Software companies are constantly trying to add more and more AI features to their platforms, and AI companies are constantly releasing new models and features. It can be hard to keep up with it all, so we’ve written this roundup to share 10 notable updates around AI that software developers should know about.
Google announces Gemma 3
Gemma 3 is Google’s latest AI model, offering improved math, reasoning, and chat capabilities. It can handle context windows of up to 128k tokens, understand 140 languages, and comes in four sizes: 1B, 4B, 12B, and 27B.
It is a multimodal model, and it supports images and videos as inputs, which allows it to analyze images, answer questions about a picture, compare images, identify objects, or reply about text on an image.
Gemma 3 is available as either a pre-trained model that can be fine-tuned for specific use cases, or as a general-purpose instruction-tuned model. It is available in Google AI Studio, or can be downloaded through Hugging Face or Kaggle.
OpenAI reveals Responses API, Agents SDK for building agentic experiences
OpenAI is releasing new tools and APIs to help developers build agentic experiences. The Responses API allows developers to more easily integrate OpenAI’s tools into their own applications.
“As model capabilities continue to evolve, we believe the Responses API will provide a more flexible foundation for developers building agentic applications. With a single Responses API call, developers will be able to solve increasingly complex tasks using multiple tools and model turns,” OpenAI wrote.
The Responses API comes with several built-in tools, including:
- Web search, which allows for retrieval of information from the Internet
- File search, which allows for retrieval of information from large volumes of documents
- Computer use, which captures mouse and keyboard actions generated by a model so that developers can automate computer tasks.
OpenAI also announced the Agents SDK, an open source tool for orchestrating multi-agent workflows. According to OpenAI, the Agents SDK can be used for a variety of scenarios, including customer support automation, multi-step research, content generation, code review, and sales prospecting.
Boomi launches AI Studio
Boomi AI Studio is a platform for designing, governing, and orchestrating AI agents at scale. It consists of multiple components, including:
- Agent Designer, which provides no-code templates for building and deploying agents
- Agent Control Tower, which provides monitoring of agents
- Agent Garden, which allows developers to interact with agents in natural language
- Agent Marketplace, where developers can find and download AI agents from Boomi and its partners.
“With Boomi AI Studio, we’re giving organizations a powerful yet accessible way to build, monitor, and orchestrate AI agents with trust, security, and governance at the core,” said Ed Macosky, chief product and technology officer at Boomi. “As of today, Boomi has deployed more than 25,000 AI Agents for customers. This strong market adoption of our AI agents highlights not only the real value they’re delivering, but also the need for a solution that enables organizations to leverage AI responsibly while accelerating innovation and achieving transformative outcomes.”
Amazon SageMaker Unified Studio is now generally available
The platform allows developers to find and access all of the data in their organization and act on it using a variety of AWS tools, such as Amazon Athena, Amazon EMR, AWS Glue, Amazon Redshift, Amazon Managed Workflows for Apache Airflow (Amazon MWAA), and SageMaker Studio.
It was first announced as a preview at AWS re:Invent last year, and new capabilities added since then include support in Amazon Bedrock for foundation models like Anthropic Claude 3.7 Sonnet and DeepSeek-R1, and integration with the generative AI assistant Amazon Q developer.
Amazon SageMaker Unified Studio is available in US East (N. Virginia, Ohio), US West (Oregon), Asia Pacific (Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London), and South America (São Paulo) AWS regions.
“SageMaker Unified Studio breaks down silos in data and tools, giving data engineers, data scientists, data analysts, ML developers and other data practitioners a single development experience. This saves development time and simplifies access control management so data practitioners can focus on what really matters to them—building data products and AI applications,” Donnie Prakoso, principal developer advocate at AWS, wrote in a blog post.
Visual Studio now includes access to GPT-4o Copilot code completion model
The code completion model was trained on over 275,000 public repositories in 30 different programming languages, on top of the GPT-4o training. This results in more accurate completion suggestions, Microsoft explained.
It will be available for users working in Visual Studio 17.14 Preview 2, which was released this week.
SUSE AI is updated with new features for agentic AI use cases
SUSE AI is an open infrastructure platform for running AI workloads, and the latest release includes a number of new features, such as:
- Tools and blueprints for developing agentic workflows
- New observability features that provide insights into LLM token usage, GPU utilization, performance bottlenecks, and more
- LLM guardrails to ensure ethical AI practices, data privacy, and regulatory compliance
- SUSE AI Library now includes support for OpenWebUI Pipelines and PyTorch
“Through close collaboration with our customers and partners since the launch of SUSE AI last year, we’ve gained additional and invaluable insights into the challenges of deploying production-ready AI workloads,” said Abhinav Puri, general manager of Portfolio Solutions & Services at SUSE. “This collaborative journey has allowed us to bolster our offerings and continue to provide customers strong transparency, trust, and openness in AI implementation. These new enhancements reflect our commitment to building on that partnership and delivering even greater value, while strengthening SUSE AI.”
Eclipse Foundation releases Theia AI
Theia AI is an open source framework for integrating LLMs into tools and IDEs. It gives developers full control and flexibility over how AI is implemented into their applications, from orchestrating the prompt engineering flow to defining agentic behavior to deciding which data sources are used.
Additionally, the organization said that an AI-powered Theia IDE based on the Theia AI framework is now in alpha. The Eclipse Foundation says this IDE will give developers access to AI-enhanced development tools while also allowing them to maintain user control and transparency.
Both tools are being contributed to the Eclipse Foundation by EclipseSource. “We believe that openness, flexibility, and transparency are key success factors for the innovative and sustainable adoption of AI in tools and IDEs,” said Jonas Helming, CEO of EclipseSource. “Large language models inherently introduce a significant level of indeterminism into modern workflows. Developers don’t need yet another proprietary black-box layer they cannot control and adapt. For tool builders developing reliable industrial solutions, it is even more crucial to have full customizability and control over every aspect of an AI-powered tool while also benefiting from a robust framework that allows them to focus on their domain-specific optimizations.”
Anthropic makes changes to reduce token usage
The company announced several new features to help users spend fewer tokens when interacting with its models:
- Cache-aware rate limits: Prompt cache read tokens don’t count against the Input Tokens Per Minute (ITPM) limit anymore on Claude 3.7 Sonnet, allowing users to optimize their prompt caching to get the most of their ITPM limit.
- Simpler prompt caching management: When a cache breakpoint is set, Claude will now automatically read from the longest previously cached prefix. This means that users won’t have to manually track and specify which cached segment to use, as Claude will automatically identify the most relevant one.
- Token-efficient tool use: Users can now specify that Claude calls tools in a token-efficient manner, resulting in up to a 70% reduction on output token consumption (the average reduction has been 14% among early adopters).
Diffblue releases tool for verifying its AI-generated unit tests
Diffblue Test Review was designed to give developers more confidence in accepting AI-generated unit tests. A recent Stack Overflow study found that only 2% of developers have confidence that AI-generated code is accurate. Test Review aims to give developers the insights needed to make an informed decision about accepting tests into their codebase.
Developers can review each test and accept them all in one click, or send specific tests back or edit them before accepting them into the codebase.
“We hope to win over developers who are apprehensive about integrating a fully-autonomous agent into their development workflow,” said Peter Schrammel, co-founder and CTO of Diffblue. “By lowering the barrier to adoption, developers can ease into an AI-powered iterative unit testing workflow, and ultimately, evolve into full autonomy and the remarkable scalability that results from it.”
ScaleOut Software adds generative AI to Digital Twins service
ScaleOut Digital Twins provides a framework for building and running digital twins at scale. Version 4 adds capabilities such as automatic anomaly detection using AI, the ability to use natural language prompts to create data visualizations, the ability to retrain machine learning algorithms in live systems, and other performance improvements.
“ScaleOut Digital Twins Version 4 marks a pivotal step in harnessing AI and machine learning for real-time operational intelligence,” said Dr. William Bain, CEO and founder of ScaleOut Software. “By integrating these technologies, we’re transforming how organizations monitor and respond to complex system dynamics — making it faster and easier to uncover insights that would otherwise go unnoticed. This release is about more than just new features; it’s about redefining what’s possible in large-scale, real-time monitoring and predictive modeling.”
Read last week’s AI announcements roundup here.
