Back to List
Industry NewsEntertainmentTelevisionComedy

Rob Grant, Co-Creator of Iconic Sci-Fi Sitcom Red Dwarf, Has Passed Away

Rob Grant, widely recognized as the co-creator of the beloved science fiction comedy series 'Red Dwarf,' has died. The news was published on February 27, 2026, by Hacker News, citing Beyond The Joke as the source. Further details regarding his passing were not provided in the original announcement, which only contained the title and a 'Comments' section, indicating a forthcoming discussion or lack of immediate detailed information.

Hacker News
Share

Rob Grant, the acclaimed co-creator of the enduring science fiction sitcom 'Red Dwarf,' has passed away. The announcement of his death was made public on February 27, 2026. The news originated from Hacker News, which referenced Beyond The Joke as its primary source. The original report was notably concise, consisting solely of the headline 'Rob Grant, creator of Red Dwarf, has died' and a 'Comments' section, suggesting that further details or a more comprehensive article were either pending or not immediately available at the time of publication. 'Red Dwarf,' which Grant co-created, has garnered a significant following and critical acclaim since its inception, becoming a staple in British comedy and science fiction.

Read Original Article

Related News

Meituan Showcases AI Innovations at ACL 2026: Advancing LLM Evaluation, Reasoning, and Generative Recommendations
Industry News

Meituan Showcases AI Innovations at ACL 2026: Advancing LLM Evaluation, Reasoning, and Generative Recommendations

The Meituan technical team has achieved significant recognition at the ACL 2026 conference, with six papers accepted into this premier international forum for computational linguistics and natural language processing. These research contributions span critical frontiers in the AI landscape, including large language model (LLM) capability evaluation, complex process reasoning, and the optimization of competition-level mathematical thinking. Additionally, the papers explore advancements in reinforcement learning and the evolution of generative recommendation systems. By addressing these diverse technical directions, Meituan is actively shaping a new paradigm for generative AI, focusing on bridging the gap between theoretical research and practical industrial applications. This selection of papers highlights Meituan's commitment to enhancing model intelligence and reasoning capabilities to solve sophisticated real-world problems.

Meituan LongCat Releases General 365: A New Benchmark for AI Reasoning Evaluation
Industry News

Meituan LongCat Releases General 365: A New Benchmark for AI Reasoning Evaluation

Meituan's LongCat team has officially launched General 365, a rigorous new benchmark designed to evaluate the reasoning capabilities of large language models. In a comprehensive test of 26 mainstream models, the results revealed a significant performance gap in the industry. Even the top-performing model, Gemini 3 Pro, achieved an accuracy rate of only 62.8%. Furthermore, the vast majority of the models tested failed to reach the 60% threshold, which is considered the passing mark for this evaluation. This release sets a challenging new standard for AI development, highlighting that complex reasoning remains a major hurdle for even the most advanced artificial intelligence systems currently available.

Managing AI-Driven Development: Meituan’s Strategy for Refactoring 310,000 Lines of Code Using Agent Evaluation Logic
Industry News

Managing AI-Driven Development: Meituan’s Strategy for Refactoring 310,000 Lines of Code Using Agent Evaluation Logic

Meituan's technical team has shared a comprehensive analysis of their experience refactoring 310,000 lines of code in an environment where over 90% of code is AI-generated. The core insight is that while AI significantly accelerates code production, it can also amplify technical debt and systemic chaos without proper constraints. To mitigate this, the team adopted an 'Agent evaluation' mindset to manage AI coding. By implementing a framework consisting of technical debt sorting, rule construction, standardized operating procedures (SOPs), and a Pre-PR (Pull Request) mechanism, they successfully transformed large-scale refactoring from a high-cost, specialized effort into a continuous, daily iterative process. This approach ensures that AI remains a productive tool rather than a source of unmanaged complexity.