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I didn't understand the OS I built with AI until the MCP gave it analytics For my birthday this year I got a Pimoroni Presto — a little developer gadget with a 240×240 touchscreen and not much else. I also got an environment sensor which can track CO2, temperature, and humidity. I wanted to build something for it. The thing is, I'm not an engineer. I'm a marketer. Until I joined PostHog I couldn't code at all . I've made effort to learn and slowly got myself to the point where I could ship product features , but it wasn't until DeskHog came along that I saw how AI could supercharge my progress. My goal was a desk dashboard that worked at any level of attention. Something I could glance at between meetings and read idly, but that also had deeper things to poke at while I waited for someone to join a call or had lunch. What I built I call it Joe-OS, because naming things is the one part of this I'm actually qualified for. The Presto runs MicroPython. The top two-thirds of the screen is a carousel of panels; the bottom third permanently shows temperature and CO2 from the sensor, with little sparklines of the last half hour. You move between panels by tapping the corners of the screen. The panels are deliberately a mix of useful and useless: Three news readers that stream the latest RSS headlines from The Guardian, BBC News, and Rock, Paper, Shotgun. Oblique Strategies — Brian Eno's deck of creative prompts, plus a few dozen I wrote myself to build it out more. A rotating daily joke, a random fractal, and a Sky panel showing the current moon phase, sunrise, and sunset. The most ambitious part of Joe-OS was Vigil It's a tiny medieval roguelike text adventure about a knight keeping watch over a dying realm. Every turn something happens — a ghoul rises, a house burns, a bandit approaches — and you respond by choosing an Honorable or a Craven option. Sometimes you get a third choice based on your inventory or companions, each of which has their own simple personality. It runs across five acts and 400+ hand-written events, with a two-and-a-half min...

Introducing Claude Agent for Jira

Meet the Knowledge Architect: The Role Every AI-First Organization Will Need Soon

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

Best practice for validating admin payloads before storing data in Forge KVS Hi Atlassian Forge community, I have a security design question about Custom UI, Forge resolvers, and storing administrative configuration in Forge KVS. In a Forge app, an administrator can update some configuration from the frontend. The frontend sends data using invoke() , the resolver receives { payload, context } , and then the backend stores the final configuration in KVS. For example, imagine a generic configuration for a user history feature: { enabled: true, displayMode: "compact", maxItems: 20, showUserName: true, showDate: true, allowedEvents: ["CREATED", "UPDATED", "DELETED"] } The expected backend contract is: enabled must be boolean. displayMode must be one of: compact , detailed . maxItems must be a number within an accepted range. showUserName and showDate must be boolean. allowedEvents must only contain known internal event names. Unknown fields should be rejected or ignored. The storage key should be defined in the backend, not received from the frontend. updatedBy should come from context.accountId , not from the payload. During a security review, we tested the scenario where a browser request is captured and replayed or modified while the administrator session is still valid. In that case, even if the user is a valid administrator, the payload can still be changed before reaching the resolver. For example, instead of receiving the expected payload, the resolver could receive something like: { enabled: "true", displayMode: "admin", maxItems: 999999, showUserName: true, storageKey: "another-key", updatedBy: "fake-user", context: { accountId: "fake-account-id", moduleKey: "fake-module-key" }, isAdmin: true, extraConfig: { unexpected: true } } My current understanding is that checking Jira administrator permissions is necessary, but not enough. The resolver should still treat the entire payload as untrusted and validate it against a strict backend schema before writing anything to KVS. We also noticed that some contextual information could be passed f...

Your Jira Board just got a new kind of teammate Most teams have already adopted AI in some form. The bottleneck now is orchestration: knowing who’s accountable, what agents are actually doing, and how they fit into your existing process. AI coding tools boost productivity by 10-15%, but the orchestration gap limits teams from realizing AI’s full value ( Source: DX ). Agents living outside of where the work happens means more context switching, manual bridging, and accountability blind spots. Jira closes that gap by giving agents first-class citizenship, making them assignable, mentionable, and woven into the workflows your team already uses every day. At Team ’26 Anaheim, our Jira teams shared a look into what’s possible to bridge the gap between your real teammates and AI agents. Read on to understand how to bring these learnings to your organization and catch the on-demand recording . The orchestration problem A year ago, AI helped developers write unit tests or reword a paragraph. Useful, but still a single-player tool. You asked it something, it answered, and you moved on. In 2026, LLMs can reason through complex, multi-step goals. Agents then execute entire workflows, not just providing answers to individual prompts, and AI task complexity is roughly doubling every seven months. The problem still stands: most agents still operate in silos, causing developers to lose time they could spend within their workflows. You receive output from a chat window, copy it, and manually integrate it into your team’s workflow. Jira agents eliminate this gap entirely. Agents as true teammates in Jira Agents in Jira are AI teammates that live directly in your workspace. They’re assignable, mentionable, and integrated into your workflows just like your real-life teammates. Every agent action is visible and traceable, so accountability is built in from the start of your projects. The same coordination patterns your team already uses for people now work for agents. Here’s what that looks like across four core capabilities. 1. Ass...

Open Tab: Hunter Harris is the creator of , the twice-weekly Substack newsletter where she takes a forensic lens to our culture’s obsessions and her own. She says she and her audience of nearly 200,000 subscribers share the same “disease”—insatiability—and that they’re bonded by a specific tunnel vision: her willingness to write a dozen pieces about A Star Is Born or spend time tracking down the marriage license of Taylor Swift’s publicist, if that’s what curiosity demands. is part cultural criticism, part late-night group chat, and an ongoing argument for why pop culture matters. Her chat , where paid subscribers debrief everything from season premieres to their own lives, has become one of the most devoted, lore-dense places on the internet and serves as a master class in how to turn an audience into a social world. Harris grew up in Tulsa, Oklahoma, where she watched melodramas with her aunt, crime capers with her dad, and, at 12, wrote a letter to the local film critic disputing his review of The Other Boleyn Girl. In this season finale of Open Tab, she sat down with Substack’s head of new media, , to talk about studying journalism (and thinking she might break the next Watergate), her four years at New York magazine’s Vulture, and launching on Substack in 2020. They met at Trees Lounge inside the Nitehawk Cinema in Prospect Heights, Brooklyn, one of Harris’s home theaters, sipped tequila gimlets, and filled in the rest. Hanne: So you have subscription revenue as your core, but you also have a podcast, you do TV writing, you still do some freelance for legacy media, and you do some brand stuff. How do you think about the whole mixed-revenue stream, the business of Hung Up? Hunter: The newsletter is my job. Everything else feels like something fun, something I feel curious about. But I do feel very seriously that people who read the newsletter are customers, and I am fulfilling my contract with them. I just think it’s the most disrespectful thing in the world to be like, “Okay, maybe it...

Introducing Claude Agent for Jira Built on Anthropic’s Claude Managed Agents infrastructure, Claude Agent for Jira lets you assign work items directly to Claude. Your agent’s work now sits in Jira alongside everything else: tracked in your project, visible in workflows, and connected to the goals it’s helping ship. The agent automatically processes the work item context, implements the required changes in a secure sandbox, and opens a draft pull request for your review. The plan-to-code bottleneck Moving from a planned task to pull request carries repetitive overhead. A longitudinal study across 400 engineering organizations found that even with AI adoption as high as 90% for coding tasks, overall productivity gains have plateaued at 10–15%. For routine tasks, developers often spend significant time copying codebase and business context into chat interfaces, setting up local branches, and translating issue descriptions into agent-readable specs. This integration eliminates that context switching. By connecting Jira directly to code execution, agents always have the full context needed to build accurately. Every action stays visible and traceable in the same system your whole team already uses to plan, review, and ship. Using coding agents in Jira Delegating tasks to Claude. Mention Claude Agent for Jira in a comment, select Claude in the assignee dropdown menu, or create automation rules that automatically assign certain tasks to Claude. The agent reads directly from the work item, including acceptance criteria, target repository, and relevant design/engineering standards. Sandboxed execution. Operating within Anthropic’s isolated infrastructure, Claude clones the repository, analyzes the codebase, implements the changes on an independent branch, and pushes the code. Human-in-the loop review. The agent opens a draft pull request in your connected GitHub repository and streams real-time status updates directly back to the Jira work item. Your engineering team retains complete control over code review...

Feature request: ability to get reactions to comments, and get event payload via trigger when reaction is added/removed Essentially: the title. 1 post - 1 participant Read full topic

For some reason i keep getting this error https://preview.redd.it/esjkirpld08h1.png?width=1094&format=png&auto=webp&s=9ef042ec6170716ee27b4ac24e2bc93d1752565b submitted by /u/Salmaniuss [link] [comments]

Meet the Knowledge Architect: The Role Every AI-First Organization Will Need Soon While most enterprises are talking about how they approach AI transformation, increasingly knowledge management and arranging the required context are important factors in whether or not AI can work effectively. This shift is creating demand for a new role called the Knowledge Architect. We have Systems Architects to design the overall structure of software and hardware systems. Knowledge workers execute on work across different functions like Sales, Marketing and Engineering to achieve business goals. But the concept of a Knowledge Architect is new, and perhaps what’s needed to untangle the complexity of an organization’s documentation and enable better collaboration across functions. A Knowledge Architect is the bridge between the history of what an organization has documented, how it makes decisions today, and how it should operate in an AI-augmented world. Put simply, it’s the role that architects the conditions for AI to do useful work. It could be the difference between a successful or unsuccessful AI transformation. Measuring success in your organization should not be about how much AI is deployed, but instead by how effectively the workforce is transformed. At Atlassian, we’ve been thinking about where this role can add most value. Here’s how it could look. What is a Knowledge Architect? The purpose of this role is really two-fold: Architect the systems and frameworks that enable AI tools to draw on organizational knowledge effectively. Upskill team members to leverage the tools and systems effectively across their functional areas. The concept draws on the structural rigor of a Systems Architect and leverages the documentation of knowledge workers within the business, while introducing a new dimension to adapt for AI transformation: context engineering. Context engineering is the process of understanding how to structure, curate, and deliver the right information so that both humans and AI agents can make high-quality decisions. It requires thinking deeply ...

Long Horizon: How Atlassian Built a Reasoning Engine for Complex AI Tasks Last December, when the Rovo hybrid orchestrator was introduced, a hierarchical multi-agent system was introduced, where specialized subagents last introduced, one for Jira, one for Confluence, one for Slack, and so on — each handled their own domain. The hybrid orchestrator would decompose a user’s query, route subtasks to the right agent, and combine the results. This architecture was a strong fit for both sides of the equation: customers asking focused questions, and LLMs of the era reliably handling them in one or two tool calls. As frontier models matured and users began asking Rovo to plan, branch, and execute across many steps, the same patterns that made it fast and predictable became the bottleneck: rigid routing, shallow context, and little room for the model to reason about how to approach a problem. Why we needed a new architecture As Rovo Chat matured, several architectural constraints of the Hybrid Orchestrator surfaced — each compounding as tasks grew in complexity. Building a Reasoning-Native Orchestrator Each subagent ran in its own LLM context. The orchestrator routed work to them and received summaries back, but never saw raw tool outputs, intermediate reasoning, or error details. It made downstream decisions on a lossy, secondhand view of its own work, leading to missed connections across products, redundant searches, and no ability to recover gracefully when something failed mid-task. Limited iteration depth Even when routing worked, the system was optimized for quick, two- to five-step tasks: search, read, answer. Its iteration budget was low, and its timeout was conservative. Tasks that required sustained reasoning across multiple data sources — “analyze our sprint velocity across the last three quarters” or “compare what different teams documented about this incident” — would frequently hit the ceiling before producing a thorough answer. Models outgrew the box The multi-agent design wasn’t an accident — it was a workaround. Earlier-generation...

Atlassian named a 4x Leader in the 2026 Gartner® Magic Quadrant™ for DevSecOps Platforms

Atlassian named a 4x Leader in the 2026 Gartner® Magic Quadrant™ for DevSecOps Platforms Today, Gartner® named Atlassian a Leader in the 2026 Magic Quadrant for DevSecOps Platforms, and placed us highest in execution. It’s our fourth consecutive year as a Leader across the Gartner DevOps and DevSecOps research – a category that has changed underneath all of us as security shifted from a downstream checkpoint to a first-class concern in every team’s delivery loop. This one matters to me for a specific reason: it validates a bet we made four years ago. We bet that the future of software delivery wouldn’t be won by generating code faster, but by giving entire teams – humans and agents – the shared context, governance, and trust to ship better software, safely, at scale. Why DevSecOps is now an agentic problem Every engineering leader I talk to is asking the same question: how do we get real ROI from AI? The data is sobering. Most teams report only 10–15% productivity gains from their coding agents, even as adoption climbs far higher. The gap comes from a simple oversight. Most AI deployments speed up the 16% of a developer’s time spent writing code and ignore the 84% spent on planning, review, security checks, incident response, and cross-team coordination. As writing code keeps getting faster and cheaper, the pressure only moves further up the lifecycle. How do you keep agent workflows aligned across thousands of engineers? How do you make sure an agent’s output is grounded in your business context and your code standards – not generic best practice? We work on this every day, for our customers and for ourselves. Here’s what we’ve found: context is the difference. In our internal benchmarks, agents grounded in your organization’s context graph return 44% more accurate results than agents relying on MCP connections alone. With that context in place, the gains get real – we’ve used Rovo and Jira to automatically resolve 51% of vulnerabilities over the past six months. We believe the Gartner recognition reflects that shift. DevSecOps isn’t only about deploym...

3 ways teams use Rovo and automation in Jira Service Management to speed up resolution Real-world customer stories of automated triage, translation, and incident follow-through. Agentic automations in Jira Service Management are growing 3.4x faster than traditional automations. The reason is simple: teams aren’t just speeding up tasks anymore — they’re handing entire workflows to AI agents that triage, translate, investigate, and follow through on their behalf. Three Atlassian customers — Axpo, SpotOn, and Vodeno — show what that looks like in production today. Three teams. Three workflows. Zero hand-offs. Axpo: end-to-end auto-translation for multilingual support The problem: Axpo, a leading Swiss energy provider, serves German-speaking users from a second-level support team in Madrid that doesn’t speak German. Every ticket meant copy-paste into translation tools, waiting for results, and back-and-forth clarification. The solution: With Rovo embedded into Jira Service Management, Axpo turned translation into a fully automated workflow. When a German ticket lands, Rovo detects the language, translates the summary and description into English, and stores it in a dedicated field. Every German comment is auto-translated and posted as an internal-only English note — original author and timestamp preserved. Key outcome: Madrid agents now work every German ticket directly in Jira Service Management — no tool switching, no context loss, zero translation hand-offs. How it works Trigger Agent action New German-language ticket created Rovo detects language → translates summary & description → writes to custom field New German-language comment added Rovo translates comment → posts internal-only English version with original author + timestamp SpotOn: production ticket intelligence in a single view The problem: SpotOn, a San Francisco-based restaurant technology and financial services provider, runs its internal IT support on Jira Service Management. Every request carries rich institutional context — past tickets, related docs, prior resolutions — but analysts ha...

Why we're bullish on loops When the creators of both OpenClaw and Claude Code speak, people listen. And last week Peter Steinberger and Boris Cherny were both talking about the same concept: loops. Their argument? You shouldn’t be prompting agents to write code, but building loops that prompt themselves to write code, so agents can complete long running tasks and you can use multiple agents at once to go further, faster. What’s needed to engineer a loop? You need four things: 1. A goal Agents are capable, but you need to scope the loop so they know what you want them achieve. A loop without a goal is a slop cannon. 2. Context Context is fuel and loops are often starved of it. Context can include tools, skills, analytics data, errors, memories – any information that helps the agent find work and complete the loop. It’s best to curate context and feed it throughout, rather than dump it all upfront. The agent needs to be able to fetch and react to new inputs. 3. Evaluation This is how the agent checks itself. Tests , evals, metrics, LLM-as-judge , playgrounds. Test-driven development is so back , or maybe it never left? This is a big difference between loops and prompting: agents do the verification, not engineers. 4. An agent (obviously) The most basic is using something like Claude Code with a while true (aka Ralph ) or using /goal . More complicated is purpose-built harnesses and context systems – e.g. an agent on a cron that pulls signals from your product data and emit work to subagents, or a loop that codegens its own test suite to verify itself. Good examples of loops include: PR babysitter. The goal is to get a pull request to pass tests and “get CI green.” The context is the changes (diff) as well as the testing suite, and the evaluation is done by the CI. Bug fixer. The goal is to fix the bug. The context is the bug report and error trace. The evaluation is the test suite, snapshots, logs. Flaky test hunter. The goal is to kill flaky tests. The context is CI history and retry logs. The...

Navigation to Forge jira:adminPage - now that upm is gone from canary instances Hi Team, Overnight (Jun 17, 2026) one of my instances that is enrolled in developer canary program now have “Marketplace apps” under Jira Admin Settings redirected to admin.atlassian.com directly instead of requiring the admin users to click “Take me there”. Instances that are not in canary program don’t seem to have been affected yet as of writing. I also noticed that the canary program itself is only visible in the same side bar of /plugins/servlet/upm and it’s also no longer visible anywhere in Jira admin settings. This appears to be an intentional change to get rid of /plugins/servlet/upm (Long live upm). But isn’t plugins/servlet/upm the only entry point to Forge module jira:adminPage ( https://developer.atlassian.com/platform/forge/manifest-reference/modules/jira-admin-page/ )? How would an admin navigate to jira:adminPage after this change? Thanks, Shu 1 post - 1 participant Read full topic

Multi platform app throws "Failed to validate FCT" on jira:fullPage We are currently trying to implement a forge app for jira and confluence. So following the tutorial we added the compatiblity section to the manifest making jira the required app and confluence optional. compatibility: confluence: required: false jira: required: true Now as soon as we additionally connect confluence every resolver call fails in Jira with the message "errors": [ { "message": "Failed to validate FCT: 'contextIds' claim mismatch", "extensions": { "__typename": "GenericMutationErrorExtension", "errorType": "FCT_VALIDATION_CLAIM_MISMATCH", "statusCode": 400 } } ] This does only happen if we use the Fullpage module. Does anyone know what we do wrong here? 3 posts - 2 participants Read full topic

Deeplink to your AI coding tool from Jira

Track Deployment status for your PRs (Beta)

Track Deployment status for your PRs (Beta) You shouldn’t have to leave your PR list to know where your code is deployed. Yet, developers constantly lose time context-switching just to see if a change hit staging or production. To solve this, we are launching the Beta version of Deployment Status Tracking for your PRs. This feature surfaces live deployment statuses directly within your PR list view as code moves through your pipeline. By tracking environment data against the exact commit executed, the platform provides immediate, effortless visibility into exactly where your code lives. What this unblocks for your team? Previously, developers had to dig through pipeline histories or log into external UIs just to check if a commit was live—making troubleshooting slow and opaque. Bringing native deployment visibility directly into the PR fixes this by: Eliminating manual tracking: No more hunting through pipeline run lists or logging into third-party tools to verify code status. Accelerating incident response: By linking live deployment statuses to exact commits, teams can instantly see what code is active in which environment during an outage. Getting started with Deployments If you already use Pipelines deployments, you’ll see the new column automatically. If you dont, here’s how you can start tracking deployments natively inside Bitbucket. Here is a sample bitbucket-pipelines.yml configuration that triggers deployments against the relevant production environment: image: atlassian/default-image:5 pipelines: branches: main: - step: name: Deploy to production deployment: production script: - echo "Deploying to production" How it works? Whenever a commit is deployed to an environment, Bitbucket tracks it in real time and maps the deployment status directly to that commit and its associated PR. This creates an automatic, unambiguous record of exactly what is live and where. Instant Deployment Tracking: A dedicated Deployment column on the PR list view displays the exact environments (such as dev, staging, or prod...

Deeplink to your AI coding tool from Jira AI coding agents work fast. Getting them up to speed isn’t. There’s the copy-pasting from Jira, the hunting for the right docs, and the careful prompt-crafting to make sure nothing gets lost. It’s a tax on every task, and it compounds across every sprint. Today, we’re removing the friction. One click from Jira. Your agent is ready to go. Deep linking to local AI coding tools is now available in Jira. Now, from any work item, you can open your preferred coding agent directly — with the full context of that work item automatically pre-filled in the prompt. No copy-paste or custom prompt assembly required. Just press enter and build. At launch, Jira supports six coding agents: Claude Code Cursor GitHub Copilot OpenAI Codex Rovo Dev CLI VS Code For desktop apps like Cursor, GitHub Copilot, and Codex, the tool opens with a ready-to-go, context-rich prompt. For terminal tools like Claude Code and Rovo Dev CLI, Jira opens a modal with the prompt ready to copy into your terminal in one action. Context is where agents win or lose Agents run on context. A vague prompt requires additional tokens and refinement by the engineer. A prompt that includes the work item summary, description, comments, and linked resources gives it what it needs to do useful work on the first pass. When you click “Open in [your tool]” from a Jira work item, Jira packages that context automatically through Atlassian MCP . The work item summary, description, and linked docs go directly into the starting prompt. Your agent can also pull additional context through MCP as it works, so you don’t have to anticipate every question upfront. The result: your agent starts informed. Get started Deep linking to local AI coding tools is available now in Jira. Head to any work item, open the Development panel, and click “Open in coding tool” to try it. → Get started https://support.atlassian.com/jira-software-cloud/docs/open-a-jira-work-item-in-an-ai-coding-tool/ The post Deeplink to your AI coding tool from Jira ap...

MCP Server OAuth (authv2) – Validation failed / Missing PKCE verifier issue We have an internal AI application We want to connect it with Jira Cloud via MCP Authentication should be OAuth-based (per user login) MCP Server URL used: https://mcp.atlassian.com/v1/mcp and https://mcp.atlassian.com/v1/mcp/authv2 We also have an OAuth 2.0 (3LO) app created in Atlassian Developer Console Validation failed Authentication Failed Missing PKCE verifier in state Looks like MCP requires OAuth 2.1 with PKCE flow However, our platform validation seems to be happening server-to-server without a browser redirect We are unsure: Whether MCP supports integration from custom backend/UI tools Whether we should use our own 3LO OAuth app or MCP-managed OAuth How PKCE state is expected to be handled in such integrations Can Atlassian MCP Server be used with custom AI/backend applications , or is it only supported with MCP-native clients (like Claude, VS Code, etc.)? Should we use our own OAuth 2.0 (3LO) app , or is MCP expected to manage OAuth internally? How should the PKCE flow (code_verifier/state) be handled when the client is not a browser-based MCP client? Is there any supported pattern or reference implementation for integrating MCP with custom applications? 1 post - 1 participant Read full topic

Is there an upper time limit for how long it takes for the CQL index to update? In our migration tooling we have a page that lists all migrated pages that contain our macros where the customer has to perform some actions to complete the app migration. For this we need to wait for the CQL index to update and include the freshly migrated pages. Sometimes this takes very long. Is there an upper time limit for when the index should include the new pages? I would like to add this to our customer facing documentation. 1 post - 1 participant Read full topic

Cheapest AI observability tools for developers, compared Teams shipping AI features run into the same blind spots: latency, token costs, and model failures don't show up until a user runs into them. AI observability tools are how you see them coming. A lot of LLM observability tools like to flex their "free" muscles, but "free" can mean a lot of things; it can be as generous as 100,000 events a month with no seat limit ( yes, really ), or as limited as a 24-hour retention window and a single seat. This guide ranks these tools by how far their free tiers actually take you. If you specifically want open source, we have a separate guide to the best free open source LLM observability tools . What features do you need in an AI observability tool? At a minimum, a useful AI observability tool should: Capture LLM traces with input, output, latency, and token counts Track cost per model and per user Visualize aggregate metrics (p50/p99 latency, error rates, total spend) Support the LLM providers you actually use (OpenAI, Anthropic, and others) Offer free access without requiring a credit card The best tools go further with: Prompt management: Version prompts without redeploying code Evaluations: Score outputs with LLM-as-judge, human annotations, or automated metrics User and session context: Tie model behavior to product analytics , session replay , or feature flags Dataset curation: Build golden datasets from production traces Self-hosting: Keep all data in your own infrastructure for privacy or compliance AI observability tools with the best free tiers 1. PostHog PostHog is an all-in-one developer platform where AI observability sits alongside product analytics , session replay , feature flags , experiments , error tracking , logs , and more. Since LLM data is stored as regular events , you can connect it to user behavior, replay sessions where an agent failed, and ship prompt changes behind a feature flag without ever having to switch between tools. PostHog's AI Evals score outputs with LLM-as-judge or your own code, and run a...

Four warning signs your AI and collaboration investments aren’t paying off

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

Bulk Edit endpoint to handle multiple values for multiple issues Problem: Bulk edit endpoint doesn’t allow to modify multiple issues with different fields and values as it was on Data Center and Server. Endoint: POST /rest/api/3/bulk/issues/fields Request: Modify the bulk edit endpoint or create a new one Ticket: [JRACLOUD-98587] Support setting different field values per issue in a single bulk edit request - Create and track feature requests for Atlassian products. 1 post - 1 participant Read full topic

A business model that works for creators Illustration credit: Joro Chen, Brand Designer at Substack Today we’re introducing the next phase of Substack’s native sponsorships program, with an inaugural cohort of flagship partners who are collectively investing millions of dollars into creators on Substack. If you are a Substack bestseller who is interested in participating and shaping the program, you can publish your Creator Kit now. The deal most of the internet offers creators goes something like this: We’ll give you reach if you give us your audience. Perform for the algorithm. Hope for a minority share of a platform’s own revenue, or figure out the money part yourself. Build your following on our platform, under our rules, subject to our priorities—and if any of that changes, good luck. Most creators accept this deal because they feel like they have no choice. The platforms are where the audiences are. And over time, the system shapes you. You learn to make things the algorithm rewards instead of things you believe in. Substack is built on a different theory: Give publishers a direct connection to the people who believe in them. Let people pay for the work they value. We know that model works. Now more than 100,000 publishers make money on Substack through the subscription model. The top 10 collectively make more than $100 million a year, while tens of millions of subscribers across the network give creators the opportunity to build both audience and business in one place. At the same time, some of the most successful creators have been finding ways to expand their business and better serve their community by partnering with organizations that help them offer more perks, invest more deeply in their editorial, and introduce new features for subscribers. ’s sponsored letter with Hinge ranked as ’s fourth-most-engaged letter in 2024. She wrote about this herself that year: advertisements on Feed Me, done right, are not compromising her audience-first model. built his own Product Pass —a collection of compl...

Atlassian’s DESIGN.md is here: what we learned testing portable design context in practice When AI generates a user interface, the results tend to look the same: gradient buttons, all-caps headings, generic card layouts and hover animations that nobody asked for. It works , technically, but it doesn’t look like your brand. The design community has started calling this UI “slop”: output that may be functional, but lacks any real visual identity or intentional design decisions. The underlying problem isn’t hard to diagnose. Without context about your brand, components and patterns, AI defaults to the average of everything it’s trained on. Generic in, generic out. At Atlassian, our design system team has been hard at work on building a context engine for the AI era. We’re creating tools that give AI agents rich design context through our ADS MCP server and detailed AI skills, powered by a structured content model that encodes our documentation for both agents and humans. We’ve found these tools to be successful in reducing our AI token costs — as well as improving the accuracy and quality of what is generated by thousands of product builders at Atlassian. Learn more about this from our recent blogs: Atlassian Design System: Building the context engine for the AI era Teaching AI to speak our design language More recently, a new format has entered the conversation. DESIGN.md is an open-source Markdown format designed by Google for their Stitch design tool — a portable snapshot of a team’s brand and UI patterns that’s gained a lot of traction as a simple fix for slop. The idea is straightforward: include this file in your prompt, and the generated output starts to look more like your product. We’ve explored this format and tested how it fits alongside our existing tools, and we want to share our findings. The portability of a single Markdown file shows genuine promise for certain workflows and use cases — but we also found that portability comes at the cost of sophistication and efficiency. DESIGN.md: a primer What it is: A portable markdown file that describes...

Complete change log is not returning by api "/rest/api/3/search/jql&expand=changelog" Endpoint: /rest/api/3/search/jql&expand=changelog HTTP method: GET JQL query used : jql=“project= AND issuetype=Defect” 1 post - 1 participant Read full topic

How to invoke Forge LLM API from a Remote Backend? (Alternative to Web Triggers) Hi everyone, I am currently building a Jira cloud app using Forge Remote. My architecture relies on a frontend that communicates directly with my own external/remote backend rather than using standard Forge hosted functions. I am trying to integrate the Forge LLM API into this setup, but I have hit a major roadblock regarding how it is exposed: Forge Function Limitation: The Forge LLM API is natively designed to work within Forge functions, which I cannot use efficiently given my remote backend architecture. No Public REST API: There doesn’t seem to be a standard, public Atlassian REST API endpoint for the LLM service that I can hit directly from an external server. Web Trigger Complexity: I looked into using Forge Web Triggers as a bridge, but managing them is highly complex because a unique URL is generated for every single Jira site/tenant where the app is installed. My Current Workaround for Jira REST APIs For context, when my remote backend needs to interact with Jira, I seamlessly use the standard Jira REST APIs by authenticating with the User and System tokens ( appUserToken and appSystemToken ) sent from the frontend. I am looking for a similar approach for AI features. My Questions: Is there any way to securely invoke the Forge LLM API from a remote backend using system/user tokens, similar to how we access Jira REST APIs? If a direct REST endpoint doesn’t exist, what is the recommended architectural pattern for Forge Remote apps to utilize Atlassian’s LLM capabilities without getting tangled in complex web trigger management per site? Any insights, workarounds, or upcoming roadmap updates on this would be greatly appreciated! Thanks in advance, Ankit Jangid 2 posts - 2 participants Read full topic

Everything (and everyone) is build mode now There's a future version of you who isn't boxed into one job function. Someone who has an idea in the morning and the means to ship it the same afternoon. Farfetched? Hardly. That version of you isn't waiting for 2030 – they exist today. They're in build mode , and they're not looking back. What is build mode? Build mode is the version of you that ships a lot more. You could call it everyone being an engineer now , but I'd put it more precisely: product engineers , product managers, and product marketers can finally play in each other's sandboxes. Look at who's actually building with PostHog's AI products. In the last 30 days, people: Made millions of MCP tool calls Held more than 400,000 conversations with PostHog AI Created more than 500,000 insights That's people building things. And a growing share of them would never call themselves engineers. In other words: build mode isn't really about more output. It's about making everyone who builds, markets, sells, or supports a product more capable. Who's actually chatting with PostHog AI – plenty of them don't sit in engineering. What you can build Reading this won't change your job title, so what does build mode mean for you? Here are some concrete things you can do today with @PostHog in Slack, PostHog AI, the MCP, and PostHog Code: 1. Turn a question or complaint into a merged PR. In case you haven't met it: PostHog has a Slack app . Mention @PostHog in any channel and it can answer any question about your users or product usage. Tag it with "fix this" or "build that" and it can code too. It spins up a sandbox, makes a plan, edits files, runs your checks, and opens a draft PR – answering your review comments right there in the thread. ( Here's how to set it up. ) In the first week it was available, usage split roughly into a quarter engineers, a quarter founders, and the rest marketing, product, support, and sales. People in every one of those roles have shipped real PRs . 2. Settle an argument in the thread where i...

Webhooks never fire for the personal Inbox board (board.type="inbox") A webhook registered on the personal Trello Inbox board is created successfully (active=true) but never receives any action events when cards are added to the Inbox. consecutiveFailures stays 0 (Trello never even attempts delivery). An identical callback on a normal board delivers fine. Repro: GET /1/members/me?fields=inbox → note inbox.idBoard (board’s type is “inbox”). POST /1/webhooks with idModel=, callbackURL= → returns active:true. Add a card to the Inbox (UI, or POST /1/cards into inbox.idList). Observed: no POST to the callback; webhook consecutiveFailures stays 0. Control: same callback on a regular board receives createCard normally. Expected: either Inbox board actions fire webhooks, or the API documents/rejects webhooks on type=“inbox” boards so integrators don’t get a silently-dead webhook. Env: REST API; standard API key; token has full access to the Inbox board (can read it + its cards). 1 post - 1 participant Read full topic

Looking for feedback on an open-source Confluence > Markdown crawler/exporter Hey, I’ve been working on an OSS CLI called confluence2md that exports Confluence Cloud content to local Markdown. It starts from one or more seed pages, traverses linked pages up to a configurable depth, converts pages to Markdown, downloads referenced attachments, fetches comments, and rewrites links between crawled pages to local relative paths. It also produces a full crawl metadata graph that captures page relationships, crawl provenance, and traversal structure. Confluence content is treated as a graph-first system and exported via a two-phase materialization pipeline, with metadata.json acting as the source-of-truth representation of structure and relationships. Repo: GitHub - gkoos/confluence2md: Confluence Cloud to Markdown crawler with incremental updates and local link rewriting · GitHub I’m interested in hearing from people who have built Confluence export, migration, indexing, backup, or AI-ingestion tooling: What API edge cases surprised you? Which content types or macros tend to be painful? Is there metadata you wish export tools preserved? Are there common failure modes I should test for? 1 post - 1 participant Read full topic

How is the work life balance and culture? Considering an offer for a G&A role. Coming from another tech company that is doing very well. How is everyone feeling post layoff? submitted by /u/chachacha01111 [link] [comments]

No, everyone is not using AI for everything. Last year around this time The New York Times Magazine ran an A.I. issue with an introduction titled “ Everyone Is Using A.I. for Everything. Is That Bad? ” It’s an edited transcript from the Hard Fork podcast , which I think assumes two things are true that are turning out to be false. Once you’ve tried AI, you use it “for everything.” No, in fact most people who’ve tried it are just occasional AI users. AI has gotten so good that despite any misgivings, “everyone is using A.I.” No, in fact large chunks of the population aren’t using AI at all. (It isn’t really strictly defined in the article, but I’m taking AI to mean generative AI accessible via a chat interface.) “ Everyone is using AI for everything ” is actually “ Some people are using AI for some things ” Take Gen Z, where AI awareness is the highest : in the last year, even though AI has supposedly gotten a lot better, Gen Z AI adoption has all but stalled, with a meaningful percentage of the Gen Z population still using AI rarely, if at all. Here’s Gallup’s year-over-year (2025/2026) breakdown: 79/81% use AI at least rarely 41/42% are anxious about AI 32/31% use AI only monthly/every few months 22/31% are angry about AI 21/19% never use AI This tracks with Microsoft’s new United States AI Diffusion site , based on “anonymized, aggregated Microsoft telemetry.” Their associated blog reports “more than 30 percent of the US working-age population is using AI [meaning about 70% isn’t], an increase of 3 percentage points from the end of 2025 . ” The underlying academic paper specifies that usage is defined as “engagement with major AI services including ChatGPT, Google Gemini, Anthropic Claude, Microsoft Copilot, and others….with at least 90 minutes of usage time in a given month.” The Microsoft data is brand new, and it mirrors another usage study from Datos from last year, also based on real-world usage data. The Datos study found similarly that, as of last June, only 21% of desktop devices visited “AI Tools” 1...

Fix your ad Roomamte? submitted by /u/CantankerousOrder [link] [comments]

JSM portal customers hit "allow access on your behalf" consent — asApp-only app Since ~12 June 2026, all JSM portal-only customers opening our Forge app ( jiraServiceManagement:portalUserMenuAction , unlicensedAccess: [customer] ) get a Forge “Access required — allow the app to access Atlassian products on your behalf” dialog. Clicking Allow access sends them to an atlassian.com login they can’t complete, so the app is hard-blocked for every customer. The app is asApp() -only — no asUser() calls, no frontend product calls. Scopes/manifest are unchanged; we redeployed an older build with an identical manifest and it still happens, so it’s not our code. It lines up exactly with the 12 June “JSM Customer Account Support” changelog. Opening the app fires the new …/customer/.../forge/context-token endpoint, then the resolver invokes — all HTTP 200, so the gate is platform-side, not our resolver. Per Access to Forge apps for unlicensed users , asApp() for customers is allowed without per-user consent — so why are customers being prompted? Questions: Is this expected from the new rollout? How is an account-less portal customer meant to complete it? Is there any opt-out or manifest setting to stop prompting customers? This is blocking all customer access to a previously working app. 1 post - 1 participant Read full topic

We are hosting our first developer event on June 18th, in SF. Join us! Hear from leaders at OpenAI, Polsia, Exa, Stripe, LlamaIndex and more. Sign up: https://localhost.render.com/ submitted by /u/hazal-mestci [link] [comments]

"Unauthorized; scope does not match" when reading issue worklogs Hello, I’m trying to access the endpoint /rest/api/2/issue/{issueIdOrKey}/worklog and getting the “Unauthorized; scope does not match” error. I’ve specified the following scopes in the manifest.yml - read:app-system-token - read:app-user-token - read:jira-work - read:jira-user - read:project:jira - read:issue-details:jira - read:board-scope:jira-software - read:board-scope.admin:jira-software - read:epic:jira-software - read:issue:jira-software - read:sprint:jira-software - read:issue:jira - read:issue-worklog:jira - read:issue-worklog.property:jira - read:user:jira - read:group:jira - read:project-role:jira - read:avatar:jira According to the documentation here: https://developer.atlassian.com/cloud/jira/platform/rest/v2/api-group-issue-worklogs/#api-rest-api-2-issue-issueidorkey-worklog-get the scopes should be enough. Any help would be appreciated. Thanks, Tuomas 1 post - 1 participant Read full topic

New wave of Atlassian layoffs? I had 3 colleagues that received the email today saying that they were "impacted". Is this bigger than what I'm aware of? submitted by /u/leobarao86 [link] [comments]

New research shows honesty about AI use at work is backfiring

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

Open Tab: Lachlan Cartwright In this week’s Open Tab, Australian-born, New York–based muckraker Lachlan Cartwright sat down with Substack co-founder to walk through two decades covering media, money, and power—from The Sun and the New York Post to executive editor of the National Enquirer, where he went from breaking news to becoming an anonymous source in one of the biggest political scandals of the decade. His New York Times Magazine cover story on the catch-and-kill operation that protected Donald Trump and Harvey Weinstein took a year to write and earned him multimillion-dollar legal threats in the process. These days he runs Breaker , the twice-weekly media newsletter and podcast he founded after years of people telling him to strike out on his own—it’s tabloid flair paired with the standards of the broadsheets and, as he puts it, a healthy appetite for fun. Hamish: You’re a newshound, a scoop-getter, a news-breaker, and a tabloid journalist of sorts. Lachlan: Allegedly. Hamish: Cut from the cloth of an old-school type of reporter. And that’s kind of special in today’s world, because lots of people are just random opinionators and mouthers-off. Lachlan: I’m an old-school newshound. Some would say a muckraker. I spend a lot of time meeting and greeting and ingratiating and sourcing. I always say I’m only as good as my sources, and that’s how I get scoops—being out and about, going out into the world and finding out new information. And then coming back, standing it up, tapping it up. And as we would say, putting it in the paper, which is now a newsletter. Hamish: You were the number two editor at the National Enquirer, and through your work there you discovered that they were essentially paying for stories so that they could kill those stories on behalf of some powerful figures: Donald Trump, Harvey Weinstein. Lachlan: That’s correct. Hamish: Tell me, what was it like to write that story and have it out in the world? Because it was complicated, and it involved so much personal stuff and was...

FCT_VALIDATION_CLAIM_MISMATCH on "Log in as User" We’ve root-caused why our Forge app (Custom UI + Forge Remote) shows an endless loading screen whenever a site admin uses “Log in as user” — and we believe it affects every Forge app rendered in an impersonated session. Filed as ECOHELP-139577 ; posting here for community visibility Symptom Admin uses “Log in as user” → opens a Forge module . Use Forge App.. In DevTools, every invokeExtension ( useInvokeExtensionRelayMutation ) fails with: { "message": "Failed to validate FCT: 'accountId' claim mismatch", "extensions": { "errorType": "FCT_VALIDATION_CLAIM_MISMATCH", "statusCode": 400 } } The app’s backend is never invoked — the rejection happens entirely inside Atlassian’s gateway, so vendors see nothing in their logs. Direct login as the same customer works perfectly. Root cause (verified by decoding both sides) The session cookie ( tenant.session.token , decoded) knows both identities — during impersonation it contains sub: plus an impersonation: [{ "aaId": " ", … }] entry. There’s even a cleartext um.user.impersonated.userid= cookie. FCT minting honors the impersonation: the contextToken sent with invokeExtension (decode its middle JWT segment) carries accountId: . FCT validation apparently resolves the session to the raw sub — the admin — compares it with the FCT’s accountId (the user ), and rejects. Mint says user; validate says admin; these always differ under impersonation → 100% of Forge invocations in a “Log in as User” session fail, deterministically. Asks for Atlassian Make FCT minting and FCT validation resolve the impersonated session to the same identity, so Forge apps work under “Log in as User”. If impersonation is intentionally unsupported for Forge apps: document it. Has anyone else hit FCT_VALIDATION_CLAIM_MISMATCH this way? @HeyJoe @ChandanaMeka @PhilipGrove @EkinBukulmez 2 posts - 2 participants Read full topic

Forge testing framework In the latest Forge webinar, there was a link to a repository with examples. In there, I found that Atlassian appears to have some platform/library abstractions for testing Forge apps. The repository contains hints of this, but the actual code appears to be private. bitbucket.org Bitbucket Is there any plan to have a public testing framework for partners to use as well? It would be nice to have more/better first-party developer tooling to support app development. 1 post - 1 participant Read full topic

Architecting Scalable ML Platforms: The Integrated Infrastructure and Acceleration Behind Rovo A novel, enterprise-scale architecture for modular ML development, high-velocity experimentation, and embedded governance – powering thousands of production workflows that underpin AI systems serving millions of Rovo users globally. Introduction: Engineering for Speed, Scale, and Governance in Enterprise AI As enterprise adoption of machine learning accelerates, organizations must operate ML systems across large, distributed teams and complex, interdependent workflows – while maintaining strict data governance and high development velocity. Traditional ML infrastructure, often fragmented and tightly coupled, fails to scale under these demands, creating bottlenecks in experimentation, compliance, and production reliability. In response to these systemic challenges, we architected ML Studio at Atlassian – a unified, enterprise-scale ML platform that standardizes modular development, centralizes workflow orchestration, and embeds governance directly into the execution layer. This architecture serves as the mission-critical backbone for AI systems including Rovo Search and Chat, Teamwork graph and Confluence , enabling thousands of production workflow runs daily and serving millions of users globally. In this technical blog, we examine the architectural evolution of ML Studio and the design principles underlying scalable, high-velocity, and governed ML systems. These patterns extend beyond a single platform, offering a reusable foundation for building reliable and compliant machine learning infrastructure at enterprise scale. What is ML Studio? ML Studio is Atlassian’s ML development platform, designed to streamline the end-to-end ML lifecycle. Key capabilities Reusable ML modules: Modular components for data processing, model training, packaging, evaluation, and deployment, enabling rapid experimentation. Granular data access controls: Automated, column-level enforcement to ensure teams access only what they are approved for. Unified workflow orchestration: A portal an...

New research shows honesty about AI use at work is backfiring New research from Atlassian’s Teamwork Lab surfaces a tension at the heart of AI at work: adoption is nearly universal, but acceptance is lagging far behind. Key findings AI at work is near-universal and employees are remarkably transparent about using it. 94% of U.S. knowledge workers report using AI at work and roughly 3 in 4 are vocal about that use with their manager and peers. But disclosing use of AI carries a steep penalty. In a controlled experiment of nearly 1,000 U.S. knowledge workers, participants rated a hypothetical peer who disclosed their AI use as 10x lazier , and were 24 percentage points less likely to recommend them for a high-visibility project, even when the output was identical. Framing matters, but silence is safer. Workers who framed their AI use as team-focused were rated 11 percentage points higher on effort and 8 points more likely to be recommended than those who cited personal efficiency. But both groups still ranked well below employees who didn’t mention AI at all. Culture is the real fix. In companies that actively celebrate AI use, the laziness penalty almost entirely disappears, and AI users are rated as more efficient than peers who don’t disclose. Everyone does it, but the honest ones get punished The secrecy around workplace AI is officially lifting. While 94% of knowledge workers report they used AI last month (and two-thirds did so regularly), they aren’t hiding it: nearly 80% are entirely transparent with their managers and peers. And yet, researchers found that disclosing AI use carries a steep professional cost. Even when evaluating identical work, colleagues rate AI users as 10 times lazier and are 24 percentage points less likely to recommend them for high-visibility projects, indicating that while the secrecy around AI use may be fading, the stigma is still severe. These insights come from a controlled experiment with 961 U.S. knowledge workers, run alongside a 1,006-person pulse survey on AI adoption and attitudes. In t...

I made an app that automatically updates Confluence Pages linked to Jira tickets once these transition to Done Would really appreciate your feedback. As a PM, I've always struggled keeping documentation up to date and I think I found a pretty easy way of handling this. Pretty satisfied with it, but would really like to hear someone else's opinion. submitted by /u/drunkules [link] [comments]

From repetitive tickets to instant resolution: How AI agents help reclaim your service desk for the work that matters 90,000 tickets auto-classified. Zero wait time. A practical guide to deploying Rovo agents in Jira Service Management for instant resolution and measurable ROI. The learnings in this white paper are based on the session, “Rovo Agents in action: Accelerating self-service deflection”, presented at Atlassian’s Team ’26 conference. You can check out this session and others on demand. Service desks across industries share a common challenge: a significant portion of incoming tickets are repetitive, low-complexity requests with known answers. These aren’t just operational inefficiencies; they represent a systemic drain on skilled human resources and a degraded experience for the employees seeking help. Custom Rovo agents in Jira Service Management change the equation. They take on repetitive tasks and intercept routine requests at the point of need – on the help center, the service portal, or in Slack – resolving them in seconds with intelligence grounded your organization’s actual documentation. In this paper we dive into the architecture behind these agents, a step-by-step guide to building and deploying them, and a real-world case study from Atlassian’s own IT service desk that proves the math works at scale. The problem: Repetitive work is crushing service teams Service leaders are under constant pressure to do more with less, yet capacity is often overwhelmed by routine demand. The volume of repetitive requests is predictable. The answers are documented. Yet the workflow still demands human intervention, every single time. The hidden cost of known-answer tickets Every service desk handles a predictable stream of recurring requests: password resets, application access instructions, policy lookups, and misdirected tickets. These are questions with documented answers, yet they still consume agent time, clog queues, and delay resolution for genuinely complex issues. The numbers tell a stark story: 20 to 60% of tickets that arrive at a service desk have known answers and ...

Improving Code Reviewer with Atlassian PR Context

Any jira devs here? submitted by /u/tolkien_12 [link] [comments]

any suggestions to make it’s more Realistic submitted by /u/Ibrahim-Antar3d [link] [comments]

I made a native app to unify Salesforce, Jira, Confluence and 365. Need your brutal feedback. Giving away 10 permanent keys submitted by /u/uknowimright_right [link] [comments]

Improving Code Reviewer with Atlassian PR Context Code Reviewer already knew the rules. We gave it history. Rovo Dev Code Reviewer catches PR issues related to code bug, code design, code readability, maintenance, testing, etc. It works well. But we saw an opportunity to take it further: to give it something closer to the institutional memory that experienced reviewers build over time, an awareness of past review discussions, team conventions, and the patterns that have shaped a codebase. This is the story of how we pursued that, what we learned, and what the data showed along the way. The idea: past PRs are the best context data you have The knowledge an experienced reviewer brings to a PR doesn’t come from a style guide. It comes from history: the last time this pattern came up, what was flagged then, what the team agreed to change. That knowledge lives in past pull requests that show what changed and the review conversations/comments that explain why it mattered. So we built similar issue context via Atlassian Teamwork Graph Collection. When Code Reviewer processes a PR, it retrieves associated Jira issue and looks for semantically similar past issues from the same repository and provides two types of context to the model: PR diff context : the code changes from a similar past PR, showing what correct patterns look like in this codebase PR comment context : the review conversation from that PR, capturing what the team cared about and why Diff context encodes technical correctness and comment context encodes team culture. Together they approximate institutional memory. Experiment 1: Offline evaluation, establishing signal We started with an offline evaluation on our internal benchmark of 3,492 issues. Of these, 2,620 were linked to valid Jira issues. It is important to note that the availability of similar issue PR context not only related to Jira issue linkage but also the ‘similarity’, where we set this to be 0.75. High selectivity of context causes the scarcity, which is an important property of the feature: s...

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

LLMs are picking winners. Here’s how to become one In the last two years, our traffic from LLMs grew 41x. Every quarter set a new record with no signs of slowing down. If you’re a skeptic like me, you’re probably about to ask: “WeLL, BuT dOeS iT cOnvErT?” Yes, yes it does. Better than almost anything else we have. I’m not telling you this to humble brag (well, maybe a little) . I’m telling you because the inverse is also true. PostHog has 14 products and counting , and (sadly) not all of them are recommended by LLMs at the same scale or rate. While most models know us for product analytics , session replay , error tracking , and feature flags , fewer acknowledge we also offer a data warehouse , AI observability , logs , and a lot more. This is likely the case for your product too. Changing it requires dipping into the mystical new field of answer engine optimization (AEO), which I’ve learned a lot about as the person responsible for it at PostHog over the last year. 1. Do LLMs even know you exist? Your first step should be finding out if you’re being mentioned at all. Go to ChatGPT, Claude, and Gemini (make sure memory is off or private mode is on). Run 3-5 basic prompts a real customer would use like “best X tool” or “alternative to Y” or “recommended Z product.” Being more specific is less helpful here. See where you show up, are missing, or are misrepresented. If you want a more robust opinion, pick one of the dozens of AEO tools that have launched in the last year. 1 A decent tool runs hundreds of prompts for you every day, which gives a clearer picture into your LLM visibility over time and across models. Don’t overthink this, choose a tool that fits your budget comfortably and matches your usual analysis workflow. The LLMs and your AEO tool will reveal you almost certainly aren’t in the place you want to be (we aren’t). This doesn’t mean you’re doomed, it’s where the rest of this post comes in. Models rely on both their training data (often from 6+ months ago) and search to create responses. To get into the ga...

I assume i'm not the only one losing all it's repo's and code ? I'm the only one losing all it's repo's ? Everything is empty and my php repo's are gone. Is there a way to still beable to download my public repo's ? submitted by /u/Lucifer_iix [link] [comments]

Connect to Forge migration, or how I learned to love responding to customer requests Atlassian, We need to talk about Connect to Forge migration. This has become more urgent because of to the recent decisions regarding notifying customers of any use of Connect modules in Forge apps. The thing is… in order to migrate our customer from Connect to Forge, we need to map the Forge installation to the previous Connect installation. As per recommendation by Atlassian, we used the Connect clientKey property as the unique identifier. However, Forge does not provide us with that identifier upon installation. Forge does not have a migration lifecycle event. Forge does not care about Connect. It treats the migration as a fresh install. We initially solved this using the connectModules lifecycle event in our manifest, as per Atlassian documentation , which allows us to capture the uninstall event which is triggered when a customer upgrades from Connect to Forge. However, adding this module to our manifest will trigger a “OH MY GOD, OH MY GOD, OH MY GOD, YOUR ENTIRE INSTANCE IS ABOUT TO BURST IN FLAMES” message to end-users. So we went with the second option in the documentation , and instead called the API to get the connect key using the magic property. The problem with that approach? That is documented here : During app installation, the Atlassian account used by your app to authenticate with Atlassian APIs is granted various permissions and group memberships for the site it is being installed into. This process is eventually consistent, and the installed event may be sent before the app is granted the permissions needed to call certain APIs using .asApp() . For large sites, it may take a very long time (minutes) for your app user to be fully granted the requisite permissions. We’re seeing situations in which the app does not yet have the required permissions to retrieve the clientKey from the API because the app is still being provisioned, even though the end-user can already interact with the app. So we can’t map the Forge installation with the Connect app, ...

[ Removed by Reddit ] [ Removed by Reddit on account of violating the content policy . ] submitted by /u/Quiet-Law2251 [link] [comments]

Retrieving info for JSM Customers Hello: I have this issue since 2020. I’m doing a customer portal for our organization, with Jira service desk integration using .NET Core. We need our customers to access our portal and check their opened issues, add coments and attachments, and create new issues. I don’t mind using Basic Auth or OAUTH2, because I already tried to implement both ways, but I can’t figure it out how to do it. Using Basic Auth, I need an API Token, but it can be created only for Atlassian users and “service accounts” Manage API tokens for your Atlassian account | Atlassian Support . I don’t know how to create an API Token for a JSM customer, or how to relate JSM customer with a service account. Using OAUTH2, the Atlassian login page only allow users to log in, not JSM customers. I can create a “technical user” to get only user information with the customer log in information on our platform, but it means we need to share the “technical user” and password to all our customers, in order for them to write the credentials manually on Jira login page. What about using “technical user” with “Basic Auth”, and then filtering? Ok, it’s possible, but all issues and comments are created by that technical user. In comments I can add “created by…” at the start of the comment, and I can use the field “CreateOnBehalfOf” to point to the customer adding the issue… BUT I need its Atlassian customer ID, and Basic Auth can’t retrieve customers info, it’s only avaiable on OAUTH2 (and Forge), and only on Jira API, Service-Desk API can only create customers. docs.atlassian.com servicedeskapi/customer - JIRA Service Desk 3.6.2 This is the reference document for the REST API and resources provided by JIRA Service Desk. The REST APIs are for developers who want to integrate JIRA Service Desk with other applications, and for administrators who want to script configuration... So, I don’t know how to allow our customers to ask for their issues, check an send comments using any Jira Rest API. I could attach the Ser...

Google I/O 2026 Developer Keynote 5-Minute recap submitted by /u/EffectiveRock3795 [link] [comments]

Bulk‑updated 1,224 Jira tickets with 20 fields each… in 1 hour This run saved roughly 40 hours of manual updates. Jira’s bulk edit can’t handle large multi‑field updates like this, so I built a tool that can. Run details: • 1,224 issues • 20 fields each • 18,360 successful updates • 0 ticket failures • 40 hours saved instantly If your team spends too much time doing repetitive Jira updates, this might be useful. Happy to answer questions. submitted by /u/Ok_Strength4594 [link] [comments]

Render Blueprint for ParadeDB Hi all. We kept getting requests for it, so we (ParadeDB) made a Render blueprint in partnership with the Render team itself! Would love your feedback. submitted by /u/philippemnoel [link] [comments]

A "patchwork" of AI laws is a feature, not a bug Consider a few of the big AI risks: violating privacy, taking jobs, and destroying the world. Here’s what Sam Altman (CEO, OpenAI) and Dario Amodei (CEO, Anthropic) have to say about each, representing the two largest AI companies. On AI Violating Privacy Sam : If you go talk to ChatGPT about your most sensitive stuff, and then there's a lawsuit or whatever, we could be required to produce that. Dario : AI seems likely to enable much better propaganda and surveillance, both major tools in the autocrat's toolkit. On AI Taking Jobs Sam : AI is going to eliminate a lot of current jobs, and there will be classes of jobs that totally go away. Dario : AI could wipe out half of all entry-level white-collar jobs—and spike unemployment to 10-20% in the next one to five years. On AI Destroying the World Sam : And the bad case—and I think this is important to say—is like lights out for all of us. Dario : My chance that something goes really quite catastrophically wrong on the scale of human civilization might be somewhere between 10 to 25%. So, the leaders of AI think there is a decent chance it will destroy the world, are pretty sure it is going to wipe out lots of jobs, and are absolutely sure it is violating our privacy and has the potential to enable much more mass surveillance. We need government to help manage these risks, let alone all the other concerns surrounding AI like data centers, child safety, etc. There is currently a debate whether Congress should prevent states from regulating certain AI risks. For example, the recently introduced Great American AI Act preempts (stops) states for three years from “regulating the development of any artificial intelligence model” (from § 121 in the full text and explanatory text ). It would be one thing if Congress was super effective, but it’s just not, especially with regard to tech regulation. Case in point, Congress has failed to pass a general privacy law for the entire life of the Internet! In fact, aside from narrow, rea...

i want to delete my account someone made the account with my mail and i cant get rid of the free subscriptions in order to delete it. the bit bucket thingy might have other stuff i have no clue i cant access to it since my mail was hacked to make that account is there a way some admin delete the account? ive been up to 4 am trying to solve this but i just keep going back to square 1 submitted by /u/East-Mousse-5316 [link] [comments]

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 ...