If you've lost your motivation—or as some would say, your mojo—as a software developer, it's likely because it hasn't been the most fun time to be in the industry with layoffs rocking teams and worker morale since mid-2022.
Given this context, it isn't too surprising that Stack Overflow's most recent Developer Survey found that less than a third (32.1%) of professional developers are happy with their current job. Common frustrations include technical debt (62.4%), as well as a reliance on unreliable tools and systems at work (31.2%).
Those aren't great numbers, but negatives are generally countered by positives. Within programming, there have been a number of interesting new developments that are a shot in the arm for jaded software pros. New languages, including Finch, are emerging, even as older ones, like Zig and Go, get some renewed time in the sun. Meanwhile others are being built with the specific purpose to help developers ease into the fast-growing AI field.
5 jobs to apply for this week
- Cloud Data Engineer AWS / Google Cloud, adesso SE, Essen
- Senior Python Developer (m/w/d), Eberlein Kunz, Berlin
- Software Developer, InTraffic, Utrecht
- Senior Azure data-engineer Douane, Douane, Rotterdam
- Ingénieur Cloud HPC (H/F/X), Atos, Toulouse
Mojo, for example, is a relatively recent programming language launched in May 2023. It was developed by Modular AI with the goal of combining the simplicity and ease of a dynamic language, such as Python, with the speed and efficiency of systems languages, such as C++ and Rust.
Not to be confused with the podcasting app of the same name or H20.ai's identically named deployment format for machine learning models, Mojo has particular promise in AI and machine learning domains where performance optimization is essential.
The language aims to provide both the flexibility of Python for developers, and the performance optimization necessary for intensive computational tasks like AI workloads. It permits Python-like syntax and dynamic typing, and it allows for the import and utilization of any Python library, ensuring complete interoperability with the language.
Right now, of course, AI is the holy grail tech firms are chasing. According to data from Statista, the sector will grow exponentially by up to $2 trillion by 2030, and AI programming talent is in such hot demand, that it's reported Mark Zuckerberg is personally attempting to poach staff from Google's DeepMind.
Forward-thinking software developers will be highly aware of what's happening in the sector, so given the context and possibility the language offers, perhaps it isn't surprising that Modular AI open-sourced Mojo's core components in March. Not even two years in, the company says it has 175,000 developers, 23,000 stars on Github, and 22,000 community members.
Mojo has strengths in areas such as parallel and asynchronous computing, memory safety, and control and interoperability. It's a high-performance option for developers in AI, machine learning, and data science, especially for computationally demanding tasks requiring low latency and efficiency.
Cautious considerations
At the same time, because it is so new, there are some considerations programmers should take into account. Mojo isn't yet a mature language. It has fewer libraries and frameworks, and its own native ecosystem of libraries, tools, and frameworks is still growing.
Developers may not yet have access to the rich ecosystem that other mature languages offer, and the developer community around it is still small. This can make finding support, tutorials, and third-party resources more challenging compared to more established languages like Python or Rust.
Languages take time to develop and evolve of course, but while this is a drawback for some, other developers may see this as an opportunity. That's because early adopters can help to shape a language, and when Mojo released version 24.4 in June of this year, it made the point that many of the improvements were thanks to its community of users.
“One of the biggest highlights of this release is that we received 214 pull requests from 18 community contributors for new product features, bug fixes, documentation enhancements, and code refactoring. These contributions resulted in 30 net new features in the standard library, accounting for 11% of all improvements in this release,” a company blog post noted.
If you're the sort of developer who understands the value of future-proofing your skills, then learning Mojo now may give you an essential advantage for machine learning or hardware optimization roles into the future.