Amperity Launches Chuck Data: A Vibe-Coding AI Agent for Customer Data Engineering

 Amperity Introduces Chuck Data: An AI Agent to Automate Customer Data Engineering with Natural Language

Seattle-based customer data platform (CDP) startup Amperity Inc. has entered the AI agent arena with the launch of Chuck Data, a new autonomous assistant built specifically to tackle customer data engineering tasks. The tool aims to empower data engineers by reducing their reliance on manual coding and enabling natural language-driven workflows, a concept Amperity calls "vibe coding."

Chuck Data is trained on vast volumes of customer information derived from over 400 enterprise brands, giving it a "critical knowledge" base. This foundation enables the agent to perform tasks like identity resolution, PII (Personally Identifiable Information) tagging, and data profiling with minimal developer input.

A Natural Language AI for Complex Data Tasks

Amperity’s platform is well-known for its ability to ingest data from disparate systems — from customer databases to point-of-sale terminals — and reconcile inconsistencies to form a cohesive customer profile. Chuck Data extends this capability by enabling data engineers to communicate using plain English, allowing them to delegate repetitive, error-prone coding tasks to an intelligent assistant.

With direct integration into Databricks environments, Chuck Data leverages native compute resources and large language model (LLM) endpoints to execute complex data engineering workflows. From customer identity stitching to compliance tagging, the agent promises to significantly cut down on time and manual effort.

Identity Resolution at Scale

One of Chuck Data’s standout features is its use of Amperity’s patented Stitch identity resolution algorithm. This powerful tool can combine fragmented customer records to produce unified profiles — a key requirement for enterprises aiming to understand and engage their audiences more effectively.

To promote adoption, Amperity is offering free access to Stitch for up to 1 million customer records. Enterprises with larger datasets can join a research preview program or opt for paid plans with unlimited access, supporting scalable, AI-powered data unification.

PII Tagging and Compliance: A High-Stakes Task

As AI-driven personalization becomes more prevalent, the importance of data compliance continues to grow. Liz Miller, analyst at Constellation Research, emphasized that automating PII tagging is crucial, but accuracy is non-negotiable.

“When PII tagging is not done correctly and compliance standards cannot be verified, it costs the business not just money, but also customer trust,” said Miller.

Chuck Data aims to prevent such issues by automating compliance tasks with high accuracy, minimizing the risk of mishandling sensitive information.

Evolving the Role of the CDP

According to Michael Ni, also from Constellation Research, Chuck Data represents the future of customer data platforms — transforming from static data organizers into intelligent systems embedded within the data infrastructure.

“By running identity resolution and data preparation natively in Databricks, Amperity demonstrates how the next generation of CDPs will shift core governance tasks to the data layer,” said Ni. “This allows the CDP to focus on real-time personalization and business decision-making.”

The End of Manual Data Wrangling?

Derek Slager, CTO and co-founder of Amperity, said the goal of Chuck Data is to eliminate the “repetitive and painful” aspects of customer data engineering.

“Chuck understands your data and helps you get stuff done faster, whether you’re stitching identities or tagging PII,” said Slager. “There’s no orchestration, no UI gymnastics – it’s just fast, contextual, and command-driven.”

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With Chuck Data, Amperity is betting big on agentic AI to usher in a new era of intuitive, fast, and compliant customer data management — one where data engineers simply describe what they want, and AI does the rest.

OpenAI Surpasses $10 Billion in Annual Recurring Revenue as ChatGPT Adoption Skyrockets

 OpenAI has crossed a significant financial milestone, achieving an annual recurring revenue (ARR) run rate of $10 billion as of mid-2025. This growth marks a nearly twofold increase from the $5.5 billion ARR reported at the end of 2024, underscoring the explosive rise in demand for generative AI tools across industries and user demographics.

According to insiders familiar with the company’s operations, this growth is largely fueled by the surging popularity of ChatGPT and a steady uptick in the use of OpenAI’s APIs and enterprise services. ChatGPT alone now boasts between 800 million and 1 billion users globally, with approximately 500 million active users each week. Of these, 3 million are paid business subscribers, reflecting robust interest from corporate clients.

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A Revenue Surge Driven by Strategic Products and Partnerships

OpenAI’s flagship products—ChatGPT and its developer-facing APIs—are at the heart of this momentum. The company has successfully positioned itself as a leader in generative AI, building tools that range from conversational agents and writing assistants to enterprise-level automation and data analysis platforms.

Its revenue model is primarily subscription-based. Businesses pay to access advanced features, integration capabilities, and support, while developers continue to rely on OpenAI’s APIs for building AI-powered products. With both individual and corporate users increasing rapidly, OpenAI’s ARR has climbed steadily.

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Strategic Acquisitions Fuel Growth and Innovation

To further bolster its capabilities, OpenAI has made key acquisitions in 2025. Among the most significant are:

• Windsurf (formerly Codeium): Acquired for $3 billion, Windsurf enhances OpenAI’s position in the AI coding assistant space, providing advanced code completion and debugging features that rival GitHub Copilot.

• io Products: A startup led by Jony Ive, the legendary former Apple designer, was acquired for $6.5 billion. This move signals OpenAI’s intent to enter the consumer hardware market with devices optimized for AI interaction.

These acquisitions not only broaden OpenAI’s product ecosystem but also deepen its influence in software development and design-forward consumer technology.

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Setting Sights on $12.7 Billion ARR and Long-Term Profitability

OpenAI’s trajectory shows no signs of slowing. Company forecasts project ARR reaching $12.7 billion by the end of 2025, a figure that aligns with investor expectations. The firm recently closed a major funding round led by SoftBank, bringing its valuation to an estimated $300 billion.

Despite a substantial operating loss of $5 billion in 2024 due to high infrastructure and R&D investments, OpenAI is reportedly aiming to become cash-flow positive by 2029. The company is investing heavily in building proprietary data centers, increasing compute capacity, and launching major infrastructure projects like “Project Stargate.”

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Navigating a Competitive AI Landscape

OpenAI’s aggressive growth strategy places it ahead of many competitors in the generative AI space. Rival company Anthropic, which developed Claude, has also made strides, recently surpassing $3 billion in ARR. However, OpenAI remains the market leader, not only in revenue but also in market share and influence.

As the company scales, challenges around compute costs, user retention, and ethical deployment remain. However, with solid financial backing and an increasingly integrated suite of products, OpenAI is positioned to maintain its leadership in the AI arms race.

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Conclusion

Reaching $10 billion in ARR is a landmark achievement that cements OpenAI’s status as a dominant force in the AI industry. With a growing user base, major acquisitions, and a clear roadmap toward long-term profitability, the company continues to set the pace for innovation and commercialization in generative AI. As it expands into hardware and deepens its enterprise offerings, OpenAI’s influence will likely continue shaping the next decade of technology.

Building a Real-Time AI Assistant with Jina Search, LangChain, and Gemini 2.0 Flash

 In the evolving landscape of artificial intelligence, creating responsive and intelligent assistants capable of real-time information retrieval is becoming increasingly feasible. A recent tutorial by MarkTechPost demonstrates how to build such an AI assistant by integrating three powerful tools: Jina Search, LangChain, and Gemini 2.0 Flash. 

Integrating Jina Search for Semantic Retrieval

Jina Search serves as the backbone for semantic search capabilities within the assistant. By leveraging vector search technology, it enables the system to understand and retrieve contextually relevant information from vast datasets, ensuring that user queries are met with precise and meaningful responses.

Utilizing LangChain for Modular AI Workflows

LangChain provides a framework for constructing modular and scalable AI workflows. In this implementation, it facilitates the orchestration of various components, allowing for seamless integration between the retrieval mechanisms of Jina Search and the generative capabilities of Gemini 2.0 Flash.

Employing Gemini 2.0 Flash for Generative Responses

Gemini 2.0 Flash, a lightweight and efficient language model, is utilized to generate coherent and contextually appropriate responses based on the information retrieved. Its integration ensures that the assistant can provide users with articulate and relevant answers in real-time.

Constructing the Retrieval-Augmented Generation (RAG) Pipeline

The assistant's architecture follows a Retrieval-Augmented Generation (RAG) approach. This involves:

1. Query Processing: User inputs are processed and transformed into vector representations.

2. Information Retrieval: Jina Search retrieves relevant documents or data segments based on the vectorized query.

3. Response Generation: LangChain coordinates the flow of retrieved information to Gemini 2.0 Flash, which then generates a coherent response.

Benefits and Applications

This integrated approach offers several advantages:

• Real-Time Responses: The assistant can provide immediate answers to user queries by accessing and processing information on-the-fly.

• Contextual Understanding: Semantic search ensures that responses are not just keyword matches but are contextually relevant.

• Scalability: The modular design allows for easy expansion and adaptation to various domains or datasets.

Conclusion

By combining Jina Search, LangChain, and Gemini 2.0 Flash, developers can construct intelligent AI assistants capable of real-time, context-aware interactions. This tutorial serves as a valuable resource for those looking to explore the integration of retrieval and generation mechanisms in AI systems.

OpenAI's Sora Now Free on Bing Mobile: Create AI Videos Without a Subscription

 In a significant move to democratize AI video creation, Microsoft has integrated OpenAI's Sora into its Bing mobile app, enabling users to generate AI-powered videos from text prompts without any subscription fees. This development allows broader access to advanced AI capabilities, previously available only to ChatGPT Plus or Pro subscribers. 

Sora's Integration into Bing Mobile

Sora, OpenAI's text-to-video model, can now be accessed through the Bing Video Creator feature within the Bing mobile app, available on both iOS and Android platforms. Users can input descriptive prompts, such as "a hummingbird flapping its wings in ultra slow motion" or "a tiny astronaut exploring a giant mushroom planet," and receive five-second AI-generated video clips in response. 

How to Use Bing Video Creator

To utilize this feature:

1. Open the Bing mobile app.

2. Tap the menu icon in the bottom right corner.

3. Select "Video Creator."

4. Enter a text prompt describing the desired video.

Alternatively, users can type a prompt directly into the Bing search bar, beginning with "Create a video of..." 

Global Availability and Future Developments

The Bing Video Creator feature is now available worldwide, excluding China and Russia. While currently limited to five-second vertical videos, Microsoft has announced plans to support horizontal videos and expand the feature to desktop and Copilot Search platforms in the near future. 

Conclusion

By offering Sora's capabilities through the Bing mobile app at no cost, Microsoft and OpenAI are making AI-driven video creation more accessible to a global audience. This initiative not only enhances user engagement with AI technologies but also sets a precedent for future integrations of advanced AI tools into everyday applications.

Anthropic Introduces Claude Web Search API: A New Era in Information Retrieval

 On May 7, 2025, Anthropic announced a significant enhancement to its Claude AI assistant: the introduction of a Web Search API. This new feature allows developers to enable Claude to access current web information, perform multiple progressive searches, and compile comprehensive answers complete with source citations. 

Revolutionizing Information Access

The integration of real-time web search positions Claude as a formidable contender in the evolving landscape of information retrieval. Unlike traditional search engines that present users with a list of links, Claude synthesizes information from various sources to provide concise, contextual answers, reducing the cognitive load on users.

This development comes at a time when traditional search engines are experiencing shifts in user behavior. For instance, Apple's senior vice president of services, Eddy Cue, testified in Google's antitrust trial that searches in Safari declined for the first time in the browser's 22-year history.

Empowering Developers

With the Web Search API, developers can augment Claude's extensive knowledge base with up-to-date, real-world data. This capability is particularly beneficial for applications requiring the latest information, such as news aggregation, market analysis, and dynamic content generation.

Anthropic's move reflects a broader trend in AI development, where real-time data access is becoming increasingly vital. By providing this feature through its API, Anthropic enables developers to build more responsive and informed AI applications.

Challenging the Status Quo

The introduction of Claude's Web Search API signifies a shift towards AI-driven information retrieval, challenging the dominance of traditional search engines. As AI assistants like Claude become more adept at providing immediate, accurate, and context-rich information, users may increasingly turn to these tools over conventional search methods.

This evolution underscores the importance of integrating real-time data capabilities into AI systems, paving the way for more intuitive and efficient information access.

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Explore Claude's Web Search API: Anthropic's Official Announcement

OpenAI Reportedly Acquiring Windsurf: What It Means for Multi-LLM Development

 OpenAI is reportedly in the process of acquiring Windsurf, an increasingly popular AI-powered coding platform known for supporting multiple large language models (LLMs), including GPT-4, Claude, and others. The acquisition, first reported by VentureBeat, signals a strategic expansion by OpenAI into the realm of integrated developer experiences—raising key questions about vendor neutrality, model accessibility, and the future of third-party AI tooling.

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What Is Windsurf?

Windsurf has made waves in the developer ecosystem for its multi-LLM compatibility, offering users the flexibility to switch between various top-tier models like OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini. Its interface allows developers to write, test, and refine code with context-aware suggestions and seamless model switching.

Unlike monolithic platforms tied to a single provider, Windsurf positioned itself as a model-agnostic workspace, appealing to developers and teams who prioritize versatility and performance benchmarking.

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Why Would OpenAI Acquire Windsurf?

The reported acquisition appears to be part of OpenAI’s broader effort to control the full developer stack—not just offering API access to GPT models, but also owning the environments where those models are used. With competition heating up from tools like Cursor, Replit, and even Claude’s recent rise in coding benchmarks, Windsurf gives OpenAI:

• A proven interface for coding tasks

• A base of loyal, high-intent developer users

• A platform to potentially showcase GPT-4, GPT-4o, and future models more effectively

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What Happens to Multi-LLM Support?

The big unknown: Will Windsurf continue to support non-OpenAI models?

If OpenAI decides to shut off integration with rival LLMs like Claude or Gemini, the platform risks alienating users who value flexibility. On the other hand, if OpenAI maintains support for third-party models, it could position Windsurf as the Switzerland of AI development tools, gaining user trust while subtly promoting its own models via superior integration.

OpenAI could also take a "better together" approach, offering enhanced features, faster latency, or tighter IDE integration when using GPT-based models on the platform.

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Industry Implications

This move reflects a broader shift in the generative AI space—from open experimentation to vertical integration. As leading AI providers acquire tools, build IDE plugins, and release SDKs, control over the developer experience is becoming a competitive edge.

Developers, meanwhile, will have to weigh the benefits of polished, integrated tools against the potential loss of model diversity and open access.

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Final Thoughts

If confirmed, the acquisition of Windsurf by OpenAI could significantly influence how developers interact with LLMs—and which models they choose to build with. It also underscores the growing importance of developer ecosystems in the AI arms race.

Whether this signals a more closed future or a more optimized one will depend on how OpenAI chooses to manage the balance between dominance and openness.

Google's Gemini 2.5 Pro I/O Edition: The New Benchmark in AI Coding

 In a major announcement at Google I/O 2025, Google DeepMind introduced the Gemini 2.5 Pro I/O Edition, a new frontier in AI-assisted coding that is quickly becoming the preferred tool for developers. With its enhanced capabilities and interactive app-building features, this edition is now considered the most powerful publicly available AI coding model—outperforming previous leaders like Anthropic’s Claude 3.7 Sonnet.

A Leap Beyond Competitors

Gemini 2.5 Pro I/O Edition marks a significant upgrade in AI model performance and coding accuracy. Developers and testers have noted its consistent success in generating working software applications, notably interactive web apps and simulations, from a single user prompt. This functionality has brought it head-to-head—and even ahead—of OpenAI's GPT-4 and Anthropic’s Claude models.

Unlike its predecessors, the I/O Edition of Gemini 2.5 Pro is specifically optimized for coding tasks and integrated into Google’s developer platforms, offering seamless use with Google AI Studio and Vertex AI. This means developers now have access to an AI model that not only generates high-quality code but also helps visualize and simulate results interactively in-browser.

Tool Integration and Developer Experience

According to developers at companies like Cursor and Replit, Gemini 2.5 Pro I/O has proven especially effective for tool use, latency reduction, and improved response quality. Integration into Vertex AI also makes it enterprise-ready, allowing teams to deploy agents, analyze toolchain performance, and access telemetry for code reliability.

Gemini’s ability to reason across large codebases and update files with human-like comprehension offers a new level of productivity. Replit CEO Amjad Masad noted that Gemini was “the only model that gets close to replacing a junior engineer.”

Early Access and Performance Metrics

Currently available in Google AI Studio and Vertex AI, Gemini 2.5 Pro I/O Edition supports multimodal inputs and outputs, making it suitable for teams that rely on dynamic data and tool interactions. Benchmarks released by Google indicate fewer hallucinations, greater tool call reliability, and an overall better alignment with developer intent compared to its closest rivals.

Though it’s still in limited preview for some functions (such as full IDE integration), feedback from early access users has been overwhelmingly positive. Google plans broader integration across its ecosystem, including Android Studio and Colab.

Implications for the Future of Development

As AI becomes increasingly central to application development, tools like Gemini 2.5 Pro I/O Edition will play a vital role in software engineering workflows. Its ability to reduce the development cycle, automate debugging, and even collaborate with human developers through natural language interfaces positions it as an indispensable asset.

By simplifying complex coding tasks and allowing non-experts to create interactive software, Gemini is democratizing development and paving the way for a new era of AI-powered software engineering.

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Conclusion

The launch of Gemini 2.5 Pro I/O Edition represents a pivotal moment in AI development. It signals Google's deep investment in generative AI, not just as a theoretical technology but as a practical, reliable tool for modern developers. As enterprises and individual developers adopt this new model, the boundaries between human and AI collaboration in coding will continue to blur—ushering in an era of faster, smarter, and more accessible software creation.

🚀 IBM’s Vision: Over a Billion AI-Powered Applications Are Coming

 IBM is making a bold prediction: over a billion new applications will be built using generative AI in the coming years. To support this massive wave of innovation, the company is rolling out a suite of agentic AI tools designed to help businesses go from AI experimentation to enterprise-grade deployment—with real ROI.

“AI is one of the unique technologies that can hit at the intersection of productivity, cost savings and revenue scaling.”
— Arvind Krishna, IBM CEO

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🧩 What IBM Just Announced in Agentic AI

IBM’s latest launch introduces a full ecosystem for building, deploying, and scaling AI agents:

• AI Agent Catalog: A discovery hub for pre-built agents.

• Agent Connect: Enables third-party agents to integrate with watsonx Orchestrate.

• Domain Templates: Preconfigured agents for sales, procurement, and HR.

• No-Code Agent Builder: Empowering business users with zero coding skills.

• Agent Developer Toolkit: For technical teams to build more customized workflows.

• Multi-Agent Orchestrator: Supports agent-to-agent collaboration.

• Agent Ops (Private Preview): Brings telemetry and observability into play.

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🏢 From AI Demos to Business Outcomes

IBM acknowledges that while enterprises are excited about AI, only 25% of them see the ROI they expect. Major barriers include:

• Siloed data systems

• Hybrid infrastructure

• Lack of integration between apps

• Security and compliance concerns

Now, enterprises are pivoting away from isolated AI experiments and asking a new question: “Where’s the business value?”

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🤖 What Sets IBM’s Agentic Approach Apart

IBM’s answer is watsonx Orchestrate—a platform that integrates internal and external agent frameworks (like Langchain, Crew AI, and even Google’s Agent2Agent) with multi-agent capabilities and governance. Their tech supports the emerging Model Context Protocol (MCP) to ensure interoperability.

“We want you to integrate your agents, regardless of whatever framework you’ve built it in.”
— Ritika Gunnar, GM of Data & AI, IBM

Key differentiators:

• Open interoperability with external tools

• Built-in security, trust, and governance

• Agent observability with enterprise-grade metrics

• Support for hybrid cloud infrastructures

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📊 Real-World Results: From HR to Procurement

IBM is already using its own agentic AI to streamline operations:

• 94% of HR requests at IBM are handled by AI agents.

• Procurement processing times have been reduced by up to 70%.

• Partners like Ernst & Young are using IBM’s tools to develop tax platforms.

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💡 What Enterprises Should Do Next

For organizations serious about integrating AI at scale, IBM’s roadmap is a strategic blueprint. But success with agentic AI requires thoughtful planning around:

1. ✅ Integration with current enterprise systems

2. 🔒 Security & governance to ensure responsible use

3. ⚖️ Balance between automation and predictability

4. 📈 ROI tracking for all agent activities

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🧭 Final Thoughts

Agentic AI isn’t just a buzzword—it’s a framework for real business transformation. IBM is positioning itself as the enterprise leader for this new era, not just by offering tools, but by defining the open ecosystem and standards that other vendors can plug into.

If the future is agentic, IBM wants to be the enterprise backbone powering it.

Google’s AI Mode Gets Major Upgrade With New Features and Broader Availability

 Google is taking a big step forward with AI Mode, its experimental feature designed to answer complex, multi-part queries and support deep, follow-up-driven search conversations—directly inside Google Search.

Initially launched in March as a response to tools like Perplexity AI and ChatGPT Search, AI Mode is now available to all U.S. users over 18 who are enrolled in Google Labs. Even bigger: Google is removing the waitlist and beginning to test a dedicated AI Mode tab within Search, visible to a small group of U.S. users.

What’s New in AI Mode?

Along with expanded access, Google is rolling out several powerful new features designed to make AI Mode more practical for everyday searches:

🔍 Visual Place & Product Cards

You can now see tappable cards with key info when searching for restaurants, salons, or stores—like ratings, reviews, hours, and even how busy a place is in real time.

🛍️ Smarter Shopping

Product searches now include real-time pricing, promotions, images, shipping details, and local inventory. For example, if you ask for a “foldable camping chair under $100 that fits in a backpack,” you’ll get a tailored product list with links to buy.

🔁 Search Continuity

Users can pick up where they left off in ongoing searches. On desktop, a new left-side panel shows previous AI Mode interactions, letting you revisit answers and ask follow-ups—ideal for planning trips or managing research-heavy tasks.

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Why It Matters

With these updates, Google is clearly positioning AI Mode as a serious contender in the AI-powered search space. From hyper-personalized recommendations to deep dive follow-ups, it’s bridging the gap between traditional search and AI assistants—right in the tool billions already use.

Apple and Anthropic Collaborate on AI-Powered “Vibe-Coding” Platform for Developers

 Apple is reportedly working with Anthropic to build a next-gen AI coding platform that leverages generative AI to help developers write, edit, and test code, according to Bloomberg. Internally described as a “vibe-coding” software system, the tool will be integrated into an updated version of Apple’s Xcode development environment.

The platform will use Anthropic’s Claude Sonnet model to deliver coding assistance, echoing recent developer trends where Claude models have become popular for AI-powered IDEs such as Cursor and Windsurf.

AI Is Becoming Core to Apple’s Developer Tools

While Apple hasn't committed to a public release, the tool is already being tested internally. This move signals Apple’s growing ambition in the AI space. It follows their integration of OpenAI’s ChatGPT for Apple Intelligence and hints at Google’s Gemini being considered as an additional option.

The Claude-powered tool would give Apple more AI control over its internal software engineering workflows—possibly reducing dependency on external providers while improving efficiency across its developer teams.

What Is “Vibe Coding”?

“Vibe coding” refers to the emerging style of development that uses AI to guide, suggest, or even autonomously write code based on high-level prompts. Tools like Claude Sonnet are well-suited for this because of their ability to reason through complex code and adapt to developer styles in real-time.

Takeaway:

Apple’s partnership with Anthropic could redefine how Xcode supports developers, blending Claude’s AI-driven capabilities with Apple’s development ecosystem. Whether this tool stays internal or eventually becomes public, it’s a clear signal that Apple is betting heavily on generative AI to shape the future of software development.