COBOL
AssessLanguages & Frameworks
A long-established programming language often used in legacy business systems.
Why it's here
Placed in Assess: 5 article(s) of evidence from 4 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 65%.
Evidence (5)
- 7Hacker News·9/4/2026product_launchIBM launches Bob, an AI coding and modernization partner
IBM Bob is presented as an AI-powered development partner for coding, agent-based task execution, and enterprise modernization. The product includes features such as subagents, natural-language implementation, command-line integration, analytics, and support for modernization workflows across Java, mainframe, IBM i, and enterprise tools.
- 3Martin Fowler·8/19/2026researchCitizens build, agents execute, experts govern
Martin Fowler argues that AI has made it much easier for non-technical people to create working software, but that this is not the same as building enterprise-grade systems. He emphasizes that experienced engineers remain essential for design, governance, risk management, and long-term maintainability when software enters production.
- 2Simon Willison·7/29/2026researchHipp on SQL Simplifying Programmer Work
D. Richard Hipp is quoted comparing SQL to the earlier era of COBOL programmers who manually generated software for querying large datasets. He argues that SQL reduced the amount of code needed and changed programmers' work rather than eliminating it.
- 6InfoQ·7/14/2026framework_updateSwiftData adds external type support and store observation
SwiftData 2027 adds support for persisting custom and third-party types through Codable, making it easier to work with external data models. The release also improves querying with SwiftUI list sections and introduces ResultsObserver and HistoryObserver for observing data store changes.
- 5Hacker News·7/9/2026researchAI changes the economics of software rewrites
The author argues that AI makes software rewrites more attractive when the target codebase has clear, consistent patterns that models can readily learn from. In contrast, proprietary languages, legacy systems, and inconsistent frameworks increase context requirements, token usage, and output variance, making AI-assisted rewrites more costly and less effective.