📰 English IT Daily · 2026-06-15

The curl project says it will not accept or process vulnerability reports during July 2026. Curl is a widely used open-source tool and library for transferring data over networks. Its maintainers announced this pause as the “summer of bliss.” The project’s submission form on HackerOne, a platform for reporting security problems, will be paused from July 1, 2026, at 00:00 CEST. Submissions will resume on August 3, 2026, at 09:00 CEST.

During this period, the project also will not handle reports sent to its security email address. In other words, people who find a possible security issue in curl during July will need to wait until August to report it through the normal process. The maintainers said they have been under heavy pressure for several months and now need real rest. They hope this break will give them time to recover, spend time away from work, and possibly return with more energy for bug fixes and new code.

The announcement also notes a practical side effect. Because reports may build up during the break, curl has pushed the release date of version 8.22.0 back by two weeks. It is now scheduled for September 2, 2026. At the same time, the project’s GitHub issue tracker and pull-request system will stay open as usual. This means general development activity can continue, even though vulnerability handling will be paused for a month.

The maintainer also encouraged other open-source projects to consider similar steps if they need them. The message is that people should take care of themselves, even in security work, where pressure can be constant. The post admits that attackers probably will not take a break. However, the team decided that rest is still necessary. One exception remains: users with paid support contracts will continue to receive normal service during this period.

A recent discussion on Hacker News asked a practical question: have any developers fully replaced Claude or GPT with a local model for daily coding work? The post was not about small side experiments. Instead, it focused on real everyday use as a main coding tool. The writer also asked people to share their setup and performance, such as tokens per second, which is a common way to measure how fast a model generates text.

This question matters because local models offer clear benefits. A local model runs on a user’s own computer or private hardware instead of a remote service. That can improve privacy, reduce dependence on an internet connection, and sometimes lower long-term cost. For software engineers, especially those working with sensitive code, keeping data inside a company environment can be very attractive. However, local use also depends heavily on hardware limits and model efficiency.

In coding tasks, performance is not only about speed. Developers also care about code quality, reliability, and how well a model follows instructions over long sessions. A model may generate text quickly but still perform poorly if its answers are inaccurate or inconsistent. That is why setup details matter. Factors such as the model size, available memory, and software tools can strongly affect the experience. In practice, a useful local assistant must be both fast enough and good enough.

The Hacker News post reflects a broader shift in AI use. Many teams are now comparing cloud-based tools with local alternatives, not just on price but also on control and trust. For daily coding, the main question is whether local models can deliver stable results in real workflows, not only in demos. As local AI improves, more developers may test this option seriously, but the decision will likely depend on each team’s security needs, budget, and technical goals.

TinyWind is a pixel pirate sailing game that focuses on wind. According to its website, players have already sailed more than 380,000 kilometers in the game. The project stands out because it tries to model real wind physics, not just simple movement across water. In other words, the boat does not move only because a player presses a key. Its speed and direction are also affected by changing wind conditions.

This idea gives the game a more realistic feel. In many games, sailing is reduced to basic controls, so movement is predictable and easy to learn. TinyWind appears to take a different approach by making the wind an active system. A system is a group of connected parts that work together. Here, that system includes the boat, the sail, the wind, and the player's decisions. As a result, players may need to think more carefully about timing, angle, and route.

Although TinyWind has a simple pixel style, the design choice does not mean the technology is simple. Small visual details can hide complex simulation work in the background. A simulation is a computer-based model of how something behaves in real life. For developers, this kind of game shows how physics can improve gameplay without requiring highly detailed graphics. It also shows that a clear concept can help a small project attract attention online.

The project is interesting for people in AI and software because it highlights the value of realistic behavior in interactive products. Even without advanced visuals, users can feel that a digital world is believable when its rules are consistent. That lesson matters beyond games. In many digital products, from training tools to robotics interfaces, a strong model of real-world behavior can improve user experience. TinyWind is a reminder that good engineering often depends on making invisible systems work well.

CrankGPT is a playful project that presents a very unusual idea: a local, human-powered AI system. Instead of sending requests to large cloud servers, it suggests that users can generate the energy themselves by turning a crank or using pedals. The website describes several levels, from basic question answering and simple conversation to more advanced tasks such as coding support, video generation, and larger agent-based workflows.

Behind the joke, CrankGPT points to serious issues in today’s AI industry. One issue is privacy. Many AI tools depend on remote data centers, so users may worry about sharing personal questions, creative ideas, or business information with large companies. CrankGPT promotes an entirely on-device approach, meaning the software runs locally on the user’s machine. In simple terms, local processing means data does not need to leave the device to be handled.

The project also comments on energy use and climate impact. As AI systems grow larger, they require more electricity for training and daily use. CrankGPT turns this concern into satire by saying people should burn calories instead of fossil fuels to produce their own tokens. Tokens are small units of text that AI models read and generate. The message is humorous, but it reflects a real debate about whether the rapid growth of AI is increasing pressure on power systems and climate goals.

CrankGPT is not presented like a normal product launch. Its language is clearly exaggerated, and ideas such as gym partnerships for enterprise AI are meant to be funny. Still, the site raises real questions for technology users and companies. How much AI work should happen in the cloud, and how much should stay local? How should organizations balance convenience, privacy, cost, and energy use? Even as satire, CrankGPT shows that public discussion about AI infrastructure is becoming more critical and more creative.

A recent security report described how a fake job offer sent through LinkedIn was used to deliver a backdoor. A backdoor is a hidden way for attackers to enter a computer or network later. The case is a reminder that social engineering, which means tricking people into trusting a message, is still one of the most effective attack methods. Even professional platforms can be used to make a message look normal and safe.

In this kind of attack, the message usually pretends to be part of a hiring process. It may include a file, a link, or instructions to download something. The target may believe the content is related to a job opportunity, so the request feels reasonable. Once the victim opens the file or runs the program, malicious software can be installed. After that, the attacker may try to gain remote access, collect information, or prepare for later actions inside the system.

Security researchers often call this a multi-stage attack. The first step is the lure, such as a convincing offer or recruiter message. The next step is delivery of a payload, which is the harmful code used in the attack. A backdoor can then contact a command-and-control server, a system that sends instructions to infected devices. This gives attackers a way to control the machine quietly and continue the intrusion without being noticed immediately.

The lesson for workers and companies is clear. People should be careful with unexpected contact, even on trusted platforms. It is important to verify the sender, avoid opening unknown attachments, and check whether a request fits a normal hiring process. Organizations also need good monitoring, endpoint protection, and employee training. For security teams, this case shows that human trust can be the weakest point, so technical defenses must be combined with strong security awareness.

In 2014, programmer Gary Bernhardt gave a talk called The Birth and Death of JavaScript at PyCon. The talk used science fiction and comedy to describe the history of JavaScript from 1995 to 2035. Although the title sounds dramatic, the message was not simply for or against the language. Instead, it offered a creative way to think about how JavaScript grew, how programmers reacted to it, and how software development kept changing around it.

JavaScript began in the mid-1990s as a language for web pages. Over time, it became one of the most important tools in programming. Bernhardt’s talk discussed its flaws frankly. A flaw is a weakness or problem in a system. JavaScript has often been criticized for confusing behavior and design choices. Even so, its wide use on the web helped it spread into many areas of the industry, shaping how developers build applications and learn new tools.

The talk was not a normal technical lecture. It mixed jokes with a serious message about the industry. By using an imagined future, Bernhardt showed that programming languages do not succeed only because they are perfect. They also succeed because of timing, adoption, and the needs of real users and companies. In simple terms, adoption means that many people and organizations start using a technology. Once that happens, the technology can influence the direction of software development for years.

One reason the talk remains interesting is that it treats JavaScript as part of a bigger story about programming in general. It suggests that engineers should look beyond personal likes and dislikes. A language may have limitations, but it can still create huge positive impact if it becomes a common platform for building products. For software professionals today, the lesson is clear: understanding a technology’s ecosystem, community, and practical value can be just as important as judging its technical quality.