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Mistral announces new connectors, Memories Mistral announced that its generative AI chat Le Chat now connects with over 20 new connectors, including tools like Asana, Atlassian, Box, Databricks, GitHub, Outlook, Snowflake, Stripe, and Zapier. Users will also now be able to add their own connectors via MCP. The company also announced a beta for Memories, which allows users to set preferences to get more personalized responses. They can also import their memories from ChatGPT. Both of these features are available for any Le Chat user, including free users. OpenAI adds several minor updates to ChatGPT The company announced that users can now branch off conversations in ChatGPT to explore a specific direction while preserving the direction of the original thread. Additionally, Projects are now available to free users, and the company has added larger file uploads per project, the option to select colors and icons, and project-only memory controls. Google announces new open embeddin...

Most reports comparing AI models are based on benchmarks of performance, but a recent research report from Sonar takes a different approach: grouping different models by their coding personalities and looking at the downsides of each when it comes to code quality. The researchers studied five different LLMs using the SonarQube Enterprise static analysis engine on over 4,000 Java assignments. The LLMs reviewed were Claude Sonnet 4, OpenCoder-8B, Llama 3.2 90B, GPT-4o, and Claude Sonnet 3.7. They found that the models had different traits, such as Claude Sonnet 4 being very verbose in its outputs, producing over 3x as many lines of code as OpenCoder-8B for the same problem. Based on these traits, the researchers divided the five models into coding archetypes. Claude Sonnet 4 was the “senior architect,” writing sophisticated, complex code, but introducing high-severity bugs. “Because of the level of technical difficulty attempted, there were more of these issues,” said Donald Fischer,...

The graph database company Neo4j today announced Infinigraph, a new distributed graph architecture that allows Neo4j’s database to run both operational and analytical workloads in one system. According to the company, silos often keep these workloads separate, leading AI applications to suffer, decision-making to be delayed, and costs to increase as a result of complex integration. Currently, some of the workarounds companies go through to bring these workloads together include having one database and one copy of data, one database and two engines (column-based and row-based), or having two or more synchronized databases. “Infinigraph eliminates the need for these workarounds. It enables organizations to run both analytical and transactional workloads in the same system, at unprecedented scale, while avoiding ETL pipelines, sync delays, and redundant infrastructure,” the company wrote in a blog post . Some examples of use cases that Infinigraph unlocks include the ability to detect ...

Kong Inc ., a leading developer of cloud API and AI technologies, today announced the acquisition of OpenMeter , a leading open-source and SaaS platform for usage-based metering and billing. The acquisition will bring usage-based monetization capabilities to Kong Konnect, the unified API platform. This will enable organizations to productize and bill for their APIs, AI, and data streams, seamlessly turning digital assets into new sources of revenue. In the AI era, billing becomes a metering problem. Agents will exchange labor via APIs. Large Language Models (LLM) – if not chats – are already sold via APIs. This requires a new kind of platform that brings together API infrastructure and monetization all in one. A unified approach to enforcement, security, and monetization. As AI adoption accelerates, digital connections are no longer deterministic or limited to the pace of human activity. Instead, they are continuous, machine-driven, and orders of magnitude larger. AI agents can trigg...

Cloudsmith, providers of an artifact management platform, announced its ML Model Registry , which can act as a single source of truth for all AI models and datasets a company is using. The registry integrates with the Hugging Face Hub and SDK so that developers can push, pull, and manage models and datasets from Hugging Face and then use Cloudsmith to maintain centralized control, compliance, and visibility. Once data has been pushed from Hugging Face to Cloudsmith, security and compliance data can be utilized by Enterprise Policy Management so that teams can apply consistent policies to automatically quarantine, block, and approve specific models. It can also integrate with training, validation, and deployment pipelines, and provides protection of proprietary models and datasets via fine-grained access controls, entitlement tokens, and audit trails. Models and datasets are also managed in the same repositories as a company’s other artifacts, and can be organized by project, enviro...

Microsoft has announced it is going to be retiring the Microsoft Graph CLI, with a deprecation phase starting now and full retirement scheduled for August 28th, 2026. During the deprecation phase, Microsoft will not add any new features and will only address critical vulnerabilities. According to Microsoft, this change is part of the company’s efforts to streamline the developer experience for Microsoft Graph by focusing its attention on PowerShell. The company recommends that users begin switching over to the Microsoft Graph PowerShell SDK, which offers broad API coverage and regular updates, integration with scripting and automation workflows, community and documentation support, and long-term support with Microsoft’s servicing commitments. The company explained that it initially released the CLI to offer a lightweight, cross-platform tool that developers could use to interact with the Microsoft Graph APIs. However, it was experiencing declining usage due to its limited extensibi...

Talk to any DevOps vendor today, and they’ll proudly tell you about their AI roadmap. Most vendors have already built something that will tick the checkbox, if that’s among your requirements. But checkboxes don’t solve problems. A feature that’s hard to use or adds extra manual steps to a developer’s processes doesn’t save you anything — and may end up costing you more than you expect. Just like you, vendors today are at the start of their AI journey. In some cases, the proof of concept gets packaged and shipped. The box is checked, the product goes out the door, and now it’s up to you to figure out if it’s worth using. Most DevOps AI Tools Are Still Point Solutions The truth is that nobody’s using one AI solution to address the entire software development lifecycle (SDLC). The vision of AI that takes you from a list of requirements through work items to build to test to, finally, deployment is still nothing more than a vision. In many cases, DevOps tool vendors use AI to build solu...