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AI

Which artificial intelligence topics are rising and falling this week, with a glossary and searchable archive of La Rebelión AI articles.

Artificial intelligence: news and trends

AI.larebelion · live

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It answers only from what we have published, cites every post and shows you what it read to reply.

It can be wrong, says “I don’t know” when it finds nothing to go on, and does not store what you type.

How a post gets written

The latest post, step by step

The real path of the machine’s most recent post, with what it records. It does not keep track of how long each step takes, so we do not make it up.

Source:Exam Code→ published asSolved Exercise: Validate and Extract Data with Regex in Python

  1. Step 1Curation1 of 829News read in the 24 h before the draft
  2. Step 2FormatexerciseThe format the classifier picked
  3. Draft · 3 Nov 12:10Writing2,058 wordsWritten by claude-sonnet-5-5
  4. Step 4Images1 · 1200×630Real size measured, never stretched
  5. Published · 3 Nov 12:41ApprovalAutomaticNobody replied within 30 min, so it published itself
The map of what we cover

Topic constellation

The 14 labels with the most posts out of the 745 published: each dot’s size is its number of posts and the lines join topics that share posts. Select a topic to see the latest.

447 posts

AI

See all posts about AI
Weekly radar

AI topics rising this week

Stories mentioning each topic among those the pipeline reads: 4,280 from 2 Nov – 9 Nov against 4,404 from 26 Oct – 2 Nov. The change compares each topic’s weight within its own week, not the total, so reading more news does not lift every topic.

Topics in the news: rank and mentions last week and this weekAI: from 1846 to 1795 stories (−7%); Security: from 329 to 178 stories (−48%); Google: from 103 to 189 stories (+76%); Microsoft: from 121 to 57 stories (−55%); Chatgpt: from 95 to 79 stories (−20%); Economy: from 30 to 55 stories (+76%); Programación: from 47 to 41 stories (−16%); Management: from 36 to 27 stories (−28%)Last weekThis weekAI: 1846 → 1795 stories (−7% in weight)1,846AI · 1,795 −7%Security: 329 → 178 stories (−48% in weight)329Security · 178 −48%Google: 103 → 189 stories (+76% in weight)103Google · 189 +76%Microsoft: 121 → 57 stories (−55% in weight)121Microsoft · 57 −55%Chatgpt: 95 → 79 stories (−20% in weight)95Chatgpt · 79 −20%Economy: 30 → 55 stories (+76% in weight)30Economy · 55 +76%Programación: 47 → 41 stories (−16% in weight)47Programación · 41 −16%Management: 36 → 27 stories (−28% in weight)36Management · 27 −28%

Rising

  • Management27 → 41 · +56%
  • Linux16 → 22 · +41%
  • IA agéntica22 → 29 · +36%

Falling

  • Microsoft78 → 33 · −56%
  • Security190 → 170 · −8%
  • Google159 → 145 · −6%
Transparency

The machine, in numbers

How much it writes, how much we review and what gets discarded in the last 30 days.

Posts in 30 days231 different formats
Approved by a person13%the rest automatic after 30 min (20)
Discarded0drafts that never got published
Verified links100%108 of 108 were live when published

Formats used: exercise (23).

Approvals, discards, formats and links are recorded since 1 Oct 2026.

Living glossary

AI glossary

They are extracted from the blog’s AI posts and updated with every new one. Select a term to see where it appears.

3 postsAI agentsAI agents are programs that don't just answer questions but make decisions and carry out tasks on their own, such as searching for information, using apps, or completing workflows. They matter because they promise to automate real work, but if they fail or act on wrong information, the consequences can hit a business. That is why companies remain cautious.
3 postsAI alignment and safetyAI alignment and safety aim to make AI systems behave according to human values and intentions and avoid causing harm, whether through mistakes, misuse or unexpected behavior. It is a key topic because trust in these tools depends on it, and so do the laws and rules that governments are beginning to pass.
2 postsArtificial general intelligence (AGI)Artificial general intelligence, or AGI, is a hypothetical AI able to learn and solve almost any intellectual task at a human level, rather than only specific functions. There is no single agreed definition or test for it, so claims that it has arrived are controversial and shape investment, regulation and public expectations.
3 postsBenchmarksBenchmarks are standardized tests that measure how well an AI handles tasks such as reasoning, coding or answering questions, letting people compare models against one another. They matter because companies use them to showcase progress, but a strong score on a controlled exam does not always reflect how useful a model is in everyday use.
3 postsContext windowThe context window is the maximum amount of text a language model can consider at once, including your question, the conversation history and any documents you provide. It works like the model's short-term working memory. The larger it is, the more long material it can analyze without forgetting the beginning.
3 postsDeepfakesDeepfakes are audiovisual content manipulated using artificial intelligence to realistically impersonate real people. Their importance lies in the serious security, fraud, and misinformation risks they generate in today's society.
2 postsEmbeddingsEmbeddings are numerical representations of the meaning of a text, image or other content, so that similar things end up close together on a mathematical map. This lets an AI find related documents or products even when they share no exact words. They underpin smart search engines and recommendation systems.
2 postsEU AI ActThe EU AI Act is the world's first comprehensive regulation on artificial intelligence. It sorts systems by risk level, bans certain uses, and demands transparency and safeguards for the most sensitive ones, with fines for those who break the rules. It matters because it affects any company offering AI in Europe, and so the products people use.
3 postsFine-tuningFine-tuning means taking an already trained AI model and training it further on more specific data so it masters a particular task or field, such as legal language or customer support. It is far cheaper than building a model from scratch. It lets companies adapt AI to their own needs.
3 postsGenerative AIGenerative AI is the branch of artificial intelligence that creates new content, such as text, images, audio, or code, based on what it has learned from large amounts of data. It matters because it is changing how we work and create, but it also opens new risks, from confidently stated errors to cyber threats that can act autonomously.
3 postsGPUs and AI chipsGPUs and other AI chips are processors built to perform millions of calculations at once, which is essential for training and running artificial intelligence models. They matter because they determine how much AI costs, how fast it runs, and who can afford it. They have also become a security target, since hardware flaws can compromise the systems that rely on them.
3 postsHallucinationA hallucination happens when an AI model produces false, invented or nonsensical information while presenting it with complete confidence and apparent coherence. It can be a made-up fact, a fake quote or a misread image. It matters because convincing-sounding answers are easy to trust, so it pays to verify what an AI claims before relying on it.
3 postsInferenceInference is the moment when an already trained AI model is put to work generating an answer, such as when you type a question into a chatbot. It happens every time you use AI, so its speed and cost determine how fast a service responds, how much it costs to run and how much energy it consumes.
3 postsLarge language model (LLM)A large language model (LLM) is an AI system trained on vast amounts of text to understand and generate human language. It is the engine behind today's chatbots and assistants. It matters because it writes, summarizes, and answers fluently, but it can also make mistakes or be manipulated, so it needs safeguards and should not be trusted blindly.
3 postsMCP (Model Context Protocol)The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external tools, databases and services in a uniform way, without custom-built integrations. Think of it as a universal connector. It matters because it expands what AI can do, but it also opens new security doors that deserve careful attention.
1 postModel distillationDistillation means training a smaller or newer model to imitate the answers of a more powerful one, using it as a teacher. It makes it possible to build cheaper, faster systems, but it also sparks disputes when done with someone else's models without permission, since it leverages their work and may breach terms of use.
3 postsMultimodal AIMultimodal AI can understand and combine different kinds of information, such as text, images, audio or video, instead of working only with words. It matters because it comes closer to how people perceive the world and enables practical uses like describing a photo, reading a chart or holding a spoken conversation.
3 postsOn-device AIOn-device AI runs directly on your phone, laptop or other gadget instead of sending your data to cloud servers. It often relies on specialized chips called NPUs. It matters because it can be faster and more private, but it also creates new security challenges for companies.
3 postsOpen-source AIOpen-source AI refers to artificial intelligence models whose design and weights are published so anyone can download, use and modify them. Unlike closed systems from a few companies, it lets more people innovate and compete. For the general public, it means more choices, lower costs and greater transparency about how these tools work.
3 postsOpen-weight modelsAn open-weight model is one whose internal parameters, meaning what it learned during training, are published so anyone can download, run and adapt it. This allows use on your own hardware with more privacy, although it does not always include the training data or full code, and it also raises security concerns.
3 postsPromptA prompt is the instruction or question you give an AI to get a response, and the quality of the result depends largely on how you word it. It also matters for security: in prompt injection attacks, someone hides malicious commands inside text to trick the AI into ignoring its rules or leaking data.
2 postsQuantizationQuantization is a technique that lowers the numerical precision a model uses to store its parameters, for example moving from very detailed numbers to more compact ones. The model takes up less memory and runs faster, with a small loss in quality. Thanks to it, powerful models can run on an ordinary laptop or home PC.
3 postsRAG (retrieval-augmented generation)RAG is a technique that connects language models to external databases to search for information before responding. This matters because it enables more accurate and up-to-date answers without needing to retrain the entire model from scratch.
3 postsReasoning modelsReasoning models are advanced artificial intelligence systems designed to solve complex problems through step-by-step analysis before answering. They are crucial because they allow tackling very difficult scientific and logical questions with greater precision.
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House rules

Always sourcedEvery post links the original story and says it was written by AI.larebelion.
A person decidesEverything goes through approval on Telegram or in the dashboard; if nobody replies within 30 min, it is published.
No making things upIf it is not in what we have published, LaRebelionBOT answers “I don’t know”.
Updated 15:48 (Europe/Madrid)