances scalability and allows workflows to be tied directly to user-specific or application-specific contexts, creating a more personalized experience.

Although MCP excels as a standardized integration protocol, kontekst.cloud takes these concepts further by emphasizing context as the foundation for organizing data and processes. This focus enables richer workflows and simplifies the design of reusable systems. With its ability to support MCP and other protocols, kontekst.cloud isn’t limited by any single system but instead embraces interoperability as a core strength. By combining the standardization provided by MCP with the context-driven modularity of kontekst.cloud, developers can build more scalable and flexible applications tailored to diverse needs.

A Practical Solution for Local Shopping and Service Searches

Finding specific products in local stores can often feel like an impossible task. Whether you’re looking for a niche item like black, 34 cm round chair cushions or something slightly out of the ordinary, there’s no straightforward way to figure out which store has exactly what you need nearby. Despite its dominance, search engines like Google have generally been poor at helping users discover local products and services—and they were never particularly good at it to begin with.

What’s missing is a dedicated service that makes searching for local products simple and efficient. Imagine a tool where every store could upload their inventory into an easily manageable catalog system, which they could keep updated with minimal effort. This would allow people to search for specific items and instantly see where they can find them locally, instead of wasting time going from store to store or trawling through irrelevant online results.

Such a service would be especially helpful for precise shopping needs that aren’t based purely on price or broad functionality. Sometimes you need something highly specific, like a particular size or design, and the usual tools simply don’t deliver these results. A focused local search tool could address this gap, empowering shoppers to save time while highlighting the unique offerings in their surrounding area.

It would also benefit local businesses tremendously. For smaller shops that might struggle to stand out against big retailers, this tool could be a game-changer. By connecting their product catalog directly to nearby customers searching for specific items, they could increase foot traffic and reach people who might otherwise have overlooked them.

In essence, the ability to connect people with local goods and services quickly and effortlessly would strengthen both shopping experiences and community ties. It’s time for a better way to search and shop locally—one that prioritizes both convenience and personal connection.

How Thinking in Reverse Unlocks Hidden Insights

Some connections can be difficult to understand because we aren’t looking at them from the right angle—or, more specifically, the wrong way around. What we see as cause and effect might actually be reversed, with the effect influencing the cause in ways we hadn’t considered. When approaching situations, challenges, or questions, our usual forward-thinking mindset might miss something crucial simply because it follows familiar paths.

A useful way to uncover these hidden connections is to reverse your thought process. By intentionally flipping events or sequences—whether in terms of time, cause-and-effect relationships, or other types of order—you force yourself to view things from a new perspective. This shift can reveal ideas or explanations that might otherwise remain unnoticed.

Looking at things backwards isn’t just about questioning assumptions; it opens up new possibilities for problem-solving and understanding. Reversing your thinking could lead to entirely different explanations for events or inspire alternative ways of approaching challenges. What seems like a clear sequence might turn out to be something completely different when viewed in reverse. Letting go of linear thinking and embracing the opposite can lead to breakthroughs that redefine how we understand the world around us.

Understanding Task Completion Through Communication

Why do we get different responses when we ask someone to “do something,” “finish something,” or “completely finish something”? The phrasing of a request can drastically change how people interpret and approach it. Saying “do something” often implies starting the task without necessarily focusing on completion. For example, asking someone to “clean the living room” might only result in tidying up visible clutter. “Finish something” shifts the focus to completing the task, but the level of thoroughness can vary. If the instruction is to “finish cleaning the living room,” one person might vacuum and dust, while another might only consider the task done once everything is spotless. The most explicit phrasing, “completely finish something,” leaves little room for misinterpretation. It clarifies that the task should be done thoroughly and to the highest standard—whether that means scrubbing every corner or polishing every surface. These distinctions show how subtle wording changes create different expectations about what “finished” actually means.

Context adds even more depth to how we interpret tasks. When you ask someone to “clean the kitchen,” the details of the situation influence understanding. Are you expecting a quick wipe-down before guests arrive, or do you need a deep clean of every cabinet and appliance? Without context, the task may result in a completely different outcome than intended. Clear communication benefits from specifying what “finished” looks like in a given scenario. Context also provides purpose—it lets people know why the task is important and what role completion plays in the larger picture, whether it’s preparing for an event or meeting a deadline.

The way we phrase requests also has a significant impact on how we think about and approach tasks. Slight adjustments in wording can shift focus. Asking someone to “start vacuuming” emphasizes getting the task underway, while asking them to “finish vacuuming thoroughly” sets an expectation of both progress and thoroughness. This isn’t just about clarity—it’s also about psychology. The phrasing we use creates mental cues that guide our actions, whether it’s a simple reminder to begin something or a directive to wrap it up completely.

By being mindful of the words we choose, the context we create, and the clarity we aim for, we can reduce misunderstandings and align expectations more effectively. When asking someone to complete a task is as clear as possible, collaboration flows more smoothly, and everyone involved can focus on what truly needs to be done.

Stories – A Way to Transfer Knowledge

Since the dawn of civilization, storytelling has been our primary way of sharing and preserving knowledge. From oral traditions filled with myths and legends to written texts, films, and interactive media, stories have shaped how we understand the world.

But why are stories so effective? Because they create experiences. Instead of just presenting isolated facts, they embed knowledge in a context, making it easier to understand, remember, and apply. This principle isn’t just useful for humans—it can also transform how we train language models.

How Stories Shape Learning

Stories are more than just entertainment. They act as cognitive frameworks, helping us connect new information to what we already know. Think about how we learn history—not through a list of dates and events, but through narratives about the people who lived them. The same applies to scientific discoveries, moral lessons, and even problem-solving strategies.

By structuring knowledge within a story, we make it relevant and engaging. A well-crafted narrative provides context, emotion, and meaning, making learning a natural and immersive experience.

Using Stories to Train Language Models

The way we train language models today often relies on vast amounts of structured and unstructured data. But what if we approached this process more like teaching a human?

Instead of feeding language models disconnected data points, we can frame information within meaningful stories. This method allows the model to understand not just words and syntax but also the deeper relationships between concepts. Context-rich learning could lead to more intuitive and adaptable language models, capable of reasoning and responding in more human-like ways.

A Future Built on Narrative Learning

Imagine a world where language models learn through carefully curated stories—absorbing knowledge in the same way we do. This could revolutionize fields like education, research, and communication.

By embracing storytelling as a core method for training, we’re not just improving language models. We’re reinforcing the fundamental truth that knowledge, when placed in the right context, becomes something more than just data—it becomes wisdom.

How to Catch Hidden Assumption Errors in Your Code—And Can a Language Model Help?

Every developer has encountered a bug that “shouldn’t have happened.” Often, these bugs stem from hidden assumptions in the code.

Take the example of a system handling substitute employees. It assumes that every substitute is assigned to replace someone. But in reality, substitutes may exist because no one currently holds the position. This faulty assumption leads to a null pointer exception, and a database constraint failure makes things worse.

These issues could have been caught earlier. But how? Could a language model (LM) help uncover such flawed assumptions before they break production?

Understanding Assumption-Based Bugs

An assumption-based bug happens when code is built on an unchecked belief about how the system works.

In the substitute employee example:

The system assumes every substitute has a direct assignment.
In some cases, no one holds the position, making the assumption false.
This leads to a null pointer exception and a database constraint failure.

Such bugs are common because assumptions often go unchallenged during development.

Can a Language Model Help Detect These Issues?

LMs could assist by:

Extracting assumptions from code and documentation.
Identifying weak spots, comparing them to past failures.
Suggesting fixes, pointing out missing cases or alternative logic.

While today’s LMs aren’t perfect at reasoning, they can help detect patterns and highlight potential problem areas.

Practical Ways to Reduce Assumption-Based Bugs

Even without an LM, there are ways to catch these issues early:

Document Assumptions – Clearly state system assumptions and challenge them.
Use Static Analysis Tools – Linters and type checkers can catch logic inconsistencies.
Implement Defensive Programming – Always check for null values and validate inputs.
Explore AI-Assisted Code Review – Emerging tools can help flag logical inconsistencies.

Conclusion

Many software bugs come from flawed assumptions rather than syntax errors. While LMs may assist in uncovering them, developers can take proactive steps today: document assumptions, use static analysis tools, and test for edge cases.

AI-Powered Blog Writing Agent

In today’s fast-paced digital world, consistently creating high-quality blog content is a challenge. Whether you’re a business, a marketer, or an individual looking to publish thought-provoking articles, writing takes time, effort, and creativity.

Our AI-powered blog writing agent is designed to automate, enhance, and optimize content creation—from ideation to publication—so you can focus on what truly matters: sharing valuable insights with your audience.

Key Features

Basic Capabilities

Our AI agent simplifies blog creation with intelligent automation:

Effortless Content Generation – Provide an idea, rough notes, an example post, a concept, or a vision, and the AI will generate a structured, well-written blog post. It can even include relevant images.
Flexible Writing Styles & Formats – Choose from different styles, including short teasers, long-form articles, press releases, visionary thought pieces, or technical breakdowns.
Smart Meta-Tagging – Automatically generates relevant tags, categories, and metadata to optimize searchability and SEO performance.
Seamless Publication – Offers full or semi-automated publishing, so you can review and approve or let the AI handle the entire workflow.

Advanced Capabilities

For those looking for more control, variation, and optimization, our AI offers powerful content evaluation and management tools:

Multi-Proposal Generation & Evaluation – Instead of producing just one draft, the AI creates multiple versions of a post and evaluates them, allowing you to select the best for publication.
Diverse Input Pipelines – Generate completely different blog posts around multiple topics and ideas, providing a variety of content options. This ensures you always have fresh and diverse articles to choose from, making it easier to maintain a dynamic publishing strategy.
Authoring Process Archive – Keep a detailed record of revisions and iterations for each post, enabling easy tracking of changes, content strategy insights, and future repurposing.
Smart Trash-Can Feature – Discarded posts aren’t lost—they go into a special archive where they can be analyzed for trends, revisited for future use, or evaluated for performance improvements over time.

Why Use This AI Agent?

With this AI-powered writing tool, content creators can:
✔ Save time and effort on writing and editing
✔ Generate multiple blog post options across different topics
✔ Improve content quality through AI-driven evaluation and selection
✔ Maintain full control over the publishing process

Whether you’re a business scaling content marketing, a tech writer streamlining production, or a visionary sharing new ideas, this AI agent enhances creativity, productivity, and content quality like never before.

Ready to Revolutionize Your Content Creation?

Try our AI-powered blog writing agent today and experience faster, smarter, and more effective content generation. 🚀

Design Alternatives for Using Path, HTTP Methods, and Actions

When designing an API, choosing how to structure endpoints and model the interaction between client and server is a critical design decision. The three alternatives outlined – data-driven, object-oriented, and action/process-driven – represent different approaches with distinct strengths and weaknesses. The choice of approach should be based on both technical and business needs, as well as user expectations and workflows.

1. Data-Driven Approach

Description

This approach focuses on data as the primary entity in the API. Clients perceive the API as a system for storing and retrieving data, without directly interacting with actions or processes. Business logic and processing happen invisibly on the backend, and clients only see the results through the data produced.

Characteristics

Clear separation between data and processes.
Clients interact only with resources (e.g., submissions) and their lifecycle.
Process statuses are represented as fields in the data.
Resembles a CRUD (Create, Read, Update, Delete) approach.

Advantages

Simple for clients – they only retrieve and store data without needing to understand domain logic.
Fewer endpoints with a consistent URL structure.
Well-aligned with REST principles.

Disadvantages

Business logic can be difficult for clients to understand and discover.
Risk of logic being spread across clients if the API does not provide enough guidance.
Less suitable for complex processes involving multiple steps or data types.

Example

GET /data/submission
GET /data/submission/1199930
POST /data/submission
PATCH /data/submission/1199930

When is this approach suitable?

For simple systems where processes are not highly complex.
When clients primarily work directly with data (e.g., case handlers).
When minimal coupling between clients and domain-specific business logic is desired.

2. Object-Oriented Approach

Description

In this approach, each resource is treated as an object that has both data and associated operations (methods). Clients can not only retrieve and update data but also trigger specific actions on each resource. This makes business logic more explicit in the API.

Characteristics

Each resource has its own set of operations/actions.
Clients must understand domain-specific concepts and processes.
The approach resembles object-oriented systems, where objects have methods.

Advantages

Clearer process support – clients receive explicit signals about available actions.
Easier for clients to navigate business logic.
Well-suited for resources with many specific actions governed by business rules.

Disadvantages

Can lead to an explosion of endpoints when multiple resources have multiple actions.
Maintaining a consistent structure across various object types can be challenging.
Can become cumbersome if many actions are not resource-specific but apply across multiple resources.

Example

POST /data/submission/search
POST /data/submission/submit
POST /data/submission/1199930/selectPractice
POST /data/submission/1199930/cancel

When is this approach suitable?

When resources have specific, business-related operations.
When it is important for clients to understand the processes around resources.
When the API is part of a larger domain application with domain-oriented users.

3. Action/Process-Driven Approach

Description

This approach explicitly separates actions from data. Clients retrieve and manage data in one way, while business processes and operations are modeled as separate process resources or services. This allows actions to involve multiple data types simultaneously and handle more complex workflows.

Characteristics

Clear distinction between data and processes.
Processes have dedicated endpoints that handle multiple resources and complex logic.
Suitable for larger, cross-cutting processes.
Often inspired by Command-Query Responsibility Segregation (CQRS).

Advantages

High flexibility in modeling business logic.
Easier to version or modify process logic without changing data models.
Well-suited for systems with complex, multi-step workflows.

Disadvantages

Can create uncertainty about which data the processes operate on.
Requires more documentation and client adaptation.
May result in an artificial separation of data access and process handling, even when logically connected.

Example

POST /process/submitReimbursementClaim
POST /process/updateReimbursementClaim
POST /search

When is this approach suitable?

When processes involve multiple different data types.
When processes have high complexity and multiple steps.
When processes should function as “black box” operations with clear input and output.
When supporting both manual and automated workflows via the same interface.

Summary Evaluation

Approach	Client Simplicity	Flexibility	Process Support	Suitable for Complex Domains
Data-Driven	✅ Very simple	❌ Limited	❌ Weak	❌ Not well-suited
Object-Oriented	⚠️ Moderate	⚠️ Moderate	✅ Good	⚠️ Partially suitable
Action/Process-Driven	⚠️ Requires learning	✅ High	✅ Very good	✅ Highly suitable

Recommendation

Choosing an approach should be based on:

The complexity of the domain.
How self-sufficient clients need to be.
How clearly processes need to be defined for clients.
Whether the API is primarily a CRUD interface or a process-driven system.

In many cases, a hybrid model may be the best solution, where basic data is managed using a data-driven approach, while more complex workflows are exposed via process-driven endpoints. This provides both simple data handling and flexible process support.

Knowledge-Augmented Model Training (KAMT)

Knowledge-Augmented Model Training (KAMT) is a structured approach to transforming a Foundation Language Model (FLM) into a Specialized Language Model (SLM) by incorporating domain-specific knowledge. This process leverages Knowledge Packs (KPs)—curated datasets containing expert-level information—to enhance the model’s proficiency in targeted areas.

By systematically integrating structured knowledge, KAMT ensures that AI models maintain their foundational language capabilities while gaining deep expertise in specific fields. This makes it a powerful strategy for organizations looking to build high-performance AI systems without the need to train models entirely from scratch.

Key Components of KAMT

1. Foundation Language Model (FLM)

At the core of KAMT is the FLM, a pre-trained general-purpose language model with broad linguistic knowledge. This model serves as the starting point and provides strong baseline capabilities in natural language understanding and generation. However, its general nature means it lacks deep expertise in specialized areas.

2. Knowledge Packs (KPs)

Knowledge Packs (KPs) act as modular data units containing structured domain-specific information. These are designed to systematically enhance the FLM’s knowledge in a particular field. A KP may include:

Industry-Specific Literature – Research papers, textbooks, whitepapers
Technical Documentation – Manuals, software documentation, engineering specifications
Expert-Curated Datasets – Annotated corpora, structured knowledge bases
Real-World Data – Case studies, financial reports, patient records (where applicable)
Interactive Feedback – Human-in-the-loop refinements and reinforcement learning

3. Specialization Training Process

KAMT involves a structured fine-tuning process that adapts the FLM using the KPs. The key steps include:

Supervised Fine-Tuning – The model is exposed to high-quality labeled data to refine its accuracy in a given domain.
Reinforcement Learning with Human Feedback (RLHF) – Expert reviewers evaluate and adjust the model’s outputs to improve reliability.
Knowledge Injection Techniques – The model learns to integrate structured knowledge without erasing its foundational understanding.
Task-Specific Optimization – The SLM is fine-tuned for specialized applications such as legal analysis, medical diagnosis, or scientific research.

4. Specialized Language Model (SLM)

The result of KAMT is a Specialized Language Model (SLM)—a version of the FLM that is finely tuned for a specific domain. The SLM offers:
✅ Enhanced Accuracy – Greater precision in handling complex domain-specific queries.
✅ Deep Context Understanding – Improved comprehension of industry terminology and specialized concepts.
✅ Task-Specific Adaptability – Optimized for use cases such as research assistance, legal document processing, medical diagnosis, or financial modeling.
✅ Scalability and Continuous Learning – Additional KPs can be integrated over time, keeping the model up to date with new knowledge.

Why Use KAMT?

KAMT provides a scalable, cost-effective, and modular approach to AI specialization. Instead of building models from scratch, organizations can leverage pre-trained FLMs and enhance them with domain knowledge, resulting in a faster, more efficient, and adaptable AI solution.

Use Cases

Healthcare & Medicine – Specialized AI for medical diagnostics, patient data analysis, and research.
Law & Compliance – AI systems that understand legal language, contracts, and regulatory requirements.
Finance & Trading – AI-driven market analysis, risk assessment, and fraud detection.
Engineering & Technology – Enhanced AI assistants for software development, manufacturing, and automation.
Education & Research – Custom AI tutors and academic research assistants.

Conclusion

Knowledge-Augmented Model Training (KAMT) is a powerful paradigm for AI specialization, bridging the gap between general-purpose language models and expert-level AI systems. By leveraging KPs and targeted training processes, organizations can rapidly develop domain-specific AI models that offer superior accuracy, contextual understanding, and adaptability in real-world applications.