Thoughtworks, a global technology consultancy, has unveiled the latest edition of its Technology Radar, highlighting trends in AI tools meant to simplify the use of large language models (LLMs) in practical applications.
The report, which is released biannually, emphasises the rise of generative AI tools, platforms, and frameworks. These emerging technologies aim to enable developers to not only create more efficient solutions but to do so with greater responsibility. Managing the "context window" or delivering structured outputs are identified as strategies to manage the documented risks associated with generative AI, helping organisations effectively integrate this technology.
Thoughtworks advises that, while the growing landscape of generative AI tools presents advantages, it also poses navigational difficulties. Organisations are recommended to evaluate their own use cases, particularly considering the objectives and risks, before adopting these technologies. This adoption must be accompanied by reliable engineering practices to ensure that solutions remain dependable and of high quality.
Rachel Laycock, Chief Technology Officer at Thoughtworks, commented on this development: "While generative AI and LLMs dominated our Technology Radar discussions as anticipated, the rapid advancement of AI-adjacent tools, techniques and frameworks from trial to adoption was a notable development in productionising AI solutions." She added, "While organisations are finding a staggering number of AI tools to use for real-world problems, it's important to assess them in the traditional model of good engineering practices in order to drive adoption of AI that is safe, transparent and reliable."
The current edition of the Technology Radar also explores several key themes. One significant theme is AI usage antipatterns, which highlights the importance of maintaining human oversight and strong engineering practices. The report warns against risks like overreliance, code quality issues, and codebase bloat, suggesting a balanced approach to maximise AI's potential while mitigating its risks.
Another focal point in the report is the popularity of the Rust programming language. Thoughtworks observes a rise in Rust-based tools and libraries, attributing its appeal to the language's fast execution and ability to avoid common pitfalls. Its vibrant ecosystem and supportive developer community further enhance its status as a prominent systems programming language.
The gradual rise of WebAssembly (WASM) is also detailed in the report. WASM offers a robust method for running complex applications within web browsers by utilising existing JavaScript virtual machines. This technology facilitates the creation of sophisticated, portable, and cross-platform applications, significantly reducing infrastructure costs. Despite its established presence, WASM was frequently discussed in this edition, indicating its potential imminent expansion within the developer community.
A further highlight is the marked growth in the AI-adjacent ecosystem. As AI models progress, their surrounding ecosystem of tools and frameworks has seen rapid development. This phenomenon resembles the explosive growth of the JavaScript ecosystem in 2015, hinting at significant future developments in AI.
