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February 27.2026

2 Minutes Read

Atlassian’s 100% Off Teamwork Collection: Empowering Social Impact Teams Worldwide

Atlassian Teamwork Collection for Social Impact Teams engaged in discussion.

Unlocking Potential with Atlassian's Teamwork Collection

Atlassian's latest initiative—offering a 100% discount on their Teamwork Collection for nonprofits and social enterprises—marks a significant shift towards empowering teams aiming for social impact. Designed for teams working on community initiatives, this no-cost access for up to 25 users aims to alleviate collaboration challenges that many organizations face. By embracing tools like Jira, Confluence, and Loom, these nonprofits can align their efforts with broader goals, streamline processes, and ultimately maximize their impact on society.

A Focus on Global Access for Nonprofits

The introduction of an 85% discount for eligible nonprofits in low and lower-middle income countries further emphasizes Atlassian's commitment to inclusivity. With tools critical for effective project management, these organizations can expedite workflows and foster innovative solutions without the constraints of financial burden. This is essential as these teams work on the frontline of addressing global crises.

Free Resources to Enhance Collaboration

Atlassian is not only providing access to tools but also offering support through various resources such as office hours and webinars. This engagement helps teams learn to use these technologies effectively, ensuring they are equipped to manage projects, track service delivery, and build knowledge bases. With more than 190,000 social impact professionals utilizing these tools, there is a wealth of knowledge and community support available for organizations looking to integrate Atlassian's offerings into their missions.

The Bigger Picture: Enabling Social Change

The essence of Atlassian's initiative lies in their recognition that collaboration is key to any successful social endeavor. Their tools foster a collaborative environment where knowledge can be shared, and challenges can be tackled collectively. By equipping social impact teams with the right tools and support, Atlassian is playing a crucial role in facilitating significant global change, offering not just discounts but a pathway to a better world through enhanced teamwork.

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02.26.2026

How Atlassian's Rovo Dev CLI is Redesigning Homepages in Minutes

Update Transforming Web Design with Modern Tools In today's fast-paced tech landscape, rapid prototyping and agile methodologies have become crucial for successful product development. The recent capability to redesign a homepage within 20 minutes, integrating Atlassian's Rovo Dev CLI with Figma MCP and the Atlassian Design System, serves as a remarkable example of how AI can streamline workflows in software development. The Breakthrough Process: A Game Changer for Developers Traditionally, redesigning an application homepage could take several days of meticulous work, involving a frontend engineer interpreting design elements, verifying tokens and component APIs, and implementing styles and tests. However, relying on AI’s power can dramatically collapse this timeframe into minutes, showcasing a transformation from a painstaking process to a swift and efficient procedure. The AI-powered workflow starts by extracting design data from Figma, followed by querying the Atlassian Design System for component details. By sourcing accurate design tokens and components instead of using generic values, the resulting code not only aligns with the visuals but also adheres to accessibility standards, thus ensuring consistency and quality. Why This Matters: Speed, Consistency, and Accessibility The implications of this rapid redesign are profound. First, the enhanced speed allows developers to focus on creative elements rather than get bogged down in routine tasks. Second, leveraging the Atlassian Design System guarantees that every piece of generated code is consistent with the product's design language, minimizing errors associated with manual coding. Furthermore, this method emphasizes accessibility from the outset. By incorporating access control and standards directly into the development process, teams can confidently deliver applications that consider all users, including those with disabilities. This forward-thinking approach enhances the overall user experience and complies with contemporary compliance regulations. The Future of Workflow: A New Era for Development As we advance, tools like Rovo Dev CLI challenge traditional systems by embracing a model where efficiency and quality coexist harmoniously through AI integration. This paradigm assists developers in maintaining focus on critical aspects of software engineering, paving the way for innovative solutions to emerge at an accelerated pace. The synergy between tools like the Atlassian Design System and Rovo Dev CLI signals a future where developers can harness the power of AI to augment their capabilities and enable responsive design changes. This operational shift not only enhances productivity but also aligns development practices with agile principles, ensuring that the software can adapt swiftly to evolving user needs and market demands. In conclusion, the ability to redesign a homepage in just 20 minutes represents a significant leap in agile application development. As this technology matures, it can encourage a broader adoption of AI tools, fostering a culture of innovation and efficiency in tech.

02.23.2026

Building Trust in AI Adoption: Rachel Shepard's Insights from Atlassian

Update Understanding Trust in AI Adoption In the rapidly evolving landscape of artificial intelligence (AI), trust is emerging as a critical factor that influences user adoption. Rachel Shepard, an AI design leader at Atlassian, emphasized this point during her presentation at the World Summit AI. With the increasing prevalence of AI tools like Rovo, understanding how to build trust with users becomes paramount. Breaking Down Complex Systems The challenge lies in effectively introducing AI agents without overwhelming users. Shepard led a design sprint at Atlassian aimed at addressing this issue. She questioned whether the AI features being created truly aligned with users' expectations and mental models. Rachel's experienced insight suggests that when AI tools are perceived as overly complex or inconsistent, they foster skepticism, deterring potential adoption. Trust-Driven Design Principles Shepard highlighted several key design principles that underpin successful AI integration. First and foremost, meeting users where they are is vital. This means designing AI systems that resonate with user experiences, thereby reducing cognitive load. In replacing traditional personified agents with simpler "Skills," the Atlassian team created a user-focused approach where capabilities are seamlessly incorporated into daily workflows. This shift not only simplified the user experience but also reduced anxiety around using AI tools. Beyond Agents: A Skill-Based Approach By dissolving the concept of agents into manageable Skills, users can now access functionalities that cater to their immediate needs without the clutter of unnecessary choices. This results in an increase in feature utilization, highlighting the efficiency of presenting AI capabilities in a direct and intuitive manner. Shared Knowledge Drives Success The establishment of a shared skills registry allowed different teams within Atlassian to effectively utilize and share these Skills. This framework not only organized resources but also facilitated easier access to various AI capabilities, further removing barriers to user trust. As Rachel Shepard noted, creating a less rigid and more accessible AI environment invites greater user engagement. The Importance of Transparency Complementing Shepard’s insights, principles from the broader dialogue surrounding responsible AI systems emphasize transparency. Many industry leaders advocate for clear visibility into AI decision-making processes, allowing users to understand how AI-generated recommendations come to be. Such clarity reinforces trust and aids in overcoming adoption hurdles. Conclusion: The Path to Trustworthy AI As organizations look to integrate AI, the lessons discussed by Rachel Shepard and supported by industry-wide frameworks stress the importance of trust. By aligning AI features with user expectations, deploying transparent practices, and focusing on making AI accessible, companies can foster an environment where AI becomes a reliable partner in productivity. Cultivating trust is not just about avoiding risks but about setting the stage for successful AI adoption across diverse user bases.

02.22.2026

Catch Performance Regressions Early in Jira Cloud: Enhance User Experience

Update Unraveling Performance Regressions in Jira Cloud In the dynamic environment of Jira Cloud, performance regressions can often go unnoticed until they impact user experience. A minor change made by developers can have varying effects across the platform, especially with millions of active tenants utilizing unique configurations and datasets. This makes catching regressions—a scenario where the performance of an application deteriorates compared to its historical levels—an intricate endeavor. The Challenge of Multi-Tenancy Jira Cloud caters to a diverse range of users, each with distinct traffic patterns, data structures, and integrations. This variability significantly influences how performance regressions manifest. For example, a harmless update might disrupt service for 0.01% of tenants, leading to severe latency issues for a handful of users while remaining undetected in broader performance metrics. Why Conventional Metrics Fall Short Many performance monitoring tools rely on aggregate data, assessing metrics like Service Level Objectives (SLOs) at a high-level overview. This approach grossly oversimplifies the problem, masking regressions that could materially affect large enterprise customers. Hence, Jira’s engineering team has developed a system that focuses on per-tenant, per-endpoint metrics. This allows teams to receive alerts tailored to specific regressions affecting targeted user groups. Leveraging Advanced Analytics for Detection The innovative system in place utilizes statistical process control techniques to monitor each endpoint with precision. Instead of relying solely on global alerts, the framework examines individual performance histories. This method has proven effective—recent months have seen prompt identification and resolution of multiple production regressions. All this is supported by a robust data analytics engine that dives deep into operational metrics. Automated Root Cause Analysis: The Game-Changer To further streamline the mitigation process, Atlassian has integrated AI-driven root cause analysis (RCA) through their Rovo Dev CLI tool. This cutting-edge technology autonomously queries performance data and identifies changes in the codebase causing regressions, significantly reducing the time engineers spend diagnosing issues. In a recent alert concerning latency spikes due to a feature flag rollout, the RCA agent was able to pinpoint the cause in no time, allowing for swift action before the problem escalated to customer complaints. The Future of Performance Management As Jira continues to scale and evolve, the monitoring and management of performance regressions will increasingly rely on automation and sophisticated analytics. By leveraging a combination of tenant-specific monitoring, automated RCA, and refined alert systems, Atlassian aims to enhance the user experience across all levels of their platform, ensuring any arising issues are swiftly addressed. In an era where agile development is paramount, maintaining the balance between rapid iterations and stable performance is crucial. With these innovative measures in place, Jira not only addresses past challenges but is also well-equipped for future scalability.

Atlassian’s 100% Off Teamwork Collection: Empowering Social Impact Teams Worldwide

Unlocking Potential with Atlassian's Teamwork Collection

A Focus on Global Access for Nonprofits

Free Resources to Enhance Collaboration

The Bigger Picture: Enabling Social Change

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