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Great companies are built in hackathons PostHog hackathons have generated millions in revenue. Session Replay , Data Warehouse , Logs , Workflows , and PostHog AI wouldn’t exist without them. We take multiple days out of every year to do them. They are days our team looks forward to all year. They’re an integral part of our culture. Yet for many companies and people, hackathons are seen as a waste of time. A distraction from important “real work.” An act of performative participation. This is because they think about and do hackathons wrong. Hackathons are an innovation engine, but only if separate from work Hackathons fail when they’re not separate from regular work or used as cover to accelerate existing work. We have two simple rules to prevent this: You should work on totally new things. Hackathons are for ambitious, weird ideas, trying new things, and learning new technologies. Pitching ideas on the roadmap is forbidden. You should focus 100% on the hackathon. We protect people from regular work by running our hackathons during offsites , where we have dedicated time and coverage plans for support and incidents. DigitalOcean employs work embargoes for hackathons run in an office. These rules fuel the innovation engine because people are unconstrained by their day-to-day work, and have a safe space to work on ambitious bets without distractions. We’ve seen first-hand how running hackathons like this can lead to transformative new products and features that influence the direction of the whole company. In PostHog’s earliest days, an engineer decided to build Session Replay during a hackathon based on user feedback, despite co-CEO James arguing it would take too long and split the focus of the company. It ended up being wildly popular, with over 50,000 daily active users, and led us to become the multi-product company we are today. As an early Facebook engineer noted, code wins arguments and hackathons are a great venue for this to play out. Remember this: Give permission and cover for your team to focu...

Side project: a “Daily Puzzle” game that teaches SHA-256 (hashing) in 60 seconds submitted by /u/Polydeuces32 [link] [comments]

Enhancing Developer Workflow with Rovo Dev and Notifications

Rovo Dev in Frontend Platform Engineering – AI for small tasks, AI for big tasks

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How to migrate Jira Issue Field (Connect) to Jira custom field (Forge) in private app Hello, I’ve a private custom addon that uses what before (in Connect) was called Jira Issue Field, and now I’ve to migrate to Forge using Jira Custom Field. The problem is that I want to preserve the same ID to not break all the JQL queries and not need to bullk the data from the “old” issue field to the “new” custom field. Some help? thank you ! 1 post - 1 participant Read full topic

When adopting connect-on-forge does the forge app have a new accountId of accountType "app"? Hello! I was under the impression that when adopting connect on forge all previous app settings would be kept but it appears that we have duplicate users of accountType “app” on our instance. They both have the app name as displayName (guessing one is from the connect app and one is now from the forge app). Is this expected behavior? I couldn’t find any reference to this in the documentation. If someone has some insight on this, I’d appreciate it. Thanks in advance, Paulo Mendes 1 post - 1 participant Read full topic

Beta testers wanted for Simple Fetcher — a no-code external data sync app for Jira built on Forge (genuine feedback only, not a promo) Hi r/atlassian , I am a solo developer and I have spent the last few months building a Forge app called Simple Fetcher. I am about to submit it to the Atlassian Marketplace but before I do I want 5 real people to install it and tell me what is broken. This is not a marketing post. I genuinely need feedback from people who use Jira regularly before I go public. The Problem Simple Fetcher Solves Most Jira teams have context scattered across multiple tools. A support agent needs to check Stripe for payment status. A PM needs to check GitHub for PR status. An ops lead needs to check an internal API for order status. None of that context lives in Jira so people copy paste, switch tabs, or just work without it. Existing solutions like Elements Connect and External Data for Jira Fields solve this well for technical users who understand SQL, JsonPath, OAuth configuration, and field dependencies. But a project manager who has never written code cannot set those up alone. Simple Fetcher is built for the non-technical user who just wants to see external data in Jira without involving IT. Full Feature List Template Gallery: Pre-built one-click connections for GitHub, Google Sheets, HubSpot, and Stripe. Pick a template, enter your API key following the plain English instructions, and data starts flowing. No URL configuration. No field mapping. No technical knowledge. Custom API Connection: Paste any REST API URL and our AI analyzes the response. It gives each field a plain English name, tells you what type of data it is (text, number, date, yes or no), and recommends which fields to include in Jira. You just check the boxes you want. Self-Healing Sync: When an API goes down temporarily, we do not clear your Jira fields or show error messages. We keep the last successful data visible and send a Slack alert. When the connection recovers we sync automatically. Users never see broken data. Visual Preview: Before activating any connection you see a realistic mock of how the synced da...

Your team’s best ideas are trapped in the wrong format. AI just fixed that.

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Atlassian Enables Default Data Collection to Train AI. Gentlemen, another route of attack has opened. Discussion on Hacker News: https://news.ycombinator.com/item?id=47833247 submitted by /u/RNSAFFN [link] [comments]

Subscription setup generic error - Confluence Enterprise Anyone dealt with this issue? Atlassian is investigating for almost 1 month with no fix. The moment I click Begin setup I get the error message. submitted by /u/Maximum_Fun5791 [link] [comments]

Forge bulk upgrade docs misleading The documentation for forge version bulk-upgrade states: The forge version bulk-upgrade command lets you upgrade a maximum of 500 installations in 1 request It looks like the limit is actually 1000 installations per request. We ran a bulk upgrade today targeting approximately 1450 installs. We watched as the pending count decreased as the completed/failed counts increased (all the time totalling ~1450). Once the completed + failed count reached exactly 1000, the pending count dropped to zero. If we run forge version bulk-upgrade start again, we can see the remaining ~450 installations that can be upgraded. We were expecting it to stop after 500 upgrades so that we could verify no issues before starting the next batch. It’s not a huge problem (at least we hope it isn’t at this early stage) that it upgraded twice as many as expected in a single batch, but the documentation is clearly wrong and should be corrected. 1 post - 1 participant Read full topic

Stash i managed to patch Stash and wrote a keygen for it. if youre interested, you can check it out: https://github.com/idkmaybedeveloper/restash submitted by /u/w1r3n_sh [link] [comments]

Forge app getting 401 on /admin/v1/orgs with valid Admin API key My Forge app calls the Atlassian Admin API using a Bearer token (key generated on admin.atlassian.com ). It works fine with curl but always returns 401 inside Forge : const headers = { ‘Authorization’: Bearer ${apiKey.trim()} , ‘Accept’: ‘application/json’, }; const res = await fetch(‘ https://api.atlassian.com/admin/v1/orgs ’, { headers }); // → always 401 {“code”:401,“message”:“Unauthorized”} 1 post - 1 participant Read full topic

In-depth: PostHog vs Matomo PostHog and Matomo help you understand how your users are using your site and product, but they're very different tools below the surface: Matomo is a privacy-focused web analytics platform modelled closely on Google Analytics 3 (Universal Analytics). It's popular with content publishers, governments, universities, and e-commerce sites that want full data ownership and GDPR compliance without sacrificing familiar reporting. PostHog can also replace Google Analytics , but it's built for engineering-led product teams. On top of web analytics , it includes product analytics , session replay , feature flags , A/B testing , error tracking , surveys , an LLM observability suite , and a built-in data warehouse , all in one platform with a shared data model. How is PostHog different from Matomo? 1. It's an all-in-one platform Matomo's core analytics product is strong, but features like funnel analysis, cohorts, A/B testing, and session recording are separate premium add-ons on On-Premise, and heavily capped in Cloud. In PostHog, every tool shares the same event data, so your analytics, replays, feature flags, and experiments all work together from day one. Each comes with a generous free tier . 2. It's built for engineers Rather than focusing on marketers like Matomo, PostHog focuses on the tools engineers need to build better products. On top of analytics, this includes experimentation , feature flags , error tracking , logs , direct SQL access , an MCP server , a full suite of AI engineering products for teams building AI features, a CDP , and a data warehouse for querying external data sources. 3. Seamless integrations with the tools you already use PostHog is built to work with the tools you already use. That means you can import data and query from sources like Stripe, Hubspot, Zendesk, S3, and more. You can also export data in batches to your data warehouse like Snowflake or BigQuery as well as a range of real time destinations like Google Ads and Slack. Matomo's inte...

Would non-technical Jira users actually use a no-code API sync tool? Hi all, I’m building a Forge app called Simple Fetcher and I’m trying to validate whether this solves a real problem or if I’m overthinking it. The concept: Connect any REST API to Jira Use templates for common tools (GitHub, Sheets, HubSpot) Or paste a custom API URL and let AI suggest field mappings Data syncs automatically into Jira custom fields If the API goes down, we keep the last good data and alert the user It’s designed specifically for non-technical users — project managers, product owners, smaller teams without dedicated Jira admins. There are already powerful integration tools on the marketplace, but most seem built for IT teams. So I’m curious: Do non-technical Jira users actually need this? Or are integrations always handled by admins anyway? Would a flat per-site price make sense? Or is this category already “good enough” as-is? Really just looking for reality checks before investing more build time. Appreciate any thoughts. submitted by /u/Crimson_Secrets211 [link] [comments]

I made buttons for my stream deck I made buttons for my stream deck that shows when my Front End & Backend are Deployed, Deploying, or Failed. I wanted to share. I used Cursor to help code it. I made PNG file for each graphic b/c the Stream Deck app does not show Emotes, Text File Tools [Bar Raider]: Regex Display. Has anyone else done this? Wanted to share. I just have a bat file that runs checking the status and changing the image in the code that is linked to the Stream Deck app/button. submitted by /u/Repulsive-Camp2715 [link] [comments]

How do I delete my acc/site or organization when I'm the billing admin???? I just found out that I have an Atlassian account because they kept spamming me in my email, telling me that I must enter my payment info so the my subscription is not deactivated. And there was an estimated bill(?) I panicked, because I don't remember anything about this website or that I was in a subscription. Anyway, I quickly downgraded, then deactivated the payed subscription. But I still have 3 other apps (idk what they are called) that are on free plan. I want to delete my account (and my organization), but these free plans are stopping me. And also that fact that I'm the billing admin. What should I do? Their bot is telling me to send a help form so that the support team manually assists me, but they don't have one. submitted by /u/Unlucky_Pop_4280 [link] [comments]

Is it possible to impersonate another user via Jira Cloud API using a service account or any other mechanism? I’m trying to understand if it’s possible to perform actions in Jira Cloud on behalf of another user via APIs, using a backend service or service account. The use case is to run automated operations (e.g., creating issues, exports, updates) while attributing the action to a specific user and respecting their permissions. I haven’t implemented a specific approach yet, but I’m exploring options such as Forge Remote with offlineUserAuthToken flow. Questions: Is user impersonation supported in Jira Cloud APIs? Can a service account be used to impersonate another user? If not, what is the recommended approach to perform actions “on behalf of” a user from a backend service? How can we ensure that the actions respect the target user’s permissions and are properly audited? Looking for guidance on the correct pattern or supported approach for this use case. 1 post - 1 participant Read full topic

Elena Verna on AI growth: Automate the basics, elevate the creative When Elena Verna joined Lovable, an AI-native startup, she wasn’t chasing another growth role. “I actually wanted to retire before I hit Lovable,” she admits. After leading growth at Dropbox, SurveyMonkey, Miro, and Amplitude, she’d seen the same patterns play out again and again. “I couldn’t do another gig where I felt like I’m doing growth 101. Onboarding flows, lifecycle emails, A/B tests. They all felt the same.” But the speed at which AI-native tools are changing how teams build pulled her back in. “I wanted to automate myself out of the job,” she says. “All of these baseline optimizations should be codified into products themselves so growth leaders can actually focus on creativity and innovation again.” That’s Elena’s operating principle: Use AI to automate the basics so humans can elevate the creative. AI isn’t 100%. It’s ‘average intelligence’ that can take you 40 or 50 percent of the way. The real lift still comes from human judgment layered on top.” Elena Verna, Head of Growth, Lovable Listen to the full episode of Product in Practice with Elena Verna to hear her workflow demo and deeper reflections on how AI is changing the way we build products. Automate the 101 For Elena, AI’s real value is simple: Do the busywork so people can focus on the good stuff. “We all spend too much time doing the same baseline optimizations,” she says. “AI should free us from that. What can you automate that you as a human don’t add value to?” She argues that the foundational “growth 101” work — A/B tests, lifecycle emails, onboarding flows — should be baked directly into products. “No company should need to hire a growth leader just to get the fundamentals right,” she says. “Growth should be about creativity and innovation, not doing the same things over and over.” Solve the empty-state problem Everyone knows the paralysis of a blank page. For Elena, this is exactly where AI shines. “Humans struggle with the empty-state problem,” she says. “We have an empty Google Doc, and o...

Atlassian + Anthropic? Lots of chatter in the usual SF coffee shops that Anthropic is in advanced talks to buy Atlassian. Hearing institutionals and Mike and Scott want $150 a share, all cash. Guess that explains why they closed Austin office and moved everything to SF. Interested to hear what everyone thinks about this. Upside: Tight integration, Confluence/JIRA getting the latest models probably for free. Downside: Probably some redundancies. submitted by /u/TurbulentTiger2567 [link] [comments]

Which assets import tutorial is the most up to date recommendation for using async queues? There are two tutorials having to do with assets import: Import Third Party data into Assets (former tutorial) Use Async Events API to queue jobs to import objects into Assets (later tutorial) They both tell you to use the same forge template: jira-service-management-assets-import-type However, the template seems to be directly related to the former tutorial. The later tutorial similarly suggests two separate queues, but defines different responsibilities for each queue. The former tutorial has an older modification date and is listed on the later tutorial as a dependency of sorts in the Before you begin section: This tutorial assumes you’re already familiar with developing an Assets Import app on Forge and the Imports REST API for Assets. If not, see Import third party data into Assets and Imports REST API Guide . So my hunch is that the guidance provided in the later tutorial is the current best practice for async queues associated with importing external assets, but the template just hasn’t been updated to reflect that and you are on your own to adapt the template. Can anyone confirm this? 1 post - 1 participant Read full topic

New data contribution settings What are your opinions on this? ( https://www.atlassian.com/trust/ai/data-contribution ) I don't want anybody to learn from my data or to train LLMs with it. Neither do I like the fact that you can't switch off the use of metadata when you're on a standard plan (hey, for enterprise users no problem, you might need it for compliance reasons). Really don't like what I see here. submitted by /u/derkasek [link] [comments]

Guests in the times of... 250K user Confluence sites Adding outside collaborators as guests is a no-brainer as g uests are essentially free users. Free as in ‘you don’t have to pay for their seats'. Sure, there are limitations but you can add up to five guests per one paid seat. Admins are less thrilled and with the advent of 250K user sites , so they are naturally biased toward saying no to protect the org and their sanity. https://preview.redd.it/gakc4ui63jvg1.png?width=2200&format=png&auto=webp&s=9c8a2b09bf8c284529160ea9251cb6fdba5f3730 I only had to deal with guests on a small site (50 seat license) but it felt like I'm manning revolving doors at times. As I was the project lead AND an admin, it wasn't that bad. Anyway, here's something I observed and practiced and it may scale up if you enforce it. I hope this will be useful for both admins and project leads. The Math A small team of 10 people who are about to change the world may add 50 guests. That’s an incredible money-saving measure! And that you can manage with ease. Let’s move things up a notch . Now you have a site with 1000 users. That means 5000 potential guests. Managing 1000 carefully provisioned users is one thing. Managing even 1000 guests is what admins' nightmares are made of . And before you ask if it scales… yes, it does, unlike an admin’s stress tolerance, all the way up to the 250K-user limit per a Confluence site. And that might happen on Standard where you don't have all the perks. Define the Guest's Role The guest role was designed to enable ad hoc collaboration of teams with outsiders. Not to get a free ride. Although… Anyway, before you invite a guest user, the following best practices will help you develop a manageable workflow. Define the project guests will participate in. Determine the Confluence space the guest will access. Guests can only access a single space at a time. Do not hesitate to create an ad hoc space just for this specific collaboration. Determine the guest’s permissions within that space. Don’t forget to check the market...

JSM and Jira cloning For the past few days I am trying to do one automation, and the answer somehow always includes branching and parentkey/etc. But it shouldn't need this, or? It's a clone from JSM new item at a particular time, make new items, 5x of them. Clone them to Jira and link them. (Example is quarterly export of systems) I thought a simple sequence of create, clone and linking is suffice. I can also do all Create instead of cloning. How do you deal with certain generic errors in this part of Jira? Do you find Rovo helpful? submitted by /u/Terrible_Ad3822 [link] [comments]

Merge Queues for Bitbucket Cloud, now in open beta Teams are shipping more code, faster than ever, as they increasingly automate their processes with CI/CD and AI. But high-velocity pull-request workflows and large monorepos, where many PRs are merged continuously, are feeling the pain as they grow: pull requests race to merge before the branch changes again, “green” builds still break due to semantic merge conflicts, and developers are stuck babysitting merges instead of building features. The result is friction, slower releases, and too much time spent managing branch stability instead of improving the product. Today, we are announcing the open beta of Bitbucket merge queues for Standard and Premium plans in Bitbucket Cloud . Merge queues allow teams to automate, sequence, and validate pull request merges, ensuring every change lands on a green, production-ready main branch, without last-minute breakages or endless rebases. And developers stay in the same Bitbucket pull request experience they use today, just with a smarter, automated merge path that cuts down on traffic jams, rework, and flaky releases. What’s new: merge queues, built in Bitbucket now automatically sequences and validates your pull requests by allowing you configure merge queues. No need for extra scripts, plugins, or manual rebases. Just open your PRs as usual, and Bitbucket does the rest: Queue pull requests instantly: Just queue your pull request whenever all checks on your Pull request pass. Keep “target branch” green: The Pull requests run against the latest target-branch code plus dependent pull-requests, and merged only when all the checks pass, keeping the target branch green and eliminating last-minute surprises. Stay in control: Prioritise urgent fixes, clear or drain the queue with a click. Why it matters Merge queues are especially valuable in high‑traffic repositories where multiple developers are frequently merging to the same target branch. For those cases, the benefits are clear: 1. Ship faster: No more waiting on manual rebases o...

Rovo Dev in Frontend Platform Engineering – AI for small tasks, AI for big tasks TL;DR Rovo Dev really shines with big scale automating platform engineering tasks – like migrating between libraries, where similar patterns need to be applied across whole repo and where codemods might not be able to cover all the cases. Rovo Dev doesn’t limit to just execution – it can help with all the stages of delivery : planning / spikes – building PoC / playgrounds / classifying changes writing harness / orchestration for executing migration tasks building validation steps which will be used as a feedback loop for the coder agent The experiment to migrate styled-components to @compiled css-in-js resulted in migrating majority of usages (most of them tricky edge cases, where codemods couldn’t update them) in short amount of time (total time to generate PRs within 3 days and minimal supervision – optimistically estimated as 14 engdays using artisan way of development). Additionally, to lower PR review pain (and decrease time to merge), Rovo Dev helped to build validation tools – used by both Rovo Dev to correct its mistakes and give the snapshot of styles to the reviewing team with highlighted differences (if applicable). Rovo Dev can help a lot with many common platform tasks – one of them which I have stumbled upon most recently is migration from library A → library B (outdated styled-components to Compiled css-in-js ). For migration-type of tasks the execution can take significant amount of time, despite the work being quite repetitive (however nuanced from case to case) – that’s where I hoped Rovo Dev help to close the gap of codemods. Preparation for the big task – migrating Styled Components to Compiled The main experiment was how to run bigger spec-driven project of mass migration of almost 200 files including styled-components library to their Compiled replacement. To make Rovo Dev to execute on an one shot bigger task we need to organise spec document describing whole project. Particular agents will consume / get as a context relevant parts of the docu...

Streaming Server-Side Rendering in Confluence Over the past two years the Confluence team has spent a significant amount of time investing into improving performance – halving the 90th percentile latency for completing page loads https://community.atlassian.com/forums/Confluence-articles/Confluence-Performance-Journey-2x-Faster-in-1-Year/ba-p/3097191 . One of the big change we have made in the last 12 months has been to introduce React’s new streaming capabilities for server-side rendered (SSR) apps. This has allowed us to significantly lower the amount of time it takes to make content visible in the browser and opens up further opportunities to improve Confluence’s performance. We’ve observed about 40% improvement for the first contentful paint. In this post, we cover how we integrated React 18’s streaming capabilities into Confluence to reduce the time it takes to display content on page load. These foundations have enabled us to further optimize how the page is loaded – ensuring pages are visible and interactive faster. Page Load Performance Metrics In order to understand how streaming impacts page load performance – we will first cover what metrics we use at Atlassian to measure and understand page loading performance. We track a fairly broad range of metrics for page load times – but some key metrics we track include: First Contentful Paint (FCP) measures the time it takes for the first visual elements to be rendered within the browser when loading a page. This could be the entire page or just a small first subset like the navigation menu. A low FCP typically helps users see a page loading earlier, making the page feel more responsive. Time to visually complete (TTVC) measures the time it takes to render the entire page, in particular, how long it takes for the entire page to settle. Importantly, TTVC requires elements to be loaded and not to shift; as such, a low TTVC number makes the page feel significantly faster. On the Confluence team, our target is to have 90% of the page visible and stable as quickl...

Editor-common heading icons broken Editor-common heading icons broken by tsRewriteRelativeImportExtensions in published ESM** Affected package:** @atlaskit/editor-common@112.18.9 , file dist/esm/quick-insert/assets/index.js Description: The importHeading() function wraps paths in _tsRewriteRelativeImportExtensions() , which is a TypeScript 5.7 --rewriteRelativeImportExtensions runtime helper. This is a no-op in compiled .js output, but prevents Webpack 5 from statically analyzing import() expressions, resulting in “Critical dependency” warnings and heading icons failing to load (not displayed) . All other ~24 icons in the same file use plain string literals and work fine. 2 posts - 1 participant Read full topic

PostHog showed me where my “simple” UX was actually causing friction One thing PostHog helped me realize while building Kestor.app : a flow can look simple and still create a frustrating experience. I found this in my invoice creation flow... Thankfully, PostHog helped me catch a usability issue that regular analytics probably would’ve missed. The problem showed up in the invoice creation flow. I designed it to feel simple and lightweight by breaking things into small, focused steps. On the surface, it looked clean. But once I started watching real sessions in PostHog, I noticed the same pattern over and over: people would move through the flow pretty confidently, then suddenly get blocked because they were missing required setup in settings, usually business details or tax configuration. So the flow wasn’t actually simple. It just delayed the friction. Instead of finding out upfront what was missing, users only discovered it later, had to leave invoice creation, go into settings, fix things there, and then come back. Nothing was broken, but the experience was clearly more frustrating than I expected. Watching session recordings made that obvious almost immediately. The fix wasn’t adding more complexity. I just made the missing requirements much clearer throughout the flow, so users could understand earlier what still needed to be set up. I also created a smoother first-time setup path, so they could complete those missing steps without losing the invoice they were already working on. That change made the whole experience feel much better. What I liked about PostHog was that it helped in two different ways: analytics showed me where people were slowing down or dropping off, and session recordings showed me why. Without the recordings, I probably would’ve only known that something felt off. With them, I could actually see the hesitation and fix the real issue. Good reminder that making a flow feel minimal doesn’t always make it better. Sometimes it just hides the dependencies and pushes the friction further down the line. submitted by...

Is there an API to get groups by product in Atlassian Admin API? Hi, Is there an official endpoint to get the groups associated with a specific product (e.g. Jira Core, Confluence)? Currently I’m guessing the product from the group name/description using regex, which is fragile. 1 post - 1 participant Read full topic

Curl search cql with hex encoded url no longer working We are using a bash script to update content on confluence cloud. First the script is searching a the page id with the page title using $CONFL_API_END= https://my-company.atlassian.net/wiki/rest/api/content/ curl -so- -H “$AUTH_HEADER” -X GET -L “$CONFL_API_END/search?cql=space=%22$_space%22%20and%20title=%22$_query%22%20and%20type=page” $_query and $_space are hex encoded , the space is ok , but since a couple days the title is no longer found "error: Page ‘%4F%76%65%72%76%69%65%77%20%41%63%63%65%73%73%20%53%77%69%74%63%68%65%73’ not found. is something changed on the API ? 1 post - 1 participant Read full topic

Inconsistent adf PDF Export Hi there, i have an Confluence Macro written in Forge. The macro defines an adfExport function in the manifest.yml. In general, the export works but sometimes the font size is extremly big and sometimes it is what i would expect to be normal. I tried adding CSS into my confluence Instance but that does not seem to do the trick. I also added 2 Screenshots of the exports. I was on the export page and the only thing i did was pressing f5 to re-render the export and this are the 2 results of that. Does any1 have any idea how to get an consistent result everytime a user exports my plugin? Best regards 1 post - 1 participant Read full topic

How are you keeping Jira automation from turning into admin debt? I keep seeing the same pattern in bigger Jira setups: nobody is too worried when the first few automation rules get added, but after enough years/projects/owners, the environment gets hard to trust. The mess usually looks like: - duplicate or overlapping rules - unclear ownership - noisy audit history - not knowing what is safe to remove If you have admin responsibility in Jira, I would really value a reality check: - how do you review old rules today? - what part wastes the most time? - do native tools feel sufficient here, or is cleanup still mostly manual? I’m specifically trying to understand the automation governance / cleanup side once the rule count gets big.I keep seeing the same pattern in bigger Jira setups: nobody is too worried when the first few automation rules get added, but after enough years, projects, and owners, the environment gets hard to trust. submitted by /u/Diligent-Resolve8023 [link] [comments]

Introducing Agentic Pipelines: AI automation for chores devs don’t want to do Bitbucket Pipelines has always been an engine for automating more than just CI/CD, but today, Pipelines takes a first step towards a full agentic automation platform for all the manual, tedious, repetitive work that happens before and after code creation. You’ve probably seen the stat: Development teams spend 84% of their day doing things other than building features . A lot of this work is: Toilsome tasks : Constantly updating documentation to keep up with changes in code Tech debt repayment : Cleaning up feature flag code after a feature is released Troubleshooting automation issues : Reading through logs to understand why a build failed This work matters, but it’s not very fun. It’s often high-effort but low-value; manual, repetitive, and easy to de-prioritize in the moment. Sometimes we skip doing it entirely, but it always turns into tech debt and you pay the price later. Agentic Pipelines is a new way to delegate those tasks to AI, orchestrating a fleet of agents to automatically, and routinely, handle the repetitive chores so you can get back to solving the fun, cool problems. Why Agentic Automation for this kind of work? Many teams already rely on tools like Bitbucket Pipelines to automate CI/CD workflows, with Bitbucket acting as the central orchestration platform. Agentic Pipelines takes that model and extends it beyond the core dev loop of “build, test, deploy.” Think about all the tasks you could continuously automate that are straightforward but time-consuming, easy to defer until later, and most importantly, painful when they pile up. A concrete example is maintaining documentation. Keeping a repo’s README or knowledge base in sync with reality involves: Keeping the project description aligned with how the code actually works Updating installation and configuration instructions as the setup changes Refreshing examples or API docs (and suffering the compounding pain when they’re out of date) Maintaining contribution guidelines, ownership info, and code ...

GDP.pdf: Can $100B AI Models Master the Documents that Run the World?

AWS App Runner -> Render AWS App Runner is ending new feature development. Render is the natural new home for your App Runner services. Same flow, no extra setup: Link your repo Set build & start commands Deploy (Docker images welcome too 🐳) https://render.com/docs/your-first-deploy submitted by /u/shifra-dev [link] [comments]

routeApi access from another Forge application Hi all, I have create a test App leveraging the routeApi module ( https://developer.atlassian.com/platform/forge/manifest-reference/modules/api-route/ ). I followed the tutorials: declared custom scope push them to forge environment added the route Api module declaration deployed the app on my instance. I see the part of the tutorial to create an 3LO to access the route from “outside”, I would like to do it from another Forge App. When hitting the url, I get a 401. My questions: is there a trick to make this work ? is this even supposed to work ? what else could I use to make 2 Forge applications communicate more than payload less AppEvents ? Regards, 1 post - 1 participant Read full topic

All calls against Rovo from Jira automation suddenly fail TL;DR Calling agent from Jira automation is now failing 100% due to the agent timing out. Anyone else seeing the same thing? Full I have been testing some automation rules that trigger an agent to do some work on Jira issues. Today it suddenly isn’t working anymore. The automation calls the agent, but then never gets any response, so it eventually aborts due to taking too long. So then I started testing a bit, and it seems it fails 100% of the time whenever an automation calls any agent. Any automation calling Rovo ends up aborted and the audit log ends with the agent call (no additional info provided, presumably because it never got a response). Anyone seen this before or is facing the same issue? 2 posts - 1 participant Read full topic

Bitbucket Packages now supports Maven and npm Few months back we launched Bitbucket Packages with a native container registry , giving teams a way to manage container images right alongside their code and pipelines. Today, we’re expanding that functionality and now Bitbucket Packages support Maven and Node Package Manager (npm), bringing JavaScript and Java package management into the same platform where your team already writes code, reviews pull requests, and runs pipelines. Why we built this? When we launched container registry support, it addressed a genuine need for teams managing container images. But customers told us containers alone weren’t enough. Their workflows cut across multiple package types, and while container images were taken care of, npm and Maven artifacts still lived in entirely separate registries, each with its own billing, access controls, and learning curve. That fragmentation causes real problems: Duplicated permissions: Teams that have already configured repository access shouldn’t have to repeat that setup in a separate artifact tool. Broken flow: Jumping between tools mid-workflow slows developers down and opens the door to security gaps. Unnecessary cost: Every extra platform in the toolchain adds its own price tag and administrative burden. What’s included Publish and install Maven and npm packages: Create a package in Bitbucket, configure authentication with an Atlassian API token, and start publishing and installing packages using your existing tools like npm, Yarn, Maven, or Gradle. If you’re publishing from Bitbucket Pipelines, built-in environment variables ( BITBUCKET_PACKAGES_USERNAME and BITBUCKET_PACKAGES_TOKEN ) can be used as your authentication credentials . Unified permissions with no extra setup: Every package is scoped to a Bitbucket repository. That means the repository permissions you’ve already configured (Admin, Write, Read) automatically apply to every package tied to that repo. There’s nothing additional to configure, and no separate access control system to ...

Opensource Browser extension for AI Code Reviews for Bitbucker PRs - powered by Ollama Hi Everyone , I want to share some exciting news , I have added support for Bitbucket cloud and Bitbucket data center to ThinkReiew browser extension The extension works on your bitbucker PRs , generates Code Review - allows you to chat with your PR Give you code suggestions as well as you can add custom review agents that run on specific conditions on your PRs The extension is fully open source , It has local ollama support as well as all frontier LLMs https://github.com/Thinkode/thinkreview-browser-extension Its available on both chrome and firefox https://thinkreview.dev/ submitted by /u/Brilliant-Vehicle994 [link] [comments]

You're doing lifecycle emails wrong Messaging tools like Customer.io and ActiveCampaign were built for a specific job: broadcast email with some segmentation on top. You upload a list, you define some basic audience filters, and set up a drip sequence. That model worked well for a long time – and it's still fine for newsletters, product announcements, and campaigns where you're addressing a stable audience with a consistent message. PLG lifecycle messaging is a different job entirely. It needs to reason about sequences of behavior. Has this user completed onboarding and then gone quiet? Have they invited a teammate before hitting the feature wall? Did they upgrade, or did they hit the paywall three times and leave? This logic requires asking arbitrarily complex questions about what a user has and hasn't done, in sequence, in real time. Messaging tools weren't designed to answer these questions – they were designed to send emails to lists. So teams end up with trigger logic that's dumber than their product data. Not because they're doing lifecycle marketing wrong, but because they're using a broadcast tool to do behavioral work. How this plays out in practice Most teams build their messaging stack early, when event tracking is still basic. Signup event, maybe a key action or two. So the sequences were time-based, because that was all you could reliably trigger on. Day 1 welcome. Day 3 nudge. Day 7 check-in. It shipped, it ran, and it was good enough. Then the product got more instrumented. Feature usage, activation milestones, plan changes – all of it flowing into the analytics tool. But the sequences kept running on the old architecture, because the open rates looked acceptable and there was always something more urgent. The result is a hybrid nobody designed: time-based triggers standing in for behavioral logic, audience definitions evaluated at sync time rather than the moment of truth, and a messaging tool working from a portrait of each user rather than a live feed. Here's what that actually looks...

Behind the scenes of Atlassian’s AI Builders Week At Atlassian, we are often asked how we are adapting internally to AI and transforming our R&D organization to be AI native. One lever we have used successfully is an event we call AI Builders Week . Each quarter, we bring together hundreds of product managers and designers to learn new AI skills and land tangible changes within a few days. We use this dedicated time to experiment together and embrace the change that AI presents, so our customers can thrive in this new era. When we build for ourselves, we’re also building the playbook for every team. Our first AI Builders Week focused on establishing the foundations. We grounded teams in responsible AI use, built early prototyping skills, and introduced basic agent workflows and evaluation practices. The goal was to help PM and Design crafts gain familiarity with AI, understand its potential, and learn the basics. Our most recent week was about taking it a layer deeper, with the goal of driving measurable impact. 77% of our Product Management org and 80% of our Design org attended a highly technical curriculum designed to help them use AI to make day-to-day work easier. We focused on advanced agentic workflows, production-ready prototyping, replacing time-intensive tasks with agents, and improving both evals and the golden data sets that power them. One thing that made this week especially impactful was connecting the dots from ideation to production through real, working prototypes. With support from our Design Technologists and Engineering leaders, we helped Product Managers and Designers get closer to the environments engineers use every day. As AI collapses the boundaries between crafts, there are general skills that all builders should have. We are actively enabling our teams to succeed in that future with teams across crafts learning how to commit production code and turn ideas into experiences customers can actually use. How did it go? 298 Agents were built. We saw a jump in AI intensity with super users incr...

Four warning signs your AI and collaboration investments aren’t paying off 96% of companies haven’t seen meaningful business value from AI. And yours might be one of them. IT leaders are spending big anyway – and wondering why projects still slip and service metrics refuse to improve. According to Atlassian’s AI Collaboration Index , 96% of companies haven’t yet seen meaningful business value from AI, despite widespread investment. AI has made it easier to get work done, but not easier to work together. If that sounds uncomfortably familiar, it may be because you’re bumping into one (or more) of these four warning signs. These four warning signs aren’t isolated problems but instead stages. Most IT organizations experiencing one are already sliding toward the next. The further along you are, the wider the gap between your AI investment and your actual ROI. 1. Faster people, slower teams On paper, AI should be a game-changer for IT with faster incident triage, better root-cause analysis, cleaner documentation, and more efficient change management. However, in practice, many IT leaders are seeing something different: Individual contributors generate more tickets, more docs, more “work artifacts” Local tasks get done faster But end-to-end project timelines and service metrics barely move This is the AI efficiency paradox for IT: AI accelerates individual tasks inside a fragmented system . Without a connected way of working, those gains don’t add up to meaningful organizational impact. If your AI rollout is focused purely on “personal productivity” use cases – code suggestions here, summarization there – without changing how work flows across teams, you’re likely optimizing the wrong thing. What this may look like in your team: AI drafts a spotless PIR, but the incident commander spends 40 minutes chasing context across 4 tools before anyone reads it. Agents closed twice as many low‑level tickets, yet the major incident MTTR didn’t budge because handoffs and ownership remained unclear. If this is where you are, you might think the fix is simply...

Not able to make field mandatory using UI Modifications in Jira cloud forge app on Transition screen Hi, I am trying to create a Atlassian Forge app to make fields mandatory on Create(GIC) and transition screens using UI Modifications module. I am able to make fields mandatory on Global Issue Create screen by registering the context for specific project, issue type and viewType. I have added a screen during a transition to Request Input and if I try to add context for IssueTransition viewType, the fields are not getting mandated on transition screen and not able to see any console logs as well. Can anyone please guide me on How to make this work? How to make fields mandatory or hidden on Issue transition screens in Jira cloud using Forge? Thanks & regards, Preethi H R 1 post - 1 participant Read full topic

Engineering the Forge Billing Platform for Reliability and Scale Introduction Forge is Atlassian’s cloud app development platform. It’s a serverless, Atlassian-hosted environment that allows developers to extend products like Jira and Confluence, and it powers thousands of apps running inside customer organizations. As Forge grew, we introduced usage-based pricing, so developers only pay for what they use above a free tier. With that shift came an engineering challenge that my team was tasked to solve: how to reliably measure, attribute, and bill that usage at scale. At a high level, we: Collect usage events from Forge services (compute, storage, logs, etc.) through a shared usage pipeline Transform and route that usage into Atlassian’s internal billing systems to create subscriptions and invoices Feed usage and cost back to developers via the Developer Console and APIs so they can understand, predict, and control their spend Building this end‑to‑end flow sounds like “just metering and billing,” but it quickly became a deep engineering problem: making sure we neither lose nor double‑count usage, keeping different systems in sync, and enforcing limits or budgets without impacting the reliability customers expect from Atlassian cloud. This post walks through how we put that platform together, from the high‑level architecture and core components to some of the toughest technical challenges we hit on the way to launch. Architecture Overview At a high level, Forge Billing sits in the middle of four worlds: Forge services that generate usage (compute, storage, logs, and more) A shared usage pipeline that standardizes and validates that usage Atlassian’s billing systems that turn usage into subscriptions, invoices, and payments Developer‑facing surfaces that expose usage, cost, and controls in a way that’s easy to understand You can think of the system as a left‑to‑right flow: Forge services → Streamhub -> Usage pipeline → Billing systems → Developer Console / APIs Forge services emit usage In Forge, every billable resource—such as func...

Onboarding Partner Form broken Getting the following error when trying to submit the new partner form at marketplace.atlassian.com Atlassian Marketplace Tried multiple times, a day later, still failing. The underlying service call failed. The underlying service marketplace_console_bff status code is: 412 1 post - 1 participant Read full topic

Dac-get-help forge-getting-started dac-get-help forge-getting-started 1 post - 1 participant Read full topic

Posthog mobile client made a small posthog mobile client for ios shows recent events, quick metrics, and current logs so you can check what’s happening without opening the full dashboard no login, just api key stored on device only https://apps.apple.com/us/app/posthog-client-app-phog/id6761744097 submitted by /u/rajsleeps [link] [comments]

AI’s speed paradox: why faster individuals don’t mean faster organizations The learnings in this blog post are based on sessions from Atlassian’s Teamwork in an AI era event featuring Forrester Senior Analyst Will McKeon-White and Atlassian’s Teamwork Lab. You can check out these sessions and others on demand. Everything’s moving faster. Decks are drafted in an afternoon. Customer emails write themselves. Reports that used to take a week now show up in your inbox overnight. But the faster your teams move, the harder it gets to keep everyone on the same page. At Atlassian, we recently surveyed 12,000 knowledge workers and over 170 Fortune 1000 executives for our forthcoming State of Teams report, building on findings from our AI Collaboration Index . The good news? We found that 89% of executives say AI has made execution faster. Unfortunately , half of those same leaders say cross-team coordination hasn’t improved at all. So what’s happening? AI can make it easier to get work done, but harder to work together Leaders know the goal isn’t “make each person faster.” When we asked where they focus their AI strategy, only a small minority said at the individual level. Many more said they want to drive value for the organization as a whole. But 58% of executives say they don’t know how to measure AI ROI at the company level, and nearly half say their high-level AI objectives are unclear. So when it comes time to show progress, they default to what’s easy to measure: individual productivity and efficiency. The actual questions should be: Are we making better decisions as a company? Are teams moving in the same direction? Is it any easier to work across functions? For many organizations, the honest answer to all three is still “not really.” Why speed without coordination backfires According to a Harvard Business Review study , about 80% of enterprise work is collaborative. By focusing almost exclusively on individual AI gains, companies may be leaving four-fifths of AI’s real potential on the table. 70% of knowledge workers say their processes and...

AI Magic in Atlassian Forge: Local Semantic Search with Forge SQL Introduction Did you know that Forge SQL can be used for semantic search ? Since Forge SQL is backed by TiDB, and TiDB supports Vector Search , it is now possible to store embeddings in the database and query them by semantic similarity. That means your app can search by meaning , not only by exact keyword matches. PingCAP highlights semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG) as key use cases for this capability. This is a very practical AI pattern. Traditional search works best when users type the same words that already exist in the stored content. Semantic search works differently: both the documents and the user query are converted into embeddings, and the database returns the closest matches based on vector distance. Because of that, a query can still find the right result even when the wording is completely different. This is also one of the core ideas behind RAG . Before an LLM generates an answer, the system first retrieves the most relevant documents and passes them as context. Better retrieval usually means better answers. In this article, I will show how to build this pattern inside an Atlassian Forge app while still keeping the architecture aligned with Runs on Atlassian . In this example , embeddings are generated locally in the Custom UI frontend , stored in Forge SQL using Forge SQL ORM , and queried directly with vector search. No external AI API is required for the semantic search flow. Runs on Atlassian eligibility depends on meeting platform requirements, including around egress, so this kind of local approach can be especially useful for Forge developers. The article has two parts: How embeddings are generated in Custom UI and processed in the Forge backend A short demonstration of the app in action, with a link to the full video walkthrough Building the Local Semantic Search Flow To build semantic search inside a Forge app, we need a way to generate embeddings locally in the browser, send them to the backen...

[ Removed by Reddit ] [ Removed by Reddit on account of violating the content policy . ] submitted by /u/Federal-Register-870 [link] [comments]

Request for Update on RFC-124: Evolving the Marketplace Trust Program Hi @PhilipGrove , Can you please provide an update on RFC-124, and more specifically with regard to the timelines of CFA and CSP badge retirement? RFC-124: Evolving the Marketplace Trust Program Request for Comments (RFC) 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 fi… Cheers, Remie 1 post - 1 participant Read full topic

is atlassian dead? Fallen angel or sleeping giant? submitted by /u/app385 [link] [comments]

Rovo Chat in Bitbucket now understands your Pipelines Why did your build fail? Ask Rovo, get a clear answer, and even a way to fix it, from anywhere in Bitbucket Pipeline debugging is one of the most common and most painful parts of the development workflow. In our Atlassian research: AI adoption is rising, but friction persists , over 50% of developers reported losing more than 10 hours each week searching for information, onboarding to new code, or toggling between apps. Failed pipelines make that worse, with developers spending 20–45 minutes per failure, most of it just parsing logs. With Rovo Chat, when your build goes red, you don’t have to pause to scroll through thousands of lines of output. Just ask Rovo, and you’ll get the answer – the failing step, the root cause, and what to do next – summarized, clear, and in context, right where you’re already working. What you can do Open Rovo Chat in Bitbucket and ask questions the way you actually think about your builds, no log spelunking required. Here are three simple examples: “Why did my last pipeline fail?” Get a plain‑English explanation of the root cause and not a log dump or just an error code, but a relevant answer that tells you which step failed, why it failed, and how to fix it. “When was the last successful run on main?” Check the health of your main branch at a glance. “Explain the error in this step” Turn cryptic exit codes and stack traces into plain English, so you can decide what to do next faster. Why we built this The problem isn’t that the information isn’t there. It’s that extracting meaning from raw logs is a specialized skill that takes years to develop and even experienced engineers spend more time than they should on it. Rovo Chat changes the interface. Instead of reading logs, you ask a question and get an answer. The same information, but dramatically less friction. How it works Rovo Chat brings your Bitbucket Pipelines context into every answer: The pipeline run and overall status The failing step The logs for that run Your pipeline configu...

Your feedback tool is not there to tell you what to build We spent years building and selling a feedback tool. We talked to thousands of product teams. In most of those conversations, the ask was the same: “I want to find trends in my feedback data.” Every time we leaned in and asked “what trends, specifically?” – people went quiet. It was a reflex, not a real need. Strong product people already have a view on where the product should go. They get it from the founder’s vision, the company’s strategy, and from customer signal. Think of how an LLM works: feed it enough data and it learns to predict the next token. A great PM does the same thing – every customer conversation, every chat with a Sales or Support colleague, every session recording they watch, every review of a usage dashboard, every skim through CSAT responses or online reviews, even playing with new products and vibe-coding their own prototypes. All of it nourishes their judgment until they can make confident calls without a spreadsheet. A feedback tool does not tell you what to build. It gives you the evidence to build the right thing, the right way. It supplies the rich customer context – the verbatim words, the edge cases, the current workarounds – that no roadmap captures. But evidence only flows if people keep contributing it. Sales reps logging deal objections tied to product limitations, Support agents flagging the same issue for the tenth time, customers giving up their time to help with your research – they all need a reason to keep doing it. That reason is trust: the belief that their input actually leads somewhere. A feedback system has two jobs: give product teams the customer context to build the right thing, and keep the people who supply that context invested in the process. This article is about how to do both. Don’t ship what people ask There is a quote often attributed to Henry Ford: “If I had asked people what they wanted, they would have said faster horses.” Whether Ford actually said it is debatable – but the lesson holds. Steve Jobs put it...

AI isn’t a productivity hack. It’s a team sport AI is everywhere. It drafts emails, summarizes meetings, analyzes data, and writes a first pass of project briefs. On an individual level, it’s working. In our research at Atlassian, we found that knowledge workers say AI is helping them save 76 minutes per day . That’s the shallow end – useful, energizing, and a good way to build momentum. But here’s the part we don’t talk about enough: most of that speed isn’t yet showing up in team-level outcomes. Why is that? According to Atlassian’s AI Collaboration Index , only 4% of executives say AI is helping their teams solve previously unsolvable problems. And 37% say AI has caused teams to waste time or head in the wrong direction. Those aren’t technology issues alone – they’re culture and coordination issues. I’ve been digging into this gap and rewatched sessions from Teamwork in an AI era , featuring Atlassian’s Teamwork Lab, Forrester researchers, and Adam Grant. A pattern emerged: leaders frame AI as an individual productivity tool, while the real shift lies in how teams coordinate, share context, and build trust together. The good news: you don’t need a massive rollout. You need a few practical moves you can start this week. Here are practical recommendations to help your team build momentum. 1. Use AI visibly, so your team knows it’s expected If your team never sees you using AI, they’ll assume it’s optional or risky. Silence sends a signal. I hear leaders say they want adoption, but when I ask how they use AI daily, there’s a pause. Some only tried a demo. Others use it quietly. The message their teams receive? If the boss isn’t using it out loud, maybe I shouldn’t either. Insights from Atlassian’s AI Collaboration Index shows that when leaders model usage, teams experiment more. Psychological safety research echoes this: people take cues from what leaders do . So don’t just encourage adoption – show it. Narrate your use in the flow of work. For example: “I used Rovo for the first draft.” “This agenda came from la...

First time using Render submitted by /u/hazal-mestci [link] [comments]

Experiences with JSM? Hi everyone! I'm a current MBA student working on a project on Jira Service Management. We are now conducting a diagnostic case study on the friction points of Jira Service Management (JSM). Specifically, we're looking at why JSM often struggles to displace incumbents like ServiceNow in large enterprises or why engineering-heavy teams find it "clunky" compared to other tools. We'd appreciate any perspective. Feel free to share thoughts here or DM to chat! submitted by /u/SemiempiricalArm [link] [comments]

The Atlassian Rovo MCP Server now supports Bitbucket Cloud The Atlassian Rovo Model Context Protocol MCP Server now supports Bitbucket Cloud. AI clients like Claude, ChatGPT, Cursor, and VS Code can now browse repositories, create commits, open pull requests, and check pipeline results, all through the same secure MCP connection that already works with Jira and Confluence. Workflows Bitbucket Cloud is where your code lives. It’s where teams commit changes, review pull requests, run pipelines, and ship releases. With Bitbucket now in the Atlassian Rovo MCP Server, your AI assistant has the full picture. Here are a few ways to put it to work: Workspace and repository management: List workspaces and repositories, browse branches, read file contents. Ask your AI assistant to explore an unfamiliar codebase without leaving your editor. Pull request lifecycle: Create PRs, get diffs, leave comments, approve, merge. Ask “show me all open PRs on platform-core waiting for my review” and get an actionable answer. Pipelines and deployments: View pipeline runs, list environments, and deployments. When a build breaks, your AI assistant can help you find the failing commit fast. Available tools Here’s the full set of Bitbucket Cloud tools now available through the Atlassian Rovo MCP Server: bitbucketWorkspace: list, get bitbucketRepository: list, get bitbucketPullRequest: create, get, list, merge, approve, comment, comments, diff bitbucketRepoContent: branch.get, branch.create, commit.get, commit.create, files.get bitbucketPipelines: list, run, get, steps, step.get, step.log bitbucketDeployments: list, get bitbucketEnvironments: list, get, create, delete, update Review our support docs on more details on the tools available today and the scopes required to use them. Current limitations Before you begin, be aware of several limitations that we are actively addressing to enable access to the Bitbucket Cloud tool on the Atlassian Rovo MCP Server. Keep the following points in mind: Org-linked workspace required: Bitbucket Cloud tools are avail...

When doing more with AI isn’t enough: Three patterns quietly undermining your team’s impact AI has made it easier to move faster, produce more, and check off tasks at a pace that would have felt impossible just a few years ago. But there’s a catch: more output doesn’t automatically translate into more visibility, trust, or impact. In fact in some cases, it quietly does the opposite. Subscribe to Work Life Get stories like this in your inbox Subscribe In our recent webinar, Work Smarter, Not Harder: 3 AI Traps to Avoid , Atlassian’s Teamwork Lab unpacked this “AI productivity paradox” and heard from hundreds of attendees who feel stuck in it. This article distills those insights into a practical guide for leaders and individual contributors alike. Read on to learn why “doing more with AI” isn’t enough, how to spot three hidden AI patterns that quietly drain visibility and impact, and how to turn AI from a simple productivity booster into a true engine for team and career growth. Three hidden AI patterns that quietly undermine team visibility and impact — and how to fix them In our research and hands‑on work with teams, the Teamwork Lab has spotted three AI patterns that often backfire. Pattern 1: The Quality Sacrificer When speed replaces intention. This shows up when AI makes it incredibly easy to produce a lot of work, very quickly. How invigorating! But when does speed start standing in for quality? A shipped document might have pristine grammar, expert structure, and a professional look. Upon a closer look, the document doesn’t answer any new questions. The key context is entirely missing or buried in niceties. This is “AI slop”: work that looks polished but lacks deeper context and judgment. In one recent survey, 37% of executives say AI has wasted their teams’ time or led them in the wrong direction due to low-quality or poorly framed outputs. “With AI, the cost of doing work drops,” says Teamwork Lab researcher Ben Ostrowski. “At an individual level, that sounds great. But when everyone is suddenly turning in five to ten times more content, the reali...

Struggling to understand who’s working on what in Jira, so I built this. submitted by /u/Thereallario [link] [comments]

Rendering a Svelte application (not SvelteKit) on Render submitted by /u/hazal-mestci [link] [comments]

The golden rules of agent-first product engineering Companies building for agents often treat them as a bolt-on feature. This is a mistake. Agents today are more like a new form factor – an interaction layer that sits between your product and your users: That means you need to build for agents as a primary surface, not an afterthought. When done right, this opens up a whole new space of possibilities for your product, like autonomous work and asynchronous flows. Implement it poorly and you’ll lose users’ trust due to slow, buggy experiences and incorrect results. We learned this the hard way and overhauled our AI architecture two times in the last year. Now, our agent and MCP have 6K+ daily active users. Here are the golden rules of agent-first product engineering we learned along the way. 1. Let agents do everything users can If a human can do something in your product, an agent should be able to do it as well. For example, let’s say you ask an agent to set up an A/B test for a new pricing page . The agent would use the PostHog MCP to create the feature flag , create the experiment, link it to the flag it just created, and set up the insight and metrics. Now imagine if the experiment-create tool was missing because we just hadn’t prioritized it yet. The agent creates the flag and insight, but must stop and ask you to go to posthog.com, create the experiment yourself, and paste the experiment ID. This is annoying and defeats the entire point. The benefit of agents is that they reduce the time, attention, and expertise needed to complete a task. If your product doesn’t give agents the same capabilities as users, you’ll always be limited by the human in the loop. Of course, there are situations where requiring human input makes sense, like if you’re dealing with sensitive data. But these should be deliberate exceptions, not accidental gaps. What this looks like in practice In practice, this means nearly everything in your API needs to be accessible to agents. That doesn’t mean converting every endpoint into an MCP tool...

They committed to 60 engineers’ worth of work with 35 engineers Overcommitment rarely starts with bad intent. It starts with optimism and limited visibility. When Jensen Fleming, a Principal Product Manager at Atlassian, realized her team had effectively overcommitted, the issue wasn’t ambition, it was math. “We’ve committed to the work of 60 engineers, but we’re a team of 35,” she says. There was no buffer for emergencies, no reliable view of when anything would ship, and no data to back up a polite “not yet” when new requests rolled in. Something had to change. Here’s how Jensen used Jira Product Discovery (JPD) to bring the math back into the conversation. Related Article Stop rewriting your roadmap and publish it By Atlassian In Jira Product Discovery The spreadsheet that died every two weeks Like many before her, Jensen tried a Google spreadsheet. She’d list all the projects, plug in dates, assign engineers, and feel organized for about two weeks. Then dates would shift, new projects would appear, and someone would forget to update a row. “It was so much work to repeat every two weeks,” she says. This was frustrating and costly. She had to rebuild the spreadsheet and chase down updates before she could plan what came next. Building a capacity view that stays alive About four months before the webinar, Jensen built a dedicated capacity planning view inside Jira Product Discovery. She described it as “a spreadsheet in JPD,” but unlike the old version, it pulls from live project data and stays up to date. Here’s how she structured it: Split by engineering team. Jensen works with two teams: Quakus and Ragents (short for Rovo Agents). Each has its own section in the view, with separate tracking for workstreams. Monthly engineer columns. For each project, she enters the number of engineers needed per month. These columns feed into calculated roll-up fields that show total committed capacity per period. A “yes/no” resourcing column. If the monthly engineer columns are populated, the project is resourced and gets a “yes” If not, it...

If your roadmap only works for you, you’re doing it wrong A roadmap that works perfectly for you can still fail everyone else. Jensen Fleming, a Principal Product Manager on Atlassian’s Rovo Agents team, had built what felt like the ideal board in Jira Product Discovery (JPD). It tracked workstreams, timelines, delivery status, design progress, legal review, engineer assignments, Figma links … everything. Then her designer opened it and froze. “There was just too much,” says Jensen. The board wasn’t wrong. It was comprehensive, but it was optimized for the PM, not the people who needed to use it. That realization changed how Jensen thought about roadmaps. Instead of expecting every partner to navigate her control tower, she started building purpose-built views for each of them. From ‘helpful’ to ‘cannot live without’ Jensen had been using Jira Product Discovery for years, but about a year ago, her approach changed. “I unlocked JPD’s value when I discovered micro views,” she says. “It went from helpful to something I couldn’t live without.” Instead of expecting one board to serve everyone, Jensen started building purpose-built views for each recurring conversation: design, legal, support docs, front-end engineering, capacity planning. Each view showed only what that audience needed and nothing more. What clarity looks like for design Jensen’s designer, Kate, was the first beneficiary. Jensen’s main control tower has 21 fields, most of which Kate doesn’t need. Kate needs to know what requires design work, in what order, and when it starts blocking engineering. Her dedicated design roadmap includes: Design status. Projects tagged “not needed” disappear, immediately filtering out 30–40% of items. Priority order. Jensen arranges the remaining work by what Kate should tackle next. Notes. A shared field captures action items between one-on-ones so they can pick up where they left off. Start dates. If designs aren’t ready by the start date, engineering stalls, so blockers are obvious. Engineer assignments. Kate can go directly to ...

New on Substack: Post templates, Notes scheduling, drop caps, and more This month, we’re shipping a range of updates that give you more control over your publishing workflow—from scheduling Notes in advance to new live video tools, post templates, and design details that make your publication feel unmistakably yours. Here’s what’s new. Post templates You can now create and save templates for longform posts, directly within the post editor. If your workflow includes recurring elements like calls to action, disclaimers, section structures, sponsorship placements, or code blocks, you can save them once and reuse them in new drafts instantly. This removes repetitive setup work and helps you stay consistent without starting from scratch each time. Build your format once, and reuse it whenever you need it. Adding a saved call to action using the template feature Learn more about using post templates in our Help Center . Drop caps Enable a drop cap—the large, stylized first letter that opens a piece of writing—for your Substack publication. You can turn this on in the Website Editor in your publication settings. From there, click “Posts,” toggle on “Drop caps,” and click “Save.” Visible on web, drop caps are a small detail that can give your writing a more considered, editorial feel. A drop cap, as seen on Racket News Learn more about adding a drop cap to your post in our Help Center . Live video: more control over how you stream and manage events We’ve expanded Substack Live with new tools for publications running regular video programming: Choose where you stream: You can now decide which publication to go live from each time you stream, instead of being limited to your primary publication. This is available across iOS, Android, and web. From the new live video management dashboard page on desktop, you can now: Schedule on behalf of a host. Contributors and admins can now schedule a livestream for another creator on your publication. Edit scheduled streams. You can now update the title, timing, host, and notification settings for any schedu...

Atlassian down? Me and another employee are getting "Something went wrong on our end". We are in west coast of United States. Anyone else experiencing the issue? submitted by /u/The_Comma_Splicer [link] [comments]

Your team’s best ideas are trapped in the wrong format. AI just fixed that. Introducing Remix with Rovo and partner agents in Confluence — a new way to instantly transform Confluence pages into charts, prototypes, presentations, and apps The last mile of knowledge Something strange happened over the past decade of work. Teams got incredibly good at creating knowledge — documenting decisions, capturing meeting notes, writing specs. But all that effort exposed a different problem: most of that knowledge never reaches the people who need it, in a format they can actually use. Confluence pages with 1 or more visual element are 18% more likely to be read by a wider audience. This isn’t a search problem. It isn’t an access problem. It’s a format problem. The knowledge exists. It’s just stuck in a form that doesn’t match how the next person needs to consume it. This means manual work: copying from docs into slides, reformatting for different audiences, and losing context, repackaging existing knowledge instead of creating new. We’re introducing two new experiences to close that gap to change how teams get value from work they’ve already created. Remix with Rovo transforms content on any Confluence page into new formats like charts, infographics, and other visuals. Pre-built third party partner agents for Lovable, Replit, and Gamma, turn Confluence content into working prototypes, starter apps, and presentations in those tools without manual copy-pasting or custom integrations. What’s available today Remix with Rovo starts rolling out today in open beta for Confluence Cloud customers with Rovo. At launch, Remix supports — data visualizations, infographics, diagrams, and charts — with more formats coming soon. Out-of-the-box partner agents for Lovable, Replit, and Gamma are in open beta and start rolling out next week. Admins can enable partner agents in Atlassian Administration under Connected Apps, with no custom agent creation or scripting required. Introducing Remix with Rovo Confluence has always been where teams go to create and share knowledg...

Why JQL can’t really show time between statuses in Jira I used to think JQL could help answer questions like: How long did an issue stay in Review? What’s the actual time between In Progress and Done? Where are issues getting stuck in the workflow? But the deeper I looked, the clearer it became: JQL is great for finding issues, not for calculating real time metrics between statuses . Yes, you can use queries like: status CHANGED FROM "In Progress" TO "Done" DURING ("2026/04/01", "2026/04/08") That helps you find issues where the transition happened. But it still doesn’t tell you: how long the transition actually took, what the average time is between two statuses, where bottlenecks are building up, or which issues are aging in a specific step. That’s the gap a lot of teams run into. What seems to work better is using JQL as a filter , and then adding a tool that actually measures time between statuses as a metric. For example, with Time Metrics Tracker , teams can: calculate time between any statuses, create custom metrics for their own workflow, account for working hours / holidays, spot outliers and aging issues, and visualize everything with gadgets like Scatter Plot and WIP Run Chart. That makes a big difference, because instead of just seeing that a transition happened, you can finally see how long it took and where flow slows down . So for me the takeaway is: JQL is excellent for search. But if you want real cycle time, time in status, or transition analytics, you need something more than native JQL. Curious how others handle this in Jira: Do you rely on JQL + dashboards? Automation + custom fields? Or a marketplace app for time metrics? submitted by /u/KneeStriking3866 [link] [comments]

Untangling Tokio and Rayon in production: From 2s latency spikes to 94ms flat Tokio, Rayon, and a Kubernetes pod walk into a bar. Nobody gets served. Even though Rust is known for its performance and safety, overlooking its inner workings often causes you to shoot yourself in the foot. We know because it happened to us. We discovered a big performance bottleneck in the hot path of our feature flags service. Solving it reduced mean p99 latency by 4x, from 2.5 second spikes to a flat 94 ms line. Here's how we found it and everything we learned along the way. The usual suspect Feature Flags sits in a uniquely latency-sensitive spot compared to most PostHog products. It needs to be as close to real time as possible. A slow flag evaluation means a customer's application is stuck waiting for a response before it can proceed. That’s a tough requirement for a service that also needs to scale well while doing compute-intensive work. Although the Rust rewrite of our feature flag service resulted in a bunch of performance gains, as we grew to serve twice the traffic, problems arose again. Latency, which the rewrite made up to 10.6x faster, was spiking. Our application was "randomly" freezing up. Our initial thought was that it was a database issue. Connection pools were maxing out at 100% multiple times a day, and connection acquire times were spiking in direct correlation with the p99 latency spikes. That's usually a clear signal. But as we dug deeper, the database turned out to be innocent. An airtight alibi We pulled up the Aurora RDS metrics expecting to find the culprit. Instead: query execution times were fast and stable, CPU utilization was healthy, no external pressure. Something else was making it look slow from the application's point of view. We then looked at scaling. The Horizontal Pod Autoscaler (HPA), Kubernetes' mechanism for automatically adding or removing pods based on load, was kicking in during spikes, but always too late. More importantly, having more or fewer pods didn't change the latency at all. This showed it wasn't about capac...

Render Workflows is now in beta: durable background jobs and AI agent logic without the infra headache Hi everyone, I'm Shifra, a Developer Relations Engineer at Render. We just launched Render Workflows via a TypeScript and Python SDK for building reliable, long-running background processes like AI agent loops, data pipelines, and billing flows, without having to wire up your own queues, workers, and retry logic. Here are some of the things you can build with it: AI agent loops Chain LLM calls, sandboxes, and previews into a single durable workflow Let individual steps fail, retry, and recover without losing the whole run Data pipelines Parallelize heavy workloads across hundreds of concurrent containers Pay only for what you use, down to the second — scales to zero when idle Background jobs Define retry behavior, timeouts, and compute per task, right in your code Trigger tasks from your app, the Render API, or the dashboard Render handles all the execution infrastructure (queuing, worker pools, state management, and observability) so you just write code. Read the blog post to learn more. You can also try it yourself by running: render workflows init or install the agent skill to let Claude Code, Cursor, or Codex build and debug workflows for you: render skills install render-workflows Example apps: Data engineering use case (Trender) https://render.com/templates/trender Deployed on https://trender.onrender.com/ Blog thumbnail generator: https://render.com/templates/blog-thumbnail-generator-with-render-workflows Voice agent: https://render.com/templates/voice-agent-with-render-workflows Webhook: https://render.com/templates/webhook-workflow Data processor: https://render.com/templates/data-processor-workflow SEO audit: https://render.com/templates/seo-audit-with-render-workflows Resources: Docs: https://render.com/docs/workflows GitHub: https://github.com/render-oss/sdk Questions? Drop them below! We're actively extending Workflows during beta with cron triggers, pause/resume, state checkpointing, and more language support are all on the roadmap. Feedback and featur...

30.8% Faster PRs: How AI-Driven Rovo Dev Code Reviewer Improved the Developer Productivity at Atlassian Rovo Dev code reviewer reviews your pull requests (PR), proactively catching quality, security, and performance issues early while recommending best practices for code quality and maintainability. Recognized by ICSE 2026, code reviewer is observed to reduce 30.8%of the PR cycle time. Introduction Code review is a critical pillar of high-quality software engineering at Atlassian. It enables our teams to find bugs early, maintain coding standards, and share knowledge effectively. However, with the rapid growth of our products and the complexity of our codebase, manual code reviews can become a significant bottleneck in our development lifecycle. In today’s fast-paced, context-driven environment, we need ways to help our engineers ship high-quality code faster, without compromising our high standards. To address this challenge, we developed Rovo Dev Code Reviewer , an innovative and large scale AI software development agent designed to automate parts of the code review process. Code reviewer isn’t meant to replace human judgment; instead, it’s a human-in-the-loop AI partner, designed to augment our engineering workflow. We’re proud to share that after a year-long, large-scale online evaluation across over 1,900 repositories, Rovo Dev has shown significant impact—speeding up our development cycles by 30.8% and cutting human-written review comments by 35.6%. It went Generally Available (GA) on Oct 2025 after receiving widespread positive feedbacks from open Beta customers. In this blog post, we’ll dive into how we built Rovo Dev Code Reviewer , our human-centric approach to AI automation, and the compelling results from our internal online evaluation. Our research on Rovo Dev code reviewer’s online evaluation has been accepted by the 48th IEEE/ACM International Conference on Software Engineering (ICSE’26) , a prestigious software engineering conference. This research was a collaboration between Atlassian DevAI engineering team, data science team and associate professor K...

Adam Grant on how AI reshapes work: why agility beats ability The learnings in this blog post are based on Adam Grant’s session at Atlassian’s Teamwork in an AI era event. Don’t miss your chance to see Grant’s full session, available only until June 30th. When you bring AI into teams, the hard part isn’t the technology. It’s the people. That was the through-line from Adam Grant during our event, Teamwork in an AI era . Grant is an organizational psychologist, bestselling author of six books, and Wharton’s top‑rated professor for seven years running. His work focuses on how people lead, work, and live in fast-changing environments – exactly the conditions AI is creating inside today’s teams. Grant dug into how leaders can rethink team structures, culture, and their own habits so AI actually makes teamwork better, not just faster. The new currency of success For most of modern management, careers have been built on ability: demonstrating mastery, proving expertise, and being “the person who knows the answer.” According to Grant, that era is over. We live in a world now where the currency of success is not ability, but agility. It’s the people who are fastest to adapt to change, who are quickest to learn and unlearn, that are ultimately rising and succeeding and in the process, building job security.” – Adam Grant In other words, the teams that win won’t be the ones who already know how to use AI perfectly. They’ll be the ones who constantly run small experiments, such as trying multiple prompts instead of just one, comparing outputs across teammates, and learning what AI is good at, what humans are better at, and where collaboration works best. And because the tools are evolving so quickly, those experiments can’t be a one‑off pilot. Grant warns: if you keep using last year’s prompts in this year’s world, you’re already behind. Treat AI as a thought partner, not an oracle By shifting perspective on how to use AI, it invites teams to use it the way they’d use a sharp colleague; someone who helps you brainstorm options, challenge ...

A 3d Automotive project i will share it's for free when done 🤩🤩 (Any advice) submitted by /u/Ibrahim-An [link] [comments]

Can't create a workplace! submitted by /u/NISMO1968 [link] [comments]

Bitbucket Data Center app publishing blocked Hi, I’m encountering a contradiction between Atlassian support guidance and Marketplace behavior. I attempted to publish a Bitbucket Data Center app via the Marketplace vendor portal. What happens: The app uploads successfully. On submit, the portal returns: “App creation failed. DATA_CENTER hosting type is no longer supported. Please use Cloud hosting. Data Center apps can no longer be published.” At the same time, Atlassian support previously indicated that Bitbucket Data Center will continue under a hybrid licensing model. This raises a simple question: Are new Bitbucket Data Center apps allowed to be published on the Marketplace — yes or no? If not: does this mean that Bitbucket Data Center continues as a product, but Marketplace distribution for new apps is no longer supported? Also: for apps that were created before the December 2025 deadline and remain private — is it still possible to make them public? Right now there seems to be a mismatch between policy communication and actual Marketplace behavior. 1 post - 1 participant Read full topic

Bitbucket Data Center app publishing blocked despite official statements (topic deleted by author) 1 post - 1 participant Read full topic

Atlassian support Has anyone else had a hard time with Atlassian, specifically their Loom support team? I’ve been trying to request support for 3 months to no avail. Each time I start a ticket they reach out, I respond, then nothing happens and the ticket is changed to resolved in the portal. I’ve never encountered a support team this passive and unhelpful. submitted by /u/Krohnowitz [link] [comments]

Creating with Rovo: How We Built a Collaborative AI Canvas Rovo is our hero AI solution at the core of our platform. Through the Teamwork Graph , it can search across internal and third‑party knowledge, surfacing the right information from tools like Confluence, Jira, and connected apps. It can also interact with and create objects across the ecosystem – from Jira issues to Confluence pages. What it didn’t have was a fully featured creation canvas : a collaborative space where people and AI can co-create, iterate on, and refine rich content together in real time. That gap was the catalyst for Creating content with Rovo . The core insight behind creating with Rovo was simple: Creation should be a collaboration between the user and the AI, not a handoff from one to the other. This post goes behind the scenes of content creation with Rovo and covers: Why we designed it as a collaborative AI canvas, not a one‑shot writer How we handle different content types reliably (pages, whiteboards, databases) The technical choices behind ADF, SVG, and CSV‑based schemas The streaming infrastructure that lets Rovo generate and edit rich content safely in real time Core principles We didn’t want to build a standalone AI writing tool siloed inside a single product. Creating with Rovo has an entry point in Confluence, but it’s natively built on Rovo and accessible across every Rovo surface . Creation should feel like a natural extension of the chat experience: you ask a question, co‑author a page, iterate on a whiteboard, and refine a database – all in the same conversation, with the same AI that understands your context. That led to a few core design principles: Creation lives inside chat. Content starts in a canvas within Rovo chat. Once you’re happy with it, you move it into the right space or product surface. It’s a platform, not a feature. From the start, the canvas was designed to support multiple content types (pages, whiteboards, databases) and to extend to other products. Create with Rovo for Jira work items recently launched in EAP a...

Stop rewriting your roadmap and publish it Every product manager knows the feeling. You’re deep in a sprint review or halfway through a strategy session when a Slack message lands: “Hey, can you send me the latest version of the roadmap?” It’s never just one request, either. Marketing wants the roadmap grouped by launch moment; your VP requests a slide format; and legal needs their must-review items. Each request means rewriting the same information in a different way – and by the time you hit send, the content is dated. Jensen Fleming, Principal Product Manager on Atlassian’s Rovo Agents team, leads about 40 engineers and four PMs to build the custom AI agent platform inside Atlassian Studio. She described this exact spiral during a recent webinar. “One of the things that brought Jira Product Discovery (JPD) to life for me was how many times I was asked to write a new roadmap page, but in some different format, in some different way, by some different team,” she says. It’s when she turned to Jira Product Discovery to create a live source of truth that the rewrites stopped. Luckily, building that system didn’t require starting from scratch. Static roadmaps don’t scale The core issue is that static roadmaps create more work than clarity. “My problem with roadmaps and writing things down on pages is that the content is always stale,” says Jensen. “It’s not up to date unless I link back to Atlassian projects.” This is the trap: You write a roadmap on Monday. By Wednesday, two dates shift and there’s a new project. But the page still says what it said on Monday. Stakeholders read it, make decisions, then discover it’s wrong. Trust erodes, and the next time they need an update, they ping you again – because the last page you sent was unreliable. Make the roadmap the source, not the summary Jensen’s breakthrough was deceptively simple: Stop rewriting and start sharing. She built what she calls her “mega board,” a comprehensive Jira Product Discovery view organized by workstream (Admin, Maker, Platform, Quality, Se...

I built a dedicated Jira desktop client for Mac and Windows: open source, session restore, multi-tab support https://preview.redd.it/nukb6cvdxktg1.png?width=3022&format=png&auto=webp&s=625a57bcf3075d27b72ac8eadf99813412202690 Hey everyone, I recently shipped Jira Desktop — a free, open-source desktop client for Jira built with Electron, available for macOS and Windows. Why I built it: I was tired of Jira living inside a sea of browser tabs. I wanted a dedicated, distraction-free workspace that felt like a real desktop app — not just another pinned tab. Current features: Vertical sidebar with multi-tab Jira browsing in one window Pinned tabs and session restore Keyboard shortcuts for new tab / close / reload Theme toggle and lockable sidebar Restricted navigation to Jira / approved Atlassian-related hosts macOS + Windows builds on GitHub Releases It stores your Jira Cloud URL locally on first launch, and packaged builds work without extra setup. It’s MIT licensed and I’d love blunt feedback from heavy Jira users: What’s missing from your daily workflow? What breaks in SSO / login flows? What would make this better than just using Jira in a browser? Repo: https://github.com/prakharbhardwaj/jira-desktop Releases: https://github.com/prakharbhardwaj/jira-desktop/releases/latest submitted by /u/prakhar-bhardwaj [link] [comments]

Oneshot Engineering Work from Slack w/ Claude submitted by /u/KnightDotTech [link] [comments]

Forge Workflow Validator Deployment via REST API returns Invalid Request Payload or Update in Progress Hi everyone, I’ve developed a custom Forge-based Workflow Validator (static/ps-workflow-extention-linkedissue-validator) and I am now trying to automate its deployment across approximately 100 Jira Cloud workflows. The Context: The app is fully functional. I can successfully add the validator to any workflow via the UI Workflow Editor, and it behaves exactly as expected during issue transitions. However, I need to scale this across 100+ workflows, and doing so manually is will take longer time. When I attempt to replicate the UI’s action using the REST API, I am hitting constant roadblocks. The Setup: Endpoint: POST /rest/api/3/workflows/update Method: Attempting to inject the validator into the “Close” transition. The Journey so far : Initial Failures: I originally tried to use the ruleKey from the manifest directly, which resulted in Invalid Request Payload. The “Forge Wrapper” Discovery : By inspecting browser network traffic while saving via the UI, I discovered that Forge rules must be wrapped in a forge:expression-validator key, with the extension ARI placed inside parameters.key. Schema Sensitivity : Even with the “correct” structure (including the root statuses array and the workflow version object), the API is extremely sensitive to read-only fields. The Current Blocker : Despite matching the JSON structure seen in the browser’s developer tools, I am consistently receiving: {“errorMessages”:[“Other workflow updates are in progress. Check the workflow editor…”]} This occurs even when no one is editing the workflow and all drafts have been discarded/published. Current Payload Snippet -JSON: { “ruleKey”: “forge:expression-validator”, “parameters”: { “key”: “ari:cloud:ecosystem::extension/…/static/ps-workflow-extention-linkedissue-validator”, “id”: “NEW_UUID”, “config”: “” } } Any advice from developers who have successfully automated Forge rule injection would be a lifesaver! 1 post - 1 participant Read full topic

ECOHELP ticket access issue Hello, I have submitted an app for review, and got this ticket: https://ecosystem.atlassian.net/browse/ECOHELP-121121 However I can not access it, the error is “We can’t show this project. You don’t have permission to view it, or it doesn’t exist. Contact your admin to request access.“. I have clicked the “Request access” button, first about 4 days ago, but nothing changed. Could some please help me solve this? My email is gabor.vitez@gmail.com (AdminHelm). Thank you! 1 post - 1 participant Read full topic

Dear Jira Users, how does the process of requirements engineering work in your organization, and what are your thoughts on it? Our team is building an application for product managers and software teams to streamline requirements engineering/gathering by increasing visibility of progress to clients and pointing out unclear requirements in a PRD/RFP. We also want to enable some sort of rapid prototyping so PMs can see what the client’s requirements would look like in the product and work on them accordingly. We would like to ask everyone here: How does the flow go from a client providing requirements to an approved, finished product for your team? Is this process relatively smooth in your team, and why or why not? All input would be extremely valuable. submitted by /u/SimilarSetting3097 [link] [comments]

MCP really bad, any hints? So after using the Posthog AI for some time, I started using it in Claude Code, as I do with most tools - using skills+API or MCP. With PostHog it's MCP. It was really great - a session knowing my whole app, the functions, the data, the flow, we could setup lots of insights and dashboards. Alas, the PostHog MCP eats A LOT OF TOKENS. I mean I'm on max and I hardly use up my limit, unless I work on two-three projects simulatneously. And this happenes usually one hour before the window closes, so it's not that bad. PostHog MCP eats really a lot, i see this often: "● claude.ai PostHog - cohorts-list (MCP) ⎿ ⚠ Large MCP response (~15.4k tokens), this can fill up context quickly " And it does. Recently i used my limit in like one hour! It's very inefficient even when working on a dashboard with 5-6 insights. Any hints, or ways to work efficiently woth PH from CC? submitted by /u/g00rek [link] [comments]

Loom Automation I love loom as a platform but it is so siloed. 2 things I cant get over: there is not api to grab transcripts or a way to download the videos in bulk, and the team feature is more like a shared folder, so every time you do a video you have to find it in your file and move it to the team. It's killing us because it's our meeting recorder too and for Friday meetings I won't get up with them until Monday or Tuesday to remind them to move it to the group. Then also as were trying to build out our AI internally there is so much information on those meetings and videos and only on person has the ability to download the video or the transcript (the recorder). Does anyone have any thoughts on how to solve for these so I'm not on here just to complain? submitted by /u/Last-Bar-4305 [link] [comments]

How Amdahl’s Law still applies to modern-day AI inefficiencies Across industries, leaders are rushing to add AI to everyday work. Developers now push code in hours, writers shape first drafts before meetings, and analysts spin up models by lunch. It all looks like a wave of productivity on the surface. However, Amdahl’s Law offers a quiet warning underneath that wave. In any system, the maximum speedup islimited by the slowest process in the workflow. If you only accelerate one slice of the work, the rest of the workflow sets a hard ceiling on how fast the whole thing can go. Right now, AI is mostly speeding up individual work: drafting, coding, and analysis. Yet on most teams, those activities make up only a fraction of the lifecycle. The bulk of the time is still spent on human collaboration and process: reviews, decisions, sign‑offs, risk checks, and alignment across teams. That means there’s a built‑in cap on how much faster AI alone can make the system, if we only look at increasing efficiency of individuals. We are currently witnessing a 43% gap between ‘felt’ speed and actual output, where individual efficiency gains are being swallowed by the friction of team-level handoffs. Team-level velocity charts stay flat, cycle times refuse to budge, and people feel more stretched instead of relieved. That is the AI efficiency paradox. AI can make an individual much faster, but unless we redesign the way teams work, the gains leak out through handoffs, reviews, and decisions long before they reach customers. Engineering teams were early adopters of AI, so we can see this paradox most clearly in the software development lifecycle. While the same pattern shows up across modern organisations, this article will focus on examples from software development and software systems. Amdahl’s Law for teams Use Amdahl’s Law to decide where to improve, not just to restate that one slow step can cap the whole system. Let the parts that rarely speed up guide your design of the rest of the workflow, so that human attention is reserved for moments...

What actually changes when AI joins your research process If you’re a PM or PMM, your research cycle probably looks something like this: Interviews and surveys land in a tool like Dovetail You promise yourself you’ll “just take a day” to organize everything A week later, you’re still tagging, finding quotes, and rewriting bullets for a deck By the time your findings are polished enough to share, the moment to act is already closing We recently ran this entire cycle completely asynchronously in a single afternoon, and ended up with an interactive, stakeholder-ready research report. It was all about changing how we spent our time and seeing what actually changes when AI is part of the whole research workflow, from raw interviews to a report that stakeholders will read and trust. The study we ran We ran a research study to talk to customers directly about how they manage work today. We’d already used AI to speed up market research, persona work, and planning from weeks to days. The next test: could we do the same for primary research synthesis, without losing nuance? After speaking to customers, our interview recordings were ready in Dovetail. The strategic context, including our research plan, customer hypotheses, and competitor positioning, was already documented in Confluence. The question wasn’t “Can AI summarize our interviews?” Interviews and handwritten notes housed in Dovetail It was: Can AI help us connect all of this fast enough to inform real decisions, while keeping humans in charge of judgment? Step 1: Connect your research data directly The first unlock was connecting tools instead of copy‑pasting between them. Using the Dovetail MCP server, we let Rovo Dev pull: Full interview transcripts Existing highlights Tags and themes we’d already applied What normally would have been two or three days of transcript review was available in minutes. For teams, this step is simple but important: AI works best when it’s plugged into the system of work you already use . Step 2: Give AI strategic context, not just your transcr...

Errors thrown by the `invokeRemote` function The documentation on invokeRemote https://developer.atlassian.com/platform/forge/apis-reference/ui-api-bridge/invokeRemote/ is pretty sparse of details on the actual errors thrown by the invokeRemote. I would like to categorize errors in: Timeout ( That’s possible, as I get an error with a Remote invocation timed out message) Response size over the limit (I get a generic An error occurred while trying to invoke ) Rate limit hit (I don’t even know how to trigger this) … other unknown error What are my options? 1 post - 1 participant Read full topic

Unexpected response form an Automation action that gets data I have an automation action that uses an endpoint for its execution. The configuration and invocation are working as expected, but the response I see in the automation rule includes the whole endpoint information including headers and status code. When I log the response form my action using a Log Action I see this {success=true, payload={headers={date=[Fri, 03 Apr 2026 10:23:18 GMT], content-type=[application/json], transfer-encoding=[chunked], connection=[close], cache-control=[no-cache, no-store, max-age=0, must-revalidate], expires=[0], pragma=[no-cache], referrer-policy=[origin-when-cross-origin], x-content-type-options=[nosniff], x-xss-protection=[0], cf-cache-status=[DYNAMIC], nel=[{"report_to":"cf-nel","success_fraction":0.0,"max_age":604800}], report-to=[{"group":"cf-nel","max_age":604800,"endpoints":[{"url":"https://a.nel.cloudflare.com/report/v4?s=KOqlJcoaqmJuW%2BzdNnoyBvrtibNugIcdhPLeXn4yYFq%2FEWm%2FRfD4c%2BmQdIii5lkZ8%2BXOtAIujAQMCiEGzvyfpFx70899IQKujls%2F%2F5Pt1ncsAuwdhYnR74HdslApnJc0xp2ZvCJjL1%2BRK1E%3D"}]}], content-encoding=[gzip], server=[cloudflare], cf-ray=[9e675b5b0cf71625-IAD], alt-svc=[h3=":443"; ma=86400]}, status=200, body=....} Where I would only expect the contents of the body field. Now to use the content of the action I need to select it using {{fetchedJenkinsJobList.payload.body}} instead of {{fetchedJenkinsJobList}} to access the response from the action. This response also makes no sense when looking at the outputContext and outputs I need to configure for the action in the manifest. Any specific reason the action using an endpoint is behaving this way that doesn’t match the documentation? @JakubMierzewski I know you moved on to other parts of the code base, but as PM of the EAP do you know? Or know who to contact about this? 1 post - 1 participant Read full topic

Built a 16-bit RPG inside Jira because I kept forgetting to log my time and estimate tickets Hey r/atlassian , I'm a dev and I am notoriously bad at Jira hygiene. I always forget to log my hours, update ticket statuses, or add story points until the last minute. So I got an idea for gamification app to bribe my own brain with dopamine to actually do it. Basically, it puts a little pixel-art troll in your issue panel. When you do your normal Agile chores (transitioning tickets, logging time, estimating, leaving comments), your troll gets XP, levels up, and evolves through a few stages. There's also a shared Village tab for the project. When a sprint closes, the village gets "raided". If the team actually met their sprint goals and didn't leave a ton of aging issues in the backlog, the village defends itself and building upgrades unlock. If not, it takes damage. Since everyone works differently, I drew 15 different troll types you can pick from: Chrono-troll gets bonus XP for worklogs. Estimation Sage gets a boost for accurate story points. Comment Kobold gets a multiplier for helping out on other people's tickets. App is free to use with 2 project limit. Let me know what you think or if you find any bugs! Marketplace User guide submitted by /u/drinkits [link] [comments]

First time at Team '26! Expectations given the AI pivot and recent layoffs? Hey everyone, Just realized Atlassian Team ’26 is only a month away. It’ll be my first time attending, so I’m curious to see what it’s actually like on the ground in Anaheim. There’s been a lot of noise lately around migration challenges and how tough it’s getting to list apps on the Marketplace. At the same time, with the stock taking a hit and the 1,600 layoffs to push more into AI, it feels like Atlassian is gearing up for a pretty aggressive shift this year. For those who’ve attended before, especially as exhibitors or Marketplace partners, what was your actual experience like? Was it worth it from a business or networking perspective? And what should a first-timer focus on to not waste time? Would appreciate any honest insights. submitted by /u/AmbitiousYudi1991 [link] [comments]

The best HIPAA-compliant A/B testing tools A lot of popular A/B testing tools aren't HIPAA-compliant, and if you work in healthcare, the last thing you want is to realize too late that the tool you're using doesn't sign BAAs. Skipping experimentation isn't the answer, though. That's like navigating an ocean by sailing roughly in the right direction – you'll probably arrive somewhere, just not where you intended. The good news is there are solid, privacy-focused options out there. Here are a few HIPAA-compliant A/B testing tools worth considering. What you need for HIPAA compliance You need to comply the Privacy Rule and the Security Rule . Breaching either can result in hefty financial penalties, but for the sake of this guide we're mostly interested in how the Privacy Rule impacts analytics and A/B testing. There are three ways to comply with the Privacy rule when adopting analytics and testing tools: Anonymize all PHI and identifiers: There are two so-called " De-identification Standards " – "Expert Determination," where an expert verifies that data isn't personally identifiable, and "Safe Harbor" where all 18 types of identifier are removed. The former is preferable simply because applying the Safe Harbor approach can render data effectively useless for analytical purposes. Sign a BAA with a third-party tool: You must sign a Business Associate Agreement (BAA) with any third-party platform that handles your protected health information (PHI). This can mean signing multiple agreements, though, such as one with your analytical partner, but also any tools you use for importing and exporting data from your data warehouse . Self-host and keep control of all your data: The less common is to self-host tools for analytics and experimentation on your own infrastructure. This reduces the number of BAAs and general legal wrangling needed to generate user insights. The only downside is you'll need the expertise to manage self-hosted instances, or third-party support to do so, and you are wholly liable for any security...

A beginner's guide to testing AI agents Software testing used to be relatively straightforward. You wrote business logic, defined expected outputs, and verified them with unit, integration, and UI tests. With the same input, your system behaved the same way every time. AI agents changed this. Instead of fixed logic, we now build systems powered by models that are probabilistic. The same input can produce different outputs. Behavior depends on prompts, context, tool calls, and external data. The "correct" answer is fuzzy. And yet, many teams still try to test AI agents the old way, or worse, not at all. Manual testing and bug reports quietly become the default. The good news is that testing hasn’t become impossible, it’s just changed. Like before, there is a way to systematically test and improve agent behavior. It just requires a different mental model. In this post, we’ll walk through what testing AI agents well looks like by following the life of an agent, from first build to production and beyond. The birth of the agent It all begins with the classic "Hello World", though now it's wrapped inside an SDK for creating agents, and we're not greeting the world, but rather being a helpful assistant. Testing at this stage is manual. You work hard to tweak that system prompt, choose the right model, and change the available tools and their descriptions until the agent successfully completes your happy paths and edge cases. It's just you and the agent locally battling back and forth. Once you're ready, you ship your agent to production and start rolling it out to your users. Reproducing bugs from your agent's first users How you tested the agent locally is a lot different than how people use it in production, and that leads to issues. Fixing an issue starts by replicating it, attempting a fix, checking whether it is solved, and repeating. This works well, but it has a requirement: being able to reproduce the issue. This isn't hard in theory because you just need to feed the same input to the agent, but the real...

Introducing: Final Steps in Bitbucket Pipelines If you’ve ever run a pipeline, you’ve certainly encountered the following situation: The pipeline fails halfway through, and the cleanup script you needed at the end to tear down test infrastructure or archive the logs never gets to run. Until now, there was no built-in way in Bitbucket Pipelines to guarantee that a step always executes at the end of your pipeline, regardless of what happened before it. Today, we’re fixing that. We’re introducing final steps : a new step in Bitbucket Pipelines that lets you define “always-run” logic directly in your bitbucket-pipelines.yml . Cleanup, telemetry, notifications, audit events — the final step always runs, ensuring whatever you needed to happen at the end, always happens. How to use the Final Step The final step is a special construct in Bitbucket Pipelines that: Runs after all other steps in a pipeline. Executes regardless of the outcome of previous steps (failed, passed, skipped, or stopped). Runs again on re-runs, resumes, and redeploys . Is clearly marked in the Pipelines UI as the final step. Behaves like a regular step, with a few targeted restrictions to keep pipelines predictable. To use it, just add a final: section as the last entry in your pipeline definition: pipelines: default: - step: name: Build & Test script: - echo "This is the build step" - step: name: Deploy script: - echo "This is the deployment step" - final: name: Cleanup script: - echo "This is the final step" What happens? The final step always runs last, even if earlier steps fail or are skipped. In the UI, it’s visually identified as the final step for quick visibility. Real word examples In this example, the final step runs regardless of whether the build or deploy steps pass or fail: pipelines: default: - step: name: Build & Test script: - echo "This is the build step" - exit 1 # simulate a failure - step: name: Deploy script: - echo "This is the deployment step" - final: name: Cleanup script: - echo "This is the final step" You can also defin...

Auto-complete your backlog. Unleash your favorite AI models with deep context, from plan to code, with Rovo Dev in Jira Rovo Dev , Atlassian’s context-aware AI agent for software teams, is now directly available inside Jira so simpler, repetitive work can move from task item to merge-ready PR in the background while your team stays focused on the hard problems. Completing Jira work without breaking focus Most development work already begins in Jira as a task, a bug, an idea. But the actual coding? That happens somewhere else. Developers instinctively reach for an IDE or CLI for every task. They bounce between tools when taking an idea through to deployment, searching for context and manually tracking work across tools, branches, pipelines, and PRs. With every tab switch, important context is lost and time is spent regaining focus. Not every task needs this much overhead. Some work is strategic and demands deep design and engineering time. But others, like resolving security vulnerabilities, cleaning up stale feature flags, and fixing minor bugs, are repetitive and well understood. What if developers could delegate those routine tasks to an AI agent that understands their work? We’re meeting development teams where they already plan and track work by making Rovo Dev available directly inside Jira. And because Jira holds the requirements and context behind what teams intend to get done, Rovo Dev can autonomously handle tasks along the software development lifecycle. Developers stay in the driver’s seat, choosing what to delegate versus tackle themselves, reviewing plans and progress, and approving changes before anything ships. The result is more merge-ready PRs with less boilerplate and context‑switching, and an AI teammate that helps your whole team move faster on real Jira work. Rovo Dev takes on the work that slows you down Rovo Dev in Jira turns your Jira work items into an AI-powered execution surface. You can spin up and orchestrate multiple Rovo Dev sessions in the cloud from any Jira work item. Rovo Dev gathers the right context, proposes a concrete plan you can review and ref...