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Google has announced that it is open sourcing a new Java-based differential privacy library called PipelineDP4J .  Differential privacy, according to Google, is a privacy-enhancing technology (PET) that “allows for analysis of datasets in a privacy-preserving way to help ensure individual information is never revealed.” This enables researchers or analysts to study a dataset without accessing personal data.  Google claims that its implementation of differential privacy is the largest in the world, spanning nearly three billion devices. As such, Google has invested heavily in providing access to its differential privacy technologies over the last several years. For instance, in 2019, it open sourced its first differential privacy library , and in 2021, it open sourced its Fully Homomorphic Encryption transpiler . In the years since, the company has also worked to expand the languages its libraries are available in, which is the basis for today’s news.  The new library, PipelineDP

Opsera, the Unified DevOps platform powered by Hummingbird AI trusted by top Fortune 500 companies, today announced that it has partnered with Databricks, the Data and AI company, to empower software and DevOps engineers to deliver software faster, safer and smarter through AI/ML model deployments and schema rollback capabilities. Opsera leverages its DevOps platform and integrations and builds AI agents and frameworks to revolutionize the software delivery management process with a unique approach to automating data orchestration. Opsera is now part of Databricks’ Built on Partner Program and Technology Partner Program. The partnership enables: ● AI/ML Model Deployments with Security and Compliance Guardrails: Opsera ensures that model training and deployment using Databricks infrastructure meets security and quality guardrails and thresholds before deployment. Proper model training allows customers to optimize Databricks Mosaic AI usage and reduce deployment risks. ● Schema D

The AI coding assistant provider Tabnine is releasing a private preview for its Code Review Agent, a new AI-based tool that validates software based on the development team’s unique best practices and standards for software development.  According to Tabnine, using AI to review code is nothing new, but many of the tools currently available check code against general standards. However, software development teams often develop their own unique ways of creating software. “What one team sees as their irrefutable standard, another team might reject outright. For AI to add meaningful value in improving software quality for most teams, it must have the same level of understanding as a fully onboarded, senior member of the team,” Tabnine explained in a blog post .  Code Review Agent allows teams to create rules based on their own standards, best practices, and company policies. These rules are then applied during code review at the pull request or in the IDE. Development teams can provi

Creatio , a global vendor of a no-code platform to automate workflows and CRM with a maximum degree of freedom, today unveiled its most innovative release yet – Creatio Energy 8.2. This launch marks a new era of automation, where AI and no-code together set a modern market standard, delivering unprecedented speed, agility, autonomy, and a remarkable increase in productivity. Businesses are often overwhelmed by the cost and complexity of traditional SaaS applications, which are slow to implement, overengineered, and have low adoption rates. In contrast, Creatio “Energy” heralds a new era of enterprise software – built on no-code and AI – that delivers greater economic value, provides engaging user experiences, and replaces static forms and data with conversational prompts that drive deep insights.   This new approach enables businesses to realize productivity savings of up to 80% for key knowledge worker roles, unlocking new levels of efficiency. “With the launch of ‘Energy,’ Creatio 

Creatio , a global vendor of a no-code platform to automate workflows and CRM, today unveiled its most innovative release yet – Creatio “Energy” (8.2) – during a dynamic online event. This launch marks a new era of automation, where AI and no-code together set a modern market standard, delivering unprecedented speed, agility, autonomy, and a remarkable increase in productivity. Businesses are often overwhelmed by the cost and complexity of traditional SaaS applications, which are slow to implement, overengineered, and have low adoption rates. In contrast, Creatio “Energy” heralds a new era of enterprise software – built on no-code and AI – that delivers greater economic value, provides engaging user experiences, and replaces static forms and data with conversational prompts that drive deep insights.   This new approach enables businesses to realize productivity savings of up to 80% for key knowledge worker roles, unlocking new levels of efficiency. “With the launch of ‘Energy,’ Crea

Crowdbotics today released an extension for GitHub Copilot, available now through the GitHub  and  Azure   Marketplaces. The Crowdbotics platform uses AI to help business stakeholders and IT collaborate and generate high-quality requirements definitions for application development projects. The platform further uses AI to turn these business requirements into technical requirements and implementation recommendations. The new Crowdbotics extension for GitHub Copilot takes advantage of all the requirements and context in the Crowdbotics platform to help developers generate more accurate code with Copilot. Integrated with GitHub Copilot Chat, the extension enables developers to benefit from this accuracy improvement without ever having to leave their development environment. A recent joint research study conducted by Crowdbotics, GitHub, and Microsoft using a subset of the Crowdbotics extension features, found that injecting business requirements from Crowdbotics PRD AI into GitHub Copi

GitHub is hosting its annual user conference, GitHub Universe , today and tomorrow, and has announced a number of new AI capabilities that will enable developers to build applications more quickly, securely, and efficiently.  Many of the updates were across GitHub Copilot . First up, GitHub announced that users now have access to more model choices thanks to partnerships with Anthropic, Google, and OpenAI. Newly added model options include Anthropic’s Claude 3.5 Sonnet, Google’s Gemini 1.5 Pro, and OpenAI’s GPT-4o, o1-preview, and o1-mini.  By offering developers more choices, GitHub is enabling them to choose the model that works best for their specific use case, the company explained. “In 2024, we experienced a boom in high-quality large and small language models that each individually excel at different programming tasks. There is no one model to rule every scenario, and developers expect the agency to build with the models that work best for them,” said Thomas Dohmke, CEO o

The Open Source Security Foundation (OpenSSF) is updating its Developing Secure Software (LFD121) course with new interactive learning labs that provide developers with more hands-on learning opportunities.  LFD121 is a free course offered by OpenSSF that takes about 14-18 hours to complete. Any student who passes the final exam gets a certificate that is valid for two years.   The course is broken down into three parts. The first part covers the basics of secure software development, like how to implement secure design principles and how to secure the software supply chain. Part two covers implementation of those basics and then part three finishes up with security testing and also covers more specialized topics like threat modeling, fielding, and formal methods for verifying that software is secure.  The new interactive labs are not required for completing the course, but do enhance the experience, OpenSSF explained. The labs launch directly in the web browser, meaning no addi

In today’s rapidly evolving digital landscape, the complexity of distributed systems and microservices architectures has reached unprecedented levels. As organizations strive to maintain visibility into their increasingly intricate tech stacks, observability has emerged as a critical discipline. At the forefront of this field stands OpenTelemetry, an open-source observability framework that has gained significant traction in recent years. OpenTelemetry helps SREs generate observability data in consistent (open standards) data formats for easier analysis and storage while minimizing incompatibility between vendor data types. Most industry analysts believe that OpenTelemetry will become the de facto standard for observability data in the next five years. However, as systems grow more complex and the amount of data grows exponentially, so do the challenges in troubleshooting and maintaining them. Generative AI promises to improve the SRE experience and tame complexity. In particular,

In 2020, Martin Fowler introduced domain-driven design (DDD), advocating for deep domain understanding to enhance software development. Today, as organizations adopt DDD principles, they face new hurdles, particularly in data governance, stewardship, and contractual frameworks. Building practical data domains is a complex undertaking and comes with some challenges, but the rewards in terms of data consistency, usability, and business value are significant.   A major drawback to achieving DDD success often occurs when organizations treat data governance as a broad, enterprise-wide initiative rather than an iterative, use-case-focused process. In this way, the approach often leads to governance shortcomings such as a lack of context, where generic policies overlook the specific requirements of individual domains and fail to address unique use cases effectively. Adopting governance across an entire organization is usually time-consuming and complex, which leads to delays in realizing t

The Open Source Initiative (OSI) today released its open source AI definition version 1.0 to clarify what constitutes open source AI. This gives the industry a standard by which to validate whether or not an AI system can be deemed Open Source AI.  The definition covers code, model, and data information, with the latter being a contentious point due to legal and practical concerns. Mozilla, a long-time open source advocate, is partnering with OSI to promote openness in AI, advocating for transparency in AI systems. The need to understand how AI systems work, so they can be researched, scrutinized and potentially regulated, is important to ensure the system is truly open source. Ayah Bdeir, senior strategic advisor on AI strategy at Mozilla, told SD Times on the “What the Dev?” podcast that AI systems are influenced by a number of different components – algorithms, code, hardware, data sets and more.  As an example, she cited that there are data sets to train models, data sets t

The explosion in interest in AI, particularly generative AI, has had many positive benefits: increased productivity, easier and faster access to information, and often a better user experience in applications that have embedded AI chatbots.  But for all its positives, there is one huge problem that still needs solving: how do we power it all?  As of August of this year, ChatGPT had more than 200 million weekly active users, according to a report by Axios .  And it’s not just OpenAI; Google, Amazon, Apple, IBM, Meta, and many other players in tech have created their own AI models to better serve their customers and are investing heavily in AI strategies. While people may generally be able to access these services for free, they’re not free in terms of the power they require. Research from Goldman Sachs indicates that a single ChatGPT query uses almost 10 times as much power as a Google search.  Its research also revealed that by 2030, data center power demand will grow 160%. Rela

Prague, October 24 – JetBrains , a leading creator of professional software development tools, announces the launch of a non-commercial license for WebStorm, a JavaScript and TypeScript IDE, as well as Rider, a cross-platform .NET and game development IDE. Starting now, developers using these IDEs for non-commercial purposes, such as learning, open-source project development, content creation, or hobby development, can do so for free. This can significantly expand the availability of both WebStorm and Rider, as over two-thirds of developers  code outside of work as a hobby, and almost 40% code outside of work for educational and learning purposes. “We have been providing professional tools to help millions of developers worldwide for over two decades,” said Hadi Hariri, VP of Program Management and Communications at JetBrains. “This change allows us to lower the barrier of access to our products for those learning software development, hobbyists, and content creators, amongst others

Google is making it easier for companies to build generative AI responsibly by adding new tools and libraries to its Responsible Generative AI Toolkit . The Toolkit provides tools for responsible application design, safety alignment, model evaluation, and safeguards, all of which work together to improve the ability to responsibly and safely develop generative AI.  Google is adding the ability to watermark and detect text that is generated by an AI product using Google DeepMind’s SynthID technology. The watermarks aren’t visible to humans viewing the content, but can be seen by detection models to determine if content was generated by a particular AI tool.  “Being able to identify AI-generated content is critical to promoting trust in information. While not a silver bullet for addressing problems such as misinformation or misattribution, SynthID is a suite of promising technical solutions to this pressing AI safety issue,” SynthID’s website states.  The next addition to the Tool

Open source software is prevalent in almost any codebase today, and that’s probably not changing anytime soon.  According to a 2024 analysis by the Harvard Business School , the supply side value of open source software is $4.15 billion, while the demand-side value is $8.8 trillion. With numbers like those, it’s easier to see how the financial benefits of using open source are just too good for most companies to turn their nose at.  But in recent years, there have been several instances where an open source project has suddenly changed their license to a more restrictive one, causing headaches for any developer who had incorporated that project in their code. For context, there are a variety of types of open source licenses, typically falling into two categories: permissive and copyleft, according to a blog post by OpenLogic by Perforce.   Permissive licenses, such as the MIT License and the Apache 2.0 License, “grant users freedom in using, modifying, and distributing the sof

DevOps platform provider Opsera today announced AI Code Assistant Insights, empowering enterprises to improve developer productivity, impact, time savings and accelerate the ROI of their investment in AI Code Assistants. “IDC research finds that on average, developers estimate a 35% increase in their productivity with the use of an AI coding assistant. However, it is challenging to have visibility into adoption and measure these gains across the organization,” said Katie Norton, Research Manager, DevSecOps at IDC. “The metrics available in Opsera’s Unified Insights should enable organizations to demonstrate the ROI of GitHub Copilot adoption, enhancing their ability to track and quantify productivity improvements.” For enterprises looking to proactively measure the increase in ROI of their AI Code Assistant investments and improve productivity across all software delivery tools, teams, and environments, the new AI Code Assistant Insights in the Opsera Unified DevOps Platform

Anthropic has a number of updates to share about its AI models, including an updated version of Claude 3.5 Sonnet, the release of Claude 3.5 Haiku, and a public beta for a capability that enables users to instruct Claude to use computers as a human would.  The new version of Claude 3.5 Sonnet features improvements across the board compared to the original version. It outperforms the original in graduate level reasoning, undergraduate level knowledge, code, math problem solving, high school math competition, visual question answering, agentic coding, and agentic tool use. “Early customer feedback suggests the upgraded Claude 3.5 Sonnet represents a significant leap for AI-powered coding,” Anthropic wrote in a post . The company also revealed that GitLab tested the model for DevSecOps tasks and found up to a 10% improvement in reasoning across different use cases.  Claude 3.5 Haiku is the company’s fastest model, and has a similar cost and speed compared to Claude 3 Haiku, but impr

Google Cloud is revealing the results of its 10th annual DORA report , which was designed to create industry benchmarks to give engineering teams a sense of how they are performing in relation to their peers.  DORA includes four key metrics for measuring delivery performance: lead time for changes, deployment frequency, change fail rate, and failed deployment recovery time. “DORA’s four key metrics, introduced in 2013, have become the industry standard for measuring software delivery performance,” Nathen Harvey, DORA lead and developer advocate at Google Cloud, and Derek DeBellis, senior quantitative UX researcher at Google, wrote in a blog post .  This year, the report highlights the impact of AI, the growth of platform engineering, and the importance of developer experience.  Impact of AI When it comes to AI, 75.9% of respondents said they used AI for at least one of their daily tasks, the most popular being generating code, summarizing information, and getting explanations of