Closing a notebook in OneNote does not delete it. Cloud-based notebooks are stored in services such as OneDrive or SharePoint, so permanent deletion must happen at the storage location.
Close it in OneNote. This removes the notebook from the app but leaves its data untouched.
Open OneDrive or SharePoint and locate the notebook in its actual storage location.
Delete the complete notebook rather than individual sections.
Check the recycle bin. A normally deleted notebook remains recoverable until it is removed from the recycle bin or the retention period expires.
Empty the recycle bin if you want to remove it immediately from your accessible storage.
Microsoft 365 business and SharePoint environments may have additional recycle-bin stages or organizational retention policies. These can preserve deleted information even after a user empties the recycle bin.
The key distinction is simple: closing removes a notebook from OneNote; deleting removes it from cloud storage. For permanent removal, always check the underlying storage and its recycle bin.
Europe’s AI ambitions just received a major financial boost. French AI company Mistral has raised €3 billion, giving it a valuation of about €21 billion ($24 billion) and marking the largest equity funding round by a privately owned European technology company.
Mistral develops large AI models and competes in a market dominated by much larger American companies. The new capital is expected to support further model development, computing infrastructure and international expansion.
The investment also has a broader European dimension. Governments and businesses increasingly want greater choice over where their AI technology and data come from. A strong European AI provider could offer another option alongside major US and Chinese platforms.
The enormous investment required to develop advanced AI is concentrating the industry around companies capable of accessing substantial computing power and capital.
Mistral’s latest funding shows that Europe is trying to remain part of that race—not simply as a customer for AI developed elsewhere, but as a producer of its own technology.
The service brings together the key capabilities needed to build and run AI agents without having to manage every technical detail yourself:
With GPT-6 Astra, artificial intelligence is taking another step toward greater autonomy. OpenAI’s new model is designed not only to provide better answers, but also to carry out complex tasks on a computer with less human guidance.
From chatbot to digital worker
Instead of specifying every individual step, users can increasingly define the desired outcome. Astra can then plan and execute multiple steps to reach that goal.
Tasks can include:
More capabilities also mean more risks
Cybersecurity is particularly important. Astra is the first OpenAI model to reach the company’s “Critical” level for cybersecurity capabilities. With suitable tools and permissions, it can potentially discover previously unknown security vulnerabilities. OpenAI has therefore introduced additional safeguards and monitoring systems.
Why it matters
The development highlights a broader shift in AI: from conversational assistants toward systems that can take action. As these systems become more autonomous, clear permissions, strong security controls, and human review become increasingly important.
The AI boom has an unexpected side effect: smartphones and computers are getting more expensive. The reason lies in components found in almost every modern device — memory chips.
AI data centers require enormous amounts of high-performance memory. Manufacturers are therefore dedicating more production capacity to lucrative memory products for servers and AI systems. This leaves less capacity for conventional DRAM and NAND memory used in smartphones, laptops, and SSDs.
The effects are becoming noticeable:
Gartner expects average PC prices to rise by 17% and smartphone prices by 13% in 2026 compared with 2025.
TrendForce forecast further increases in DRAM and NAND contract prices for the third quarter of 2026.
Budget devices are particularly affected because memory represents a larger share of their manufacturing costs.
Manufacturers may respond with higher prices, smaller product ranges, or more conservative memory configurations.
For consumers, this could also change buying habits. Devices may be kept for longer, while used and refurbished smartphones and computers become more attractive.
AI is therefore changing more than software and the workplace. The global infrastructure required to power it is increasingly influencing the price of everyday electronics.
The race to build AI that is both fast and highly capable may be getting more interesting. Google is reportedly preparing Gemini 3.8 Flash, an AI model designed to significantly improve coding performance.
Google’s Flash models are intended to provide a faster, more efficient alternative to its largest AI models. According to reports, Gemini 3.8 Flash has been tested internally with a strong focus on software development.
In Google’s internal coding environment, engineers reportedly preferred the upcoming model over Anthropic’s Opus in some comparisons. However, these are internal evaluations, not independent benchmarks, so real-world performance remains to be verified.
The development points to an important AI trend: smaller, faster models are becoming increasingly capable.
For users, this could eventually mean sophisticated coding assistants and AI agents that respond quickly while requiring fewer computing resources. But until Google officially releases the model and publishes specifications or benchmarks, its exact capabilities remain uncertain.
PVT modules make double use of roof space: they generate electricity while collecting heat for a heat pump. This can be particularly useful where a conventional outdoor heat-pump unit is difficult to install.
A PVT (photovoltaic-thermal) module combines two functions:
Because ambient air is the main heat source, thermal energy can also be collected at night and during winter.
Unlike a typical air-source heat pump, the roof-based collector operates without a fan. Natural airflow provides heat exchange, allowing silent operation without mechanically moving parts on the roof.
PVT can therefore be particularly attractive for terraced houses, densely built areas, or properties with limited space for an outdoor unit.
The required number of modules depends primarily on the building's heating load and system design.
Inflation is falling – yet at the supermarket, in restaurants, or when paying for everyday services, things may not seem much cheaper. How can both be true? The answer lies in what inflation actually measures.
The inflation rate measures how quickly prices are rising on average. If inflation falls from 5% to 2%, goods and services do not automatically become cheaper. They are simply getting more expensive at a slower pace.
A simple example:
Inflation has dropped considerably, but the price is still rising.
For the overall price level to decline, inflation would need to become negative. This is called deflation.
In simple terms:
Individual products can still become cheaper while overall inflation remains positive. Competition, lower production costs, cheaper raw materials, or technological improvements can all push particular prices down.
For households, inflation is only part of the picture. Income growth also matters. If wages rise faster than living costs, purchasing power improves. If they lag behind, everyday life can continue to feel expensive even when inflation has fallen.
In short: As long as inflation remains positive, the overall price level continues to rise. For prices overall to fall, inflation would have to turn negative.
Building more capable AI is increasingly about more than better algorithms. It also requires enormous amounts of computing power — and Anthropic has reportedly secured another major supply.
Anthropic has signed a $35 billion cloud computing deal with AI infrastructure provider Lambda, according to reports published September 1. The agreement involves large-scale computing infrastructure using Nvidia chips.
The deal adds to Anthropic’s already extensive infrastructure commitments. The company has previously announced major capacity agreements involving Amazon, Google, Microsoft and Nvidia as demand for its Claude AI models grows.
Modern AI models require huge clusters of specialized processors both for training and for answering users’ requests. Securing enough chips, electricity and data-center capacity has therefore become a strategic priority for leading AI companies.
For everyday users, these investments may eventually translate into greater AI capacity, faster services and support for increasingly demanding applications. At the same time, the scale of the spending shows how infrastructure-intensive the global AI race has become.
Learning data science becomes much easier when you know where to find good datasets, clear explanations, and real-world examples. These resources cover different stages of that journey.
A good learning strategy is to combine them: learn a concept visually, find a suitable dataset, then build a small project around it. This turns abstract theory into practical experience.