Latest news with #JetBrains
Geeky Gadgets
5 days ago
- Geeky Gadgets
Windsurf AI Wave 11 Update Overview : Voice, Deeper Browser Integrations and More
After the turmoil of the last few weeks for Windsurf the company has, released its Wave 11 update to Windsurf AI's Cascade. With voice interaction that enables hands-free coding, deeper browser integration for seamless debugging, and JetBrains-specific enhancements that supercharge productivity, this release promises to transform the way developers approach their craft. It's not just an update—it's a leap forward in how we interact with AI to solve complex problems and focus on innovation. In the video below the official Windsurf development team provide more insights into the main features of the Wave 11 update, showcasing how they address the most pressing challenges faced by developers today. From context-aware AI that remembers your workflows to automated task planning that breaks down daunting objectives, each enhancement is designed to save time, reduce friction, and boost efficiency. Whether you're curious about how voice commands can transform multitasking or eager to see how JetBrains integration can streamline your projects, this update offers something for every developer. As you read on, consider how these tools could reshape not just your workflow, but the very way you think about coding. Wave 11 Update Highlights Voice Interaction: Hands-Free Coding Cascade now supports voice commands, allowing you to interact with the assistant without relying on manual input. This feature is particularly valuable in multitasking scenarios, allowing hands-free operation while coding or managing tasks. By simply speaking commands, you can trigger actions, retrieve information, or navigate workflows. This makes the development process faster, more intuitive, and less reliant on traditional input methods. Whether you're debugging, writing code, or managing project tasks, this feature ensures seamless integration into your workflow. Enhanced AI Context Awareness The update introduces improved context awareness, allowing Cascade to recall past interactions and maintain continuity in multi-step or complex tasks. This eliminates the need to repeatedly provide background information, as the assistant intelligently references previous conversations. By offering relevant suggestions and maintaining workflow consistency, this feature ensures that your interactions with Cascade are both efficient and productive. Developers working on intricate projects will particularly benefit from this enhancement, as it reduces interruptions and enhances focus. Windsurf AI Coding Assistant Update Watch this video on YouTube. Learn more about Windsurf with the help of our in-depth articles and helpful guides. Browser Integration: Streamlined Debugging and Research Cascade's deeper browser integration enables direct interaction with your open tabs, offering significant improvements in debugging and research processes. Using app mentions, you can instruct Cascade to collect data such as screenshots, console logs, or DOM elements from specific tabs. This feature simplifies the traditionally time-consuming task of manual data collection, allowing you to focus on solving problems rather than gathering information. By streamlining these processes, Cascade helps you save time and effort, making sure smoother project execution. Workflow Automation and Task Management The Wave 11 update introduces several features designed to enhance task management and workflow automation. These include automated task planning, JetBrains workflow enhancements, and conversation checkpoints, all aimed at improving clarity, precision, and efficiency. Automated Task Planning: Cascade now breaks down complex objectives into manageable steps, making sure clarity in your workflows. For those who prefer a more hands-on approach, this feature can be disabled, offering flexibility to adapt to your preferred working style. Cascade now breaks down complex objectives into manageable steps, making sure clarity in your workflows. For those who prefer a more hands-on approach, this feature can be disabled, offering flexibility to adapt to your preferred working style. JetBrains Workflow Enhancements: Developers using JetBrains tools benefit from targeted improvements, such as file-based scoping rules and turbo mode for terminal commands. These updates streamline workflows and improve consistency across team projects. Developers using JetBrains tools benefit from targeted improvements, such as file-based scoping rules and turbo mode for terminal commands. These updates streamline workflows and improve consistency across team projects. Conversation Checkpoints: Named checkpoints within conversations allow you to revisit specific points in your interactions. This is particularly useful for managing long or intricate discussions, making sure you can return to critical moments without losing context. These features collectively enhance the way developers plan, execute, and manage their tasks, making sure a smoother and more efficient workflow. JetBrains Workflow Enhancements: Precision and Efficiency JetBrains users will notice significant improvements in how Cascade integrates with their development environment. Key updates include: File-Based Scoping Rules: This feature allows you to define specific files or directories for task execution, making sure Cascade focuses only on relevant areas of your project. This is especially valuable in large projects, where maintaining focus on critical files is essential for efficiency and accuracy. This feature allows you to define specific files or directories for task execution, making sure Cascade focuses only on relevant areas of your project. This is especially valuable in large projects, where maintaining focus on critical files is essential for efficiency and accuracy. Turbo Mode for Terminal Commands: Automate repetitive terminal tasks, such as builds or deployments, reducing manual input and accelerating processes. This feature minimizes time spent on routine tasks, allowing you to focus on higher-value activities like debugging or feature development. These enhancements are designed to improve precision and efficiency, making Cascade an indispensable tool for JetBrains users. Advanced Features for Seamless Development The Wave 11 update introduces several advanced features that further enhance Cascade's capabilities, making sure a seamless development experience: Secure Authentication for MCP Servers: Expanded support for MCP servers now includes secure authentication options, reinforcing Cascade's commitment to protecting sensitive data. This ensures that your interactions remain secure, even in environments requiring high levels of confidentiality. Expanded support for MCP servers now includes secure authentication options, reinforcing Cascade's commitment to protecting sensitive data. This ensures that your interactions remain secure, even in environments requiring high levels of confidentiality. Auto-Continue for Complex Queries: The auto-continue feature allows Cascade to deliver uninterrupted responses to complex queries or tasks. This minimizes the need for follow-up prompts, making sure that your interactions are efficient and comprehensive. These updates demonstrate Cascade's ability to adapt to the evolving needs of modern software development, providing tools that enhance both security and efficiency. A Step Forward for Developers The Wave 11 update for Windsurf AI's Cascade delivers meaningful advancements across voice interaction, browser integration, JetBrains functionality, and workflow automation. By addressing key developer needs, these enhancements simplify daily tasks, improve collaboration, and boost productivity. Cascade continues to evolve with the demands of modern software development, solidifying its position as a valuable tool for developers seeking to optimize their workflows and achieve greater efficiency. Media Credit: Windsurf Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Techday NZ
5 days ago
- Business
- Techday NZ
GitLab Duo Agent Platform beta unlocks AI-human collaboration
GitLab has opened public beta access to its GitLab Duo Agent Platform, a DevSecOps orchestration platform enabling asynchronous collaboration between developers and AI agents. Product details The GitLab Duo Agent Platform introduces an orchestration layer designed to allow specialised AI agents and human developers to collaborate within software development projects. By leveraging GitLab as the system of record, the platform delivers broad project context to AI agents, supporting informed decision-making in line with organisational standards. The company has made the public beta available to Premium and Ultimate customers. The initial set of features includes Software Development Flow - the first orchestrated multi-agent workflow that accumulates context, clarifies ambiguities with developers, and implements changes to codebases and repositories using project structures, codebase history, and supplementary context such as GitLab issues and merge requests. Specialised agents and workflows Specialised agents on the platform mirror established team roles, with capabilities to search, read, create, and modify existing artefacts across GitLab. The platform also features agent Flows, which are structured, predetermined workflows that can coordinate multiple specialised agents to autonomously execute complex or multi-step tasks. GitLab is planning an AI Catalogueueueueue in the future - this marketplace will allow organisations to create, customise, and share agents and agent flows among their teams and the wider GitLab ecosystem. Interface and support Users of the public beta have access to GitLab Duo Agentic Chat within development environments, both in IDEs and the GitLab Web UI. According to GitLab, the chat experience has been transformed into an active development partner, supporting iterative feedback and chat history, as well as streamlined delegation using new slash commands such as /explain, /tests, and /include. These commands create a quick delegation language, and the /include feature allows for context injection from specific files, issues, merge requests, or dependencies. Developers can also personalise agent behaviour using custom rules, specifying guidance tailored to individual or team preferences through natural language instructions. In addition to integration with Visual Studio Code, support has been extended to JetBrains IDEs such as IntelliJ, PyCharm, GoLand, and WebStorm. The platform also introduces Model Context Protocol (MCP) Client Support, which enables GitLab Duo Agentic Chat to connect to remote and local MCP servers. This allows agents to communicate with systems beyond GitLab, provided those systems are accessible via MCP, expanding the practical application of the platform's capabilities. Future releases GitLab stated that the scope and quality of the Duo Agent Platform will be expanded through subsequent 18.x releases, with a general availability target by the end of the year. Industry perspectives GitLab's own leadership and industry observers offered perspectives on the platform's beta release. "GitLab Duo Agent Platform enhances our development workflow with AI that truly understands our codebase and our organisation," said Bal Kang, Engineering Platform Lead at NatWest. "Having GitLab Duo AI agents embedded in our system of record for code, tests, CI/CD, and the entire software development lifecycle boosts productivity, velocity, and efficiency. The agents have become true collaborators to our teams, and their ability to understand intent, break down problems, and take action frees our developers to tackle the exciting, innovative work they love." Rachel Stephens, Research Director at RedMonk, commented, "As software development workflows grow in complexity and organisations look to leverage AI, there's an increasing need for platforms that can integrate AI capabilities without adding to existing disjointed toolchains." "As a DevSecOps platform, GitLab is already positioned to help developers collaborate both synchronously and asynchronously. Now the GitLab Duo Agent Platform intends to take this a step further, helping developers also integrate AI agents into their workflows." Bill Staples, Chief Executive Officer at GitLab, added, "Today marks a pivotal moment in software development as we introduce the public beta of the GitLab Duo Agent Platform, the first DevSecOps orchestration platform designed to unlock asynchronous collaboration between developers and AI agents." "GitLab Duo Agent Platform isn't just another AI tool; it's a fundamental reimagining of software development from isolated, linear processes into dynamic, intelligent collaboration." "By leveraging GitLab's unique position as the system of record for the entire software development lifecycle, we're providing AI agents with unprecedented context and capabilities. This enables our customers to work with AI agents that have comprehensive context about their codebase, their workflows, and their organisational goals to help boost productivity, velocity, and efficiency."
Business Wire
6 days ago
- Business
- Business Wire
GitLab Announces the Public Beta of GitLab Duo Agent Platform
SAN FRANCISCO--(BUSINESS WIRE)--All Remote - GitLab Inc., the most comprehensive, intelligent DevSecOps platform, today announced the public beta launch of GitLab Duo Agent Platform, a DevSecOps orchestration platform designed to unlock asynchronous collaboration between developers and AI agents. GitLab Duo Agent Platform represents a significant advancement in software development by establishing an intelligent orchestration layer that facilitates seamless collaboration between specialized AI agents and human developers. Leveraging GitLab's role as the system of record for software development, the platform equips AI agents with comprehensive project context, enabling informed decisions that align with organizational standards. GitLab Duo Agent Platform Capabilities Available Now in Public Beta The first capabilities for GitLab Duo Agent Platform in public beta, now available to Premium and Ultimate customers, include: Software Development Flow: The first orchestrated multi-agent workflow that gathers comprehensive context, clarifies ambiguities with developers, and executes strategic plans to make precise changes to codebases and repositories, leveraging the project structure, codebase, and history along with additional context like GitLab issues or merge requests. GitLab Duo Agentic Chat in IDE: Transforms the chat experience from a passive Q&A tool into an active development partner directly in development environments, enabling more sophisticated interactions and task delegation through enhanced capabilities, including: Iterative Feedback and Chat History: Enables Duo Agentic Chat to support stateful, conversational partnerships that help foster trust and enable developers to delegate more complex tasks with corrective guidance. Streamlined Delegation Through Slash Commands: Expanded commands such as /explain, /tests, and /include that create a "delegation language" for quick and precise intent, with /include allowing explicit context injection from specific files, issues, merge requests, or dependencies. Personalization Through Custom Rules: Enables developers to tailor agent behavior to individual and team preferences using natural language for development style guides and organizational policies. GitLab Duo Agentic Chat in Web UI: Provides direct access within the GitLab Web UI, evolving the agent from a coding assistant to a true DevSecOps agent with access to rich non-code context and the ability to make changes directly from the Web UI. JetBrains IDEs Support: Expands Duo Agentic Chat to the JetBrains family of IDEs, including IntelliJ, PyCharm, GoLand, and WebStorm, with automatic agentic capabilities for existing users and marketplace installation for new users. Model Context Protocol (MCP) Client Support: Enables Duo Agentic Chat to connect to remote and locally running MCP servers, allowing connections to systems beyond GitLab with any service that exposes itself via MCP, becoming part of the agent's skill set. Each month, GitLab plans to increase the scope and quality of the Duo Agent Platform in subsequent 18.x releases, with a target for general availability by the end of the year. To learn more about GitLab Duo Agent Platform capabilities and product roadmap, read the GitLab blog. Supporting Quotes 'GitLab Duo Agent Platform enhances our development workflow with AI that truly understands our codebase and our organization,' said Bal Kang, Engineering Platform Lead at NatWest. 'Having GitLab Duo AI agents embedded in our system of record for code, tests, CI/CD, and the entire software development lifecycle boosts productivity, velocity, and efficiency. The agents have become true collaborators to our teams, and their ability to understand intent, break down problems, and take action frees our developers to tackle the exciting, innovative work they love.' 'As software development workflows grow in complexity and organizations look to leverage AI, there's an increasing need for platforms that can integrate AI capabilities without adding to existing disjointed toolchains,' said Rachel Stephens, Research Director at RedMonk. 'As a DevSecOps platform, GitLab is already positioned to help developers collaborate both synchronously and asynchronously. Now the GitLab Duo Agent Platform intends to take this a step further, helping developers also integrate AI agents into their workflows.' "Today marks a pivotal moment in software development as we introduce the public beta of the GitLab Duo Agent Platform, the first DevSecOps orchestration platform designed to unlock asynchronous collaboration between developers and AI agents," said Bill Staples, CEO at GitLab. 'GitLab Duo Agent Platform isn't just another AI tool; it's a fundamental reimagining of software development from isolated, linear processes into dynamic, intelligent collaboration. By leveraging GitLab's unique position as the system of record for the entire software development lifecycle, we're providing AI agents with unprecedented context and capabilities. This enables our customers to work with AI agents that have comprehensive context about their codebase, their workflows, and their organizational goals to help boost productivity, velocity, and efficiency.' About GitLab GitLab is the most comprehensive, intelligent DevSecOps platform for software innovation. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100 trust GitLab to ship better, more secure software faster. Forward-Looking Statements This press release contains 'forward-looking statements' within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. Although we believe that the expectations reflected in the forward-looking statements contained in this release are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to be materially different from any future results or outcomes expressed or implied by the forward-looking statements. Further information on risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those included in or contemplated by the forward-looking statements contained in this release are included under the caption 'Risk Factors' and elsewhere in the filings and reports we make with the Securities and Exchange Commission. We do not undertake any obligation to update or release any revisions to any forward-looking statement or to report any events or circumstances after the date of this press release or to reflect the occurrence of unanticipated events, except as required by law.
Time of India
09-07-2025
- Business
- Time of India
Why AI still needs human coders 'Java' at 30
Representative AI image Java 's thirtieth birthday arrives amid the clamour surrounding generative AI, a technology already capable of drafting voluminous, enterprise-level code from a single prompt. For newcomers it can seem logical to bypass the hard graft of mastering a programming language and let the machines handle everything beneath the surface. Oracle, steward of the language since 2010, believes that view misunderstands both the role of AI and the purpose of Java itself. 'AI is just another use case— albeit a noisy one,' says Sharat Chander , senior director of Java SE (standard edition) product management at Oracle. 'Java already supplies the performance, stability and security that modern models demand; what AI tools really strip away is the tedious low-level plumbing, freeing developers to malafocus on the business logic that matters.' Chander insists that the real value of learning Java lies in the architectural judgement it cultivates rather than the keystrokes it saves. To underscore the point he draws a parallel with everyday speech. 'A language is a language, whether spoken or programmed,' he says. 'We do not abandon Spanish or Hindi simply because English is popular, and neither will developers discard a language that has earned their trust.' Java's own lane, he argues, is to remain the general-purpose, enterprise-grade option, trusted precisely because it evolves without leaving existing applications behind. That loyalty is reinforced by a global community of user groups—India alone hosts fourteen—whose members 'get attached' to the language in much the same way people become attached to their mother tongue. Compatibility, however, is not conservatism. Oracle now ships two Java releases a year, each incubated in the OpenJDK project where outsiders can probe, complain and contribute before any feature is cast in stone. Projects within the Java developer community such as Amber, Panama and the Generational Z Garbage Collector are tuned for a future in which AI inference and training happen inside Java processes. 'We're making the language more concise, more data-oriented and more maintainable,' says Chander. 'So developers can reason about large models instead of wrestling with syntax.' That distinction—between reasoning and typing—defines the debate over whether Java remains worth learning as AI gains fluency. Mala Gupta , a Java champion, author, and developer advocate at JetBrains, reaches for a medical metaphor. 'Imagine a surgeon using a robot for delicate work,' she says. 'If the robot stalls mid-procedure, the surgeon must take over. Now imagine the surgeon never learned the craft at all—that's terrifying.' In Gupta's view, AI assistants enlarge rather than diminish a developer's duty to 'know, delegate and verify'. Large models hallucinate, misread edge cases and alter their answers from one prompt to the next. The antidote, Gupta says, is systems thinking—understanding how every construct in the language interacts with memory, concurrency and downstream services. 'Ask 'why' five times,' she advises, 'and you reach the clarity AI cannot provide.' That scepticism is shared by Srikanth Seshadri , director at Confluent India, whose engineers maintain Kafka-based infrastructure for banks and telecoms (a lot of Kafka's core components are written in Java). Confluent enables AI reviews in its pull requests, yet still requires human sign-off after the machines have spoken because generated code has introduced race conditions that only seasoned eyes detect. In Java, a race condition happens when two or more threads try to read or write the same data simultaneously and the program's outcome depends on whichever thread happens to act first. This can cause mistakes as the threads 'race' to access and update the shared resource. To avoid problems like race conditions is why Seshadri warns that one cannot use AI blindly in an enterprise setting where the stakes are high. Mastery of the language, the business context and the behaviour of concurrent systems remains non-negotiable. Java's measured pace of adopting new features, he adds, helps developers grasp underlying concepts rather than chase fashions. 'AI can generate tests and implementations, but you still need to debug and maintain them. To 'trust AI but verify', you must know your craft.' Critical thinking is what matters For Zorawar Purohit, CAIO & cofounder at M37Labs, Java serves as a mental gymnasium that trains discipline. 'Learning Java isn't about memorising syntax,' he says. 'It's about wiring your brain to think in structured, scalable, battle-tested ways.' Strong typing, object-oriented design and defensive coding become habits that AI cannot replicate: good judgement, domain knowledge and the instinct to spot design flaws before they erupt in production. Even if AI delivers 80% of the boiler-plate, 'the remaining 20% is where the real game is—debugging complex failures, designing resilient systems, optimising performance and making trade-offs with your eyes open.' AI's a power tool, not a replacement for critical thinking, warns Purohit. Chander closes the circle by arguing that mathematics and statistics remain the bedrock on which every language and every AI model rests. Over that foundation sits an understanding of concurrency, memory management and datacentric programming—areas that Java formalises through its memory model, threading primitives and newer value-based structures. Gupta encourages students to embrace IDEs such as IntelliJ IDEA, whose embedded copilots can already refactor entire projects, yet streses that those tools amplify competence rather than supply it. Seshadri suggests reading the Java Language Specification, not to memorise grammar but to absorb the mental models that avert data races. Purohit would add a study of distributed-systems failure modes—the corner cases that AI prompts still fail to anticipate. For young technologists choosing their first language, the message from practitioners of the three-decade-old programming language is that AI is here to stay, but it is a collaborator, not a career ender. Java offers a curriculum in systems reasoning and a guarantee that today's code—hand-written or machinegenerated—will still matter when the next hype cycle arrives. Fluency in any language never harmed a speaker; it merely broadened the stories they could tell. In the same vein, fluency in Java will only be enriched rather than eclipsed by AI. AI Masterclass for Students. Upskill Young Ones Today!– Join Now
Yahoo
11-06-2025
- Business
- Yahoo
Introducing Lemony, The First Plug-and-Play Device For Secure, On-Premise Artificial Intelligence
AI Node Brings Businesses Fearless AI Adoption; Simplicity, Compliance, Scalability, And Affordability, Without the Risks of Cloud Access IBM, JetBrains and Carahsoft Collaborations Democratize AI Access For Every Individual, Team, And Company NEW YORK, June 11, 2025 /PRNewswire/ -- Lemony today announced the official launch of its groundbreaking on-premise artificial intelligence solution that is redefining how organizations deploy generative AI. Lemony's secure, hardware-based node offers enterprise-grade 'AI in a Box,' empowering companies to run advanced, end-to-end AI workflows privately, instantly, and without cloud dependence. Lemony's AI nodes are stackable and scalable, creating small, modular AI compute clusters that support seamless expansion across users. Lemony can host the entire technology stack, from foundation models to lightweight, use-case specific adapters and specialized agents, and gives businesses the power of secure, on-premise AI that will allow them to grow and scale. Imagine activating thousands of emails, PDFs, and other files that were previously nearly impossible to access. Lemony activates previously dormant company data, securely, at scale. The result is a flexible, powerful system that grows with a company's AI ambitions. Lemony offers a fearless, refreshingly simple solution: a powerful, on-premise AI device with sophisticated AI models that requires no specialized knowledge. It was specifically designed to address the inherent privacy and compliance risks of cloud-based AI while democratizing access to this powerful technology. Lemony empowers every organization to participate in the AI revolution on their own terms to make internal knowledge accessible, searchable and usable across teams. Lemony brings plug-and-play AI, the power of large language models (LLMs), adaptors, and Retrieval-Augmented Generation (RAG), directly to the desktop. It is exceptionally easy to use: employees simply plug it into their computer or local network, upload their reference documents, and they are ready to go. "We built Lemony to make enterprise AI simple, cost-effective, and secure," said Sascha Buehrle, co-founder and CEO. "Organizations shouldn't have to choose between capability and control. Lemony delivers both, right out of the box." Strategic Partnerships with IBM and JetBrains The launch is bolstered by two major industry partnerships: IBM is collaborating with Lemony to deploy its enterprise-grade AI models on the Lemony nodes. These models will allow Lemony users to deploy trusted IBM AI solutions securely and locally, bypassing the need for massive infrastructure investment. JetBrains, the creator of intelligent software development tools used by more than 11.4 million professionals worldwide, is integrating its coding models and tools into the Lemony node. This partnership allows software developers to leverage JetBrains' intelligent development features on Lemony hardware, making IP and workflows locally executable and fully secure. The platform offers unique team capabilities; developers will collaboratively use JetBrains development platform with transparency and auditability, making it the first to assess AI-generated vs. human code for security and compliance. Carahsoft: a certified reseller and trusted provider to U.S. government agencies, will sell Lemony's secure, privatized AI nodes to its public sector and healthcare customers, providing additional evidence that Lemony is ready for mission-critical use in sensitive, highly regulated environments. Built for Privacy And ComplianceLemony is manufactured in the United States and Europe, and is designed to empower small businesses and corporate teams alike. It is already in use across highly regulated industries, including legal, finance, government, and healthcare, as well as teams or IT leaders who are hesitant to embrace AI due to concerns over the sovereignty of their corporate data. It is particularly resonant with teams wary of cloud-based data exposure. Each node supports up to five users and comes preloaded with a curated set of among 16 high-performance open-source models, including IBM's Granite family, several Meta's Llama variants, and others. With Lemony, different teams can run their own nodes or clusters of nodes and securely connect them. This enables teams to share knowledge across the organization, but only at the depth permitted by defined AI access policies. In other words, teams can tap into the expertise of others while maintaining strict control over what GenAI-generated knowledge is accessible. This federated yet governed approach allows large institutions to scale AI collaboration without compromising data boundaries, privacy, or compliance With no cloud connectivity, no data sharing, and a physical security key option, Lemony is a robust answer to corporate governance issues, including the rise of "shadow AI," business data shared with models for training, and mounting compliance demands. Users receive quarterly USB software updates, ensuring the node continuously evolves to include the latest technology, models agents, and updates, without breaching internal security protocols. Lemony has secured a $2M seed funding round led by True Ventures. Pricing and Availability Lemony is available today with a subscription price of $499 / month, which can be accessed by up to five individuals and includes hardware and software access, updates, and support. Pricing per node is reduced as customers add nodes. Teams can stack multiple nodes for advanced multi-model or departmental usage. A two-week free trial program is available at About Lemony Lemony is the only enterprise-grade, on-premise generative AI device that's ready to use out of the box in minutes, and delivers a holistic solution - combining hardware, AI, and software. It's also the first AI compute device currently released for on- or close to-desk use. Lemony was created for business teams that demand simplicity, security and complete control over their AI infrastructure. Designed as a compact plug-and-play hardware node that comes complete with multiple open-source AI models, Lemony enables high-performance AI workflows without relying on the cloud, making it the safest, easiest and most predictable AI solution available, for every individual, team and company. Lemonys' investors include True Ventures with key influencer advisors, including Meta's Nicola Bortington. View original content to download multimedia: SOURCE Lemony Sign in to access your portfolio
