ChatGPT is getting a host of new capabilities for business users, including connectors for cloud services such as Box, Dropbox, Google Drive, OneDrive and SharePoint. In addition, OpenAI also announced that its chatbot is getting the ability to record business meetings and support for Model Context Protocol connections, enabling ChatGPT to use external tools to aid in deep research. OpenAI said it’s focused on making ChatGPT even better for office workers rather than devs, with the new connectors enabling it to search for information across some of their most commonly used cloud services. OpenAI said the connectors will follow the organization’s policies around access control to ensure that users can only gather insights or get answers from documents and files they’re allowed to open. A second update sees ChatGPT gain the ability to record and transcribe meetings, the company said. It explained that the chatbot will be able to generate notes about the meeting, with time-stamped citations, so users can check to see exactly what was said. It will also be able to suggest actions based on whatever was discussed during the meeting, as it can understand exactly what was said. It will work in tandem with the new connectors, too. Users could query their meeting notes, and ChatGPT would be able to look up any relevant information held in Box or another service to provide more insights. Users will also be able to convert action items into a Canvas document, which is OpenAI’s tool for coding and writing projects. The final update refers to new connectors that are meant to enhance ChatGPT’s deep research capabilities. This is an agentic AI capability, because ChatGPT performs the research autonomously, without any input or guidance from the user beyond the initial prompt. The new connectors leverage the MCP protocol, which provides a standardized way for AI agents to use third-party tools. Previously, ChatGPT could only perform research using a web browser, but OpenAI said it can now use select tools from Google LLC and Microsoft Corp., as well as HubSpot and Linear, to aid in its research efforts.
Perplexity Labs debuts an AI coworker Labs that is different from its “Deep Research” feature in that it can use more tools to create project deliverables turning prompts into spreadsheets, dashboards and simple web apps
Perplexity has launched Perplexity Labs, a new AI tool that can turn prompts into spreadsheets, dashboards and simple web apps. The tool functions as a virtual team that performs 10 minutes or more of self-supervised work using tools like deep web browsing, code execution and chart and image creation. According to the AI startup, “Labs can accomplish in 10 minutes what would previously have taken days of work, tedious research, and coordination of many different skills.” Perplexity said Labs is different from its “Deep Research” feature in that it can use more tools to create project deliverables.
Thread AI’s composable AI infrastructure connects AI models, data, and automation into adaptable, end-to-end workflows aligned with enterprise-specific needs to rapidly prototype and deploy event-driven, distributed AI agents
Thread AI, a leader in composable AI infrastructure, has raised $20 million in Series A funding. Despite the rapid adoption of AI, many organizations struggle integrating AI into complex, evolving environments. They often must choose between rigid, pre-built AI tools that don’t fit their workflows, or costly custom solutions requiring extensive engineering. Thread AI addresses this gap with composable infrastructure that connects AI models, data, and automation into adaptable, end-to-end workflows aligned with each organization’s specific needs. Unlike traditional RPA, ETL, or workflow engines that mirror human workflows or require large infrastructure investments, Thread AI’s Lemma platform allows enterprises to rapidly prototype and deploy event-driven, distributed AI workflows and agents. Lemma supports unlimited AI models, APIs, and applications all within a single platform built with enterprise-grade security. This speeds up deployment, reduces operational burden, and simplifies infrastructure, while maintaining governance, observability, and seamless AI model upgrades. As a result, Thread AI equips enterprises with the flexibility to keep up with rapidly changing AI ecosystem, and the cross-functionality to unlock the power of AI across their entire organization. Lemma users report a 70% improvement in process response times, along with significant efficiency gains as AI-powered workflows reduce operational bottlenecks. Early customers have expanded their AI implementations by 250% to 500%, demonstrating Thread AI’s scalability and practical impact.
Anysphere’s Cursor code editor can spot more subtle bugs that don’t render a code snippet unusable but lead to unexpected behavior or slow performance
Code editor startup Anysphere has closed a $900 million funding round. Anysphere is now worth $9.9 billion, nearly four times what it was worth after its previous funding round in December. Anysphere offers a popular code editor called Cursor that uses artificial intelligence to automate programming tasks. An embedded chatbot allows developers to generate code, ask for technical explainers and perform related tasks. The software processes user requests using more than a half dozen large language models. Cursor is based on VS Code, one of the most popular open-source code editors on the market. As a result, developers can bring over keybindings from their existing VS Code environments. Keybindings are user-defined keyboard shortcuts that speed up tasks such as jumping to the start of a code file. Cursor also works with VS Code extensions. Using Cursor, developers can describe the task they wish to perform in natural language and have an AI model generate the corresponding terminal command. The code editor also functions as a kind of spell checker. It can automatically spot and correct mistyped characters, which removes the need for developers to interrupt their workflow in order to perform troubleshooting. Mistyped characters render the code file that contains them unusable, which makes them fairly easy to detect. Developers spot the issue as soon as they attempt to run the file. According to Anysphere, Cursor can also spot more subtle bugs that don’t render a code snippet unusable but lead to unexpected behavior or slow performance.
Microsoft plans to rank AI models by safety based on its own ToxiGen benchmark, which measures implicit hate speech, and the Center for AI Safety’s Weapons of Mass Destruction Proxy benchmark.
Microsoft will start ranking AI models based on their safety performance, as the software group seeks to build trust with cloud customers as it sells them AI offerings from the likes of OpenAI and Elon Musk’s xAI. Sarah Bird, Microsoft’s head of Responsible AI, said the company would soon add a “safety” category to its “model leaderboard”, a feature it launched for developers this month to rank iterations from a range of providers including China’s DeepSeek and France’s Mistral. The leaderboard, which is accessible by tens of thousands of clients using the Azure Foundry developer platform, is expected to influence which AI models and applications are purchased through Microsoft. The new safety ranking would ensure “people can just directly shop and understand” AI models’ capabilities as they decide which to purchase. Microsoft’s new safety metric will be based on its own ToxiGen benchmark, which measures implicit hate speech, and the Center for AI Safety’s Weapons of Mass Destruction Proxy benchmark. The latter assesses whether a model can be used for malicious purposes such as building a biochemical weapon. Rankings enable users to have access to objective metrics when selecting from a catalogue of more than 1,900 AI models, so that they can make an informed choice of which to use.
Non-profit EleutherAI releases massive AI training dataset of licensed and open domain text created in consultation with legal experts and claims performing on par with models developed using unlicensed, copyrighted data
EleutherAI, an AI research organization, has released what it claims is one of the largest collections of licensed and open-domain text for training AI models called the Common Pile v0.1. Weighing in at 8 terabytes in size, the Common Pile v0.1 was used to train two new AI models from EleutherAI, Comma v0.1-1T and Comma v0.1-2T, that EleutherAI claims perform on par with models developed using unlicensed, copyrighted data. The Common Pile v0.1, which can be downloaded from Hugging Face’s AI dev platform and GitHub, was created in consultation with legal experts, and it draws on sources, including 300,000 public domain books digitized by the Library of Congress and the Internet Archive. EleutherAI also used Whisper, OpenAI’s open source speech-to-text model, to transcribe audio content. EleutherAI claims Comma v0.1-1T and Comma v0.1-2T are evidence that the Common Pile v0.1 was curated carefully enough Non-profit alternatives. According to EleutherAI, the models, both of which are 7 billion parameters in size and were trained on only a fraction of the Common Pile v0.1, rival models like Meta’s first Llama AI model on benchmarks for coding, image understanding, and math.
Amperity vibe coding AI agent connects directly to the customer’s Databricks environment via native compute and LLM endpoints to quickly execute complex tasks such as identity resolution
Customer data cloud startup Amperity Inc. is joining the agentic AI party, launching Chuck Data, an AI agent that specializes in customer data engineering. Chuck Data is trained on massive volumes of customer data from more than 400 enterprise brands. This “critical knowledge” base allows it to execute tasks such as identity resolution and personally identifiable information tagging autonomously and instantly resolve customer identities, with minimal input from human developers. The agent is designed to help companies dig up customer insights much faster. Chuck Data makes it possible for data engineers to embrace “vibe coding,” so they can use natural language prompts to delegate these manual coding tasks to an autonomous AI assistant. The company said Chuck Data connects directly to the customer’s Databricks environment via native compute and large language model endpoints. Then it can quickly execute complex tasks such as identity resolution – which involves pulling data from multiple profiles into one – as well as compliance tagging and data profiling. One of Chuck Data’s core features is Amperity’s patented identity resolution algorithm, which is based on the proprietary Stitch technology that’s used within its flagship cloud data platform. The company said users can run Stitch on up to 1 million customer records for free, and for those with bigger records, they can sign up to Chuck Data’s research preview program to access free credits. It’s also offering paid plans that unlock unlimited access to Stitch, enabling companies to create millions of accurate, scalable customer profiles. huck Data provides yet more evidence of how CDPs are evolving from activation tools into embedded intelligence layers for the customer engagement data value chain.
Hirundo’s approach to AI hallucinations is about making fully trained AI models forget the bad things they learn, so they can’t use this mistaken knowledge
Hirundo AI Ltd., a startup that’s helping AI models “forget” bad data that causes them to hallucinate and generate bad responses, has raised $8 million in seed funding to popularize the idea of “machine unlearning.” Hirundo’s approach to AI hallucinations is about making fully trained AI models forget the bad things they learn, so they can’t use this mistaken knowledge to generate their responses later on, down the line. It does this by studying the behavior of AI models in order to locate the directions users can go in order to manipulate them. It identifies any bad traits, then investigates the root cause of those bad outputs, before steering the model away from them. It pinpoints where hallucinations originate from in the billions of parameters that make up their knowledge base. This retroactive approach to fixing undesirable behaviors and inaccuracies in AI models means it’s possible to improve their accuracy and reliability without needing to retrain them. That’s a big deal, because retraining models can take many weeks and cost thousands or even millions of dollars. “With Hirundo, models can be remediated instantly at their core, working toward fairer and more accurate outputs,” Chief Executive Ben Luria added. Besides helping models to forget bad, biased or skewed data, the startup says it can also make them “unlearn” confidential information, preventing AI models from revealing secrets that shouldn’t be shared. What’s more, it can do this for both open-source models such as Llama and Mistral, and soon it will also be able to do the same for gated models such as OpenAI’s GPT and Anthropic PBC’s Claude. The startup says it has successfully managed to remove up to 70% of biases from DeepSeek Ltd.’s open-source R1 model. It has also tested its software on Meta Platforms Inc.’s Llama, reducing hallucinations by 55% and successful prompt injection attacks by 85%.
ChatGPT is the most adopted general-purpose model by developers accounting for more than 86% of all LLM tokens processed followed by Meta’s Lama
New Relic released its inaugural AI Unwrapped: 2025 AI Impact Report, offering a view into how developer choices are transforming the AI ecosystem. Drawing from comprehensive aggregated and de-identified usage data from 85,000 active New Relic customers over a year, the report reveals that developers are overwhelmingly embracing the largest general-purpose models, led by OpenAI’s ChatGPT, which accounted for more than 86% of all LLM tokens processed by New Relic customers. The data shows ChatGPT-4o has been dominating more recently, followed by ChatGPT-4o mini. However, adoption of ChatGPT from version-to-version is occurring seemingly overnight as developers pivot toward newer, better, faster, and cheaper models. New Relic users have been rapidly shifting from ChatGPT-3.5 Turbo to ChatGPT-4.1 mini since it was announced in April. This shows that developers value cutting-edge performance and features more than savings. In a countervailing trend, the findings also highlight increased model diversification as developers explore open-source alternatives, specialized domain solutions, and task-specific models, although at a smaller scale. Meta’s Llama emerged as the model that saw the second largest amount of LLM tokens processed by New Relic customers. In fact, New Relic saw a 92% increase in the number of unique models used across AI apps in the first quarter of 2025. Since its launch last year, enterprises have been adopting New Relic AI Monitoring at a steady 30% growth in usage quarter-over-quarter in the previous 12 months, giving them a solution to ensure AI model reliability, accuracy, compliance, and cost efficiency.
Uniphore’s solution unifies agents, models, knowledge, and data into a single, composable platform and offers, is interoperable with both closed- and open-source LLMs and offers pre-built enterprise-grade agents
Uniphore has launched the Uniphore Business AI Cloud: a sovereign, composable, and secure platform that bridges the “AI divide” between IT and business users by combining the simplicity of consumer AI with enterprise-grade security and scalability. Uniphore’s Business AI Cloud empowers both CIOs and business users by unifying agents, models, knowledge, and data into a single, composable platform. This balance of usability and rigor unlocks the true promise of AI, not just as a technological upgrade, but as a transformative force for business. Data Layer: A zero-copy, composable data fabric that connects to any platform, application, or cloud – querying and preparing data where it lives to eliminate migrations and accelerate AI adoption. Knowledge Layer: Structures and contextualizes enterprise data into AI-ready knowledge retrieval, enabling proprietary SLM fine-tuning. Perpetual fine-tuning, and unlocking deep, explainable insights across domains. Model Layer: Open and interoperable with both closed- and open-source LLMs, allowing enterprises to apply guardrails and governance to models, as well as orchestrate and swap models without rework as technologies evolve. Agentic Layer: Offers pre-built enterprise-grade agents and a natural language agent builder, plus Business Process Model and Notation (BPMN) based orchestration for deploying AI into real workflows across sales, marketing, service, HR, and more. The Business AI Cloud was purpose-built to address the four biggest blockers to enterprise AI adoption: The Data Layer Bottleneck, Data Sovereignty, Disconnected AI Ownership Between IT and Business, Rip-and-Replace Requirements.