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How Wendy’s turned everyday challenges into enterprise-wide transformation The learnings in this blog post are based on the session “Wendy’s recipe for launching faster with Atlassian” from Atlassian’s Team ’26 conference. Watch this session on demand. Prior to adopting a unified enterprise platform, the nationwide restaurant chain Wendy’s operated in a fragmented manner. Teams were managing their own tool subscriptions, duplicating efforts, and relying on inconsistent processes, spreadsheets, and disconnected data. Without a unified strategy or governed source of truth, it was hard to see work clearly, report on progress, support audits, or scale better ways of working across IT and business teams. That is why Wendy’s chose Atlassian as its platform for scaling. The team needed more than another project tracker; they needed a connected system that could support work management, portfolio governance, knowledge sharing, service workflows, assets, analytics, and automation without forcing every team into the same rigid process. Wendy’s Atlassian Administrator Deven Gambrell and Portfolio Manager of Technology Initiatives Emery Jordan shared how their two-person team helped grow Wendy’s Atlassian footprint from 147 users to nearly 1,000 active users in less than two years. Gambrell brings nearly a decade of Atlassian administration experience across both data center and cloud environments. Jordan has spent over 20 years in the program, project, and portfolio management space across multiple industries. Together, they are the entire Atlassian team at Wendy’s, and the growth they drove was powered by change management, relationship-building, and practical solutions delivered across the business. From focused IT adoption to enterprise-wide impact When Wendy’s began this journey, the company’s Atlassian footprints had pockets of usage, but it was not yet positioned as the central enterprise platform. Over time, that changed dramatically. What made Atlassian the right platform was not a top-down mandate. It was the combination of enterprise govern...

Atlassian named a Strong Performer in The Forrester Wave™: Strategic Portfolio Management Tools, Q2 2026

Cloud Fortified availability checks ambiguity during Connect to Forge Remote migration Hi Atlassian team, We migrated an existing Connect Marketplace app to Forge using Forge Remote, and we’re trying to understand how Cloud Fortified availability monitoring behaves during and after this transition. Our app previously had a Connect descriptor at: /atlassian-connect.json In the Forge version, we now define remote backends in manifest.yml using remotes.baseUrl, including region-specific URLs for data residency. During a recent backend deployment, we observed short-lived 502 responses in our server logs for requests with: User-Agent: Atlassian-CFA-healthcheck Path: /atlassian-connect.json This appears to have correlated with Cloud Fortified availability alerts. However, Forge apps do not use atlassian-connect.json as their descriptor, so we’re unsure whether this alert is coming from: A legacy Connect healthcheck still configured for the Marketplace app/version. A healthcheck for older Connect versions that still have active installations. A Cloud Fortified check that remains tied to the app’s historical Connect descriptor even after migration. Something related to Forge Remote availability monitoring, though the path suggests Connect. The documentation and forum posts seem to leave this unclear: CHANGE-775 says app base URLs and regions are monitored for Cloud Fortified availability. The Forge Cloud Fortified announcement says Forge reliability is handled differently, using invocation success rates and latency. Forge Remote introduces partner-hosted backend URLs, but we could not find a clear statement on whether Cloud Fortified probes Forge Remote remotes.baseUrl endpoints directly, or what SLI/SLO applies to those remotes. Could Atlassian clarify the following? For apps migrated from Connect to Forge/Forge Remote, does Cloud Fortified continue to monitor the old Connect healthcheck path? If older Connect versions still have installations, are their base URLs and healthcheck paths still monitored independently? Are Forge Remote remotes.baseUrl values di...

RFC-137: Forge App Suspension Behaviour RFCs are a way for Atlassian to share what we’re working on with our valued developer community. It’s a document for building shared understanding of a topic. It expresses a technical solution, but can also communicate how it should be built or even document standards. The most important aspect of an RFC is that a written specification facilitates feedback and drives consensus. It is not a tool for approving or committing to ideas, but more so a collaborative practice to shape an idea and to find serious flaws early. Please respect our community guidelines: keep it welcoming and safe by commenting on the idea not the people (especially the author); keep it tidy by keeping on topic; empower the community by keeping comments constructive. Thanks! Project Summary This RFC defines how Forge app installations behave when they enter a suspended state. We want to give you - as a Forge developer - a clear, predictable contract: what happens to your app’s functions, UI extensions, product events, and storage access during suspension, and what you should do to handle the transition cleanly when your app comes back online. Publish : 05 June 2026 Discuss : 19 June 2026 Resolve : 03 July 2026 Problem Your Forge app can enter a suspended state for a number of reasons - for example, when a customer’s Marketplace subscription lapses, when data is being migrated to a new region, or when Atlassian needs to take an app offline for operational reasons. Today, what actually happens to your app during suspension is not consistent: Your app may continue to appear available in some places while silently failing in others (e.g. invocations blocked). This creates a confusing experience for both, you and your customers. There is no documented, reliable contract for what you can expect during suspension - whether your functions will be called, whether your UI extensions will render, whether product events will be delivered, or whether your storage will be accessible. This also means that when ...

referral request I've read on here employees saying that the referral system at Atlassian is a blackhole but it guess it wouldn't hurt. please DM me if you'd be willing! thank you submitted by /u/ThePeggyOlson [link] [comments]

Prevent container image overwrites with immutable tags in Bitbucket Packages

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Atlassian named a Strong Performer in The Forrester Wave™: Strategic Portfolio Management Tools, Q2 2026 We improved across the board — and our Vision and Innovation scores tell a bigger story about where SPM is heading. We’re proud to share that Atlassian has been recognized as a Strong Performer in The Forrester Wave : Strategic Portfolio Management Tools, Q2 2026 . But the placement is only part of the story. We have evolved since 2024. In this recent evaluation, we received a Current Offering score of 3.10 and a Strategy score of 3.80, reflecting our continued investment in delivering comprehensive strategic portfolio management capabilities. Our scores reflect something we’ve been building deliberately: a point of view on where Strategic Portfolio Management (SPM) needs to go and the investment to get there with Atlassian’s Strategy Collection . And that point of view was validated with the highest possible score: 5 out of 5 on Vision and Innovation. From signal to action: what’s still broken in enterprise decision-making In our latest whitepaper Leading the Human + AI Enterprise , we surveyed 308 senior leaders and found a stark gap: 80% say their priorities are clear , but only 11% have their portfolio work connected to those priorities in a shared system. Fewer than one in three can act on a strategic change within a week. We call this mean time to pivot , and in most enterprises it’s measured in months, not days. AI doesn’t solve this. Our research found that the biggest factor in AI effectiveness is domain connectivity (whether strategy, work, and execution data exist in a connected model), more than any other factor. Fragmented operating models don’t just slow you down. They make AI more expensive to run and less valuable to the business. What Forrester recognized and what it signals Forrester’s vendor profile describes Atlassian as having “an AI-powered model with a connected work graph to break down organizational silos, deliver on strategic outcomes, and balance capacity and costs” and calls that vision “well in line with SPM’s expanded focus.” Our point ...

Prevent container image overwrites with immutable tags in Bitbucket Packages We’re excited to announce that immutable tags are now available for the Bitbucket Packages container registry . With immutable tags, workspace admins can set container image tags from being overwritten, moved, or modified after they’re first pushed. Tags are how your team refers to container images, but by default they’re mutable: anyone with push access can point an existing tag at a different version of the image. For a tag like v1.2.0 or prod, that means the image running in production today might not be the one you tested and approved yesterday. Immutable tags remove that uncertainty. Once a tag is locked, it points to the same image every time, so your releases stay reproducible, your deployments stay predictable, and you have the audit trail you need to meet compliance requirements. Benefits of immutable tags Improve reproducibility : A locked tag always points to the same image digest, so the image you tested is the image you deploy. Reduce deployment risk : Immutable production and release tags can’t be silently overwritten. Support compliance : Immutable release tags help teams meet audit and compliance requirements. How immutable tags work Immutable tags is configured at the workspace level from the Container settings page. You’ll choose from three options: No tags are protected : All tags stay mutable. This is the default. All tags are protected : Every tag becomes immutable after it’s first pushed. Some tags are protected : Only tags matching custom rules become immutable. When you choose Some tags are protected , you can create up to 7 custom rules per workspace. Each rule uses a rule name and an RE2 regular expression pattern of up to 100 characters, evaluated against the tag name being pushed. A built-in pattern tester lets you check a sample tag before you save. Here are a few examples: Goal Example pattern What it matches Protect a production tag ^prod$ Only the tag prod Protect semantic version tags ^v\d+\.\d+\.\d+$ Tags like v1.0.0 or v2.14.3 Prot...

How switching to Jira Service Management and Rovo helped Neta cut ticket escalations by 35% Ever wondered what’s possible when you connect service management, knowledge, and AI on a single platform? Meet Neta , the proprietary software platform for energy and utilities of Engineering Group (ENG) ENG is a global Digital Transformation Company with more than 14,000 employees across 50+ subsidiaries in 21 countries. Neta is supported by a dedicated team of around 700 professionals across Italy, Spain, and Brazil, delivering a modular platform for energy and utility providers that covers the entire “meter-to-cash” lifecycle, from CRM and billing to metering, ERP, and workforce management. They also provide Application Management Services (AMS), and the AMS team ensures business continuity and user support to a mission-critical platform for Utilities, combining application governance, domain expertise, and structured operational capabilities. The problem: too many escalations and siloed knowledge Before Jira Service Management, Neta’s AMS team was running on an older version of BMC Remedy. It worked, but it wasn’t built for what they needed next. The team needed a new approach with an effective, innovative tool that made AI an essential component for the evolution of service. The challenge Neta faced was familiar to a lot of support organizations: junior agents couldn’t handle complex tickets on their own. They’d need to pull in an already busy senior product expert. Knowledge lived in silos, scattered across teams and tools, and finding the right documentation to resolve a ticket ate up time that nobody had. “Our vision was simple,” Grazia Maria Zucca, Head of Application Management Services, told us. “Anyone in the AMS team — even at the junior level — should be empowered to be a product expert. That’s what we set out to build.” Connecting Jira, Confluence, and Jira Service Management Neta was already using Jira and Confluence for development and documentation. When they started exploring AI capabilities inside the platform — especially Atlassian Rovo agents ...

Open Tab: Joanna Coles has spent her formidable career at the center of media: London’s Fleet Street in the ’80s, New York magazine, Marie Claire, Cosmopolitan, and eventually the chief content officer role overseeing Hearst’s entire global magazine portfolio. She knows intimately what legacy media organizations have looked like at every stage of their life cycle and spent years “managing decline” from the inside. Two years ago, as Chief Creative and Content Officer, she helped take over and bring it to Substack, where it now operates a suite of newsletters and podcasts. She also writes , her own publication covering politics and power and where she co-hosts and sends out The Daily Beast Podcast to an audience of over 70,000 subscribers. Joanna invited us for a cup of tea and a slice of cake at , a West Village institution that has been importing British calm to Manhattan for over 30 years. There, she and Substack’s head of new media, , talked about the golden age of the glossy mag, the two questions Cosmo readers have never stopped asking, and what life looks like on the other side of the old media order. Hanne: For somebody growing up today in journalism school who might not know what old newsrooms were like, what was the culture? Joanna: Well, I mean, first of all, they were incredibly noisy because you had typewriters. And I am not exaggerating when I say that you would get seven pieces of paper, seven pieces of carbon paper. You would put it into the typewriter, and you would have to think very carefully about what you wanted to say, because if you made a mistake, you had to either rip the whole thing out and start all over again or you had to correct it seven times. And you would have what was called Tipp-Ex whiteout, where you had to cross it out. So there was more planning ahead of what you were going to write. It was fantastically noisy. Then computers came in, and half the newsroom, largely men, would say, “Well, I’m not going to use a computer. That’s ridiculous. There’s nothin...

CSM not available in sandbox I highly urge anyone that is currently a Jira JSM premium subscriber using CSM to go follow and vote for the following request in their portal. The fact that Jira has rolled this out without support in sandbox is ridiculous and we need to let them know with numbers that it's not alright. Enable Customer Service Management (CSM) Feature in Sandbox Environments | Atlassian Cloud - Create and track feature requests for Atlassian products. https://jira.atlassian.com/browse/CLOUD-12761 submitted by /u/CrumpinAintEasy [link] [comments]

Forge external auth completes successfully, but outboundAuth/finish tab does not close and UI shows “auth window closed” Forge external auth completes successfully, but outboundAuth/finish tab does not close and UI shows “auth window closed” Hi, I’m seeing an issue with Forge external authentication and I’d like to know whether anyone else has run into something similar. I have a Forge Jira issue panel using an OAuth2 provider configured against Microsoft Entra ID. The app requests an access token for my own backend API, not Microsoft Graph. The provider is configured roughly like this: providers: auth: - key: microsoft-backend name: Microsoft Backend type: oauth2 clientId: scopes: - openid - profile - email - api:// /access_as_user remotes: - portal-backend bearerMethod: authorization-header actions: authorization: remote: microsoft-login path: / /oauth2/v2.0/authorize exchange: remote: microsoft-login path: / /oauth2/v2.0/token refreshToken: remote: microsoft-login path: / /oauth2/v2.0/token retrieveProfile: remote: portal-backend path: /api/auth/profile resolvers: id: id displayName: displayName remotes: - key: microsoft-login baseUrl: https://login.microsoftonline.com - key: portal-backend baseUrl: https:// My backend validates the Microsoft JWT using Microsoft JWKS and checks: signature exp / nbf / iat aud = api:// tid / issuer scp contains access_as_user The retrieveProfile endpoint is intentionally simple. Forge calls it with the bearer token, my backend validates the token, extracts claims from it, and returns: { "id": " ", "displayName": " ", "email": " ", "tenantId": "...", "objectId": "..." } From the backend logs, everything looks successful: JWT valid aud=api:// scope=access_as_user GET /api/auth/profile [AUTH PROFILE] Returning Forge profile id=... displayName=... Also, if I manually refresh the Jira issue panel after the auth flow, the app correctly detects that the user is authenticated. So the credentials seem to be stored by Forge. The problem is that the auth browser window/tab remains stuck on: https://id.atlassian.com/outboundAuth/finish?code=...&state=... and the...

How to get attachments for a specific version of a page Hello, I’m trying to get the attachments of a page using the API. I’m using an API key, and to get a list of attachments i’m calling: https://{company}.atlassian.net/wiki/rest/api/content/{pageID}/child/attachment Once I get the attachment ID, I download it (this time using OAuth2 / 3LO) with https://api.atlassian.com/ex/confluence/{cloudID}/rest/api/content/{pageID}/child/attachment/{attachmentID}/download The results are correct. However, the first call returns all the attachments of all the versions of the page, not just the current version. For example, say the page has the following attachments Page version 1: Image1.jpg Image2.jpg Image3.jpg Image4.jpg Page version 2 (current): ImageAlbum.pdf (the images were deleted and merged into a single file) The results includes all 5 files. However, our app should see only the most recent one (in this case ImageAlbum.pdf). The attachment objects returned don’t have any mention of the version of the parent page where they are located, so I can’t filter them either. Is there a way to obtain the attachments of just the last version of a page using the API? Alternatively, with the ID of the page and the attachment, can I check if the attachment is present in the last version of the page? 1 post - 1 participant Read full topic

Rovo success stories Today I, the documentation department, produced release notes for a new release in 5 minutes. I copied the previous page in Confluence, opened it and told Rovo to write in the new release notes from a list of Jira tickets and a pointer to our product documentation Confluence root. submitted by /u/BlondBot [link] [comments]

What is AI observability, how it works, and which tools to use The problems with AI agents and apps often don't show up in normal monitoring tools: The code works, the API calls succeed, but the outputs are off. The model says the wrong thing. The agent calls the wrong tool. The bill is 4x what you expected. That's because normal monitoring was built for software that does the same thing every time. This gap is what AI observability fills. This guide walks through what AI observability is, the features and data it's built from, and which tools are worth using depending on your stack and stage. What is AI observability? AI observability (often used interchangeably with LLM observability and LLM analytics ) is the practice of monitoring the AI features of your application: prompts, responses, costs, latency, errors, and quality of outputs. It's a category that barely existed two years ago, because the problems it solves didn't exist either. Traditional APM tools like Datadog and New Relic were built to monitor software that behaves the same way every time. AI features don't. How is it different from traditional monitoring The short version: Traditional APM watches AI observability watches Whether API calls succeed What the model said Response times Token usage and cost per call Error rates Quality of outputs (was it a hallucination?) Service-level metrics Agent-level metrics (which tools were called, in what order) Did the code run? Did the model give a useful answer? The data model is different too. APM is built around requests, services, and traces across your application stack. AI observability is built around generations (individual LLM calls), traces (the chain of generations and tool calls that make up an agent loop), and spans (the steps inside a trace). AI observability tools also do things APM tools structurally can't, like semantic clustering of model outputs , which groups traces by what the model actually said rather than which endpoint was hit. That's a kind of analysis only possible when your monitoring layer unders...

How Ace Hardware Modernized IT with Jira Service Management Modernizing for a second century in business with integrations, automations, and hard-won lessons from a 5,200-store rollout. The learnings in this blog post are based on a discussion with Ace Hardware presented at Atlassian’s Team ’26 conference . Ace Hardware is a 102-year-old hardware co-op that’s a staple across the US. When they decided to modernize their IT service management, they teamed up with Atlassian ITSM specialized solution partner Stratacom to roll out Jira Service Management across their entire IT operation. At Atlassian’s recent Team ’26 event, Jeff Nason, a 26-year veteran at Ace Hardware and team lead for production support, and Greg Guertin, Project Architect at Stratacom with 18 years of experience, walked through their full implementation journey. Explore the story behind what they built, how they brought it to life, and the lessons that can help other IT teams modernize with confidence. A co-op at scale Ace Hardware was established in 1924, named after the fighter pilots of World War I. It operates on a co-op model — meaning the stores actually own the corporate entity. Every investment, every tool decision, every budget line item has to serve the best interests of all store owners, from chains running more than 300 locations to the individual mom-and-pop shop on the corner. With the co-op model inevitably comes scale. Ace’s 12,000 corporate employees (excluding store staff) support 5,200 domestic stores across 15 warehouses, a presence in 60 countries with 3,000 international stores, and a distribution network so wide that 75 percent of Americans live within a 15-minute drive of an Ace location. That means the stakes are high for IT. When ITSM breaks down at a company this size, such as when tickets get lost, tools go dark, or teams can’t see what’s happening across applications and infrastructure, the ripple effects reach thousands of stores and millions of customers. Getting ITSM right isn’t just an internal efficiency play; it’s a business...

Premium self-hosted runners are generally available

We asked Jira teams if sprint start/end timing actually matters. The answers surprised us. We asked Jira/Scrum teams a simple question recently: Do teams actually forget to start/end sprints in Jira? The responses were surprisingly split. Some teams said: ✅ “Never happens.” ✅ “Coverage/process handles it.” ✅ “Value delivery matters more than sprint timing.” Others said: ⚠️ PTO/time zones caused misses ⚠️ reporting got messy ⚠️ release cadence and cross-team coordination made predictable timing matter more ⚠️ sprint timing slipped because real life happened The biggest takeaway for us: There isn’t one truth: it depends on how operationally sensitive your environment is. For teams where predictable sprint timing matters, we built something to remove the operational overhead entirely: Sprint Automation for Jira Cloud - Auto Sprint Start Stop automatically start/end sprints predictable cadence without manual effort useful for distributed teams, release rhythm, or simply reducing one more operational dependency Curious where your team falls: “Timing doesn’t matter much” or “timing consistency matters operationally” ? submitted by /u/jain_archit1986 [link] [comments]

Premium self-hosted runners are generally available In December, we shared our plans to introduce pricing for self-hosted runners. You told us loud and clear that a free option matters. Today, as Premium Runners become generally available, we are happy to share that we will continue to have a free tier, which includes the use of up to 100 self-hosted runners as part of your plan. If your team needs more scale, dedicated support, or advanced management features, you can upgrade to Premium Runners when you’re ready. If you’re already using our V5 paid runners, you will automatically be migrated to the Premium tier. All other versions will remain on the free tier and you can upgrade anytime. Premium runner capabilities Premium Runners are designed for teams that need more control over how CI/CD workflows are executed and managed. Here are the upgrades that you get with the Premium tier. Feature Free Runners Premium Runners Support Community Premium Support Runner per Workspace 100 1000 Custom storage Custom volumes for docker based runners Custom resources for docker based runners Advanced queue management Runner Groups (coming soon) Auto-update (coming soon) To upgrade, go to your Workspace settings > Pipelines > Workspace runners, and choose your tier. Note that to activate Premium runners, your Bitbucket workspace must be connected to an Atlassian organization for billing purposes. Pricing Premium self-hosted runners are billed at $15/month per concurrent build slot , which is the maximum number of builds that can run simultaneously across your workspace. Standard plans include one free slot, Premium includes two slots. To manage your budget, you can set your max slots in workspace settings; any concurrent builds beyond that limit will get queued until a slot frees up. You are only billed for the max concurrent slots that you actually use in the month. Billing example If you’re on the Premium Bitbucket plan and set a concurrency limit of 50 runners but only reach a max of 30 concurrent builds during the month, you wi...

Introducing Reply Rules Reply Rules set by Le Grove , Entertaining with Beth , Sherry Ning , and Ministry of Pop Culture . Since its founding, Substack has sought to create the best possible conditions for cultures of many varieties to flourish online. Doing so often involves difficult trade-offs, but on top of our core model—where we only earn when writers, artists, journalists, musicians, and so on earn—we’ve managed to chart a distinctive course relative to other platforms. We hope that people, their work, and the communities that form around it all can be more real here than has historically been possible elsewhere, where massive scales and advertising models seem to drain reality even from good-faith creators over time. A few months ago, we shared this vision for what Substack can be: a place where you can build your own corner of the internet . That means your branding, your community, and your rules, without sacrificing the network that helps you grow. Today, we’re excited to announce the next feature in that effort, which we think will make it possible for even more people to be fully themselves and at home on Substack. We call it Reply Rules , and it combines a couple of elements: Anyone can now set their own Reply Rules, which will appear to anyone replying to them: in comments on their posts, in replies to their Notes, or in Chat. There is now a system that learns every time you hide a reply and preemptively hides replies it thinks you would hide yourself (you can always see these and un-hide them, which the system will also learn from). Soon, this system will read your Reply Rules when you set them and use them as guidance for its actions, in addition to what it sees you do. Our hope is that it gives you more time to focus on what matters: creating the work that encourages people to stop by your corner of the internet in the first place. Learn how to set up your Reply Rules here . Your house, your rules It will come as no surprise to anyone who has followed Substack’s arc over ...

In-house plugin issues compatible with jira 10.3.21 Our existing plugin is running on JIRA 9.12.30 without any problem. Recently, our jira upgraded to 10.3.21. So we need to upgrade the plugin to be compatible with jira 10.3.21. However , we ran into different build and compiling issues. The current build issue. It isn’t clear what version of dependency for com.atlassian.activeobjects is compatible with jira 10.3.21 so I can put the proper version of the dependency is the pom.xml. com.atlassian.activeobjects activeobjects-plugin 7.1.2 provided we also having below error during jira startup. 'gov.eop.omb.jira.vacms.MAX-JIRA-VACMS-Plugin' - '# JIRA VA Criteria Management System Plugin' failed to load. Cannot start plugin: gov.eop.omb.jira.vacms.MAX-JIRA-VACMS-Plugin Unable to resolve gov.eop.omb.jira.vacms.MAX-JIRA-VACMS-Plugin [197](R 197.0): missing requirement [gov.eop.omb.jira.vacms.MAX-JIRA-VACMS-Plugin [197](R 197.0)] osgi.wiring.package; (&(osgi.wiring.package=com.atlassian.plugin.webresource)(version>=7.2.6)(version =7.2.6)(version VA Criteria Management System Plugin (v1.0.93) 1 post - 1 participant Read full topic

anyone fixed the ""confluence page owner left"" problem? our IT runbooks slowly drift to wrong over months "we've got a ton of confluence pages for our IT runbooks and honestly its becoming a mess. most of the original authors either moved on or switched teams long ago. the pages slowly go stale as our systems change, and nobody updates them because nobody really owns them anymore. We tried a ""page reviewers"" rotation thing where ownership moved around the team but it just became one persons job and they burnt out fast. I also looked at Comala approvals and confluence's built-in page expiry reminders, but they still assume someone is around to act on the reminder which is exactly the gap we have. how have you guys handled this drift problem in a way that doesnt just dump it all on one person?" submitted by /u/ciggafteregret [link] [comments]

Create Custom field type view interface issue Hello, I am trying to define a new custom field type with the 3 interface, view, edit, contextConfig. I have followed the documentation and the examples here and I successfully create edit and contextConfig interfaces using custom ui, now I am facing the issue with the view interface, I tried both custom ui and ui kit but nothing is working, what I did understand is that the system always returns the formatter expression, not the resource, thank you for the help, kind regards, 2 posts - 2 participants Read full topic

ComplexConstraints: A Benchmark for Entangled Instruction Following

Interview Process for Sales Roles I’m going into the interview process for an Atlassian sales role and I was hoping to get insights on the structure of the process (how many interviews, presentations, etc). Any insights or advice from folk who have been through it would be super helpful! Thanks in advance! submitted by /u/Chris_Chilled [link] [comments]

How Loom turns customer support into a driver of business growth with Atlassian Customer Service Management Loom resolved over 80% of support inquiries with AI and reduced the likelihood of churn by 11% – transforming support into a growth driver. Key results After implementing Atlassian’s CSM and an embedded AI agent, Loom experienced: 80% AI resolution rate (without human support agent escalation) 11% less likely to churn +10.7% increase in video creation after launching in-product AI support widget Executive Summary Loom didn’t just improve support — they proved better experiences drive retention and growth. Challenge: Loom needed to scale support for millions of users without adding headcount, while reducing tickets and improving customer experience. Solution: Loom adopted Atlassian Customer Service Management (CSM) and embedded AI-powered, in-product support to move beyond ticket-based workflows. Impact: Loom transformed support into an AI-powered engine that reduces escalations, increases product engagement, and improves customer loyalty — turning support into a driver of customer value, not just a cost center. A fast-growing video messaging platform with a support model under pressure Loom, an Atlassian company and video communication platform used by millions, face a familiar challenge: how to deliver fast, high-quality customer support as demand for help continued to grow. It’s ticket-based system worked, but at scale, it created friction. Customer had to leave the product to get help, and support teams were overwhelmed with repetitive requests. The team was handling thousands of tickets each month, often spending close to 10 minutes on routine requests. A previous AI assistant had been introduced to help, but it struggled to resolve issues effectively, leaving most requests to escalate to support agents. After adopting Atlassian Customer Service Management (CSM) and embedding AI directly into the product experience, Loom transformed support from a reactive function into a proactive driver of customer engagement–resolving 80% of inquiries with AI and reducing chu...

Build AI-powered Forge apps with Atlassian-hosted LLMs

Teaching AI to speak our design language People are using AI to design and code every day, but AI tools are only as good as the context they receive. At Atlassian’s scale, information about our design language is spread across our systems: documentation sites, Confluence pages, Figma files, Looms, and hundreds of packages in code. Some sources are thorough, but others are missing key details, are out-of-date, heavy on images and visual information which can’t be read by code. Most are designed for people to view, not for machines to read effectively. When AI tools try to generate Atlassian UI from this fragmented landscape, the results are unpredictable and cost more than they should. Agents find outdated patterns, miss accessibility requirements, or invent components that don’t exist in our system. When we looked for solutions, we couldn’t see anything in the industry that had solved this at scale. So we set out to fix this by restructuring design system content into a coherent schema, providing the right level of context for AI tools to build Atlassian UI. We called this ‘structured content’. What structured content looks like in our design system Structured content, broadly, is content broken into consistent, machine-readable chunks instead of freeform prose. For example, an icon’s description appears in multiple places across the design system website, code, and Figma libraries. Consistent metadata is reused across different tools and locations, rather than being duplicated and eventually drifting out of sync. AI pushed us to take this approach further. Every component, token, and other element needs its details captured in a consistent, lightweight, and machine-readable format. We developed new content schemas for Atlassian Design System components, icons, tokens, lint rules, and foundational guidelines. The schemas provide a consistent format that covers usage, code examples, props, content standards, and accessibility requirements. Schemas live in TypeScript files alongside the code. From this sourc...

Build AI-powered Forge apps with Atlassian-hosted LLMs Summary The Forge LLMs API is now officially in Preview release and can be enabled on production environments. Developers can call Claude models directly in Forge, making it easy to create AI-powered app features without dealing with model integration or infrastructure. Forge LLMs run entirely in Atlassian’s secure environment, with no data egress, so apps that use it can qualify for the Runs on Atlassian badge . Keep reading to learn how you can go from zero to your first LLM call in just a few easy steps. LLMs have become a core tool in every app developer’s stack, powering features that feel almost magical. Users can query data with natural language, summarize long issue threads, and get smart, contextual suggestions, in an instant. Just a few years ago, these features would have been cumbersome or even impossible for developers to build. AI features clearly provide a ton of useful functionality for app users, but developers integrating LLMs have to deal with trust considerations and complexity. Where is data going and how is it moving between systems? Is my model infrastructure scalable and compliant? That’s why we’re excited to introduce the Forge LLMs API . The Forge LLMs API allows developers to call large language models hosted natively on Forge. Data stays on-platform, reducing risk and operational overhead. And because requests are routed only through Atlassian-managed infrastructure, apps that use Forge LLMs can still qualify for the Runs on Atlassian badge. “With the Forge LLMs API, we’ve been able to give our customers something they couldn’t get anywhere else: an AI that orchestrates their entire delivery lifecycle inside Jira, with no data leaving their Atlassian tenant. That data protection assurance has opened doors with enterprise and government customers that were previously closed to AI tooling entirely.” — Brett Taylor, CEO, BluBees Try it out It’s easy to get started . Because Forge handles model integration for you, you can skip the set-up and...

The independent writer’s advantage in the age of AI publishes jasmi.news , where she interviews AI researchers, eavesdrops at San Francisco house parties, and reports what she calls “an anthropology of disruption”—on-the-ground dispatches from how frontier technology is changing culture. At Substack’s recent Once and Future Media Forum, Jasmine shared her perspective on the comparative advantages she and other independent writers and creators hold in the age of AI: what skills are gaining value, which are losing it, and where economies of attention are going as a result. She’s the first to acknowledge that writers and creators hold a wide range of views—concerns, optimism, fears, curiosity — about what AI means for their work. But here she makes the case for why writers are better positioned than they might think, and what they can do to exercise that advantage whether they choose to use AI or not. What follows is a lightly edited transcript of her talk. When economists debate the future of work, they often ask the question: what will humans’ comparative advantage be? I think the key to understanding what the future of work looks like is not, How can humans race against the machine, how can we generate slop faster than the machines can generate slop? but rather, What are the human strengths, and how can we understand what skills are going to go down in value now that AI has made them commoditized, versus what skills are going to go up in value because they are still scarce and because the machines simply aren’t very good at them yet? I’m going to introduce four ideas, or provocations, that have shaped the way that I think about AI and my own media career. One: The value of summary will go down, and the value of secrets will go up. Reporting is the act of taking private knowledge and making it public. The things that people have not said, things in whisper networks, the tacit knowledge, the open secrets that have never been put in the public domain—the journalist manages to pluck them out and make them public. When yo...

In-depth: DuckDB vs Postgres We've made some apples to oranges comparisons over the years. None have been so diametrically opposing than Postgres and DuckDB. However, that also makes for a neat pairing: comparing Postgres and DuckDB is a masterclass on how databases are layers upon layers of design decisions. At almost every layer, Postgres and DuckDB go separate paths. They have some things in common. Both speak a variant of SQL. Both are tabular. And they both store data. That's about it. Postgres and DuckDB weren't built to serve the same purpose. Postgres is a general-purpose, client-server, row-based, OLTP database. It's designed for broad scenarios and is often used as the central database for an application or system. DuckDB is an embedded database and query engine that's lightweight and exclusively designed for analytics. We'll break apart the dimensions that differentiate Postgres and DuckDB. Then, we'll discuss why a single company (us) might use both. Memory structure The best place to start is memory structure and architecture. Postgres is a row-based OLTP (Online Transaction Processing) database. DuckDB is a column-based OLAP (Online Analytical Processing) database. Postgres's OLTP design uses heaps and B-trees. DuckDB's OLAP design uses min-max indices. Let's break down what all of these terms mean. Row-based versus column-based This orientation dictates how tabular data is stored on disk. In a row-based database, a single entry is stored sequentially. Imagine a database of students. In the student table, each student entry's name , age , description , height , etc. all live next to each other on disk. Retrieving a student's name and height is virtually the same as retrieving just one of those fields. In a column-based database, a column of data across all entries is stored sequentially. Imagine a columnar version of a student table. Instead of age and description living side-by-side, all the students' age values are stored sequentially. If a program needed the average age across ...

We put PostHog in Slack and now everyone's an engineer Today, we're releasing the PostHog Slack app into beta. We built it for those times when a colleague flags an annoying UI quirk, or a customer mentions a bug. The issues that normally end up on a backlog, untouched and ignored. With the PostHog Slack app, you @PostHog to "fix this" or "build that". It spins up a sandbox, makes a plan, edits files, runs checks, opens a draft PR, and answers review comments in the thread. The bot uses your product data as context and follows your repo's rules. It even reacts with emojis while it works, which makes it feel less like a coding tool and more like chatting with a clever teammate. Try it yourself → setup docs @PostHog in the #papercuts channel Paul D'Ambra was the first to fall in love with @PostHog. Among other important blitzscale duties, he owns the #papercuts Slack channel, where anyone can post the small bugs and nits they hit in the app. He'd been fixing them with PostHog Code like a good engineer. Now he mentions @PostHog in nearly every thread. PostHog/posthog#58811 Real prompts that became actual PRs It's awesome when a prolific engineer gets even more productive, but what makes @PostHog really magical is that it empowers every role. Sales, marketing, customer support – anyone can tag the bot with a bug, a papercut, or a feature idea. Here's a few examples of @PostHog usage across the org chart: The one where it built a new feature for the web app Will Wearing (technical account manager) asked @PostHog to add support to copy and paste for markdown into PostHog notebooks with proper rendering. The bot wrote the code, added 20 test cases, and auto-closed a related stale GitHub issue. PostHog/posthog is a massive production repo that most people in a sales role would never feel empowered to touch. Will's PR got merged in less than 24 hours, and the only hiccup was a flaky test (nothing wrong with the code, just CI being CI). Clearing CI is as much of a job as the code generation itself, and the bot sticks with a PR th...

In-depth: DuckDB vs SQLite DuckDB has often been called the "SQLite of OLAP". Is it an apt comparison? DuckDB and SQLite are databases with similar design principles. Both are lightweight, embedded, single-file, in-process databases that blur the line between database and library you link in. Both are also limited at distributing data over multiple nodes. The similarities end there. DuckDB and SQLite are built for completely different use cases. SQLite is a transactional OLTP database. It lives in the same class as products like MySQL, Postgres , or MariaDB. Conversely, DuckDB is an analytical OLAP database. It competes with products like ClickHouse or Apache Druid . In this article, we'll compare SQLite and DuckDB to each other as well as how they differ within their database classes, testing the "SQLite of OLAP" label. What is SQLite? SQLite is a self-contained, serverless, zero-configuration SQL database engine. That might sound like a lot of buzzwords, but it's a fancy way of saying that SQLite has as much in common with Microsoft Excel as it does with Postgres. It's entirely written in C and is one of the most thoroughly tested codebases in history. A SQLite database lives on a file (e.g. database.sqlite ) on disk. It runs as an in-process database, using a simple host application instead of client-server infrastructure. This differentiates it from MySQL and Postgres: with no client-server layer, SQLite is incredibly lightweight. It's just a utility. It's very small, living at just 750 kB compiled. It is not designed to be a distributed database. Theoretically, it can scale to hundreds of terabytes on a single machine, but cannot be horizontally scaled like most transactional databases. Concurrency is limited, too: SQLite only supports one writer at a time. The project is one of the most deployed databases in the world. It's used by applications that need transactional local storage. This includes mobile apps, browsers, coding language libraries, desktop applications, and insulated envir...

How We Cut up to 80% of Engineering “Chores” Using AI Agents in Jira

The future of product craft: Why AI-native PMs build better products, not just work faster

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

Migrating from Data Center to Cloud in Confluence — 150+ pages stuck in legacy content macro. Any help? Hi everyone, My company is in the process of migrating from Confluence Data Center to Cloud and I'm looking for advice from anyone who has dealt with a similar situation. Our setup: We are in the middle of creating a People Portal wiki with 150+ pages (and still growing) that serves as a self-service HR resource for all employees at our company. Over the past year, we carefully built every page using Panel, Section, and Column macros to create a clean, organized layout. The problem: After migration, all of this content gets wrapped in the legacy content macro. The pages: Don't look the same as they did in Data Center — formatting is broken/off Cannot be duplicated to create new pages Cannot be used as templates going forward My main question: Am I missing something about legacy content? Can it actually be edited in a meaningful way? From what I can tell, the editing capabilities are extremely limited — I can change text, but I can't restructure the layout, add/move macros, or really do anything beyond minor text updates. If there's a way to properly work with legacy content that I'm not seeing, I'd love to know. If not, that means we're looking at completely redesigning 150+ pages from scratch with no way to continue building new pages in the same format. Also looking for: Has anyone found a good workflow for rebuilding a large number of pages like this in Cloud? Are there any marketplace apps (Aura, etc.) that have helped replicate Panel/Section/Column layouts in Cloud? Any tips for making this process more efficient at scale? Is there any way to bulk-convert or batch-update legacy content pages? Any advice or shared experiences would be really appreciated. Thank you! submitted by /u/Professional-Ad-7115 [link] [comments]

How We Cut up to 80% of Engineering “Chores” Using AI Agents in Jira Our Jira engineering team was spending more time than we’d like focused on KTLO (keeping the lights on) tasks – the small, but important maintenance tasks nobody wants to spend time on. This includes work like cleaning up old feature flags, chasing flaky tests, fixing identified vulnerabilities, addressing accessibility issues, and chipping away at a long tail of bugs. As a team we wanted to shift our time towards enabling our engineering org to move faster with best-in-class tools, not just keep tech debt at bay. So we devised a way to use agents, Jira, and workflows to cut up to 80% time on key automated repetitive tasks. Jira is the heart of our strategy. Each work item is a record of the tasks we need to complete and acts as a prompt for agents. All the context an agent needs is shared using the work item, Atlassian’s Teamwork Graph , and the explicit instructions we include in our workflow automations. Our team has spent years fixing these exact categories of issues. That pattern recognition is what makes delegation to agents possible. We know what good cleanup looks like, so we can define clear parameters, build review checkpoints, and design a human-in-the-loop system that produces code meeting our standards. For our team, Jira isn’t just where work is tracked. It is where agents are given context, assigned work, and where our team controls what agentic work is actually merged into our codebase. Here are two examples of applying this framework to automating some of our engineering “chores”: How to fix flaky tests faster with AI agents in Jira Flaky tests are one of those maintenance problems that look small in isolation, but create real drag over time. They interrupt builds, reduce trust in CI, slow down delivery, and pull engineers away from product work. Our team previously spent two hours resolving a flaky test. We encountered roughly one flaky test per day, sometimes more. Each required an engineer to inspect the CI failure, reproduce the issue locally or...

A Cross-Browser (Chrome, Edge & Firefox) extension that automatically renders Mermaid diagrams in Confluence and Jira Atlassian Mermaid Viewer A Cross-Browser (Chrome, Edge & Firefox) extension that automatically renders Mermaid diagrams in Confluence and Jira. Authors write plain Mermaid code in a code block - readers see the rendered chart, with a hover toolbar to open in Mermaid Live or copy the source. https://preview.redd.it/ik27ig9uvp4h1.png?width=1280&format=png&auto=webp&s=cfd01f83a28334d4332cc02f63ea38a9d3ab90fb Available for Chrome (Approved) / Edge (Soon available): https://chromewebstore.google.com/detail/fpfcpjfohjfplfhfkihncaghkhgfccjp?utm_source=item-share-cb Available for Firefox (Approved): https://addons.mozilla.org/en-US/firefox/addon/atlassian-mermaid-viewer/ submitted by /u/____R2D2____ [link] [comments]

From alert noise to action: How 24 Hour Fitness transformed IT operations with Jira Service Management and Rovo Ops How a modernized IT Ops team cut alert noise, slashed ITSM costs by 37%, and built a fully connected Ops platform that traces change and reclaims on-call sanity. The learnings in this blog post are based on the session, “From alert noise to action: How 24 Hour Fitness modernized IT Ops with Jira Service Management”, presented at Atlassian’s Team ’26 conference. You can check out this session and others on demand. Across hundreds of fitness clubs in the United States, the 24 Hour Fitness IT Ops team is responsible for keeping critical systems running for members, employees, and club operations. Their work goes beyond building applications; the team also owns the day-to-day reliability, incident response, and operational processes that keep the business moving. Before Jira Service Management , IT operations at 24 Hour Fitness were an uphill battle: No automated on-call system . They had a homebrewed, static on-call schedule with no overrides, nothing dynamic. A human had to read alert emails coming from monitoring systems and manually figure out who to wake up at 2 a.m. Constant context switching. Once an engineer got the call, they had to log in, create an incident ticket, flip over to monitoring and telemetry tools, then flip back to the ticket to take notes. Disconnected data. Change records and incident records lived in different systems for months, so teams couldn’t reliably connect an outage to the change that caused it, or see downstream impact across locations. No change to incident traceability. If a deployment caused an incident (or dozens of incidents across multiple clubs), there was no way to trace it back. People made changes under the table because the old system was so onerous to work with. Top 4 issues for IT Ops teams not on Jira Service Management Too many alerts — a flood of notifications, many with zero actionable value Disconnected Dev and Ops tools — teams working across separate systems, without shared context for changes and incidents, or a si...

The AI-native SDLC is paying off: 19% more PRs and 2–3 hours saved per developer per week Reimagining software development around an AI-native SDLC AI is now a core part of how software gets built. 93% of developers use AI tools and nearly 30% of code is AI-authored [ source ]. Code generation has been the first breakout use case for developers but it is not the end of the story. The next chapter is AI at every stage of the Software Development Lifecycle (SDLC): Plan, Orchestrate, Code, Review, and Operate. That requires reimagining software development around a new AI-native SDLC, and building new measures to capture the impact AI is having on it. In this analysis, we focus on the parts of that shift where we have the strongest data today: coding and review workflows in Rovo Dev, measured through PR throughput and developer time saved. The role of the developer has changed already Atlassian’s State of Developer Experience report shows how quickly the developer role is changing. In 2024, only 38% of developers reported saving any time at all with AI. In 2025, 99% of developers said they were saving meaningful time every week, and re-investing this time towards improving the broader system around code such as code quality, engineering culture and documentation. But as AI accelerates building in the code phase, bottlenecks to the left and right of code become more pronounced. Clear planning, strong code review, testing, documentation, and operations matter even more when teams are managing a growing volume of agent generated outputs. The productivity challenge now for developers is how well they can orchestrate the entire system of work around the SDLC. Next come agents at every stage of the SDLC What comes next is AI doing meaningful work at every stage of the SDLC, not just code generation. We believe agents will partner with humans at every stage: The AI-native SDLC Plan: Humans establish intent and requirements. Agents draft technical breakdowns, estimates, and work items. Ideas become structured, executable plans a human can review, iterate on, and ap...

Andrej Karpathy's AI Autoresearch found a 3-year-old bug in PostHog's query engine (and improved performance across the board by 11%) A few weeks ago at a team offsite in Lisbon, we pointed an AI agent at our query engine, fed it slow queries from production, and let it run overnight. By the next morning it had found something embarrassing: for almost three years, every query with a timestamp filter had not been using ClickHouse's primary key correctly. The fix cut the number of granules ClickHouse had to scan by 62% on the benchmark query, and made the query itself meaningfully faster. This post is about the setup we used, the bug itself, and what we're building now so this kind of analysis happens automatically. What's autoresearch? The general idea isn't ours. Andrej Karpathy packaged it up and gave it a name in March 2026: give an AI agent a small but real system, a benchmark, and a budget, and let it loop; propose a change, run the benchmark, keep what helps, throw away what doesn't. Karpathy ran it for two days against a depth-12 nanochat training run and found about 20 changes that improved validation loss , some of which transferred to a bigger model. The shape isn't new (DeepMind's FunSearch (2023) and Sakana's AI Scientist (2024) are earlier examples), but Karpathy's repo is small and concrete enough to inspire you to build your own version in an afternoon. The interesting part for us is the second-order effect: the agent doesn't carry the bias that comes from living in a codebase. To us, the toTimeZone() wrap had just always been there. The kind of code you stop seeing. The agent has no priors. It runs every diagnostic, reads the surrounding ClickHouse and PostHog source for context, and treats a three-year-old expression with the same suspicion as the line you wrote yesterday. Setting up autoresearch for ClickHouse in a hackathon Every year, we run hackathons at company offsites. A lot of what's now PostHog ( session replay , the data warehouse , logs , and more) started this way. At a smaller joint team offsite for the Analytics Platform and Query Performance teams in Lisbon, our hack...

Minimum viable AI observability: what to set up after shipping your first AI feature A lot of "LLM observability" content out there is written for teams running production AI at scale: enterprise governance, fancy eval pipelines, dedicated AI platform engineers. Useful if you're there, but wildly over-engineered if you just shipped your first AI feature on a Tuesday. This guide is for the other half – the small teams, vibe coders, and solo founders who have a couple of agents or LLM calls in their app, no data team, and no idea where to start with AI observability. The answer isn't to set up everything, but to set up the minimum that keeps you from getting blindsided, skip the stuff that won't pay off yet, and add depth as your product earns it. Here's what that looks like in practice. What is AI observability? AI observability (often used interchangeably with LLM observability and LLM analytics ) is the practice of monitoring what's happening inside the AI features of your app: the prompts you send, the responses you get back, how long calls take, how much they cost, and whether the outputs are any good. Traditional APM tools ( Datadog or New Relic weren't built for this. They can tell you your API call to OpenAI succeeded with a 200, but they can't tell you the response was a hallucination, or that your prompt is costing 3x what it should be. AI observability fills that gap. The core building blocks are: Traces – the full record of an LLM call (or chain of calls) including inputs, outputs, model, parameters, and timing Cost and token tracking – how much each call costs, broken down by model, user, or feature Evaluations (evals) – ways to score the quality of outputs, not just whether they succeeded Prompt management – versioning and iterating on prompts without re-deploying code Feedback loops – capturing user signals (thumbs up/down, retries, abandonment) on AI outputs You don't need all of these on day one. Most MVPs need two. Why does my MVP need observability at all? Three reasons, in order of how painful they are when you skip them: AI featur...

How autoresearch found a 3-year-old bug in our query engine A few weeks ago at a team offsite in Lisbon, we pointed an AI agent at our query engine, fed it slow queries from production, and let it run overnight. By the next morning it had found something embarrassing: for almost three years, every query with a timestamp filter had not been using ClickHouse's primary key correctly. The fix cut the number of granules ClickHouse had to scan by 62% on the benchmark query, and made the query itself meaningfully faster. This post is about the setup we used, the bug itself, and what we're building now so this kind of analysis happens automatically. What's autoresearch? The general idea isn't ours. Andrej Karpathy packaged it up and gave it a name in March 2026: give an AI agent a small but real system, a benchmark, and a budget, and let it loop; propose a change, run the benchmark, keep what helps, throw away what doesn't. Karpathy ran it for two days against a depth-12 nanochat training run and found about 20 changes that improved validation loss , some of which transferred to a bigger model. The shape isn't new (DeepMind's FunSearch (2023) and Sakana's AI Scientist (2024) are earlier examples), but Karpathy's repo is small and concrete enough to inspire you to build your own version in an afternoon. The interesting part for us is the second-order effect: the agent doesn't carry the bias that comes from living in a codebase. To us, the toTimeZone() wrap had just always been there. The kind of code you stop seeing. The agent has no priors. It runs every diagnostic, reads the surrounding ClickHouse and PostHog source for context, and treats a three-year-old expression with the same suspicion as the line you wrote yesterday. Setting up autoresearch for ClickHouse in a hackathon Every year, we run hackathons at company offsites. A lot of what's now PostHog ( session replay , the data warehouse , logs , and more) started this way. At a smaller joint team offsite for the Analytics Platform and Query Performance teams in Lisbon, our hack...

Created a test reporter that can catch up with the pace of AI era Hi everyone, I have been working on Testream for the last couple of months after getting overwhelmed by the deep learning curve of the mainstream Jira test management tools (Zephyr/Xray) and having to create manual test cases and maintaining them instead of automating the testing. On top of that, AI has integrated into our development flows and the amount of automated test code is beyond of the point manual tests tools can catch up with. Testream has a free tier that is available without entering credit card details, so please check it out and give it a go if you like it. I would love to get all the feedback from you. Thanks in advance! Website: https://testream.app Marketplace Listing: https://marketplace.atlassian.com/apps/3048460704 Documentation: https://docs.testream.app submitted by /u/Sikallengelo [link] [comments]

cross-team access requests touch 3 different owners. how do you get them all to approve in less than a week? Every time a new PM joins a project on our side, they need access to the jira board, the confluence space, and a handful of squad slack channels and each one is owned by a different person. So approvals end up taking days because these three owners are basically never online at the same time. We tried using the approvers field in JSM with an all-approvers transition, but it falls apart pretty fast. The slack channel owner isnt even a JSM user, and the confluence admin sits in a totally different project from us. So the approver list cant really span all three systems the way we need it to. Has anyone here figured out a pattern that doesnt drag on for a whole week? submitted by /u/Pure-Mango-2481 [link] [comments]

I built this Jira automation directly into OdinFlow — here’s a 13‑second demo I’ve been building a metadata‑driven automation engine for Jira inside OdinFlow, and this workflow is already fully implemented. The system pulls the project metadata, maps the fields automatically, and executes the update. If Jira returns a 429, OdinFlow immediately backs off and re‑queues the request so it fits the instance’s actual rate limits. No mockups — the video is the real workflow running. If there’s a Jira process you’d want to see automated, drop it below. I’m building out more demos based on real user pain points. submitted by /u/Ok_Strength4594 [link] [comments]

The future of product craft: Why AI-native PMs build better products, not just work faster AI use is accelerating across the modern enterprise. Teams are moving faster. The barrier to building will continue to drop. But the cost of building the wrong thing is about to skyrocket, because teams can now ship more of it, faster. Atlassian’s State of Teams 2026 report found that 89% of executives say AI has increased the speed of work. But only 6% feel confident they can point to specific organisation-wide AI ROI. That gap is a sign that speed, on its own, doesn’t actually create better products. After watching hundreds of product managers across Atlassian navigate this shift over the past year, we think solving the speed‑without‑ROI problem is less about tools and more about three changes in how product teams work. The job hasn’t changed. How you do it has. 1. Product craft is still the differentiator The PMs who thrive won’t be the ones who use AI most. They’ll be the ones who use it with sharper judgment, who know what’s worth building and why. Atlassian’s AI Collaboration Index found that teams who treat AI as a shared partner, rather than a personal shortcut, see 2x the ROI on their AI efforts. The difference isn’t adoption. It’s judgment about where to apply AI and when to override it. AI changes how you work. Your judgment is what makes it count. 2. AI collapses the distance between idea and evidence Product managers have always sat between the customer and the solution. The problem was the distance: commission the research, wait for the readout, translate for the team, review the mocks, ship, and hope for the best. AI compresses that loop. PMs can now prototype ideas, query customer data, and test concepts directly, not to become designers or engineers, but to develop ‘product builder’ skills. That means flexing into areas that were previously out of reach. The closer you are to the work, the sharper the craft. So, discover faster, validate sooner, and communicate with a working prototype, not a document. 3. The PM’s job is to rewire how the team learn...

RFC-137: Forge Module for AI Skills RFCs are a way for Atlassian to share what we’re working on with our valued developer community. It’s a document for building shared understanding of a topic. It expresses a technical solution, but can also communicate how it should be built or even document standards. The most important aspect of an RFC is that a written specification facilitates feedback and drives consensus. It is not a tool for approving or committing to ideas, but more so a collaborative practice to shape an idea and to find serious flaws early. Please respect our community guidelines: keep it welcoming and safe by commenting on the idea not the people (especially the author); keep it tidy by keeping on topic; empower the community by keeping comments constructive. Thanks! Project Summary We are introducing rovo:skill , a new Forge module type that lets developers define AI skills directly inside a Forge app. A skill is a named, reusable AI capability backed by a SKILL.md instruction file and optional Forge tools. In v1, those tools are implemented as Forge action modules from the same app. When an app with rovo:skill modules is installed, those skills are automatically registered with the Atlassian Skills Registry, making them discoverable by Rovo Chat and other Atlassian agents without any additional configuration. This RFC describes the developer experience, manifest design, packaging pipeline, skill lifecycle, end-user experience, and open questions we’re working through. We’re seeking feedback from Ecosystem partners before we finalise the implementation. Publish: 29 May 2026 Discuss: 12 June 2026 Resolve: 17 June 2026 Problem Forge developers can already expose callable capabilities to Rovo Studio agents through Forge action modules, and those actions can appear as ‘skills’ (soon, tools) in parts of the current UI. However, that model does not give developers a first-class way to package reusable skill instructions, examples, and supporting files that an agent can load at runtime. Today, ...

"Get users from group" no longer shows email addresses for "app" users (Atlassian Service Accounts) Previously, the Get users from group API (ex: YOURSITE.atlassian.net/rest/api/latest/group/member?groupId=YOURGROUPID ) would return an emailAddress for Atlassian’s new(ish) Service Accounts . Recently, this has changed. For an account with an email like: sa-something-123456@serviceaccount.atlassian.com , the emailAddress is simply missing from the response. I don’t recall if those accounts previously had the accountType of app but they do now. It seems like Atlassian is having difficulties figuring out how to treat Service Accounts vis-a-vis regular accounts. Since their introduction if you looked that same group in a site’s Groups UI, the SA would not be visible. The only way you can view and edit an SA’s Group memberships in the SA admin interface for each one. Which I suppose makes sense - SA’s are “global”, and groups are site-specific. (Although we are still a non-Vortex organization, not the centralized user management experience.) Still it’s frustrating that the automation I wrote to pull all members of a group, including email addresses, is now broken. 2 posts - 1 participant Read full topic

Cross-Benchmark Generalization for Long-Horizon Agentic Tasks

Atlassian P50 → cleared at P40 (down-leveled twice), then hiring freeze killed the team match. Full loop. 8+ YOE, ex-Oracle SMTS (IC3), laid off Sep 2025, IITK. Interviewed for Atlassian Senior SDE (P50) Dec 2025 – Jan 2026. Cleared at P40 (down-leveled from P50), then Atlassian froze hiring and laid off 10% before team matching could start. Posting because (a) Atlassian's loop is genuinely different from typical FAANG, and (b) the down-leveling logic is worth understanding so you don't repeat it. Atlassian's format is different — read this first These aren't whiteboard problems. You set up an empty project in an IDE, write extensible clean working code with JUnit test cases and follow-ups . Not LeetCode speed-coding. The bar is "would I want this in my codebase," not "did you get O(n log n) under 20 min." Time management matters more than at most companies. R1 — DSA Coding ( Tennis Court Booking + 2 follow-ups ) Fumbled mid-way but recovered. Working code with JUnits, walked through both follow-up implementations. Verdict: P50 Hire (Medium Confidence) ✅ R2 — Code Design (Middleware Router) This is where it went sideways. Interviewer insisted on a Trie -based implementation — I genuinely hadn't drilled Trie in prep. Got it working after some struggle, but lost 5–7 minutes to a single-character typo from an IntelliJ autocorrect that wasn't obvious in my hurry. No time left for follow-ups. Verdict: P40 Hire (Strong Confidence) — first down-level R3 — System Design (Web Crawler) Thought I killed this. Prepped with HelloInterview 's WebCrawler tutorial + ran the problem through ChatGPT from a few angles. Went in confident. Feedback came back: "didn't ask enough clarifying questions." I'd prioritized speed + depth + critical design choices over upfront questions I felt were obvious. Lesson learned: even when clarifying questions feel performative at senior level, do them anyway. Atlassian's rubric scores them as a separate dimension, and "I'm too senior for obvious questions" is a fast way to get marked as not collaborative. Bitter pill but a real one. Verdict: P40 Hire (Str...

Is AI flattening your team’s creativity? Here’s how to tell.

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24 tips for giving S-tier demos A good demo can be the difference between a project shipping or shutting down, and a startup getting funded or going nowhere. Yet most developers don’t invest in learning how to demo well – probably because we enjoy building things more than presenting them. I watched 50+ hackathon demos back to back at our offsite last week and, after that many in a row, some patterns became super obvious. Here's a practical list of what set the S-tier demos apart from the rest. The basic structure 1. Pick one main point you want people to remember and make everything else in your demo revolve around it. Typically, this should be the problem you’re solving and how you’re solving it. 2. Get to your main point as soon as possible. No one needs the whole story behind the idea – if you need context, keep it 1-2 sentences max. 3. Treat the demo like a pitch, not a product walkthrough. The goal isn’t to just show off the cool thing you made, it’s to get people excited about it. 4. End with something immediately actionable. It can be as literal as a QR code, or simply calling for contributors and inviting audience questions. One team during our offsite hackathon created a system that lets you interact with PostHog over any messaging app, so they put up a phone number in their last slide (which I texted right away): The MCP Analytics team even announced that their project was already rolled out to 25% of PostHog users. Shipping before you even demo adds a huge "wow" factor, especially if you can show real user data flowing. Storytelling tactics 5. Lead with a shared pain point that everyone in the audience understands. Two of our designers built a web app that automatically tags and indexes illustrations. They opened their demo by asking who has had trouble finding a specific hedgehog in their chaotic lovely “Hoggies” Figma file. Everyone raised their hand. 6. Borrow a familiar concept to explain a new one. For example, one hackathon team explored building our own take on Claude Cowork ins...

Atlassian Design System: Building the context engine for the AI era Design systems are no longer ‘just’ about consistent UI and component libraries. They’re strategic capabilities that harness design intent, drive coherence and accelerate a team’s ability to innovate. Here’s how the Atlassian Design System (ADS) is evolving to be a full stack context engine to meet the demands of an AI-native future. Maria Christley is the Head of Design for the Atlassian Design System, leading over 35 designers globally across Design Language, Accessibility, Systems Architecture, and AI. She is a 2025 Women Leading Tech finalist and has spoken at Figma Config and UX Australia. Rachel Radford is a Design Manager on the Atlassian Design System team, where she leads designers working on the systems, components, and practices that power Atlassian’s products at scale. She is a founding leader of Friends of Figma Wellington, New Zealand. Design Systems with Figma – Sydney The value of a design system has never been higher Whether it’s 2016 or 2026, certain truths about working in design persist. Seeking buy-in is hard. Brand coherence is always at risk. Knowing which design decisions have been made, and how to apply them correctly remains elusive at scale. These aren’t new problems. But the expectations around them are accelerating. Competitive landscapes are shifting faster, and customers notice the gap between products that feel considered and those that feel assembled. Design systems have a leading role to play in how companies close that gap, and we believe the value of the design system has never been higher. Today, design systems play a leading role in: Front-end infra Components are deeply connected to the codebase and engineering practices. They are part of the performance and reliability story, not just the visual layer. This layer is now more agentic than ever in how it show up to designers and developers via the tools they use, ultimately making it easier and faster to produce high quality artefacts. Fluid collaboration, not linear handoffs. T...

Open Tab: Kevin Kelly We sat down with writer, editor, photographer, and futurist at the Interval at Long Now in San Francisco’s Fort Mason. Part cocktail bar, part science-centric museum to the future, the Interval houses an eight-foot-tall model of the solar system, artwork by the musician Brian Eno, and prototypes for the Long Now Foundation’s most ambitious project: a clock being built inside a West Texas mountain, designed to keep time without intervention for 10,000 years. Kevin serves on the board of the organization, which believes that a civilization seriously anticipating a long future would think and build very differently. Kelly is also the founding executive editor of Wired, where he spent the better part of the 1990s helping people understand technology as culture. Before that, he was a key editor and publisher at the Whole Earth Review and a central figure at the Whole Earth Catalog, the legendary tools-and-ideas resource that shaped a generation of independent builders and thinkers from the late 1960s through the 1980s. He is the originator of the “1,000 true fans” theory—the idea that a creator with direct access to their audience needs only 1,000 people willing to buy anything they make to sustain a living—one of the most practically influential ideas in independent media. In his conversation with Substack’s head of new media, Hanne Winarsky, he talks about his journey through tech and media and the subjects he writes about for his Substack audience at his newsletter, : taking a prototyping approach to life, why optimism is a daily practice, and some guesses for where we’re headed next. Hanne: There was no web at the very launch of Wired? Kevin: The web happened within a year or so of Wired. The first couple of years we were working on it, it was going to be a paper magazine with an online presence. So we started Wired Digital, which was actually in a separate building, and we developed a whole bunch of editors and people, and they were inventing what the media would be...

When evaluating third-party vendors for a migration, what technical standard should be prioritized to safeguard data and establish a foundation of trust? submitted by /u/AmbitiousYudi1991 [link] [comments]

The (for now) exhaustive list of our tools Product analytics : our bag. Session Replay : see exactly how your product is used by real users. Understand patterns and debug issues visually to improve support. We also have heatmaps(!!) 🔥 Feature Flags : Roll out features for specific users, groups, or percentages of traffic, with minimal code required. Great for beta programs and A/B testing. Speaking of A/B testing: Experiments . Measure the impact of different variations of your product, and see what's statistically significant. Error Tracking : capture, monitor and resolve exceptions within your app. Helps you debug quickly, so you can spend more time shipping code. Endpoints : build embedded analytics using data from PostHog, and display live metrics or customer-facing dashboards. Surveys : Collect feedback directly from users with templates like Net Promoter Score and Customer Satisfaction Score to choose from. Data Warehouse : one source for all of your important data, including external sources like Stripe and Postgres. A huge chunk of the real value in using PostHog. PostHog CDP : enriches that data, and allows you to pull it in real time from wherever you need it, and send it to dozens of destinations. Data Modeling : take advanced control of how your data's cleaned, enriched and modeled. Web Analytics : Simple dashboard, privacy-friendly, and with no sampling. Especially useful for marketers and content creators. AI Observability : gather data for your AI and LLM products' usage and performance, including Prompt management and Evaluations. Workflows : a no-code, drag-and-drop tool for automating actions and sending messages to users. Logs : search and analyze your application logs with PostHog and OpenTelemetry. Correlate this with your PostHog events and user data. Get deeper insights. Boom. Customer Analytics : build towards product-market fit and understand your customers' demographics without the need to build dashboards from scratch, because that sucks. Support : built-in customer assistance feat...

How AI is Changing Developer Workflows: Lessons From Engineers I sat down with engineers who use AI coding agents for the vast majority of their implementation work. Stack Overflow’s 2025 Developer Survey found 84% of developers using AI agents for software development are using them for actual implementation work, so I wanted to understand what that looks like up close. What I heard reshaped how I think about where developer tools need to go next. This piece covers what those engineers actually spend their time on, where trust in AI breaks down, and what that means for the tools built to support them. What is AI-native developer work? AI-native developer work means writing code is no longer the primary bottleneck. Engineers use AI agents to handle the bulk of implementation. They write the prompts, review the output, and spend the rest of their day on everything the agent can’t do alone: gathering context, planning work, and making judgment calls. The most rigorous study to date, a randomized controlled trial from METR , surfaced just how much the work around the code shapes whether AI tools actually help. What is changing in AI-native developers’ workflows? Most of the day goes to context, not code When I asked engineers what takes up most of their day, almost none of them pointed to writing code. They pointed to everything that happens before an AI agent can be useful: gathering context. The pattern came up in every interview. Download a PDF from the wiki. Paste it into a chat. Open the repo, copy the file path. Find the right person, message them, wait for a reply. Re-read the architecture doc someone wrote eight months ago. Stitch together enough context for the agent to do something useful. Then prompt it. Then review. I have to manually go through and find all the relevant docs. It’s scattered across our wiki, Google Docs, Slack threads, email. Very time consuming.” In 2026, code generation is fast. Context assembly is slow. That gap is where most of the friction in AI-native development lives. Atlassian’s State of Devel...

In Safari, with PostHog session replay enabled, window.fetch is often wrapped so the body becomes a ReadableStream. Safari then throws error fetchReport uses apiRequest → fetch() with body: JSON.stringify(...). In Chrome that’s a string body and works. In Safari, with PostHog session replay enabled, window.fetch is often wrapped so the body becomes a ReadableStream. Safari then throws: NotSupportedError: ReadableStream uploading is not supported The failure is caught here, so you only see a console log and no POST in the network tab: That matches: Chrome OK, Safari + PostHog broken, InView can still fire. submitted by /u/FomexSystems [link] [comments]

My Internet: Kate Lindsay Twice a month, internet-culture publication logs on with someone extremely online and asks them nearly the same set of questions: What memes are making you laugh? Where do you get your news? Do you text people voice notes? The series is called My Internet, and it’s one of several recurring formats that founders and have built since being laid off and starting their newsletter in 2021. Over the years, they’ve expanded it into a multi-vertical operation, with the free interview series drawing readers in and paid tiers that unlock essays, a Friday media gossip column, and a video series. Kate told us Embedded’s growth has been “a lesson in consistency”: nearly five years of publishing almost every week, building an audience entirely their own. Kate has made her career in part out of naming what the internet is doing, contributing to our vernacular and comments sections phrases like “millennial pause” (first seen on Embedded ) and “ rawdogging .” She’s been cited as an expert by the BBC and CBC, and co-hosts ICYMI , Slate’s podcast about internet culture. Today we flip the script on one of the internet’s shrewdest observers, asking Kate to sit for her own Embedded Q&A and talk about the business she’s built around the format. EMBEDDED Founded: 2021 Format: Twice-weekly newsletter Verticals: My Internet , a recurring Q&A series about the online habits of interesting people; media_gossip , a Friday sub-newsletter covering the media industry; Screen Time Diaries , a video series documenting what’s happening across Kate’s screens in real time Tiers: Free (creator interviews, My Internet Q&As), Paid—$5/month or $50/year (Monday essays, Friday media_gossip edition), Founding Member—$75/year (all of the above, plus your name inscribed in the “Hall of Embeddeds”) What’s been your best-performing newsletter post? Why do you think it hit? It’s never the ones you expect, but this one is far and away Embedded’s most popular post, so much so that it was syndicated and translated int...

Moved a Jira ticket to a different project in seconds. OdinFlow handles single precise updates or massive bulk operations with the exact same ease. No scripts. No headaches. CSV imports only if you want to. One workflow. Any scale. Built to solve problems and to not break the user’s bank. #DeadVikingSoftware submitted by /u/Ok_Strength4594 [link] [comments]

How to shift your mindset from “AI as a tool” to “AI as a partner”

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

Trouble accessing service-desk-ops api Hi, I am using my service account token (it has proper accesses granted) to do stuff with Jira, but also check teams on call schedules etc. It works properly for Jira api, where I am using this base url: “ https://api.atlassian.com/ex/jira/{cloud_id} ” But when I am trying to check stuff from service-desk-ops, API I am getting 401 unauthorized. The documentation ask for customerID, but I am not sure what that is, and why is that different that cloudID? developer.atlassian.com About.css-1afrefi{display:inline-block;-webkit-flex-shrink:0;-ms-flex-negativ...... This is the reference for the Jira Service Management operations REST API. The REST API is for developers who want to integrate Jira Service Management operations with other applications or administrators that want to automate their workflows and... I am getting: RuntimeError: Failed to list Jira Ops teams: 401 Client Error: Unauthorized for url: ``https://api.atlassian.com/ex/jira/{cloud_id_masked}/jsm/ops/api/v1/teams/{team_id_masked}/roles How do I get the customerID? What is that, or is it just old documentation, and no one noticed it? Is the URL I am using okay? The authentication I am using (bearer token in header) should be working fine, since it works for Jira stuff. 1 post - 1 participant Read full topic

Please help!!! How to request a Refund in at Atlassian Hello, Atlassian Team. I am from the Philippines. I am a 20-year-old college student with no work, and I honestly have no idea what Atlassian is until I received your email regarding Jira Premium. I would like to request a refund regarding the possible unauthorized auto debit charges on my GCash account. I was not aware that any auto debit transaction or subscription had been activated under Atlassian. The merchant name shown in my transaction history says “Google Merchant,” so I am not completely sure if Atlassian is the one who deducted the money from my account. However, your email regarding Jira Premium was the only related message I received before the deductions happened, so I kindly ask you to please check and verify this. The only email I received related to this was about the Jira Premium trial details at around 10:17 AM, but my GCash account had already been deducted earlier at approximately 10:03 AM. Because of this, I was unable to review or cancel anything before the charge was processed. My GCash account was deducted: ₱50 on May 12, 2026 ₱330 on May 14, 2026 ₱330 again on May 27, 2026 If these charges are connected to Atlassian, I kindly request a refund of the full deducted amounts. Thank you for your understanding and assistance. submitted by /u/Forward-Kiwi6581 [link] [comments]

Routing on full page module not working anymore Since today the routing in the confluence:full-page module is broken. It suddenly gets the whole route instead of the route from the defined routePrefix. When i try to route within the app i get this warning and a 404 not found page. This is on a development environment but production apps also have stopped working. 2 posts - 2 participants Read full topic

Training our own AI models I've never been more excited about the future of PostHog. Over the past year, we've started building more AI-powered features into PostHog, like our AI installation wizard , PostHog AI , and our MCP . They're all wildly popular, but they're only the start. PostHog's next chapter is about building more proactive, self-driving products. Products that surface answers and solutions for you, act on them, and improve over time. This is the vision for PostHog Code , which is now in beta. To enable this and more products like it, we want to try something new. We want to train models on data in PostHog. What we want to build We have two goals here: Make our existing products smarter, more proactive, and useful to you Build entirely new products, like PostHog Code, that help teams build better products, faster The first area we're interested in is session replay analysis . PostHog AI can already detect issues in replays, but it's expensive and doesn't scale well. We want replays to be as powerful at scale as they are for diagnosing the problems of individual users, and we think a model trained on the underlying data that powers replays will help us achieve this. Another idea I'm especially excited about is synthetic user testing – i.e. using our knowledge of user behavior to identify when users might get confused, or what flows might break, before you ship to production. As coding models improve, many people are seeing test and review workload increase hugely. We want to automate this, so you can focus on your product. And, if we can get better at predicting user behavior , we should be able to suggest changes that will improve conversion, and reduce user frustration, for features you've already shipped as well. If we can automate this work for you, you'll spend less time on manual analysis and burn fewer tokens in the process. Our ideas here are experimental. It will take iteration to figure out how to train models effectively, and what data is actually useful. But, so far, ...

The 5 best customer-facing analytics examples (and what to steal from each) Customer-facing analytics are the stats you show your users, not your internal team. A users' usage dashboard, a "23 people are viewing this right now" counter on a product page, a public leaderboard, or how big your support queue is. Under the hood, a lot of these are powered by the same kind of infrastructure: a fast, authenticated API layer that takes product data and ships it straight into the place users will see it. At PostHog, we call this Endpoints . Endpoints are a way to expose your PostHog data as fast, versioned, authenticated APIs you can drop straight into your product without maintaining a custom API backend. But, rather than telling you what customer-facing analytics is, we’re going to show you five companies who do it really well, and for each one, we've pulled out what's worth copying. 1. Vercel Analytics: Embedded customer usage dashboard Vercel is the textbook case of a dashboard that customers log in to check. Every project deployed on Vercel ships with a Web Analytics panel that surfaces live visitor counts, top pages, referrers, UTM campaigns, and geographic breakdowns. All one click from the deploy button, with no separate tool to install. What to steal Treat customer-facing analytics with the same latency expectations as the rest of the product. Vercel made the design choice for their Web Analytics so that data shows up " seconds after enabling Analytics, not 30 minutes ". Customer-facing analytics live or die on whether users trust the numbers are fresh — the moment they stop trusting, the dashboard quietly becomes wallpaper. Good news if you're a PostHog user: We just launched a real-time dashboard for Web Analytics . 2. Booking.com: Live metrics on the landing page Booking.com has made a science of turning live activity into social proof: "23 people are looking at this hotel right now." "Booked 4 times in the last 6 hours." "Only 2 rooms left at this price." These numbers are pulled from live booking and browsing data and dropped directly...

From cost center to growth engine: 5 lessons from Leadership Circle’s AI-first support transformation How a global leadership development company implemented human-AI collaboration to serve 3 million customers across 21 languages, and turned support into a revenue driver. The learnings in this blog post are based on a discussion from Atlassian’s Team ’26 conference. You can check other sessions like this on demand. Leadership Circle is a global leadership development company whose mission is to evolve the conscious practice of leadership through data-driven 360-degree assessments, coaching, and consulting. With 3 million customers and partners across 21 languages, and a network of over 12,000 certified practitioners worldwide, they needed customer support that could match the scale and quality of their work. In looking to modernize their support practice, they turned to the Customer Service Management app, Atlassian’s AI-first, purpose-built offering for external customer support. Before CSM, our help content was buried on our website, our cases lived in NetSuite, and nothing was connected. Now, with Atlassian CSM, we have one unified experience — self-service, AI, knowledge base, and ticketing — all in one place , making it easier for our team to quickly find information and trust that it’s current and approved, across 21 languages.” –Miranda Dunn, Director of Global Transformation and Change, Leadership Circle After implementing Atlassian’s Customer Service Management (CSM) app, Leadership Circle achieved a 71% AI resolution rate, a 65% reduction in manually handled tickets, and a support function that now actively drives revenue while delivering the best possible customer experience. Here’s what we learned as we worked with Leadership Circle as an early adopter of the Customer Service Management app: 1. Measure resolution, not containment The customer service industry has a metrics problem. As AI tools proliferate, many organizations have become laser-focused on AI containment rates , which is the percentage of interactions handled without a human. AI containment...

Customfield typewith searchtype matching issueHistory() We have implemented custom field type with integer property that works with following JQL "My Custom related issue.id" in issueHistory() Anyway, it would be nice to provide autocompletition for such “issue” generating JQL functions. I suppose that new searchType “issue” similar to “user” would be helpful. 1 post - 1 participant Read full topic

Rovo - Edit option for custom rovo agent showing blank screen Hi Buddies, Not sure if you all are facing the blank screen issue when trying to edit your custom agent that you created using Rovo. We are facing this issue for last 2 days, whenever me or other TMs are trying to edit the agent, studio open with blank screen and no further progress…wating and keep on wating. Attaching the screenshot with you all. Please let me know if there is issue at from Atlassian side or its something that we need to check our site configuration etc. Thanks, Ranvijay Singh 1 post - 1 participant Read full topic

Is Atlassian Tools Perfect for you? Hey! I’m trying to understand if people are actually able to easily use Atlassian products like Jira, Confluence, Bitbucket, Loom etc - in their day-to-day work. What are the parts that feel a bit frustrating or more complicated than they should be? In Things like setting projects up, searching for stuff, keeping track of updates, or just how everything fits together when you’re trying to get work done. Even small annoyances or “this could be simpler” moments, especially things you’ve just gotten used to working around over time. submitted by /u/Infinite-Local-6116 [link] [comments]

Is the PostHog feature available to customers? feature available to customers?" src="https://preview.redd.it/anx9oxc08a3h1.jpg?width=140&height=140&crop=1:1,smart&auto=webp&s=3b1cdc7c7ba2ff402afa53bd9286c10404bb0e93" title="Is the PostHog feature available to customers?" /> Hey all, new to the Reddit community! I’ve been using PostHog for my startup's product analytics for about two years now, and I have been extremely interested in PostHog’s micro-survey feature. Now, I’ve always been curious about PostHog’s ability to submit user feedback and have been searching for a bug reporting system as well, but didn’t know if PostHog offered this until I ran into this 3-type bug/feedback/question form on the login page. Is this bug reporting form included in their feedback/survey feature suite, or is this something PostHog custom-built for themselves? It would be super awesome if PostHog offered a bug reporting system(which included the user’s system diagnostics), since all feedback would be centralized to a degree in PostHog. Anyway, if anyone knows what this is or has an idea of what it entails, it would be super helpful. Thanks! submitted by /u/aaronorjohnson [link] [comments]

Does Cloudstaff use employee productivity monitoring software? submitted by /u/IcyKaleidoscope1212 [link] [comments]

Query around External Backend access Hi, I’ve few queries around external backend URL access setup in Forge app manifest file. Below is my current implementation and my query Intention: Jira User / Admin fills up a form where they enter their ACCELQ(External System) credentials to establish connection between Jira and ACCELQ to allow REST API calls to hit ACCELQ server. Now we have two kinds of ACCELQ servers - On Cloud whose URL is *.accelq.io and On Premise URL where client have full control over domain name (*) So the question is will Forge allow setting up star as the value under external → fetch → backend. If not, then what are other options? ``` permissions: scopes: - read:jira-work - write:jira-work - read:jira-user - manage:jira-configuration - storage:app external: fetch: backend: - '\*.accelq.io' - '\*.ngrok-free.app' ``` 3 posts - 2 participants Read full topic

JSM Work Item for Client Work Looking for advice on best practice and/or how other have done this There are a number of of JSM client portals, where each client can raise incidents and request for support via their portal They also use the portal to allow clients to raise enhancements they want. They are using ‘change’work items for this ie request a change Whats do other do, it can be confusing for the internal support portal as change is used in the ITIL Sense (Request for Change)for controlled IT changes, approval, change calender etc submitted by /u/Anom0000 [link] [comments]

Bitbucket Connect to Forge migration tooling We have added some documentation around Bitbucket Connect to Forge migration tooling available today, including: How to make Forge app distribution link available to workspace admin How to use Addon client key API to perform data migration/app cleanup after Forge app install How to list your Forge app on marketplace and retain your Connect app’s metadata/reviews How to block installation of your Connect app when you are ready Please see Connect to Forge migration tooling for details. If you have not done so already, reminder to please provide your feedback on any friction points around the migration process at the form mentioned in the previous post here . Thanks Wendy 1 post - 1 participant Read full topic

From 270GB RAM to 5GB: Moving local flag evaluation from Django to Rust I reloaded Grafana three times before I trusted the numbers. p50 latency: 40ms to 4ms. CPU usage: a fraction of before. Memory: barely there. We'd just moved the feature flags local evaluation endpoint from Django to Rust. I knew it would be better. But I wasn't ready for this. Local evaluation explained Local evaluation is a configurable endpoint that server-side SDKs hit to fetch feature flag definitions. SDKs poll it every 30 seconds by default, so it gets a lot of traffic. Until recently it had its own Django deployment, sized for that traffic: about 30 pods on average in US, with autoscaling configured up to 250. Each pod requested 2 CPU cores and 9 GB of memory, so at baseline we were running 60 cores and 270 GB of RAM. The endpoint reads cached flag definitions out of Redis, checks auth, and returns JSON. There is no flag evaluation logic, and no database queries on the hot path. The Rust feature flags service next door was already serving around 13,000 requests per second on /flags and /decide , doing the actual compute, so moving the cache read over to live in the same codebase felt overdue. Porting the endpoint Porting the endpoint itself was maybe the easy part, because most of the time went into matching Django's behavior around the edges. Auth was trickiest. The endpoint's Django view declared one authentication method, but a shared mixin added more behind the scenes. I noticed when Rust rejected requests that Django accepted. A related quirk: Django returned 403 (authenticated but not permitted) for some requests where Rust returned 401 (not authenticated). Same outcome for the client, but the codes mean different things, and our dashboards disagreed about what was happening. Matching Django's behavior took a few small changes on the Rust side. ETags were less painful. Instead of computing our own on the Rust side (which would have required byte-identical serialization with Django), Rust reads Django's stored ETag from Redis. That side-steps the serial...

More data supports science funding literally pays for itself Previously I put out a post explaining “ how science funding literally pays for itself ” that takes you through the math and some data that backs it up. Now two new data points further bolster this claim. First, the Congressional Budget Office (CBO), the nonpartisan federal agency that provides budget and economic information to Congress, published a report entitled “ Estimating the Economic Effects of Federal Investment in Research and Development . ” Usually the CBO only projects out 10 years per their mandate, but because the effects of science funding can take longer to fully manifest, they projected out 30 years. Thanks for reading Gabriel Weinberg! Subscribe for free to receive new posts and support my work. The relevant headline takeaway is highlighted below in their primary table (Table 1), showing that over this period the effects of a $30B increase in science funding for 10 years ($300B in total and about a 33% increase from today) would result in decreasing the overall deficit over 30 years (see green arrows). The decrease is about -2% on average if the “R&D funding increase [is] financed by reducing noninvestment spending” and about -1% on average if the “R&D funding increase [is] financed by borrowing.” This means that the increased science funding would grow the economy so much that the tax revenues received from this growth alone would outweigh the spending increase, leading to an overall decrease in the budget deficit. In other words, increasing science funding (at least by this amount) is a complete no-brainer, so let’s do it already! A few years ago the CBO did a similar report for infrastructure spending and compared the two in this report, finding the ROI effects of science funding to be about seven times greater than infrastructure spending. Again, so let’s do it already! The effect on the present value of GDP over the next 30 years (discounted using Treasury rates) that a dollar increase in deficit-financed R&D spending would have is about seve...

OdinFlow isn’t just for bulk updates submitted by /u/Ok_Strength4594 [link] [comments]

The r/JiraProductivity subreddit is a marketing subreddit for one company, and that’s gross I don’t recommend visiting that subreddit - they don’t disclose this fact, and the only posts in the subreddit besides mine are from the company. Not a good look. submitted by /u/musicjunkieg [link] [comments]

From Ambiguous Questions to Action: Research Mode in Rovo Dev CLI

Atlassian introduced DIA: The AI-Powered Browser for Work submitted by /u/SaaSJet [link] [comments]

Jira Product Discovery is getting a massive AI boost with the new "Feedback App" submitted by /u/SaaSJet [link] [comments]

"forge build" command starting to fail with "invalid json response..." error This morning, when trying to execute: “forge build” I get errors like below. Any idea what is going on? Yesterday all was ok. If I try to open the URL mentioned in the error I get a 404 error. Thanks. root@LP-AST03526:/opt/RD/JiraCustomFieldPlugin/forge# forge build --verbose Building and uploading your app. Press Ctrl+C to cancel. Running forge lint... Error: invalid json response body at https://developer.atlassian.com/cloud/jira/service-desk/swagger.v3.json reason: Unexpected end of JSON input Error: invalid json response body at https://developer.atlassian.com/cloud/jira/service-desk/swagger.v3.json reason: Unexpected end of JSON input at lint (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/node_modules/@forge/lint/out/lint/lint.js:131:15) at process.processTicksAndRejections (node:internal/process/task_queues:104:5) at async LintService.run (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/out/service/lint-service.js:30:29) at async BuildController.verifyPreBuild (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/out/command-line/controller/build-controller.js:71:33) at async BuildController.run (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/out/command-line/controller/build-controller.js:49:13) at async /root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/out/command-line/register-build-commands.js:48:16 at async Command.actionProcessor (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/out/command-line/command.js:283:28) at async Command.parseAsync (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/node_modules/commander/lib/command.js:1104:5) at async Command.parse (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/out/command-line/command.js:165:13) at async main (/root/.nvm/versions/node/v24.15.0/lib/node_modules/@forge/cli/out/command-line/index.js:94:5) root@LP-AST03526:/opt/RD/JiraCustomFieldPlugin/forge# 4 posts - 3 participants Read full topic

Market place app version is not updating Hey everyone I built a Forge app, listed it on the Marketplace, and when I deploy from local to production the version doesn’t update in the listing at all. I’m deploying via CLI. The app ID matches, everything looks fine, but the Marketplace still shows the old version. Has anyone run into this? Is there something I’m missing in the deploy flow or the Partner Portal? Any help appreciated 1 post - 1 participant Read full topic

From Ambiguous Questions to Action: Research Mode in Rovo Dev CLI Not every developer question starts as a coding task. Sometimes the hard part is figuring out where to look: Jira for work history, code for implementation truth, Confluence for product context, or pull requests for how decisions landed. Research Mode in Rovo Dev CLI came from that experience. What shipped We shipped Research Mode as a structured workflow for investigation before action: it breaks an ambiguous question into research domains, chooses top-down or bottom-up direction per domain, sends subagents to investigate, runs another round when dependencies exist, and returns a final report with references and next-step options. See how research mode works: The pain points Users were asking questions that were bigger than “edit this file”: Which work items are ready for automation? Why does this problem happen, and what mitigations exist? Which bugs or security issues keep resurfacing across work items? What decision led to this architecture? What evidence do we have for a launch or roadmap decision? Prompt tuning helped the agent ask better questions, search more sources, and write cleaner summaries. But the core problem was not just prompt quality. It was research strategy. From better prompts to a research workflow We borrowed from how research is normally done: define the question, split it into subquestions, gather evidence, compare findings, and write a synthesis. In Research Mode, those subquestions become research domains . Each domain starts with a search direction. That direction gives the agent a path through the available evidence. For some domains, the right path is top-down: Built on the permissions you already have, Research Mode only reads from the sources a developer already has access to. It runs on demand when a developer invokes it for a specific question, not in the background, and not across teammates’ private work. The agent’s scope is bounded by the same access controls that govern Jira, Confluence, and Bitbucket today. For other domains, ...

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Does anyone have a spare Posthog Code referral? I'd be indebted! Working on a cool project, just got accepted to Startup Program, but I don't see ready access to Posthog Code :( submitted by /u/itsjoshweaver [link] [comments]

Advancing modern teamwork AI thinking and product news to unleash the potential of teams

Open Tab: Ashlee Vance In episode 3 of Open Tab, Substack co-founder shared a few rounds with filmmaker and tech journalist at Fred’s Place, a longtime neighborhood dive in Mountain View just minutes from where the technology that gave Silicon Valley its name was born. After two decades covering tech for major publications, Ashlee circled the globe for Hello World, Bloomberg’s travel-tech documentary series exploring “the freshest, weirdest tech creations” and introducing viewers to “the beautiful freaks behind them.” That same pull toward tech’s biggest risk-takers also led him to write a bestselling biography of Elon Musk, based on months of interviews and unusually direct access—a profile Musk first praised as “95% accurate” before turning on it at publication. With , the media company he founded and now runs on Substack, Ashlee is still writing, reporting, and making films, but with his own shop and team. He says his work gives audiences a window into where the world is going six or seven years before the rest of the culture catches up. In his conversation with Hamish, he talks about what it actually takes to get close to a subject, why he walked away from what he called the best job in media, and the stories and formats he’s building as the bet seems to be paying off. CORE MEMORY Started: 2025 Subscribers: Thousands of paid (orange checkmark bestseller) Format: Newsletter, podcast, video series, documentary films Team: A crew of writers, producers, editors, and a social media team, led by Ashlee alongside a COO, a chief creative officer, and an operations lead Extensions and verticals: Multiple writers producing regular newsletter sends across beats; podcast ; merch ; feature documentary projects in production, including an upcoming Neuralink documentary produced and self-funded through the company Hamish: Why are we here at Fred’s Place in Mountain View? Ashlee: I spent most of my early 20s in the Tenderloin [in San Francisco] bonding with dive bars. It was part of my lifestyle. Fre...

Atlassian’s Teamwork Graph: The Context Engine AI Has Been Missing submitted by /u/SaaSJet [link] [comments]

Migrating a Connect + Spring Boot app to Forge Remote (and Containers) without rewriting your service layer Hi everyone, If you still run a Connect add-on on Spring Boot and need to move to Forge Remote , the usual fear is a full rewrite: new auth model, new Jira clients, new tenant keys, new UI, new manifest — all at once. To avoid a massive rewrite, I built and open-sourced atlassian-runtime-bridge . It handles the infrastructure differences for you, so you can run the exact same Spring Boot backend across Connect , Forge Remote , and Forge Containers without changing your business logic. Migration phases: What changes vs. what stays Phase What you change What you keep Hybrid Add Forge Custom UI + remotes; keep Connect iframe for a while Same Spring app, same controllers Forge Remote Remove connectModules from manifest; UI is Forge-only Same Spring app, same business code Forge Containers (optional, harder) Run Spring in a container; storage → Forge SQL / KVS Same service abstractions, different bridge module + data layer The library only standardizes the boring part: who is the tenant and how do we call Jira on each runtime. Step 1 — Fix tenancy before you “go Forge” This was the biggest data-model mistake to avoid: Your tables should not hang off AtlassianHost.clientKey or FK into Connect’s host table. Use cloudId (or installationId where needed) as your tenant key. Know the difference: cloudId is permanent for the tenant. installationId changes completely every time a customer uninstalls and reinstalls your app. If you tie your domain data to installationId , that data becomes orphaned on reinstall. During transition, a small mapping table (cloud_id, client_key) is enough: resolve cloudId first , fall back to clientKey only for stragglers on native Connect (if any remain). Migration is about restructuring your database to use cloudId , not dragging legacy Connect tables into your new architecture. Step 2 — One backend, two transports (hybrid → Forge Remote) While hybrid: Connect iframe still hits your app with Connect JWT. Forge Custom UI hits the same baseUrl via re...

Recreate on-prem alert action submitted by /u/Wonder-through-u-2 [link] [comments]

So... what if you just missed the May 19th Atlassian AI data-scraping deadline? What now? Hey everyone, just realized something that I haven't seen a ton of noise about, and it's kind of a massive deal for anyone managing Jira or Confluence. Apparently, May 19th (two days ago) was the deadline to finalize data-sharing preferences before Atlassian kicks off their new AI training policy in August 2026. If you didn’t actively change your settings by Tuesday, your org has likely defaulted into letting Atlassian use your internal data to train their cross-customer AI models. Did your org actually catch this before the 19th? submitted by /u/BarberPlayful5984 [link] [comments]

OAuth 3LO "failed to retrieve client" for non-contributors despite Sharing mode enabled I have a private OAuth 2.0 (3LO) app registered in developer.atlassian.com . It’s a Flask web app that authenticates users against a Jira Cloud instance using the standard authorization code flow. The problem: Users who are not the app creator get a “failed to retrieve client” error on Atlassian’s authorization page. The app creator can authorize fine. What I’ve done: Enabled Sharing under Distribution (toggled on, hours ago) Added a Privacy Policy URL Verified the callback URL in Authorization → Configure is correct Confirmed the client_id in the app matches what’s in the authorization URL My authorization URL structure: https://auth.atlassian.com/authorize?audience=api.atlassian.com&client_id= &scope=read:jira-work+write:jira-work+offline_access&redirect_uri= &state= &response_type=code&prompt=consent Questions: Does enabling the Sharing toggle require any additional step to take effect, or is it immediate? Is there a known propagation delay after switching from Development to Sharing? Is there any reason “failed to retrieve client” would persist after Sharing is enabled? 1 post - 1 participant Read full topic