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William Johnston, the author of the blog post, expresses concern about the future of Bun, a software owned by Anthropic, citing the decline of Claude Code, a related project, as a potential indicator of Bun's potential downfall. Johnston worries that Bun may follow the same path of "enshittification" as Claude Code, which has been experiencing issues.
Researchers Noah Arman Kouchekinia, David Neumark, and Tim A. Bruckner have found evidence suggesting that employment may slow cognitive decline in older adults. Their study, which analyzed labor market shocks, found that individuals who remained employed experienced less cognitive decline compared to those who were unemployed or retired.
Stripe's Developer Productivity team successfully formatted a 25 million-line codebase overnight using their Rust-based autoformatter, rubyfmt. The team extended and rolled out rubyfmt across the world's largest Ruby codebase, achieving the task in a single night.
Redis array type development process spanned four months, with the author working on it part-time. The development involved writing a specification document, implementing the array type using automatic programming, and reviewing the code. The author used AI tools to assist with the design and development, allowing for a more complex and efficient data structure. The final implementation included features such as ARGREP and regular expressions, and was thoroughly tested and stress-tested.
Jonathan Swift's epitaph at St. Patrick's Cathedral in Dublin has been the subject of a seven-year decoding effort by a college professor, who believes he has finally cracked the code. The epitaph, which reads "R.S. 1726," has been interpreted in various ways over the years, but the professor's solution suggests that it is a joke.
A 1966 Ford Mustang has been converted into a Tesla with working 'Full Self-Driving' capabilities. The conversion, which took two years and cost around $40,000, was carried out by a Tesla auto parts shop owner in Sacramento. The vehicle now features a Tesla powertrain and advanced autonomous driving technology.
Monero's proof of work, known as RandomX, differs from other systems by turning mining into running random CPU-friendly programs over a large memory dataset. This approach makes it less suitable for application-specific integrated circuit (ASIC) mining, which is commonly used in other cryptocurrencies. As a result, Monero's proof of work is more CPU-intensive and less energy-efficient than other systems.
In Brief