Topic
Token & Tech articles on Artificial Intelligence: explainers, analysis and guides with verifiable sources and clearly separated facts and opinions.
Research & Future Trends · Analysis
In two years, AI agents went from near zero to high scores on well-known tests. But tests age, leak and measure little of the real world. Here is what the evidence supports as of October 2026.
Artificial Intelligence · Explainer
Language models sometimes state things that are not true with full confidence. Learn where this behavior comes from and what actually helps reduce it.
Artificial Intelligence · Explainer
Automating with AI starts with the process, not the tool. This guide shows how to choose what to automate, where AI makes sense and how to keep control over the result.
Artificial Intelligence · Explainer
An assistant that answers based on your organization's manuals, contracts and policies is one of the most common uses of AI. This roadmap shows the decisions at each stage and where projects usually go wrong.
Development & Building with AI · Explainer
Claude Code doesn't just suggest code: it reads the project, runs commands and checks the result. Based on Anthropic's documentation, here is how that loop works and where the controls are.
Artificial Intelligence · Explainer
A well-written AI response is not, for that reason, a correct response. Here is how to tell the two apart with clear criteria, from personal use to systematic testing.
Artificial Intelligence · Explainer
A language model doesn't remember you the way a person does. What it uses to answer depends on what fits in the context, what was saved to memory and what is retrieved on the spot.
Artificial Intelligence · Explainer
Writing good requests for a language model is less a trick and more a method: clarity, examples, structure and testing. See what official sources and research say and how to apply it day to day.
Artificial Intelligence · Explainer
Choosing an AI platform is not choosing the model at the top of a ranking. It is deciding who will handle the company's data, how much it will cost over time and what happens when the vendor changes. Here is a roadmap to compare options carefully.
Artificial Intelligence · Analysis
Fine-tuning changes the model's behavior; RAG changes the information it consults. Confusing the two is one of the most expensive ways to go wrong in an AI project.
Artificial Intelligence · Explainer
Writing a paragraph, painting a landscape and continuing a voice recording seem like different tasks. Underneath, generative AI does the same thing: it learns what data usually looks like and produces new samples similar to it.
Artificial Intelligence · Explainer
Models that read a photo, hear a question and answer out loud are already part of everyday life. See how they bring different types of data together and which limits still apply.
Artificial Intelligence · Explainer
AI tools promise to read contracts in seconds and flag risky clauses. Understand what they actually do, where they go wrong and why professional review is still necessary.
Artificial Intelligence · Explainer
Students and teachers already use generative AI in everyday school life. See what research says about when it helps learning, when it gets in the way and what happens to authorship and assessment.
Research & Future Trends · Analysis
AI is already part of most developers' routine. The most important change, however, is not in typing code, but in what happens before and after it.
Artificial Intelligence · Explainer
Some studies show programmers 56% faster with AI, and another showed them 19% slower. Understand what each piece of evidence measures and what it does not allow you to conclude.
Artificial Intelligence · Explainer
Artificial intelligence already reads scans, summarizes medical records and answers questions about symptoms. Learn what the evidence shows, where it fails and who is accountable for decisions in Brazil.
Artificial Intelligence · Explainer
Chatbots and AI assistants already answer customers on many channels. See where they help, where they get things wrong and why the company remains responsible for what the bot says.
Artificial Intelligence · Analysis
Not every model called "open source" is open in the classic sense. Here is what changes between open weights and closed services in licensing, cost, privacy, control and risk.
Artificial Intelligence · Explainer
Some AI models “think” before answering: they write intermediate steps that you often don't even see. This improves certain tasks, but it costs time, money and transparency.
Artificial Intelligence · Explainer
Artificial intelligence has become a label for almost everything. Here you will see what the term means in official definitions, how a system learns from data and why it still makes mistakes.
Artificial Intelligence · Explainer
A chatbot answers questions. An agent receives a goal, chooses the steps, uses tools and checks the result. See how this works on the inside and where it tends to go wrong.
Artificial Intelligence · Explainer
Behind the AI assistants that write, summarize and translate is a type of program called an LLM. See what it is, how it learns and where it tends to go wrong.
Artificial Intelligence · Explainer
Many AI projects stall at the pilot stage or deliver less than they promised. Studies and surveys of companies point to recurring causes, and almost none of them is the model itself.
Artificial Intelligence · Explainer
Everything you write or attach in an AI assistant leaves your computer and is processed by another company. Knowing where it goes, how long it stays and who can read it is the first step to using these tools without exposing you, your clients or your company.
Development & Building with AI · Explainer
Feeling that AI speeds up your work is not the same as measuring that it does. See which metrics to use, which ones mislead and how to set up a simple experiment on your team.
Artificial Intelligence · Explainer
Language models do not know your contracts, manuals or internal reports. RAG solves part of this: it finds the right passages and hands them to the model before it answers.
Artificial Intelligence · Explainer
Calculating the return on an AI project requires counting every cost, measuring the before and after of the same process and separating time savings from financial gains. Here is how to do the math without fooling yourself.
Artificial Intelligence · Explainer
Every piece of text you send to an AI model is cut into chunks called tokens. Understanding that split helps you predict costs, limits and some curious errors.
