📰 English IT Daily · 2026-06-16

A recent security report described how a fake job offer on LinkedIn was used to deliver malware. The case shows how attackers are mixing social engineering with professional networking platforms. Instead of sending a simple spam message, they used a message that looked like a real recruiting approach. This made the contact seem more trustworthy and increased the chance that the target would open the file or follow the instructions.

According to the report, the attacker first contacted the target through LinkedIn with a job opportunity. After building interest, the attacker shared a file that appeared to be related to the role. In security, this kind of trick is called social engineering, which means manipulating people into taking unsafe actions. The file was not a normal job document. It was a delivery method for malware, malicious software designed to enter a system without permission.

The malware acted like a backdoor. A backdoor is a hidden way for an attacker to access a computer or network after the first infection. Once installed, it can allow remote control, data theft, or further downloads. Reports like this are important because they show that modern attacks often begin with ordinary business communication. A message about hiring, partnership, or payment can be enough to start a compromise if the target trusts it too quickly.

The incident is a useful reminder for professionals and companies. Job offers, resumes, and interview documents should be treated carefully, especially when they include unexpected files or unusual steps. Security teams often advise users to verify the sender, check file types, and avoid opening attachments from unconfirmed contacts. The wider lesson is that cybersecurity is not only about firewalls and tools. It also depends on human judgment, clear process, and healthy doubt during everyday online communication.

TimescaleDB can reduce the size of time-series data by a very large amount, sometimes up to 98% in typical cases. This matters because systems that collect sensor readings, logs, or metrics often keep huge amounts of historical data. The database uses an engine called hypercore to do this job. Hypercore is a hybrid row-columnar engine, which means it uses two storage styles for different kinds of work.

New data first goes into normal PostgreSQL row-based chunks. This design supports fast inserts and updates, which are important for active applications. Later, older chunks can be converted into a compressed columnar format. In columnar storage, values from the same column are stored together instead of saving full rows one after another. This helps the system compress repeated patterns more efficiently and lets analytical queries read fewer bytes.

The approach is different from PostgreSQL TOAST, a built-in feature that handles very large individual values such as long text, jsonb, or bytea. TOAST works on one value at a time, while TimescaleDB compression looks for patterns across many rows in time-series data. These two methods are complementary, not competitors. The source explains that TimescaleDB may even use TOAST internally as a fallback for some data types.

Hypercore uses specialized algorithms such as delta encoding, delta-of-delta, Gorilla XOR, and run-length encoding. In simple terms, these methods store changes, repeated values, or predictable sequences instead of storing every value in full. This is especially useful for timestamps, sensor floats, and other ordered data that often changes gradually. Because less data must be read, compression can also improve analytical performance, not only storage costs. For teams managing long retention periods, that can make time-series systems more practical and more affordable.

Iroh has announced version 1.0, its first stable release. The project promotes a simple idea: devices should connect by cryptographic keys instead of IP addresses. An IP address can change when a device moves between networks, and it may be hidden behind a firewall. A key, however, stays under the device owner’s control. According to Iroh, this makes devices easier to reach securely, even when network conditions change.

The company says this model could improve how the internet works. In Iroh, the same key can be used to secure a connection and identify the device. This means the key acts both as an address and as part of the security system. The blog describes this as turning the internet into a more secure version of localhost, the name developers use for their own machine. The goal is to make communication more stable, private, and direct.

To support this design, Iroh has built several networking features. It uses open standards where possible and includes an implementation of QUIC multipath, which allows one connection to use several network routes and switch between them if conditions change. It also includes NAT traversal, a method for creating direct connections across private networks while keeping connection details encrypted. Iroh can also work in local-first setups, meaning devices can find each other on a local network even without internet access.

The project says developers are already using Iroh for tasks such as video streaming, file transfer, secure chat, games, and communication between AI agents. It also reports broad language support, including Python, Node.js, Swift, and Kotlin, in addition to Rust. Version 1.0 is important because it promises stable language APIs and a stable wire protocol, which is the format devices use to communicate. For software teams, that kind of stability can reduce risk when adopting a new networking technology.

A Dutch project called GPT-NL is developing a sovereign language model for the Netherlands. A language model is an AI system trained on large amounts of text so it can understand and generate language. The project says this kind of technology is becoming important in workplaces, education, and public services. However, it also argues that control over the technology matters, especially when AI tools influence daily life and public decision-making.

GPT-NL is being built by TNO together with SURF and the Netherlands Forensic Institute. Their goal is to create an independent Dutch model and a wider ecosystem around it. According to the project, this can support digital autonomy in the Netherlands and Europe. In simple terms, digital autonomy means having more control over key technology instead of depending too much on providers from outside the region. The project presents this as a way to align AI development with local laws, values, and social goals.

The team says the model is based on governance, transparency, and public values. Governance means the rules and decision-making processes behind the system. Transparency means clearly documenting how data is collected, how training decisions are made, and how risks such as bias are handled. GPT-NL also plans to publish its source code as open source and share information about its dataset. At the same time, model weights will be available under a controlled licence, so use can be monitored without ignoring security or regulation.

Another major aim is to build a trustworthy model from scratch. The project says this helps avoid unclear data provenance, copyright problems, and hidden personal data from earlier models. It applies strict criteria to data collection, including anonymising personal data, excluding confidential information, reducing duplication, and filtering harmful content. GPT-NL also promotes a reciprocal approach by involving data providers and rights holders through a Content Board and returning part of the revenues to creators. The project says it is also focusing on energy efficiency and responsible use of computing resources.

vocabulary

TinyWind is a pixel-style pirate sailing game that focuses on one main idea: wind should feel real. Instead of moving like a simple arcade boat, the ship reacts to changing wind and water conditions. This design gives players a slower but more realistic experience. The project has also shared a striking result: players have already sailed more than 380,000 kilometers in the game, showing strong interest in this unusual approach.

The key feature is wind physics. In simple terms, physics is the set of rules that describes how things move in the world. In TinyWind, wind direction and force affect the ship’s speed and movement. That means players must pay attention to sailing angle, not only to destination. A good route depends on how the wind is behaving, so travel becomes a small strategy problem instead of a straight line.

This kind of simulation can make a game feel deeper without using complex graphics. Pixel art keeps the visual style simple, but the underlying system can still be detailed. For developers, this shows how gameplay can come from strong mechanics rather than expensive visuals. It also suggests that a clear core system, when combined with a distinct style, can help an indie project stand out in a crowded market.

TinyWind is also an example of how science concepts can be turned into interactive entertainment. By translating natural forces into game rules, developers create a product that is both fun and educational. For players, the experience may improve problem-solving and observation skills. For the wider tech industry, it highlights the value of simulation, user feedback, and careful system design in building engaging digital experiences.

US battery manufacturing output continues to reach new highs, according to data from the Federal Reserve Economic Data system. The series tracks industrial production for battery manufacturing, which means the real output of factories over time. In simple terms, it shows how much battery-related production is happening in the country, adjusted so that changes in prices do not hide the trend.

This matters because batteries are now a basic part of modern technology. They are used in electric vehicles, laptops, smartphones, home energy storage, and backup power systems. Stronger domestic production can support supply chains, reduce dependence on imports, and help companies respond faster when demand changes. For manufacturers, higher output may also suggest that more capacity has been added or that factories are operating more efficiently.

The record-setting trend is also important for the wider tech industry. Battery production depends on equipment, materials, logistics, software, and quality control systems. As factories expand, they often need more automation, better monitoring, and stronger data analysis to keep output high and defects low. That creates demand not only for hardware, but also for industrial IT systems that connect machines, track performance, and improve operations.

However, rising output does not solve every challenge. Battery manufacturing still faces pressure from material costs, energy use, safety standards, and global competition. Demand can also change with economic conditions and government policy. Even so, the long-term direction shown in the data suggests that batteries are becoming a more important strategic industry in the US, with effects that may spread across transportation, energy, and digital infrastructure.

Canadian researchers say a U.S. decision to remove some ocean sensors is a serious problem as El Niño conditions approach. These sensors collect data from the sea and atmosphere, such as temperature, pressure, and wind. Scientists use this information to understand ocean changes and improve forecasts. When sensors disappear, the flow of real-time data becomes weaker.

El Niño is a climate pattern in the Pacific Ocean that can affect weather far beyond the region. It often changes rainfall, temperatures, and storm activity in many countries. To study it, researchers depend on a monitoring network, which is a group of connected tools that send observations regularly. Without enough sensors, it becomes harder to see early signs and track how conditions are developing.

Canadian research groups were shocked because the data is important not only for science but also for public planning. Fisheries, shipping, coastal communities, and weather services can all benefit from accurate ocean information. A gap in monitoring may reduce forecast quality and make it more difficult to prepare for unusual conditions. Experts say international cooperation is especially important because oceans do not follow national borders.

The issue also shows how fragile scientific infrastructure can be. Even if satellites and computer models are available, they still need reliable observations from the ocean to stay accurate. In simple terms, sensors provide the raw input, and models turn that input into predictions. Researchers hope governments and agencies can protect key systems so that climate monitoring remains stable during important periods like El Niño.

A story from Microsoft engineer Raymond Chen describes a surprising moment in the history of software emulation. In the past, Windows sometimes ran on processors that were not x86, so it needed an emulator for x86-32 programs. In one case, the emulator used binary translation. This means it changed x86 instructions into native code for the real processor, instead of reading and executing each instruction one by one. This method was faster and worked a bit like a JIT compiler.

During testing, the emulator team found one program that needed to reserve about 64KB of stack memory and fill it with data. Normally, a compiler would create a small loop to write to that memory step by step. A loop is efficient because the same short set of instructions can run many times. But in this case, the compiler did something very different. It unrolled the loop into 65,536 separate write instructions, creating a very large block of machine code just to initialize the buffer.

The result was extreme. The program used around 256KB of code to initialize 64KB of data. Even though the code was technically valid, the emulator team thought it was a terrible use of space and processing time. According to the story, they were so offended by this function that they added special logic to the translator. When it detected this exact pattern, it replaced the huge sequence with an equivalent tight loop during emulation.

The story is funny, but it also shows a serious engineering lesson. Tools like compilers, emulators, and translators do not only run code; sometimes they must protect performance from poor code generation. It also highlights the value of profiling and careful observation. A system can become faster not only by improving hardware, but also by recognizing wasteful patterns and fixing them automatically. In some cases, the emulator may even help software run better than its original version.

A recent blog post argues that Kubernetes is now common in job interviews across many companies, including small startups. The writer says this was not true five years ago, when more teams used virtual machines, simple service managers, or serverless tools. What surprised the writer was that many of these companies were not running huge systems or many microservices. Even so, they had still chosen Kubernetes, a platform that helps teams deploy and manage applications in containers.

According to the conversations in interviews, the main reason was often not raw technical power. One important benefit was uniformity. In simple terms, every service can be deployed in the same way. This reduces special cases, such as one old service running on a single server with an unclear script while newer services use a different setup. A shared deployment pattern can make daily operations easier, especially when teams grow or when different engineers must support the same systems.

Another reason was standardized knowledge. Because Kubernetes has become a common industry skill, new engineers can understand a system faster. Much of the operational knowledge is written in configuration files, such as YAML or Helm charts, instead of living only in one person’s memory. The blog post also highlights traceability. With GitOps, engineers change configuration in Git, go through review, and let automated tools handle deployment. This creates a clear record of who changed what and when, which can also support compliance work.

The writer does not say every company needs Kubernetes. In fact, the post suggests that many organizations may be choosing it mainly for organizational benefits, not because they need advanced features like autoscaling rules or complex scheduling controls. For some teams, that trade-off may be reasonable. However, the writer still believes many companies should begin with simpler systems, because Kubernetes can be difficult to debug when problems happen. The larger lesson is that technology choices are often shaped by hiring, teamwork, and process, not only by scale.

Qwen has introduced the Qwen-Robot Suite, a foundation model suite designed for physical world intelligence. In simple terms, a foundation model is a large AI model that can be adapted to many tasks. While many AI systems mainly work with text, images, or code, this suite aims to help robots understand and act in real environments. The goal is to connect digital intelligence with physical action.

The idea of physical world intelligence is important because robots must do more than recognize objects. They need to understand space, movement, and the results of their actions. For example, a robot may need to identify a cup, judge its position, and move safely to pick it up. A model suite for robotics can support this process by combining perception, reasoning, and control into one broader system.

This type of development reflects a wider trend in AI. Companies are trying to build systems that can work across different forms of data and different tasks. In robotics, that often means using multimodal inputs such as vision, language, and sensor signals. Multimodal means the AI can process several kinds of information at the same time. This can make robots more flexible and more useful in changing environments.

For engineers and businesses, the Qwen-Robot Suite shows how AI may move from software-only applications into machines that interact with the world. The technical challenge is not only model performance but also safety, reliability, and deployment in real settings. If these systems improve, they could support automation in industry, research, and daily life. At the same time, developers will need practical testing methods and clear limits for where the technology should be used.