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

How writers are reacting to Substack’s AI transparency tools “Spot the difference” by Joro Chen, Brand Designer at Substack Earlier this week, we announced a feature that allows readers to scan text published to Substack to see how much of it is estimated to be written by hand or with AI assistance. We also launched tools for publishers, including the ability to run the program— —on drafts prior to publication, disable scanning on a post-by-post basis, and add a “How I make this” statement explaining their creative process to set expectations for their readers. These features do not use publisher content, either through Substack or Pangram, to train generative AI models and are now available on web, iOS, and Android. Norms around AI use and its disclosure are unsettled. But we have made the assessment that while the use of AI isn’t necessarily a problem, a lack of transparency around it definitely is. Our aim is to help writers get credit for the work that’s theirs and help their audiences make more informed choices about what they spend their time on. Substack has long been home to brilliant writers and thinkers exploring questions about AI disclosure, making a livelihood through writing, and whether technology can replace human effort in art. , a columnist at The Economist, is highly bullish on AI’s economic upside but recoiled from AI-written prose: “We should shun AI writing even if it is perfectly bearable to read,” he wrote. Science fiction writer evangelized intention , and said that AI output can’t be art because it isn’t the product of choices. The proposed a Mohs-style scale of AI reliance, from 0 to 10, and menaced , “If you let AI do your writing, I will come to your house and kill you.” Writers have methodically tested what AI can and can’t generate. ran an experiment to track if an LLM could write the parts of a novel that aren’t the prose itself. Engineer, founder, and former venture capitalist tried to measure the statistical signature of literary style, interested to see if a model could eventually learn to w...

Getting 401 response when calling Confluence cloud rest API search Hi, I’m trying to call the confluence rest api search function but facing some issues. I’m getting a 401 response even with the correct credentials. Here’s the simple code I’m trying out: curl --request GET –url ‘ https://corporate.atlassian.net/wiki/rest/api/search?cql=text~test ’ –user ‘corporate@email.com:GENERATED_TOKEN’ –header ‘Accept: application/json’ I tried with 2 tokens with difference scopes. The first token I created have these scopes Read read:account read:confluence-content.all read:confluence-content.permission read:confluence-content.summary read:confluence-groups read:confluence-props read:confluence-space.summary read:confluence-user read:me Search search:confluence The 2nd token that I created have a more limited scope having only these Read read:content-details:confluence Search search:confluence But still I’m getting a 401 even just by using the simplest sample code from the documentation of search endpoint - https://developer.atlassian.com/cloud/confluence/rest/v1/api-group-search/#api-group-search I can see that the tokens are being access as the page API Tokens shows when it was last accessed. Not sure how to proceed and what else to check. Please advise. 1 post - 1 participant Read full topic

A forge app adopted from connect is unable to be installed (still uses connectModules from manifest.yml) Hello, I am aware of the fact that is it no longer possible to install private connect apps ( https://developer.atlassian.com/platform/forge/changelog/#CHANGE-3096 ). But recently I have noticed that apps which have been adopted from connect (no longer using atlassian-descriptor.json , but rather manifest.yaml with connectModules key) are not possible to be installed on any jira/confluence instance via forge install . The installation fails every single time (does not matter if the app is private, or it is publicly listen on marketplace and just installing dev version of it on private jira instance). I tested it with removing connectModules and keeping only connect.key - also did not work. App installation failed. Tested multiple apps - every single installation failed. This is why I wanted to ask if there has been any change preventing from installing also connect apps that were adopted from forge. 1 post - 1 participant Read full topic

TEAM Earnings (Aug 6) – What’s your take? With Atlassian reporting soon, where do you see the stock going post-earnings? Are you expecting a positive surprise and a bounce back to $90–$100+, or is macro pressure going to keep it down? Are you holding, buying the dip, or staying away? submitted by /u/Ok-Equivalent-3439 [link] [comments]

Anyone else using Jira for SAP implementations? This came up during a project this week, and now I'm wondering how common it actually is. Most people I know use Jira for software delivery, but I've started seeing it used more for SAP implementations as well. The part I'm struggling with isn't sprint planning. It's everything around it. Requirements change, WRICEF objects keep growing, testing has to stay linked, and then someone asks, "Can you show me how this requirement made it all the way to production?" That's where things seem to get messy. I'm genuinely curious how others are handling it. Are you keeping everything inside Jira, or do you end up using Excel, Confluence, or another tool for part of the process? Would love to hear how your team does it. submitted by /u/Former-Willow-7881 [link] [comments]

Inside AI Builders Week: How teams work in the AI era

Forge-sim: a local Forge runtime for testing, AI agents, and local dev Hey everyone, wanted to share the first public release of some tooling I’ve built for Forge. github.com GitHub - ryanackley/forge-sim Contribute to ryanackley/forge-sim development by creating an account on GitHub. It’s a simulated Forge runtime that runs your app locally with no cloud deploy. There are three ways to use it: A test library for running Automated Tests. Designed specifically for CI/CD testing. Headless UIKit2 rendering, shims for most forge APIs. Drop-in configurations for vitest and jest. A CLI and stdio MCP server for AI driven development that deploys and tests against a sandboxed, simulated runtime. A development server that serves your app locally for more traditional browser driven development and testing. I originally built it for a fairly complex Forge app I got tired of redeploying to Atlassian’s cloud to test the smallest change. Then I decided to build out the tool to be more general purpose. There is a lot of surface to the Forge platform, so please let me know if you try it out and run into any problems. This is a personal project. I don’t work for Atlassian and this project is not affiliated with Atlassian in any way. 1 post - 1 participant Read full topic

I kept wanting to check PostHog from my iPhone, so I built Hogly Product analytics has been part of my work for a long time. I started with Flurry, moved to Mixpanel later, and eventually moved my apps to PostHog. PostHog is where I go when I want to understand what is actually happening in a product. The frustrating part was that once I left my desk, that workflow mostly stopped. I didn't want a widget that only showed DAU. If an error spiked while I was out, I wanted to open the issue, watch the replay, and turn off a feature flag from my phone. That's why I built Hogly, a native PostHog client for iPhone. Most days I start with the cross-project Overview. I can put one app's DAU next to another app's error rate and a third app's top events, so the morning check takes about 30 seconds. Tapping a project opens a ready-made detail page with activity, users, sessions, retention, geography, web analytics, top events, and recent activity. I don't have to build a dashboard first just to see whether the product looks healthy. And if something looks wrong, I can keep going instead of waiting until I'm back at my Mac: watch the Session Replay with native playback, inspect and resolve the error, or turn off a feature flag. I can also manage A/B experiments, ask PostHog AI, create and edit insights, browse events, and check surveys and responses. It signs in with OAuth and only asks for the permissions I choose. Multi-project overview, native Session Replay, experiments, feature flags, and PostHog AI—all on iPhone. Hogly connects directly to my existing PostHog projects. There is no separate analytics backend copying the data. It isn't trying to replace PostHog. It's the PostHog client I wanted in my pocket. Website: https://hogly.app App Store: https://apps.apple.com/app/id6772233904 Hogly is an independent app and isn't affiliated with or endorsed by PostHog Inc. If you manage more than one PostHog project, what's the first thing you usually check?And if you try Hogly and run into anything, feel free to reply here—or DM me if you'd rath...

Bitbucket Tests now supports tracking tests from multiple branches

Resolving the privacy paradox The so-called “privacy paradox” states that people say they value their privacy while acting in ways that don't reflect this stated preference, such as using services that collect and monetize their data. DuckDuckGo is often held out as an example in this way: DuckDuckGo has small search marketshare relative to Google; therefore, people have a revealed preference that they really don’t value their privacy. I believe there are two flaws with this view that I’ll expand on below: DuckDuckGo is a bad example because of particulars in the search market; Apple’s “Allow App to Track” dialog (depicted below) is a much better example that in fact shows a clear revealed preference for privacy. People sit on a continuum of privacy preferences, such that it is way too simplistic to imply nearly everyone is on one side or the other. Gross generalizations about privacy should be avoided. The latest U.S. data from the Business of Apps shows an opt-in (allow tracking) rate on Apple’s dialog of 25%, indicating a 75% strong revealed preference for privacy. This 25% allow-tracking rate is higher than the original 5-10% rates reported when the dialog first came out because both users have had years to get used to it and apps have had years to change their UIs around. Apps now often present users with the best reasons to allow tracking, usually presented right before users see the dialog, and they can customize the explanation text in the dialog itself (see below for examples). That is, I don’t think you can legitimately say anymore that people don’t understand the dialog or the privacy tradeoffs involved; the 25% steady state is the real preference. But, how do you reconcile this 75% choice for privacy with DuckDuckGo’s 3% search marketshare? The short version is most people are actually still unaware of DuckDuckGo and, for those that are and want to make the switch, many still find it difficult or impossible to do so across all platforms and devices because, unlike with the Apple one-...

Developer Space could not be published 2 posts - 2 participants Read full topic

Content of Forge macros without body not discovered by Rovo Hello, Rovo Assistant is not able to discover the content of Forge macros without a body (macros without a layout property in the manifest). For example, if you ask Rovo Chat to “Summarise this page”, the content of these macros is not included in the response. Has anyone found a way to make the content of macros without body discoverable by Rovo? Thank you, Luigi 1 post - 1 participant Read full topic

Inside AI Builders Week: How teams work in the AI era Each quarter, we ask some of our R&D teams to take a week away from the day-to-day to experiment and innovate on AI. We call this AI Builders Week, and it helps fuel our AI transformation. In past quarters, we’ve focused on building for individual workflows — how do we each get faster and more productive by inserting AI into different parts of our daily to-dos? But for our most recent Builders Week, we wanted our teams to think about how they come together and how they could reinvent entire workflows with AI. In other words, moving from isolated AI solutions to truly reshaping systems end-to-end. The results: across 1,500 registrants, 92% said they were more confident using AI in their daily work, and over 120 new workflows were built in just a few days. Almost all of our participants said they are ready to bring a new workflow to their team following the event. Shifting from individual to team-built AI workflows AI capability has become broadly accessible and useful to individuals, but what actually helps organizations solve problems faster is the ability for teams to assess and redesign how they operate with AI. More and more, the measure of success can’t be whether AI can help one person move faster. Instead, we chose to anchor on team-level transformations. We’re more focused on whether a team can work differently together because of the solutions being built. Our build day was grouped by business area and expertise so teams could focus on solving pain points they feel in day-to-day work. This intentionally brought together teams working on similar problems, codebases, and requirements. We also involved leaders, Heads of Product & Design to act as strategic coaches during build time, roaming breakout rooms and engaging directly with teams mid-flight. The goal was to create conditions for teams to learn from each other and truly embed new, AI-native workflows tailored to their own environment. We gave them a tight deadline to deliver and share their results. This...

Repositioning PostHog As PostHog enters its next chapter , we have spent the last 6 weeks going all in repositioning as the company that makes your product self-driving. This is how we are specifically doing it, and how it's going. If you are trying to position or reposition your product, hopefully this will help you! What does repositioning actually mean? "Positioning" is when you set the context for how you want your product to be seen by customers. You're trying to get across how your product is the best in the world at something that your ideal customer profile (ICP) actually cares about. "Repositioning" is when you change your positioning (woah). It's very normal to regularly review your positioning as the world around you changes. Why did PostHog need repositioning? We originally positioned PostHog as open source product analytics for developers back in 2020. Over time, we've expanded to position ourselves as a suite of tools that product engineers use to build better products. We've used terms like "Product OS" and "customer data infrastructure" that tried to capture this. Despite this, most people still see PostHog as "analytics++" today even though we make money from a lot of different products. In part, this was because we did our repositioning incrementally, but mostly it's because repositioning is just hard , especially if you don't go all in. We updated website copy! We ran ads! We wrote blogs! But never all at once, and never in a way that felt like our entire team was pointing in the same direction and moving with the same urgency. How PostHog is positioned now Self-driving is now how we are positioning ourselves. We are going all in on this in an uncomfortable way - this means putting it front and center on our website, but also focusing on it in content , video , influencers , events etc. Being all in means realizing this will probably confuse some people for a bit. It might hurt signups. We are willing to tolerate some short term pain, because we believe our long term s...

Bitbucket Tests now supports tracking tests from multiple branches Tracking tests on non-default branches was one of the most requested capabilities during the Bitbucket Tests (beta). We heard you, and today, we’re shipping it. Most teams don’t ship from main . Integration happens on develop , stabilisation on staging , releases cut from release . Until now, Bitbucket Tests only tracked the tests from default(main) branch, leaving the branches that actually matter invisible. Multiple branch tracking in Tests changes that. Bitbucket Tests can now track up to 5 branches per repository including the default branch( often main ), making failure rates, flaky scores, and test history reflect real delivery workflows. Bitbucket Tests is available on the Standard and Premium plans. Why it matters Test health where testing and releases happen. Integration, staging, and release branches are now first-class, not just the repository default. Flaky detection with full coverage. Failure rates and flaky scores aggregate across all tracked branches, surfacing the worst offenders wherever they fail. For more detail, read the earlier post on flaky test detection . Branch-specific answers. Every test execution shows its branch. A single filter isolates failures to one tracked branch. Get started 1. Ensure your test frameworks in the pipelines are generating JUnit-Xml compatible test reports. If not already generating. Know more about Bitbucket Tests 2. Open Tests → Configure branch tracking (visible to repository admins) Configure branches for Test tracking 3. Add the branches the team ships/tests from. 4. Results flow in from the next pipeline run. Left: Summary of Test results Right: Single test executions See the branch tracking section of the Get started with tests in Pipelines help document for step-by-step guidance on configuring branch tracking and making the most of test insights across your delivery branches. Share feedback Multiple branch tracking was prioritised based on customer feedback, and we’d love to keep learning from you. Share your...

Stop stitching together HR tools. Start Connecting Them. HR doesn’t have a tool problem, it has a connection problem. The Atlassian Ecosystem turns fragmented systems into one intelligent system of work. HR has quietly become one of the most cross-functional operations in any organization. Onboarding a new hire touches IT provisioning, payroll, compliance training, manager coordination, and knowledge management, often across a half-dozen platforms that don’t talk to each other. The tools exist. The problem is they’re disconnected. Onboarding checklists live in one system while the training content lives in another. Policy approvals move through email, while the policies themselves sit in a wiki that no one can find. Every handoff is a place where work stalls, data gets re-entered, and new hires wonder if anyone planned for their arrival. This isn’t a niche frustration. It’s a structural challenge, and it’s where the Atlassian Ecosystem offers something fundamentally different. One of the ecosystem’s practical strengths is flexibility in how teams adopt it. You can buy from thousands of Marketplace apps tuned to HR use case, build custom solutions with Forge or Rovo Studio when no existing app fits, or you can compose solutions by combining Atlassian products, Marketplace apps, and Rovo agents into automated workflows specific to your organization. What the Ecosystem actually is The Atlassian Ecosystem is a network of Atlassian products, 7,000+ Marketplace apps and integrations, a global partner community , and an extensible platform, all built on the same foundation. Every Marketplace app, Rovo AI agent , and partner-built solution operates on the same data model, permissions, and workflows that power Jira, Confluence, and Jira Service Management. You’re not stitching together separate platforms. You’re extending one. Four ways HR teams put this to work Across hundreds of enterprise deployments, HR success with the ecosystem falls into four categories: Planning, alignment, and optimization. Large-scale HR transformation p...

Give AI agents the full picture with Loom video prompts Your team has always been able to watch your Looms and act on them. Now, your agents can too. Loom introduces a new way to instruct your agents with video prompts . Record your screen, talk through what you want done, and get a structured action plan with rich instructions you can share with any agent, or turn into Jira work items in a few clicks. Stop engineering prompts. Start showing your agents what to do. AI agents promise to automate our most tedious workflows, but they have a massive bottleneck: human input. Getting an agent to successfully execute a task like bringing a prototype to life, making a UI change, or updating a design requires critical visual context. Up until now, that meant text prompts, copy-pasting screenshots, and cycles of back-and-forth clarification when the agent inevitably misses something. The overhead of creating these prompts often offsets the time saved by the agent. Text prompts lack visual context, voice transcripts lack technical structure, and static screenshots miss the actual click paths and screen states. Loom eliminates this manual documentation by converting a standard screen recording into structured, machine-readable instructions. By automatically parsing your narration, keyframes, target UI elements, and visited links, Loom produces an agent-ready action plan. You get the speed of real-time communication, combined with the structured metadata an AI agent needs to execute ideas, code changes, or workflows – without the need to engineer prompts. How it works Go from idea to agent handoff in just a few steps: Record your screen and talk through the task. Open the Loom Chrome extension and navigate to the Generate tab to start recording in video prompt mode. Share your screen and walk through what you want done, narrating as you go. Loom captures your transcript, on-screen clicks, links visited, and snapshots of the screens you share. For best results, record over 30 seconds. Get structured instructions for agents, without th...

Forge app translate jira? I was deeply checking for a solution to spanish people with custom languages (galician, euskera, catalan). I know the robust solution is ask for a language pack and will be ignored. I see some apps in marketplace for translate jira using browser extensions, doesn’t exist really any supported way to add a custom language in jira cloud with basic warranty of usage? 1 post - 1 participant Read full topic

Forge Object store default ttl Hi, I went through the documentation and I wasn’t able to find the default ttl value when uploading a file. In the EAP it was 90 days, but now ? Regards, Magnus 1 post - 1 participant Read full topic

Share packages across your workspace with internal packages

Connect Trello to Your Favorite AI Assistants with Trello MCP

Connect Trello to Your Favorite AI Assistants with Trello MCP Great ideas can start with a random spark at 2 a.m., grow through a planning session with ChatGPT, or take shape in a brainstorm with Claude. AI helps you plan, but getting that plan into Trello has always been a manual, momentum-killing process. Today, that changes. Trello now uses MCP (a shared standard that lets AI tools talk to apps like Trello) to connect with assistants like Claude, ChatGPT, Gemini, and Cursor. That means you can manage your boards, lists, cards, and checklists just by asking. No manual work required. AI assistants have crossed from being a developer tool into something anyone can use — nearly half the people using Atlassian’s MCP aren’t on software teams at all. And they’re not just asking AI to read their work; they’re using it to actually get things done. Now that same capability comes to Trello, which over 100 million people have signed up to use for everything from new hire onboarding, to marathon training, to wedding planning and beyond. Say you’re chatting with Claude about a two-week Italy trip. You’ve nailed down the cities: Rome, Florence, Amalfi Coast. You’ve got restaurant recommendations, museum tickets to book, and a packing list that keeps growing. Normally, this is where the conversation ends and the real work begins. You’d open Trello, create a board from scratch, try to remember everything you just talked about, and spend twenty minutes rebuilding it all by hand. With Trello MCP, you just say: “Turn this into a Trello board with a list for each city and cards for everything we need to book.” Done. Your assistant creates the board, organizes the lists, adds the cards, and even builds out checklists for things like “book Uffizi tickets” or “pack adaptor for EU outlets.” You go from planning to packing in seconds. Prompt: “Turn this into a Trello board with a list for each city and cards for everything we need to book.” Or say you’re planning a wedding. You’ve been going back and forth with your AI assistant about venues, guest ...

"Prerequisites" for set up? Hi! I'm wiring up product analytics to our web app and chose PostHog for the session replays. I'm blocked on the first prereq which is compliance. What the hell is a DPA and do i really have to do this stuff? Thank you so much, really appreciate any help! submitted by /u/Ok-Procedure-1348 [link] [comments]

The CLI couldn't securely store your login credentials in a local keychain I am getting an error while logging in to Forge. Please help. I am new to Forge: Error: The CLI couldn’t securely store your login credentials in a local keychain. If a local keychain is not available, use environment variables before trying again. See https://go.atlassian.com/dac/platform/forge/getting-started/#log-in-with-an-atlassian-api-token for more. 2 posts - 2 participants Read full topic

Render suspended my account for “suspicious activity” with no details & can’t even sign in to open a ticket submitted by /u/Djokokrish [link] [comments]

Request to Expedite Forge Remote Agent Early Access — FRGEAP-2077 Hi Atlassian Community Team, I’m currently evaluating Atlassian products during a trial period to determine whether they are a good fit for my ecosystem. Access to the Forge Remote Agent early access program is a key part of this evaluation, as remote agent functionality is one of my primary use cases. I submitted an early access request some time ago, but it is still in the queue and I have not yet received an update: Ticket: FRGEAP-2077 Because my trial period is time-limited, I would greatly appreciate it if the team could review and, if possible, expedite my request. Testing this capability before the trial ends is important to my decision about purchasing Atlassian licenses. Without the active EAP access I keep getting the following error when I try to deploy my middleware Error: Manifest validation failed: rovo:agentConnector is not a valid extension. I have reached out to the support team without any luck. Please let me know if you need any additional information from me to complete the review. Thank you for your help. 1 post - 1 participant Read full topic

Share packages across your workspace with internal packages We launched Bitbucket Packages to give teams one place to manage code, pipelines, and artifacts, and we’ve been expanding it ever since, first with Maven and npm , then PyPI and NuGet . Every one of those packages has shared one trait: it lives inside a single repository and inherits that repository’s permissions. Most of the time, that’s exactly what you want. But it also means sharing a package across repositories has meant granting people access to source code they never needed to see. Starting today, it doesn’t have to. With internal packages , a repository admin can open up read access to a package across every repository in the workspace, without handing out any access to the linked repository. Mark a package as internal, and any member of your workspace can pull it from anywhere, while write access stays exactly where it was. Why we built this Until now, a package was only as shareable as its linked repository. When another team wanted to pull a shared base image or a common library, someone first had to grant them read access to the source repository, even though all they wanted was the artifact. In practice, that created friction that had nothing to do with the code: Repetitive access requests: Repository admins and platform teams spent real time approving permission requests just so someone could pull a shared package. Oversharing to get work done: Granting repository access to share one artifact also exposed source code the requester didn’t need, and often shouldn’t see. Stalled pipelines: Any pipeline without read permission on the source repository couldn’t pull the packages it depended on, blocking automation until access was sorted out. Internal packages remove that friction directly: share the artifact, not the repository. Internal vs. private: what actually changes Every package now has one of two visibility states, set per package on the linked repository. It’s worth getting the distinction right before you turn anything on, because it’s the whole ...

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

RFC-140: Updating categories in the editor's element browser RFCs are a way for Atlassian to share what we’re working on with our valued developer community. It’s a document for building shared understanding of a topic. It expresses a technical solution, but can also communicate how it should be built or even document standards. The most important aspect of an RFC is that a written specification facilitates feedback and drives consensus. It is not a tool for approving or committing to ideas , but more so a collaborative practice to shape an idea and to find serious flaws early. Please respect our community guidelines: keep it welcoming and safe by commenting on the idea not the people (especially the author); keep it tidy by keeping on topic; empower the community by keeping comments constructive. Thanks! Project Summary We are planning on updating the categories in the editor’s element browser (both the slash menu and view more modal) to make discovering Atlassian & app elements easier for users. We wanted to give the developer community a heads up and open up discussion for feedback. Publish : 21 July 2026 Discuss until: 28 July 2026 Resolve by : 4 August 2026 Problem We want to improve the discoverability of all elements within the editor’s element browser (both the slash command menu, and the view more modal). Today: The slash menu version of the element browser is one long, continuous list without clear categories or grouping to help users browse it. The view more modal version of the element browser does show categories, however, the categories themselves are outdated, and don’t well enough reflect our offerings. Element browser - Slash menu Element browser - View more modal Proposed Solution We plan to: Update the set of categories we have to better capture our shared offerings. Update the slash menu to actually show the categories. Update the view more modal to show the new categories. Cosmetically modernize the slash menu and view more modal. Existing category names Rovo Formatting Confluence content Navigation Admin...

Custom deployment permissions for your environments (Beta)

Posthog for non-devs (vibe-coders)? Is there a Posthog for dummies tutorial somewhere? I'm trying to connect it to some apps i've build but can't seem to figure it out. submitted by /u/pondris [link] [comments]

Studio agent gets ALL of a Forge app's actions when only one skill is added. Bug? Hi there, When a no-code Rovo Studio agent has just one skill from my Forge app enabled, the agent can (and does) invoke every action the app declares, including skills that were explicitly not added to the agent. Only after removing the last app’s skill, the agent stops calling the app’s actions entirely. Skill selection in Studio therefore appears to work at app granularity, not per skill. So I wonder if the skill selection is just app-scoped? And is this a bug or on purpose? Best regards, Sven 1 post - 1 participant Read full topic

I missed PyCharm’s Git workflow in VS Code, so I built IntelliGit , looking for blunt feedback submitted by /u/Makkish_SWG [link] [comments]

“You Should Be A Little Uncomfortable”: How Cursor Is Navigating the New Shape of Product Craft Every week, it seems like the rules change for building products. We’re all figuring this out in real time, and one of the best ways we’ve found to do that is to sit down with other leaders who are in the thick of it. That’s why Atlassian recently started a series called AI Talks at our San Francisco offices with senior leaders building AI products. We break down what’s working, what’s broken, and what feels uncomfortable. Recently, our Chief Product and AI Officer, Tamar Yehoshua, was joined by Maxime Prades, who leads Product at Cursor . We recently launched our Jira and Cursor integration , allowing Jira teams to assign work directly to Cursor, where a cloud agent will pick up the task and begin working. Tamar and Maxime spoke about the biggest changes that AI has brought about, and how to think of AI as a complete mindset shift, rather than just a tool. Here are some of the key industry trends they discussed, and how senior leaders should be rethinking their product orgs. The ever-shifting bottleneck In our last AI Talks, the co-founder of Gamma, Jon Noronha, shared that one of the biggest challenges in building products right now is that with engineers getting more productive with AI, product managers have become the bottleneck. Maxime echoed this feeling. “When I was at Zendesk,” he told us, “The engineers were always the bottleneck. But now, at Cursor, I’m often the bottleneck.” If you’ve solved for engineering velocity but your decision-making, data, marketing, and review processes haven’t caught up, you haven’t actually gotten fast. The bottleneck has just moved. To get true value out of AI, you have to re-architect the whole value chain, rather than focusing on just one piece of it. Should PMs be able to code? Atlassian’s point of view is that product managers (PMs) should know how to code, though the central job of a PM is to know what to build, particularly in a world where the barrier to building is so low. Maxime believes that PMs don’t have to write c...

Custom deployment permissions for your environments (Beta) You shouldn’t have to grant full repository admin rights just to let an engineer or release manager trigger a deployment. To solve the all-or-nothing access problem, Bitbucket Pipelines introduces custom deployment permissions (Beta) . You can now gate specific environments to authorized users and groups, ensuring safer releases and easier compliance. Left: Add users and groups to gate deployments for this environment. Right: Unauthorised users are blocked from deploying and need someone with access to resume the deployment. What this unblocks for your team Deployment permissions help teams protect critical environments without slowing down their release process. Reduce production risk: limit deployment access to the people and groups trusted to release to sensitive environments. Avoid unnecessary admin access: give release managers, SREs, or on-call teams deployment access without making them repository admins. Support compliance workflows: separate who can merge code from who can deploy it, helping teams meet change-management and audit requirements. How it works Repository admins can manage permissions for each environment directly from the Repository settings → Pipelines → Deployments Add users or groups: Add individual users or groups to an environment’s permissions list. Audit access: View who currently has access, including user and group details. Revoke access: Remove a user or group when they no longer need deployment access. When permissions are configured for an environment, only the users and groups listed in the permission list can trigger deployments to that environment. Getting Started To use this feature, workspace must be on a Premium plan. Additionally, Bitbucket Pipelines must be enabled for the repository and at least one deployment environment configured. You must also have Admin permissions on the repository. Navigate to Repository settings → Pipelines → Deployments . In the Deployment permissions section, select the option to allow selected us...

What is a context warehouse? Data warehouses are optimized for analytics, business intelligence (BI), and data engineers. What would they look like if they optimized for AI agents instead? They'd look like context warehouses: a single tool that stores your business and product data, ingests and models it, and makes it directly available to agents that want to reference and use it as context. This isn't just an agent bolted onto a data warehouse, or a smarter semantic layer sitting on top of one. Plenty of data warehouses have that. A context warehouse is different because the ingestion pipeline and the modeling that make raw data mean something are part of the same tool as the storage and the querying, not separate pieces you assemble and keep in sync yourself. Nothing has to be piped from one system to another to be useful. Here's how we're thinking about the differences between a data and context warehouse, and how we're building one at PostHog. Why "data warehouse" stopped being the right word A data warehouse's job is simple: store structured data and let you query it. It doesn't know what a "customer" is, what a feature flag is, or what an experiment variant is; someone has to teach it, by building the modeling and pipelines to wire that meaning in. Traditionally that's a data engineer's job. As agents increasingly become the consumer of data, they need both the access and the tools to make sense of this data themselves. It becomes their job to know the meaning of the data so they can decide what to fix next. An agent working the self-driving product loop needs product signals (errors, funnel drops, slow sessions) and business context (revenue, plan tier, support history) in the same place so it can query directly without waiting for your daily data sync. That's what a context warehouse is: a data warehouse plus ingestion pipeline and data modeling, with direct access to your agents in one place. It’s what you get without having to build and maintain it. Data warehouse vs. context warehous...

The best log monitoring tools for developers, compared Every log monitoring tool does three things: collect, search, alert. If that were the whole job, this article would be one sentence long. It isn't because log monitoring tools do a lot more and that's where the real differences start to show. You discover these later: what a terabyte costs at your scale, whether the query language rewards or punishes you, or whether a log line can take you anywhere useful (like the trace behind it or the user in front of it). This guide compares the best log monitoring tools, which features actually matter when choosing, and who each one is built for. What features do you need in a log monitoring tool? At the bare minimum, good log monitoring tools should offer: Log ingestion from multiple sources (agents, APIs, and OTLP) Full-text search and filtering Tiered log retention Centralized dashboards and real-time visualization Alerting and notification The best monitoring tools go further to provide: OpenTelemetry support: Standardized log ingestion so you can switch between tools without rewriting your application logic. OpenTelemetry also enriches your logs with trace and span IDs, so you can trace an error back to the exact request that triggered it. Structured logging: To filter and query logs by specific fields – user ID, status code, or service name – rather than parsing raw text strings. Correlation with traces and metrics: Useful for connecting logs to related traces and metrics, so you can track performance issues or errors back to the events that caused them. Query language depth: Goes beyond basic keyword search and enables you to aggregate and analyze log data – making it easier to identify patterns, investigate incidents, and understand the root cause of recurring issues. RBAC and audit logs: Control who views, exports, or manages logs across teams and environments, and get insights into who ran a specific search or extracted sensitive data. Useful for regulatory compliance and security audits. Here are some of the best log...

New research reveals how AI is making jobs bigger

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Render Workflows and application-defined compute are changing how developers build, deploy, and scale AI agents. In the opening keynote from Render’s first user conference, the team introduces "application-defined compute": for dynamic, long-running, and stateful AI applications. https://reddit.com/link/1v1udb6/video/mziv9in1lfeh1/player submitted by /u/ojus_render [link] [comments]

2030-shaped software By 2030, AI agents will be the primary users of software. Many of the conversations and decisions about what we’re building now are impacted by this. More and more, we’re aiming towards building “2030-shaped software.” This is a vague aspiration and “ agent-first software ” isn’t much better. But, while we can’t predict the future, we can share how we’re thinking about the problem. Human and agent work is split The current era of software is designed for humans to do all the work, but in 2030, agents will be the ones doing . Agents have shown that they can do the obvious stuff themselves. What does this leave humans to do? Judging and deciding. Humans will approve, prioritize, choose direction, and resolve ambiguity. Understanding and trusting. Humans will evaluate whether it worked, if it’s safe, and what’s changed. “Doing” UI like buttons and forms are going away. They will be replaced with UI that supports the two types of work above. This helps optimize humans’ limited time and energy by pointing them where their input actually matters. Product infrastructure is agent-first If a human can do something with your product, agents should be able to do it too. This means complete APIs, an MCP server , tools, and permissions. Build for agents as a primary surface , not an afterthought. Missing capabilities are a failure mode. Say you ask an agent to set up an A/B test . It creates the feature flag , builds the insight, wires up the events, and stops because you never exposed a tool to create the experiment. It now has to ask you to do that part which defeats the purpose of using agents. This doesn’t mean that tools are all you need. It won’t be enough to just let Claude use your product on its own. Relying entirely on a model provider is too much risk. Building your own harness lets you guarantee quality, protect your brand, and make continuous improvements. Even OpenAI notes that agents struggle not because they are incapable, but because they lack “the tools, abstra...

The Future of Jira Isn’t Just Tracking Work. It’s Delegating it TL;DR: I wanted to see what would happen if I mapped AI agent capabilities to the functions of a software team. So I built a team of specialized agents in Jira, assigned each one to a stage of the software development lifecycle, and used them to build a new feature for my Forge app. It ended up being one of the smoothest development workflows I’ve tried so far. Jira is becoming the delegation layer for software teams. For a long time, Jira was where you went to track work. You updated a status, closed a ticket. Something important has shifted now that agents can be assigned to work items. Jira is still a record of what happened, but now it’s something else too: a place where work can move between humans and agents. That shift got me thinking. What if I leaned into it fully and ran an experiment? What if each stage of the SDLC had its own specialized agent and the board itself handled the routing between them? I picked a real feature, mapped a specialized agent to each stage of my Jira board, and let them coordinate the work. Here’s what that actually looked like. The Project: Launchpad To pressure-test this workflow, I wanted to use something I was already building instead of creating another demo app. I’ve been working on a Forge app called Launchpad. At its core, it’s a place where I can work through an idea with an AI agent before turning it into something a team can actually start working on. For this experiment, I wanted to add a Teamwork Graph-powered context discovery layer. The goal was simple: before I started implementing a feature, I wanted Launchpad to automatically surface the organizational context around it: related Jira issues, relevant Confluence pages, implementation patterns, known blockers, and the people who might already have context on the problem. It felt like the perfect feature to pressure-test assignable agents. It wasn’t a single coding task; it naturally broke down into research, planning, implementation, and review, giving each agent a ...

Request to enable the newest AI models in Rovo Dev Hello Atlassian Rovo Dev team, Please enable the newest models currently available for our Rovo Dev account and expose them in the Rovo Dev model selector. Specifically, we would like access to: OpenAI GPT-5.6 Anthropic Claude Fable 5 Anthropic Claude Opus 4.8 Anthropic Claude Sonnet 5 Thank you! 1 post - 1 participant Read full topic

APD — a local-first Agile Performance Dashboard for Jira CSV exports | Free 14-day trial submitted by /u/magus_prime_x [link] [comments]

How do I refund a wrong transaction on Phone Pe? 82717 45722 Cu in no KB x do KB cu HC at g BH on BH 2 posts - 1 participant Read full topic

पेटीएम से पैसा दूसरे के खाते में गलती से चला जाए तो वापस कैसे पाए✍️0827-174-5722? 1. क्लाइंट सर्विस नंबर: अपने बैंक या यूपीआई ऐप के ग्राहक सेवा नंबर 0827-1745722 पर कॉल करें। · 2. जानकारी बताएं: जब आप ग्राहक सेवा पर हों, तो सुनिश्चित करें कि आप उनसे निम्नलिखित जानकारी साझा करें:. 2 posts - 1 participant Read full topic

All PowerUp iframes failing Calling t.popup() with an args object successfully loads the iframe HTML, but window.TrelloPowerUp.iframe() throws TypeError: t is not iterable from iframe.ts:72 . Removing args allows the iframe client to initialize normally. I use a call like this: const popupOpts = { args: { view: "settings" }, title: "Settings", url: "./details.html", height: 265, }; return t.popup(popupOpts); Then in the iframe when it appears, it fails here: const t = window.TrelloPowerUp.iframe(); // We are seeing this error on ALL Power-Ups: Uncaught TypeError: t is not iterable Ut iframe.ts:72 iframe index.js:69 js details.js:15 This happens ONLY if you used args in the call of the popup or the modal. We use args extensively and therefore our Power Ups are failing. I see other Power-Ups function, and validated they are not using args. This appears to have started since early this AM. Is there anyone else seeing this same issue? @Trello NOTE : I have submitted a P1 Developer Support Request for this as well: ECOHELP-148080. Confirmed it was a change to iframe.ts in the last 24 hours that has caused this Trello Wide outage. 1 post - 1 participant Read full topic

Jonathan Wilson’s World Cup Diary We caught up with veteran British soccer writer, podcaster, and historian of the game on the eve of his sixth World Cup final. Over the past six weeks, he’s been all over the map, filing from Mexico City, Guadalajara, Houston, and Boston for The Guardian, Bloomberg, the Paris Review, and more, while bringing literary dispatches and wry analysis to his own newsletter Wilson’s World (of football) . We asked him to document the day he covered the Spain-France semifinal in Dallas and chatted with him about his Substack strategy, how he expanded his subscriber base by 40% over the course of the tournament, and his reckoning with the strange atmosphere at World Cup 2026. Your sixth world cup! How many matches did you get to this time around? Fifteen, I think. Maybe 16. I did three in Mexico City, three in Guadalajara, two in Monterrey, so that’s eight. I did four in Dallas, two in Houston, one in Boston. So 15 total! I also loved your dispatches from the games you caught along the way: watching from hotel bars, or restaurant patios, or catching the Germany-Paraguay penalty shootout at an airport Chili’s. I wasn’t even in it! I was outside the Chili’s. I was rushing through security to see the end, obviously had to make my flight, and I could see the screens. I was like, “Okay, I can just go over there and watch.” There were maybe about a dozen people there when I got there, and I got there at about 117 minutes. By the end of the penalties there were 60, 70, 80 people standing there. If you’d been selecting the most diverse group of people you could have found, you couldn’t have created a better one. There were a couple of security staff, a pilot, a couple of cleaners, a bloke in a Swiss shirt, loads of people with Mexican shirts, babies in pushchairs, old people in wheelchairs. People who clearly knew exactly what was going on, people who said, “I don’t understand this. What happens next?” It was the great World Cup moment of everybody coming together to watch this exciti...

Google broke the law. It's paid no price. Congress can change that. Opinion Google broke the law. It's paid no price. Congress can change that. Nearly two years since a judge ruled Google an illegal monopolist. Total changes Google has had to make so far: zero. That's what getting away with it looks like. The SEARCH Act can end it now. 17 Jul 2026 · 4 min read

Google broke the law. It's paid no price. Congress can change that. Nearly two years ago, a federal court ruled that Google illegally monopolized search . The judge was specific about how: Google didn't win by building a better product. It paid billions of dollars to be the default everywhere, on your phone and in your web browser, such that most Americans never actively choose their search engine at all. That ruling should have been a turning point. Instead, nothing has changed. Getting away with a monopoly. The court's decision was a diagnosis, not the cure. The remedies ordered last year fall dramatically short of what needs to happen to level the playing field in search. And Google has appealed them anyway. So too has the Justice Department, seeking the stronger fixes it originally asked for. The strongest remedies haven't taken effect and may not for years to come. Meanwhile, the court-appointed technical committee charged with putting change into practice is only just getting up and running. The result is a company operating exactly as it did before being declared a monopolist while running the same exact playbook that was ruled to be illegal. This is the definition of getting away with it. And the harm compounds each day. Google's vice grip on search was never only about defaults. It rests on two engines. The first is distribution, or the paid defaults that the court condemned. The second, less visible, is scale. Because Google sees far more searches than anyone else, it trains its systems on data no rival can touch. At trial, an analysis of 3.7 million unique search phrases over a single week found that 93% were seen only by Google. More searches produce better results, which draw more users, which produce still more searches. Every day the remedies are delayed, that flywheel spins faster and the gap a court has already ruled illegal grows wider. And the same flywheel is now spinning up in AI, threatening to rig the next era of search before it starts. Congress doesn't have to wait. It doesn't have to be this way. A solution...

What happens to Questions for Confluence data when you migrate or uninstall it? I’m researching a very specific Confluence migration problem. When an organization migrates, consolidates sites, or removes Questions for Confluence, what does it normally do with its existing questions and answers? Atlassian’s exporter/importer request is still unresolved, so I’m validating whether a read-only paid export service would be useful. A few questions for admins and consultants: Have you encountered this during a real migration? Would a searchable HTML/CSV/JSON archive solve it? Or would you only pay if the data could be imported directly into SharePoint or another Q&A product? Disclosure: I’m the person considering building the service. I’m looking for honest feedback and potentially three paid pilot projects, not pretending to be an unrelated user. submitted by /u/Able_Rope1656 [link] [comments]

Google broke the law. It's paid no price. Congress can change that. Opinion Google broke the law. It's paid no price. Congress can change that. Nearly two years since a judge ruled Google an illegal monopolist. Total changes Google has had to make so far: zero. That's what getting away with it looks like. The SEARCH Act can end it now. 17 Jul 2026 · 2 min read

Any plans for Commercial Cloud → Government Cloud / Isolated Cloud migration? What happens to Forge app data (Storage, issue properties, custom fields)? Hi all, Docs for AGC/Isolated Cloud only cover Data Center → Cloud migration. Is a Commercial Cloud → AGC/Isolated Cloud path planned? If it ever ships, a key problem: Forge installations of the same app get a different app key on Commercial Cloud vs. AGC vs. Isolated Cloud. That breaks anything keyed by app key: 1. Forge Storage (KVS, Forge SQL, Object Store) - does this data move with the site, or is it on each vendor to build their own export/import? 2. Issue properties used for JQL search - two failure modes depending on how the property key is built: Key includes the app key: issue migrates fine, but the property key no longer matches (different app key in the new cloud) → JQL search silently breaks until the app rewrites every property with the new key. Key is app-key-agnostic (same everywhere): the property key survives, but the app’s own backend data behind it may not have migrated → JQL can return stale/wrong matches until the app clears those properties. Since the app key will necessarily differ between clouds, would Atlassian rewrite entity property keys automatically during migration (old key → new key), or expose an API/notice ahead of cutover so vendors can remediate properties themselves right after migration? 3. Custom fields - field key and schema key are also app-key-scoped. Would custom field definitions + values migrate at all (given the old key won’t match any app in the destination), get dropped, or get remapped to the new installation? Any pre-GA / directional guidance from Atlassian would help us build the right safeguards ahead of time. Thanks! 1 post - 1 participant Read full topic

Hognarok (a hog-themed game for PostHog) Hey gang, I had a lot of fun building a small hog-themed game over the past few days. I built it when applying for a position at PostHog. It features Max, Robohog and Hogzilla. Yes, it has a hog mode! Sharing it here for hog cult members to enjoy. Cheers submitted by /u/psyl4x [link] [comments]

Google broke the law. It's paid no price. Congress can change that. Nearly two years ago, a federal court ruled that Google illegally monopolized search . The judge was specific about how: Google didn't win by building a better product. It paid billions of dollars to be the default everywhere, on your phone and in your web browser, such that most Americans never actively choose their search engine at all. That ruling should have been a turning point. Instead, nothing has changed. Getting away with a monopoly. The court's decision was a diagnosis, not the cure. The remedies ordered last year fall dramatically short of what needs to happen to level the playing field in search. And Google has appealed them anyway. So too has the Justice Department, seeking the stronger fixes it originally asked for. The strongest remedies haven't taken effect and may not for years to come. Meanwhile, the court-appointed technical committee charged with putting change into practice is only just getting up and running. The result is a company operating exactly as it did before being declared a monopolist while running the same exact playbook that was ruled to be illegal. This is the definition of getting away with it. And the harm compounds each day. Google's vice grip on search was never only about defaults. It rests on two engines. The first is distribution, or the paid defaults that the court condemned. The second, less visible, is scale. Because Google sees far more searches than anyone else, it trains its systems on data no rival can touch. At trial, an analysis of 3.7 million unique search phrases over a single week found that 93% were seen only by Google. More searches produce better results, which draw more users, which produce still more searches. Every day the remedies are delayed, that flywheel spins faster and the gap a court has already ruled illegal grows wider. And the same flywheel is now spinning up in AI, threatening to rig the next era of search before it starts. Congress doesn't have to wait. It doesn't have to be this way. A solution...

Thoughts headed into earnings on 8/6? Service Now reports 7/22 Atlassian reports 8/6 Given the SaaSpocalypse narrative I’m curious how we’re currently feeling. I think the market may be very wrong on Atlassian and it should be a $250+ stock but things are legitimately shifting with AI. Thoughts? submitted by /u/app385 [link] [comments]

Security vulnerability in adf-schema Is there a plan to update adf-schema library to fix the linkify high vulnerability described here https://socket.dev/npm/package/@atlaskit/adf-schema/alerts/56.1.5?tab=dependencies ? We have an app using adf-utils and security tools are flagging this vulnerability weeks ago. 1 post - 1 participant Read full topic

Bring GitHub context into Rovo with the Teamwork Graph connector for GitHub GitHub in Rovo for software teams AI is making it faster to write code. The new bottleneck is understanding the work around the code: what changed, why it changed, who reviewed it, and how it connects to the plan. That context lives across Jira, GitHub, Confluence, and the conversations that happen around them. Teamwork Graph helps bring it together. That connection is powered by the Teamwork Graph connector for GitHub, which helps make eligible GitHub data available in Atlassian experiences through Teamwork Graph. For software teams, GitHub is where implementation takes shape. Pull requests show what changed. Issues capture technical tasks and decisions. Commits and branches show how work moved. Review comments explain the tradeoffs behind the final code. For engineering leaders and software team leads, that context matters because delivery is not only about writing code. It’s about understanding progress, unblocking review, reducing handoff cost, and keeping the plan connected to your teams’ work. The opportunity: connected software context The process of building software work creates useful signals everywhere: a work item in Jira, decisions in pull request comments in GitHub, design content in Figma or documentation in Confluence. When that context is connected, teams can move faster with less manual reconstruction: Planning gets clearer because teams can see what already happened Reviews move faster because teams can find the surrounding work context Status reporting gets easier because the progress from planning and code tools can be understood together AI Agents become more useful because they can reason with more of the work record around the code Rovo is built for that context problem. With GitHub connected to Teamwork Graph, Rovo can help teams find and use GitHub context from inside Atlassian. Why this is different GitHub, Jira, and AI coding agents each solve important parts of the software delivery workflow. The gap is that each sees only part of the sy...

How Atlassian for Startups grew from an experiment into a global program

START HERE: What is PostHog + how to get help Welcome to r/posthog ! What is PostHog? PostHog is an open source platform for building self-driving products. In practice that means two things: A full suite of developer tools – product analytics, session replay, feature flags, experiments, error tracking, logs, AI observability, surveys, a data warehouse, a CDP, and more – all in one place, on one data stack. Agents that act on that data. Self-driving mode watches your product for signals (errors, rage clicks, failed queries...), researches them, and opens pull requests for you to review and merge. Nothing ships without your sign-off. You can use it from web, desktop, Slack (@ PostHog in any channel), or your own editor via the MCP (works with Claude Code, Cursor, etc.). Need help? Official support: https://app.posthog.com/home#supportModal Community forum: https://posthog.com/questions Docs: https://posthog.com/docs Status page: https://status.posthog.com Want more good stuff? Browse our blog for guides, comparisons, and an insider view on how we build: https://posthog.com/blog Subscribe to our newsletter, build mode, and stay up to date on shipping products in the AI era: https://newsletter.posthog.com/ submitted by /u/PostHogTeam [link] [comments]

Why AI alone isn’t enough for enterprises (and what Mercedes-Benz did about it)

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

IncidentIQ — evidence-grounded root cause analysis for production incidents (feedback welcome) Hi all — sharing a project I built and would love technical feedback on, especially from anyone working on incident tooling. The problem When a service goes down, an on-call engineer typically has to manually correlate signals across separate tools — metrics dashboards, log search, deployment history, and past-incident records — to figure out what changed. That correlation work is usually the slowest part of incident response, not the fix itself. What I built A small FastAPI service that automates the correlation step, not the guessing step: Retrieves deployment history, metric trends, and error logs for a given incident Computes an evidence-strength signal (strong / moderate / weak) from code — deployment recency, metric delta magnitude, historical match score — before any LLM is involved Uses an LLM only to turn already-scored evidence into a readable narrative, with every claim citeable back to a specific log line or metric. It never generates its own confidence number — that’s a deliberate design choice, since a model asked “how confident are you” will produce something plausible-sounding with no calibration behind it Similarity search over past incidents via vector embeddings, to answer “has this happened before” A real GitHub integration: pulls deployment history from the GitHub REST API, and a webhook receiver with HMAC-SHA256 signature verification for real-time push events What’s honest about scope The demo runs against two synthetic services with an injected connection-pool-exhaustion scenario — there’s no live Prometheus/Elasticsearch cluster behind it. Everything downstream of that data existing (correlation, scoring, retrieval, narrative synthesis) is real and tested; the log/metric ingestion is not yet connected to a real production source. 32 tests cover the evidence-scoring logic, the API, and the GitHub integration (including a regression test that the narrative never fabricates a confidence percentage). Stack: FastAPI, SQLAlchemy, pgvector/Postgres...

Looking for old posthog demo.mov The one that was female voiced and very slowly paced, unironically best unintentional asmr video, I need it! submitted by /u/en1k174 [link] [comments]

Can JSM portal-only customers authenticate to a third-party app via OAuth 2.0 (3LO)? Can Jira Service Management portal-only customers (external customers without a full Atlassian/product license) authenticate into a third-party web app via Atlassian OAuth 2.0 (3LO) — i.e., can we reuse their existing JSM identity to sign them into our own app? Or is 3LO limited to full Atlassian accounts? If it’s not supported for portal-only customers, what’s the recommended approach for authenticating them into an external app? 1 post - 1 participant Read full topic

The clues were there all along. Tracing is now in beta Distributed tracing is now in beta. If you've been using PostHog Logs , you probably saw this coming. PostHog starts with your user data and provides you with all the tools you need to build successful products, so debugging is an obvious must-have to keep your products working as expected. We have Error Tracking , Session Replay , and Logs in the PostHog debugging stack, so tracing will complement nicely to give you more of the context you need to resolve issues quickly, and more fuel for the agents doing it for you. A trace is the full journey of a single request through your system, from the first incoming call to every service, queue, database, and third-party API it touches along the way. Each step is a span, stamped with how long it took and whether it succeeded, and they nest into a waterfall you can read top to bottom. Where a log tells you what happened at one point and an error tells you something broke, a trace shows you the whole path: what called what, in what order, and exactly where the time went. What's in the beta Point your existing OpenTelemetry exporter at PostHog's OTLP/HTTP endpoint, drop in a bearer token, and you're sending traces. There's no need for a new SDK or rewriting the instrumentation you already have. OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=" /i/v1/traces" OTEL_EXPORTER_OTLP_TRACES_HEADERS="Authorization=Bearer " OTEL_SERVICE_NAME="my-app" Inside PostHog, you get a volume sparkline for traffic at a glance, a filterable table of root spans (slice by service, name, kind, status, duration, trace or span ID, or any attribute on the span), and a flamegraph for any trace you click into. All of it lives on the same warehouse as your PostHog Logs, so the same query can run across both. More fuel for your agents Most observability tools assume a trace is something a site reliability engineer stares at on a dashboard: spans, services, latency, hosts. The user who hit the bug lives somewhere else, in your analytics, your replay, your support tool, ...

Chartography: A Benchmark for Professional Chart Understanding

Two years in: how Atlassian for Startups grew from an experiment into a global program — and why it matters more than ever

How we’re evolving Jira for AI-native software development

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

myTeamTickets() JQL function How to develop “Team[Team]” IN myTeamTickets() dynamic JQL function? The JQL functipon should be return currentUser()'s Team issues. A user can include multiple Teams. I want to create one JSM Queue (My Team Issues) for this reason. 1 post - 1 participant Read full topic

Two years in: how Atlassian for Startups grew from an experiment into a global program — and why it matters more than ever Early-stage startups face a particular set of growing pains. It’s not just figuring out what to build and for whom – there’s also a need to measure progress, coordinate across projects, and keep knowledge accessible, all while likely making some drastic pivots. These challenges are what we call a “collaboration tax,” and if it isn’t solved in the earliest days of operating the company, the “tax” only compounds as a startup scales. In June 2024, Atlassian launched a program built on a straightforward premise: if we could get early-stage founders building with the right collaborative tools from day one, they’d be able to deliver more value to their customers and their startup would grow with Atlassian as they scale. Two years later, the premise is holding. As of June 2026, Atlassian for Startups had onboarded more than 5,000 startups from around the world – over 2,000 in the first year and more than 3,000 in the second. From AI to Semiconductors to Deep Tech to Hospitality, our startup customers span nearly every industry, and they’re bringing deep expertise to real-world problems. I work directly with startups from across the globe through the Atlassian for Startups program. The most rewarding moments are hearing a founder share that Atlassian has become so ingrained in how they work that they can’t imagine running their business without it. That’s exactly why we built this program – founders need tools that scale with them, so they can stay focused on what they’re truly passionate about: building great products for their customers. “Since joining Atlassian for Startups we’ve: shipped multiple big sprints and closed 15 new clients….” – Amir, Timber Exchange “The Atlassian Startup Program has been incredibly valuable for our team, and it has played a meaningful role in helping us advance our AI-based character infrastructure. Over the past year, we’ve made strong progress on the technology side and are now preparing to enter commercialization, which we expect to begin...

How we’re evolving Jira for AI-native software development New Jira and Teamwork Graph capabilities help engineering teams plan, assign, govern, and measure work across humans and AI agents. Whether I speak to customers or Atlassian’s own engineering teams, the message is consistent: the unprecedented adoption of powerful coding agents has transformed software development, but the hard parts of delivering software are…gasp…still pretty hard. Teams still have to decide what to build, and why it should exist. They need to understand the system they’re changing and which constraints matter. They have to know what “done” means, and whether the output is safe to ship. That is the reality behind the AI productivity gap. In a longitudinal study we ran with DX across professional engineering teams, AI usage has increased by 65%, but overall developer velocity did not. It topped out at a 15% increase, with many organizations seeing gains averaging 10% The gap is not because models are bad at writing code. It’s because software development has never been only about writing code. It is about turning business goals, strategies, and context into working software inside a real organization. For more than two decades, Jira has evolved to meet software teams of every shape and methodology: we’re the source of truth for what to build, who is doing it, how it’s going, and what shipped. Today, the shape of software teams is changing rapidly. AI-native software development teams require a new system where context for agents is a first-class citizen and tasks are delegated to agents while humans steer, and review. Engineers, product managers, designers, and security teams bring judgment and context to the work. It’s a new way for humans and agents to work together with clear plans, shared context, and validation that what comes back is something the team can stand behind. Today, we’re announcing new agentic product development capabilities in Jira built for that shift. Teams can plan work with AI, turn intent into agent-ready specs, assign work...

ContentPropertyManager and Confluence 11 Hello @Kusal , is there still a plan to add #getKeys API to the ContentPropertyManager? I am referring to the request previously raised by @jens : Confluence 10.0 release EAP is available now - #218 by jens Your answer was “Regarding the suggestion to add a #getKeys API - this is a sensible addition, but unfortunately it’s too late to commit to this for 10.0. I’ve added it to our backlog to implement prior to 11.0.”. As Jens already mentioned, this is functionality that we cannot replace on our side, and we rely on these keys in several of our products. Could you please let us know whether this is still planned and when we can expect it to be implemented? Best regards, Daria 1 post - 1 participant Read full topic

In our Condfluence DC app the Actions stoped working After upgrading to Confluence 10 our team tried to do every necessary modification in our custom app to make compatible with the new version. But we cannot get the Actions working. We changed the xwork tags to struts tags in our atlassian-plugin.xml, but we cannot find anything in the documentations that we need to do more. Now we get this error when we try to do an Action: java.lang.IllegalStateException: Error loading action class ... Caused by: java.lang.ClassNotFoundException: com.catworkx.confluence.dme.actions.ClearBrokenAttachmentsAction Here is our atlassian-plugin.xml: ${project.description} ${project.version} images/plugin-icon.png images/plugin-logo.png images/vendor-icon.png images/vendor-logo.png /plugins/servlet/cwx-dme/settings true com.atlassian.auiplugin:ajs com.atlassian.auiplugin:dialog2 page cwx-dme-copy-dialog com.atlassian.auiplugin:ajs com.atlassian.auiplugin:aui-select2 cwx-dme-settings com.atlassian.auiplugin:ajs com.atlassian.auiplugin:aui-select2 cwx-dme-settings com.catworkx.confluence.dme.ao.DmeEntityManager com.catworkx.confluence.dme.ao.SettingsEntry /pages/cwx-dme-copy-page-tree.action?idOfPageToCopy=$helper.page.id&spaceKey=$helper.space.key page action-copy /templates/cwx-dme-copy-page-tree.vm /pages/cwx-dme-copy-template.action?idOfPageToCopy=$helper.page.id&spaceKey=$helper.space.key page action-copy /templates/cwx-dme-copy-template.vm /pages/cwx-dme-copy-template-metadata.action?pageId=$helper.page.id edit action-copy /templates/cwx-dme-copy-template-metadata.vm /pages/cwx-dme-propagate-metadata.action?pageId=$helper.page.id edit action-copy /templates/cwx-dme-propagate-metadata.vm /cwx-dme-copy-all-metadata.action?pageId=$helper.page.id edit action-copy /templates/cwx-dme-copy-all-metadata.vm com.atlassian.auiplugin:ajs space /cwx-dme-clear-broken-attachments.action?spaceKey=$generalUtil.urlEncode($helper.spaceKey) /templates/cwx-dme-clear-broken-attachments.vm /plugins/servlet/cwx-dme/settings /cwx-dme/settings /plugins...

ConvoAiHomeThreadsInput must define one or more fields Is there a specific reason why I am getting this message when using Introspection? 1 post - 1 participant Read full topic

#TuesdayTip: Did you know we have a page dedicated to Render Templates? Templates help you quickly deploy popular tools, starter projects, and complete applications on Render without setting everything up from scratch. Take a look and see what you can launch: https://render.com/templates Have a template you’d like us to add? Drop your suggestion in the channel. submitted by /u/ojus_render [link] [comments]

Can a Marketplace App MCP be connected to the Heart of Rovo / TWG? Hello there, We have been exploring how external MCP servers integrate with Atlassian Rovo , and we wanted to share our current findings and open a discussion about what’s possible beyond what we’ve achieved so far. Following Atlassian’s official documentation on connecting external MCP servers ( Add an external MCP server from Atlassian Administration | Atlassian Support ), we successfully connected a c ustom MCP server for one of our Marketplace apps , Projectrak. The MCP exposes several tools that interact with our app’s API, and Atlassian correctly detects and registers those tools during setup. From our testing, we’ve confirmed that: MCP tools can be used as skills within agents created in Rovo Studio These agents are able to invoke the MCP and retrieve live data from our app However, beyond this point, the integration seems limited . The Atlassian documentation mentions that MCP tools can be used in “Rovo, Studio, and other experiences.” While Studio is clear (custom agents), it is not obvious what “other experiences” refers to in practice. So far, we have not observed: Any usage of MCP tools by the default Rovo experience MCP data being surfaced in general Rovo Chat interactions Any indication that MCP data is being indexed or incorporated into the Teamwork Graph Automatic insights or suggestions in Rovo based on MCP-connected data In other words, connecting an MCP does not currently seem to make its data part of Rovo’s core intelligence layer. Our goal is to understand whether it is (or will be) possible to: Connect external app data (via MCP) to the core of Rovo Make that data available for general Rovo insights, not only through explicitly configured agents Have MCP-backed data contribute to the Teamwork Graph At the moment, the only clear and reliable pattern we see is using MCP tools inside custom Rovo agents . While useful, this feels like just one part of the potential. We would love to hear from others who are experimenting with MCP: Have you found an...

Rolling releases and SQL We’re evaluating Rolling Releases (Preview) for our Forge native app. Some of our customers are currently on an older major version of our app that does not use Forge storage/SQL. In our latest major version, we introduced SQL. In the Preview Limitations documentation , it states: Upgrading from a version without storage to a version with storage (KVS and SQL) is not supported. We’re trying to understand how this limitation applies to our situation. If some customers are on a major version without SQL, and newer major versions use SQL: Does this limitation mean we cannot use Rolling Releases for the app at all while those customers remain on the older, non-SQL version? Or would Rolling Releases only apply to customers who are already on a version that includes SQL, while customers on the older non-SQL version would simply not receive those updates? More generally, does this limitation mean there is no supported upgrade path from a version without storage/SQL to a version that uses SQL, or is there another migration approach we should follow? If a customer on the older non-SQL major version manually upgrades to the newer SQL-based major version, would they then be eligible to receive subsequent Rolling Releases, or would the storage limitation still prevent that upgrade path? Will this be supported on the GA release? Thanks! 1 post - 1 participant Read full topic

Sourcetree API-Token Issue Hello, Two weeks ago, while using Sourcetree, I became aware of the CHANGE-3222. It took me awhile to get my API token setup and working correctly but I was eventually successful and have been able to use sourcetree. Then yesterday I was again confrunted with the inability to access bitbucket, I checked and this was during a Brownout Window. I have repeated all the steps to make sure my sourcetree is set for API-Token authentication, with it confirming that Authentication is OK. But still I am not able to access bitbucked during the Brownout Windows. Attached screen grabs of the relevent parts of sourcetree. Assisance would be greatly appreciated. 3 posts - 2 participants Read full topic

How Axel Springer is using Rovo Dev to help engineering teams focus on higher-value work The learnings in this blog post are based on the session, “Accelerating the SDLC at scale: How Axel Springer uses Rovo Dev to ship faster”, presented at Atlassian’s Team ’26 conference. You can check out this session and others on demand. Every engineering organization feels pressure to ship faster, but increasing velocity alone isn’t really the most useful goal. Martin Bilt, Lead Product Excellence at Axel Springer, frames this challenge differently. According to Bilt, the more important question is whether engineers are spending their best hours on work that moves the product forward or on the repetitive scaffolding around it. Axel Springer is a global media and technology company behind brands such as Business Insider, Politico, BILD, and idealo. Headquartered in Berlin and active in more than 25 countries, the company has spent more than 70 years evolving from a traditional publisher into a technology-driven media company by repeatedly betting on technology well before it felt safe or comfortable. This technology-driven culture has shaped the company’s approach to AI in software development. Adopting AI was never a question for Axel Springer; their real question was, “Where do we start? And how quickly can we get up to speed?” Axel Springer placed Rovo Dev in the middle of how several engineering teams build software. This pilot focused on real workflows, measurable outcomes, and the day-to-day friction that prevents engineers from spending more time on meaningful problem-solving. Speed is a byproduct, focus is the goal Axel Springer found that a more important outcome than velocity alone was redirected attention . Rovo Dev helped move senior engineering time away from repetitive setup work and toward the problems that matter most. The company used a simple framework to think about where AI could help. Work fell into four categories: quick wins, strategic bets, time sinks, and fillers. AI was especially useful for high-frequency, low-complexity work that creates...

Why individual AI speed isn’t delivering the ROI CIOs expected

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

Product for Engineers is now build mode We’re rebranding Product for Engineers as build mode to better cover everything it takes to build successful products from now into the future. Here’s why. We accomplished our original goal We started Product for Engineers to challenge the idea that engineers only care about code. We showed that engineers can flex their product muscle, talk to users directly , be opinionated about what to work on , work autonomously to ship features, and figure out if they are successful. The tens of thousands of subscribers we amassed, the rise in popularity of the product engineer role, and 100+ YC startups hiring them now are proof we accomplished this. Along the way, we learned product engineers care about more than just product . They care about building a successful company and career more broadly. After all, great products come from the people and companies who build them. Following the advice to “ build write something people want,” we wrote about everything from finding a job to scaling a startup to marketing to collaboration . But every post like this pulled us away from the “product” framing in our name. Helping people build more successful products meant a lot more than just helping engineers do product. AI changes engineering and the meaning of “engineer” The other part of our name has also seen huge changes because of AI. First, people are building AI-powered products and using AI to do so. Last week, PostHog saw 3.5M MCP tool calls, nearly 100K PostHog AI chats, and 40% of experiments created through our MCP. Our AI observability product grew 17x in the last year. Building the best products now requires building for agents , writing loops , and making full use of AI coding tools . Second, AI has made it possible for anyone to build. The engineeringification of everything is real. Salespeople ship features , product managers fix bugs , marketers build video games . Our posts are relevant to a new group who don’t exclusively label themselves as engineers. PostHog as a c...

Bitbucket-jira-cli: a gh-style CLI for Bitbucket and Jira, with branch-name-as-Jira-key automation I built an open-source CLI called bitbucket-jira-cli (command: bj ) and wanted to share it here. Full disclosure: I’m the author. The idea is simple: bring the GitHub gh workflow to Bitbucket and Jira. bj manages Bitbucket pull requests, repositories and pipelines, and Jira issues, from one command tree that mirrors gh . If you know gh pr , gh issue and gh repo , you already know bj pr , bj issue and bj repo . The part I actually built it for is branch-name-as-Jira-key. Your git branch carries the key (for example feature/PROJ-42-thing ), and bj uses it automatically. bj pr create fills the PR title from the ticket, links the PR to the issue, and moves the ticket to In Progress. bj pr merge moves it to Done. No copy-pasting keys between tabs. A few other notes: It talks to the REST APIs directly, so it works in scripts and CI, and there is no server to run. Bitbucket Cloud and Jira Cloud, with a scoped or unscoped API token for each. It ships an agent skill, so AI coding agents can drive it the same way they drive gh . Install: uv tool install bitbucket-jira-cli # or: pip install bitbucket-jira-cli bj auth login github.com GitHub - Spenhouet/bitbucket-jira-cli: The gh CLI experience, for Bitbucket and Jira The gh CLI experience, for Bitbucket and Jira Docs: Bitbucket Jira CLI - Bitbucket Jira CLI Here a quick demo: I’d like to hear where the current commands don’t match how you work. Feedback and issues welcome. 1 post - 1 participant Read full topic

Request for Proposals for Non-profit Grants For the last five years our family has been giving grants to a handful of U.S. non-profits using the following criteria. We are planning to slowly expand this effort and I’m putting this post out there in hope that more orgs, especially newish ones, find us over time. Funding criteria $25-200K annual grants U.S. non-profit within a target area (see below) Ideally founded in last 5 years with Can use grant(s) to level up impact, for example hire a new team member, start/expand a project, or unlock new funding sources Target Areas and Request for Proposals (RFPs) Science Acceleration Example grant: PubPeer RFP: Raise the U.S. science budget Voting Reform Example grant: Rank The Vote RFP: Citizen-led amendment path RFP: Enlarge the House of Representatives RFP: Multi-member, proportional House districts Government Transparency Example grants: Free Law Project , Fix The Court RFP: FOIA for the Judicial branch Criminal Justice Reform Example grant: Public Accountability RFP: Similar appellate impact litigation Direct Aid Example grants: GiveWell Top Charities Fund , DonorsChoose RFP: Coordination for starting more bail funds Mainstreaming Critical Thinking Example grant: School of Thought RFP: Help people reason about complex topics If you’re wondering where is privacy as a target area, it is covered by the independent DuckDuckGo donations , which you can learn about more in this Duck Tales episode . Thanks for reading! Subscribe for free to receive new posts or get the audio version . Share

Made A Free Discord server That Pings & Emails You The Moment A Critical CVE Drops For Dozens Of Vendors (Select & Choose), Atlassian CVEs Are One Of Them. Mitigation & Resource Documentation/Discussions As-Well I created a simple Discord server that automatically updates vendor-specific channels whenever a new CVE is published from that specific vendor, including Atlassian CVEs. It tags users based on the roles they choose, so you can follow the vendors you care about and decide whether you only want to be tagged for critical alerts. You can also choose to receive an email as well when that CVE drops. I’ve also added discussion channels where we can share patching tips, troubleshooting advice, and general networking/security/sysadmin knowledge, plus resource channels for each vendor with quick links to relevant documentation (Official Vendor Advisory Feed etc). Just wanted to help myself and other Network/Sys/Devs make their already complicated lives easier. It’s completely free to join. https://discord.gg/duxkwSSAAH submitted by /u/NoPo552 [link] [comments]

Why I build Jitly: A bridge between your working directory and Jira submitted by /u/Direct_Holiday_5934 [link] [comments]

How to automate product workflows with AI-powered video: A product manager’s guide Every product team has the same problem. The work that moves a product forward keeps getting squeezed by the work that surrounds it. Status meetings. Context-setting. Bug report write-ups. Documentation nobody has time to create. Atlassian’s 2024 State of Teams report found that knowledge workers spend 72% of their time on “work about work.” The fix is not another productivity hack or a new project management methodology. It is rethinking how context moves between people and systems. AI-powered video is emerging as the connective layer that automates the busywork product teams have accepted as inevitable. This blog walks through five workflows where AI-powered video eliminates manual effort. From automating bug reports to turning meeting conversations into updated work items, these are capabilities product teams are using today. What is AI-powered video? AI-powered video is screen recording software that uses artificial intelligence to do more than just capture what is on your screen. It transcribes what you say and understands the context of what you are showing. Then it takes action on your behalf: generating summaries, creating documentation, filing work items, and briefing AI agents on what to do next. Unlike traditional screen recording, AI-powered video treats the recording as structured data. The transcript, the on-screen content, browser context, and developer logs all become inputs that AI can process, organize, and route to the right tools automatically. Why product teams need workflow automation Product managers are the connective tissue of their organizations. They translate between engineering, design, leadership, sales, and support. That translation work generates an enormous amount of context transfer: meetings to align, documents to write, tickets to update, decisions to record. Most of this context transfer is manual. Someone watches a meeting and writes up the notes. Someone sees a bug and spends ten minutes describing it in a ticket. Someone gives...

Developer Space won't publish to Marketplace, "Failed to process request" (topic deleted by author) 1 post - 1 participant Read full topic

Scheduled/Web trigger function invocation fails with wrapper-level TypeError, regardless of module type or function name Bug: Scheduled/Web trigger function invocation fails with wrapper-level TypeError, regardless of module type or function name Summary A named exported function in index.js , referenced correctly by manifest.yml ’s handler field, cannot be invoked by the Forge runtime. Every invocation — whether via scheduledTrigger , webtrigger (manually curled), or after renaming the function to match the documentation’s own example — fails identically with: TypeError: t[w.functionName] is not a function at /mnt/wrapper/wrapper_v24_XX.cjs:2:905258 at g (/mnt/wrapper/wrapper_v24_XX.cjs:2:905264) at new Promise ( ) at /mnt/wrapper/wrapper_v24_XX.cjs:2:904992 at AsyncLocalStorage.run (node:internal/async_local_storage/async_context_frame:63:14) at BufferedInvokeProcessor.Q [as handler] (/mnt/wrapper/wrapper_v24_XX.cjs:2:904238) at BufferedInvokeProcessor.processInvoke (file:///var/runtime/index.mjs:1099:33) at file:///var/runtime/index.mjs:1185:98 at InvokeStoreSingle.run (file:///var/runtime/index.mjs:83:12) at _Runtime.runWithInvokeContext (file:///var/runtime/index.mjs:1198:24) Note the wrapper filename version changes between invocations ( wrapper_v24_5.cjs , wrapper_v24_20.cjs , wrapper_v24_25.cjs , wrapper_v24_35.cjs , wrapper_v24_50.cjs , wrapper_v24_85.cjs , wrapper_v24_100.cjs — all observed across different invocations of the same function). A different exported function ( index.handler , built via resolver.getDefinitions() ) in the same file invokes correctly on every call — the app’s primary resolver-based UI functionality (search, export, saved filters, etc.) all work without issue. Environment App ID: ari:cloud:ecosystem::app/0628f03f-e8d8-4cdc-b5cc-9dedbe26ebd3 Environment: development Site: power-exporter-test.atlassian.net (also reproduced on arzenicstech.atlassian.net ) Runtime: nodejs24.x , memoryMB: 512 , architecture: arm64 Forge CLI: updated to latest (13.1.0) partway through investigation — issue predates and persists after this update Manifest (relevant excerpt...

Bitbucket Cloud pipelines: trigger variables rejected at 10,240 bytes since 2026-07-08 — intended change or regression? Since ~05:00 UTC on 2026-07-08, triggering a pipeline via POST /2.0/repositories/{workspace}/{repo}/pipelines with custom variables started returning 400: “Variables exceed the maximum allowed count or total size. Maximum count: 50, Maximum total size: 10240 bytes.” …for requests that succeeded fine before that time. Nothing changed on our side. What’s confusing: - The 10,240-byte total-size limit contradicts the documented limits — the Variables & secrets page states 100 KB for shared variables and 120,000 chars per custom value. 10 KB appears nowhere in the docs. - It’s a hard, clean cliff: requests whose combined variable values are 10,240 bytes fail 100% of the time (verified across hundreds of triggers). - Onset was sudden and hit many separate workspaces at the same time. - No matching entry in the developer changelog. Questions: 1. Was the total-size limit for API-trigger variables intentionally reduced to 10,240 bytes? If so, where is it documented? 2. If not intentional, is this a known regression, and is a fix/rollback planned? 1 post - 1 participant Read full topic

Our session replays are opening their own pull requests Errors are the easiest data to act on in our product improvement pipeline . They arrive with a stack trace that's groupable and says exactly what broke and where. This makes it easy for agents to research each one, and if it's actionable, open a PR to fix it. Session replays are a lot trickier. Someone clicks a button that doesn't respond, clicks it three more times, and leaves. Typically, nothing lands in your logs for this type of error. The only evidence is a recording nobody has time to watch. That's the most common kind of bug, and it's almost invisible to everything except a human watching the replay. Replay Vision is our solution to this. It is our AI layer over session replay. You set up a scanner, aim it at the sessions you care about, and it reads them for you – all of them, not a sample. Scanners come in a few shapes: summarizers write up what happened in a session, scorers put a number on something like frustration, and classifiers sort sessions into buckets. When a scanner spots something worth acting on, it raises a signal. These flow into the Inbox, the queue at the center of PostHog's self-driving loop , where signals from across your product are grouped into reports, researched by background agents, and turned into work. To show you how this works, here's the details on three pull requests opened from replays. The struggle scanner The one doing the work here is a success/struggle classifier. It reads real user sessions across projects, works out whether the person actually did the thing they came to do, and tags each session success or struggled . When it tags a struggle, it drops a signal into the same pipeline our errors flow through. In the first two weeks, the scanner has read ~3,400 sessions and emitted ~1,850 signals. Whether half is the right number is an open question, and we'll come back to it. Once a signal is in, it stops being a session replay problem and becomes a normal pipeline problem: cluster the related signals into a report, promo...

From tool to teammate: How one Atlassian team made AI a real coworker

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

Stop being the code review bottleneck Agents are writing code faster than any human can review. The naive solution would be for developers to review code faster. The 500 IQ take is for developers to review as little code as possible. If you need to be involved in every code review, you will always be the bottleneck. Instead, put yourself outside of the code review loop by building a pipeline that delegates tasks to agents. We asked engineers at PostHog how they’ve been reviewing AI-generated code to keep shipping fast without losing quality. Here are four workflow changes you can steal (with prompts included) to make your life easier. 1. Make agents review code for you The number one thing to add, if you haven’t yet, is a way for agents to review code for you. The goal is to offload the simpler reviews to agents, and flag if something genuinely needs a human. The key is that the agent that wrote the code can’t be the one that reviews it. Agents are bad at checking their own work since they’re often unaware of their own blind spots. 1 For the same reason, it’s better to have multiple agents with different instructions and goals to cover more gaps, 2 as well as different models and providers for different reviewers. 3 Here’s how one of our engineers, Paul D’Ambra , makes his own custom agent review system work: First, qa-swarm spawns four reviewer agents, each with their own special instructions: qa-team – spawns technical subagents that hunt for security, database, performance, etc. security-audit – probes for vulnerabilities like SQL or prompt injections paul-reviewer – uses Paul’s voice and focuses on observability, rollouts, naming xp-reviewer – applies an Extreme Programming lens to review Then, review-triage sorts those reviews to classify threads into three categories: actionable → gets fixed and pushed nits → get resolved and replied to with a comment ambiguous → escalated and sorted for Paul to work through with the agent later An outer loop iterates up to three times or until no new actionable t...

IP Whitelisting Blocking Bitbucket - GCP Dataform Connection Hello Community, I have an issue with a connection between Dataform in GCP to Bitbucket private projects which is working only with whitelisted IPs. I have 2 GCP projects and the mentioned Bitbucket private workspace. I have successful configuration with Dataform in project A via SSH, but I get the following error in project B via Developer connect: Invalid resource state for “projects/project-b/locations/europe-west3/secrets/bitbucketCloudApiToken-21wfz/versions/latest”: failed to verify authorizer token: Get " https://api.bitbucket.org/2.0/repositories/elena-ws/repository-elena-ws/hooks": generic::permission_denied: 403: To access this repository, an admin must whitelist your IP. Dataform in GCP is part of Big Query, checked the GCP documentation where I found an IP range for Dataform for Frankfurt 3 and whitelisted it in BB, also double-checked for Big Query limitations, but according the documentation, it does not have specific IP ranges. The IP ranges for IPv4 inbound for bitbucket. org, api.bitbucket. org, and altssh.bitbucket .org are whitelisted. There are no VPCs, Cloud NATs or additional limitation from the GCP project. The token has permissions per documentation. Via the tests, the authorization one needed repository:admin and the read one needed webhook, both were granted to both API tokens, and even with the additional permissions I still get that error. For the test, I tried to add new repository connection in Dataform from project A, where we already have established SSH connection for another repository, but the result was the same. My question is: What should I whitelist more? I also did CURL of the hooks API, just to check the IPs behind, but it’s already whitelisted because it matches the given from BB range. Could it be a bug of BB related with the whitelist restriction? I created a test workspace, without that option and it works as expected. Thank you in advance! Best Regards, Elena 1 post - 1 participant Read full topic

Dynamic properties indexing I’m successfully using the https://developer.atlassian.com/platform/forge/apis-reference/dynamic-modules/ API, but I have a problem that my dynamic entity properties are not properly synced. The definition for a working and not working modules are Working { "key": "aggregator-43f05283-1ab4-400d-afe5-406d06e1e462-customfield_10001", "type": "jira:entityProperty", "data": { "propertyKey": "pwhierarchies.aggregator.43f05283-1ab4-400d-afe5-406d06e1e462.customfield_10001", "entityType": "issue", "key": "aggregator-43f05283-1ab4-400d-afe5-406d06e1e462-customfield_10001", "values": [ { "type": "string", "path": "customfield_10001.name", "searchAlias": "hierarchyEpicsAggregatedTeam" } ] } } Not working { "key": "customaggregator-43f05283-1ab4-400d-afe5-406d06e1e462-pwhierarchies-system-pending-team", "type": "jira:entityProperty", "data": { "propertyKey": "pwhierarchies.customaggregator.43f05283-1ab4-400d-afe5-406d06e1e462.pwhierarchies-system-pending-team", "entityType": "issue", "key": "customaggregator-43f05283-1ab4-400d-afe5-406d06e1e462-pwhierarchies-system-pending-team", "values": [ { "type": "string", "path": "result.name", "searchAlias": "hierarchyEpicsPendingTeam" } ] } } Properties And the properties of an issue (HT-11) are { "key": "pwhierarchies.aggregator.43f05283-1ab4-400d-afe5-406d06e1e462.customfield_10001", "value": { "customfield_10001": [ { "avatarUrl": "https://teams-directory-frontend.prod-east.frontend.public.atl-paas.net/assets/teams/avatars/v4/lime_no-highlight_3.svg", "isVerified": false, "name": "QA", "id": "0dd6a03a-e100-458a-b879-89f6f09c8848", "isVisible": true, "title": "QA", "isShared": true }, { "avatarUrl": "https://teams-directory-frontend.prod-east.frontend.public.atl-paas.net/assets/teams/avatars/v4/red_no-highlight_4.svg", "isVerified": false, "name": "Developers", "id": "0489f6ae-bbb9-4d29-b603-19274bf35e5b", "isVisible": true, "title": "Developers", "isShared": true }, { "avatarUrl": "https://teams-directory-frontend.prod-east.frontend...

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