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GitHub’s code scanning autofix enters public betaGitHub has announced that its code scanning autofix feature, powered by...
14/02/2025

GitHub’s code scanning autofix enters public beta

GitHub has announced that its code scanning autofix feature, powered by GitHub Copilot and CodeQL, is now available in public beta for all GitHub Advanced Security customers.

The autofix tool aims to remediate over two-thirds of vulnerabilities found during code scanning with minimal editing required by developers.

“Our vision for application security is an environment where found means fixed,” said GitHub in a blog post. “By prioritising the developer experience in GitHub Advanced Security, we already help teams remediate 7x faster than traditional security tools. Code scanning autofix is the next leap forward, helping developers dramatically reduce time and effort spent on remediation.”

The tool currently supports JavaScript, TypeScript, Java, and Python, covering over 90 percent of alert types in these languages. GitHub plans to add support for C # and Go next.

Python skills ‘increasingly essential’ to dev teams venturing into advanced AIPython’s status as the primary language fo...
14/02/2025

Python skills ‘increasingly essential’ to dev teams venturing into advanced AI
Python’s status as the primary language for AI and machine learning projects, from its extensive data-handling capabilities to its flexibility and portability, is well-founded. Just how well-founded can be seen in new user data from Snowflake.

The cloud data provider reported that, year on year, use of Python grew 571% in Snowpark, Snowflake’s set of libraries which securely deploy and process non-SQL code. In other words, it will not be a surprise to see Python, Scala and Java at the top of this list, but the numbers are what makes it interesting: Scala saw a 387% increase, compared with Java at 131%.

Alongside this, the report noted that usage of the most popular AI/ML open source Python libraries in Snowpark has increased by 335%. Scikit-learn saw a 474% uptick, while XGBoost, focused on gradient boosting, saw an increase of 357%.

From Snowflake’s perspective, the company was keen to note that the increase went beyond shifting existing work to its platform, but a net increase in experimentation with advanced AI. The research also found that enterprises were increasingly getting value out of their unstructured data; up 123% from a user analysis over six months.

“Given that Python in particular is the language of choice for many developers, data engineers and data scientists, its fast-growing adoption suggests that these unstructured data workflows are not just for building data pipelines, but also involve AI applications and ML models,” the report noted. “Python skills will be increasingly essential to development teams as they venture into advanced AI.”

Snowflake also explored how its community of developers in Streamlit were utilising large language models (LLMs). Between April and January, the company noted that 20,076 developers were working on 33,143 LLM-powered apps, including apps which were still in development. Almost two thirds (65%) of 1500 survey respondents said these were work projects.

Among developer concerns from working with LLMs, based on a survey of 980 users, were trust, cited by 36% of respondents, privacy (28%), cost (19%) and being able to learn the requisite skills (17%).

JetBrains launches 2024.1 with local AI code completionJetBrains has released the 2024.1 updates for its IDEs with sever...
14/02/2025

JetBrains launches 2024.1 with local AI code completion

JetBrains has released the 2024.1 updates for its IDEs with several major new features, headlined by full-line code autocompletion powered by local AI models.

The new full-line code completion functionality ensures code suggestions are processed entirely on the user’s device, minimising latency and providing a seamless offline experience.

“We’ve developed models that run directly on your device, and the IDE verifies each suggestion,” explained Mikhail Kostyukov, Product Manager in JetBrains Machine Learning Team. “This approach delivers the best user experience and ensures that the generated code never contains non-existent variables or methods.”

JetBrains’ local AI code completion provides an alternative to cloud-based offerings like GitHub’s Copilot, which has raised concerns around privacy and intellectual property. By running models directly on the user’s machine, JetBrains aims to address those issues for developers in regulated industries or security-conscious environments.

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