# DigitalNeuron

> Independent AI news, plain-language explainers and a reference glossary, published in 6 languages (en, ko, ja, th, es, de). Every article opens with a short direct answer and a key-takeaways list, and cites its sources.

## About

- Site: https://digitalneuron.net/
- Contact: editor@digitalneuron.net
- Editorial policy: https://digitalneuron.net/en/editorial-policy
- Licence for quotation: quotation with attribution and a link back is welcome.
- Localised versions of any URL use the same path with a different language prefix (/en/, /ko/, /ja/, /th/, /es/, /de/).

## News

- [The EU AI Act: which obligations are already in force, and which are still ahead](https://digitalneuron.net/en/news/eu-ai-act-what-applies-now): The EU AI Act entered into force on 1 August 2024 and applies in stages. The bans on prohibited practices and the AI-literacy duty applied from February 2025; obligations for general-purpose AI models from August 2025; and the main high-risk regime from August 2026, with product-embedded high-risk systems following in 2027.
- [Analysis: what actually changed when AI agents moved from demo to production](https://digitalneuron.net/en/news/agents-move-into-production): Demos run a short happy path once with a human watching. Production runs thousands of variations unattended, where per-step error rates compound and an unbounded permission scope turns a wrong decision into an incident. The deployments that work narrow the scope, verify each step cheaply, and gate every irreversible action.
- [Analysis: the price of AI keeps falling, so why are the bills going up?](https://digitalneuron.net/en/news/inference-cost-curve): Price per token has fallen sharply through better hardware, smaller distilled models and serving optimisations. Consumption has grown faster: longer contexts, reasoning models that generate far more tokens per answer, and agents that turn one user action into dozens of model calls. Falling unit prices with rising unit counts produce larger bills.
- [Analysis: answer engines are rewriting how people find information — and how publishers get paid](https://digitalneuron.net/en/news/answer-engines-and-publisher-traffic): Answer engines synthesise a response from several sources and show it above or instead of the traditional link list, so a query that once produced a visit can now be resolved without one. Publishers see impressions and citations rise while click-through falls, which breaks the advertising model that assumed every answer required a page view.
- [Analysis: how far behind are open-weight models, really?](https://digitalneuron.net/en/news/open-weight-models-narrow-the-gap): On common benchmarks the best open-weight models now sit close to frontier commercial models, and for many routine tasks the difference is not noticeable. The remaining gaps show up in long-horizon reliability, tool use, very long contexts and safety tuning — and in the operational work of running them yourself.

## Explainers

- [What is an AI agent, and how is it different from a chatbot?](https://digitalneuron.net/en/explainers/what-is-an-ai-agent): An AI agent is a language model that has been given tools it can call, a goal to pursue, and permission to take several steps without asking a human between each one. A chatbot answers a question and stops; an agent keeps acting until it decides the goal is met or it runs out of budget.
- [What is a context window, and why does it run out?](https://digitalneuron.net/en/explainers/context-window-explained): A context window is the maximum amount of text, measured in tokens, that a model can consider in a single request. It holds the system instructions, the conversation so far, any documents you paste in, and the answer being generated. When the total exceeds the limit, something has to be dropped or summarised.
- [What is RAG (retrieval-augmented generation), and when do you need it?](https://digitalneuron.net/en/explainers/what-is-rag): Retrieval-augmented generation is a pattern where the system searches your own documents for passages relevant to a question, puts those passages into the model's prompt, and asks the model to answer using them. The model's weights never change; the knowledge arrives as context at request time.
- [Open weights vs open source AI: what the labels actually mean](https://digitalneuron.net/en/explainers/open-weights-vs-open-source): Open weights means the trained model file can be downloaded and run yourself, under whatever licence the publisher chose. Open source is a stricter legal standard requiring freedom to use, study, modify and redistribute without restrictions on field of use. Many widely used models are open weights but not open source.
- [Why does AI use so much electricity?](https://digitalneuron.net/en/explainers/why-ai-uses-so-much-electricity): AI accelerators draw far more power per rack than traditional servers, and that power has to be delivered, cooled and paid for continuously. Training a large model is a one-off spike; serving it to millions of users is a permanent load, and inference is what dominates energy use over a deployed model's life.
- [How AI model pricing actually works: tokens, caching and batching](https://digitalneuron.net/en/explainers/how-ai-pricing-works): Almost every AI API bills per million tokens, with separate prices for input and output. Output usually costs several times more than input. Cached input, batch processing and smaller models can each cut the bill substantially, and the total for a conversation grows with history because most APIs re-send the whole thread every turn.

## Glossary

- [AI agent](https://digitalneuron.net/en/glossary/ai-agent): An AI agent is a language model connected to tools it can call, given a goal rather than a single question, and allowed to loop — calling a tool, reading the result, deciding the next step — until it judges the goal met or hits a limit. The loop is what distinguishes it from a chatbot.
- [Context window](https://digitalneuron.net/en/glossary/context-window): The context window is the maximum number of tokens a model can process in a single request. It holds the system prompt, tool definitions, conversation history, any attached documents and the generated output. Anything outside the window is invisible to the model.
- [Distillation](https://digitalneuron.net/en/glossary/distillation): Distillation trains a smaller student model on the outputs of a larger teacher model, so the student reproduces much of the teacher's behaviour at far lower cost. It is the main reason small models improved so quickly, and it is restricted or prohibited by many commercial API terms.
- [Embedding](https://digitalneuron.net/en/glossary/embedding): An embedding is a fixed-length vector of numbers produced by a model to represent a piece of text, image or audio. Items with similar meaning end up near each other in that vector space, which is what makes semantic search, clustering and recommendation possible.
- [Fine-tuning](https://digitalneuron.net/en/glossary/fine-tuning): Fine-tuning continues training a pre-trained model on a smaller, task-specific dataset, adjusting its weights so it reliably produces a particular format, tone or narrow skill. It is a poor way to teach facts, which change and are better supplied by retrieval at request time.
- [Inference](https://digitalneuron.net/en/glossary/inference): Inference is the act of running a trained model on new input to produce output, as opposed to training, which produces the model in the first place. Training is a one-off cost; inference repeats for every request, and over a widely used model's life it dominates both spend and energy consumption.
- [Large language model (LLM)](https://digitalneuron.net/en/glossary/large-language-model): A large language model is a neural network with billions of parameters, trained on large text corpora to predict the next token in a sequence. That single objective, at sufficient scale, yields the ability to answer questions, write code, translate and summarise — none of which were trained for directly.
- [Mixture of experts (MoE)](https://digitalneuron.net/en/glossary/mixture-of-experts): Mixture of experts splits parts of a network into many parallel sub-networks and uses a router to send each token through only a few of them. Total parameter count stays large while the computation per token stays small, which is why several very large models are cheaper to serve than their size implies.
- [Model Context Protocol (MCP)](https://digitalneuron.net/en/glossary/model-context-protocol): The Model Context Protocol is an open standard that defines how an AI application discovers and calls external tools, resources and prompts. A server implements it once and any compatible client can use it, replacing per-product custom connectors.
- [Token](https://digitalneuron.net/en/glossary/token): A token is the smallest unit of text a language model processes: a common word fragment produced by a tokeniser. In English one token averages roughly four characters, or about 0.75 words. Context limits and API pricing are both counted in tokens, not words.
- [Open weights](https://digitalneuron.net/en/glossary/open-weights): Open weights means the trained parameter file is published for download, so you can run, inspect and fine-tune the model on your own hardware. The licence attached decides what you may legally do with it, and many open-weight models ship under custom licences that do not meet the open source definition.
- [Prompt injection](https://digitalneuron.net/en/glossary/prompt-injection): Prompt injection is an attack in which instructions are embedded in content a model processes — a web page, an email, a document, a code comment — and the model follows them as though they came from its operator. There is no reliable way for a model to distinguish trusted instructions from text it was asked to read.
- [Quantisation](https://digitalneuron.net/en/glossary/quantization): Quantisation reduces the numerical precision of a model's weights — for example from 16-bit floating point to 8-bit or 4-bit integers — so the model occupies less memory and runs faster. Quality loss is small at 8-bit and becomes noticeable, though often acceptable, at 4-bit.
- [Retrieval-augmented generation (RAG)](https://digitalneuron.net/en/glossary/retrieval-augmented-generation): Retrieval-augmented generation searches a document collection for passages relevant to a question, inserts them into the model's prompt, and asks the model to answer using them. The model's weights are unchanged; the knowledge arrives as context at request time, so updating a document updates the answer immediately.
- [Benchmark](https://digitalneuron.net/en/glossary/benchmark): A benchmark is a fixed set of tasks with known answers, used to score and compare models. Public benchmarks give a rough capability ranking but are heavily optimised for, vulnerable to training-data contamination, and shaped like short well-specified problems rather than real production work.
- [Multimodal model](https://digitalneuron.net/en/glossary/multimodal): A multimodal model can process more than one type of input in the same request, most commonly text together with images, and increasingly audio and video. Non-text inputs are converted into the same internal representation as tokens, which is why a single image can consume a substantial share of the context window.
- [Hallucination](https://digitalneuron.net/en/glossary/hallucination): A hallucination is output that is fluent and confident but factually wrong or unsupported — an invented citation, a non-existent function, a plausible number with no source. It happens because the model generates statistically likely continuations, and a likely-sounding claim is not necessarily a true one.
- [Alignment](https://digitalneuron.net/en/glossary/alignment): Alignment is the process of shaping a trained model's behaviour so it follows instructions, tells the truth as best it can, and declines harmful requests. It happens after pre-training, mainly through supervised fine-tuning on demonstrations and reinforcement learning from human or AI feedback.

## Topics

- [models-research](https://digitalneuron.net/en/topics/models-research)
- [agents-automation](https://digitalneuron.net/en/topics/agents-automation)
- [chips-infrastructure](https://digitalneuron.net/en/topics/chips-infrastructure)
- [business-funding](https://digitalneuron.net/en/topics/business-funding)
- [policy-regulation](https://digitalneuron.net/en/topics/policy-regulation)
- [safety-ethics](https://digitalneuron.net/en/topics/safety-ethics)
- [open-source](https://digitalneuron.net/en/topics/open-source)
- [tools-products](https://digitalneuron.net/en/topics/tools-products)

## Optional

- [Model directory](https://digitalneuron.net/en/models): neutral reference table of widely deployed AI models.
- [RSS feed](https://digitalneuron.net/en/rss.xml)
- [Sitemap](https://digitalneuron.net/sitemap.xml)