Core Viewpoint - The TIOBE programming language ranking for June 2025 indicates a decline in SQL's popularity, dropping to 12th place, marking its historical low, while Python continues to dominate the rankings with a significant lead over other languages [2][20]. SQL's Historical Context - SQL reached its peak ranking at 6th place in October 2003 but was removed from the TIOBE index from 2004 to 2018 due to debates about its classification as a programming language [5]. - SQL was reintroduced to the rankings in 2018, benefiting from its stronghold in the database domain, and returned to the top ten [5]. SQL's Applications - SQL is integral to various sectors, including banking, e-commerce, healthcare, and education, where it manages vast amounts of structured data [7]. SQL Database Overview - A list of common SQL databases includes MySQL, PostgreSQL, SQLite, MariaDB, Oracle, SQL Server, and IBM Db2, with most being open-source [9]. Rise of NoSQL - The emergence of NoSQL databases poses a significant threat to SQL, as NoSQL is designed to handle unstructured data and offers greater flexibility, making it suitable for rapidly changing requirements [10][12]. - NoSQL databases, such as MongoDB and Redis, are increasingly favored for their ability to manage big data and high concurrency scenarios [13][16]. Programming Language Rankings - As of June 2025, the top programming languages are Python, C++, C, Java, C, JavaScript, Go, Visual Basic, Delphi/Object Pascal, and Fortran, with Python holding a commanding lead at 25.87% [20][22]. - SQL's ranking has dropped significantly, now positioned at 12th place, reflecting a shift in developer preferences towards more flexible programming languages [26]. Historical Trends - The TIOBE index tracks the popularity of programming languages based on various metrics, including search engine queries and community engagement, providing insights into industry trends [29].

Core Viewpoint - The article introduces a new deep thinking paradigm called PENCIL, which alternates between generation and erasure to efficiently solve complex reasoning tasks, outperforming traditional Chain-of-Thought (CoT) methods [1][3]. Group 1: PENCIL Paradigm - PENCIL operates by dynamically erasing unnecessary intermediate results during the reasoning process, allowing for a more efficient generation of final answers [3][6]. - The paradigm addresses limitations of traditional CoT, such as exceeding context window limits, difficulty in retrieving key information, and decreased generation efficiency as context length increases [5][10]. Group 2: Mechanism and Design - The erasure mechanism in PENCIL is inspired by logical rewriting rules and stack frame memory management in functional programming, utilizing special tokens to manage the process [8][9]. - PENCIL supports various reasoning modes, allowing for the simplification of complex thought processes and efficient backtracking during problem-solving [10][13]. Group 3: Training and Experimental Results - PENCIL demonstrates superior accuracy in solving larger-scale reasoning problems compared to CoT, maintaining high accuracy rates even as problem size increases [15][21]. - The training efficiency of PENCIL is enhanced by reducing the context length required for each token, leading to significant savings in computational resources [12][17]. Group 4: Theoretical Implications - Theoretically, PENCIL can simulate any Turing machine's operations with optimal time and space complexity, making it capable of efficiently solving all computable problems [23][24]. - PENCIL's approach allows it to maintain a context length that is polynomial in relation to the problem size, contrasting with the exponential context length required by traditional CoT methods [25][28].