PostHog

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Jira is making your ADHD worse, some tips from me and advice from you🤗 For a long time I genuinely thought I was just bad at Jira. Like, embarrassingly bad. Board full of tickets nobody touched in three weeks. In Progress column with 11 things in it. Same standup explanation four days running — "yeah still in progress, should be done today" (it was not done that day). Anyway. The board wasn't the problem. Everything happening before the board was. Work would show up through Slack, a DM, a comment someone dropped in a meeting and assumed I'd somehow retain. My brain would try to hold all of it. Then I'd open Jira to actually work and immediately remember six things I hadn't logged yet and now I'm not working, I'm just sitting there feeling vaguely terrible about working. Took me embarrassingly long to figure out the fix was just... don't let things live in my head. Get them into a ticket the second they arrive. Not "I'll log it later" — later is a lie I tell myself. I have no memory budget left by 11am. I set up guided forms for recurring stuff so logging something takes like 30 seconds and requires no decisions. Once a thing exists somewhere outside my skull, my brain actually releases it. Native forms lacks the customization and sharing options so I selected Smart Forms app. Also I shared the forms with teammates via links, shortcuts or Slack triggers, so every request from anyone now arrive like a jira ticket not any other way. The forms thing also accidentally solved another problem — every ticket that hits my board now has enough info to actually start. I used to pick something up, realize I needed three more things to proceed, put it back down, and then it would just follow me around as an unresolved thought for the rest of the day. WIP limit was the other one. Two things in In Progress, hard stop. I resisted this for probably six months because it felt like admitting defeat or something. But starting stuff releases dopamine and finishing stuff is just work, and without a structural limit I will start things until the end of time....

Loom evaluation - what is your take? Hello all I am a big fan of Confluence and really like the idea of us adding videos to our posts to help spread information. So, naturally, Loom came up, and I am currently evaluating the basic version. What is your take on Loom? Does it add value to your org? Good, bad? Clever use of its functions? Please share 😄 Thanks submitted by /u/PM_ME_YOUR_CLAUSES [link] [comments]

Help with PostHog setup When PostHog makes network calls (event capture, session recording, etc.), it constructs new Request(url, options) internally and calls fetch(request) . Our interceptor receives this Request object as the first argument ( input ). Inside the interceptor, we need to reconstruct the request with modified headers/URL. Doing new Request(modifiedUrl, originalRequest) transfers the body ReadableStream from the original Request , marking it as consumed ( bodyUsed: true ). submitted by /u/FomexSystems [link] [comments]

The best analytics tool stack for vibe-coded apps If you're vibe coding your way to a product, the temptation is to defer analytics until "later." Don't. There's no point in shipping fast if you don't learn fast, and you can't learn anything from metrics that aren't being tracked. You don't need a data team to get started; you only need three things: product analytics (what users do in your app), web analytics (where they came from), and – if you're shipping AI features, which you probably are – LLM observability (how your LLM is actually behaving in production). This guide walks through all three, what to look for in each, and the best tools to use. What's a "vibe-coded" app? Vibe coding is building software by describing what you want in plain language and letting AI write most of the code. The term was coined by Andrej Karpathy in early 2025. By 2026, 92% of US developers use AI coding tools daily and 41% of all new code is AI-generated . Vibe-coded apps tend to share a few traits: Built fast , often by one person or a tiny team, using tools like Cursor, Claude Code, Lovable, v0, Bolt.new, Replit, or Windsurf Stacked on opinionated infrastructure like Next.js + Vercel + Supabase for example Often AI-native , with at least one LLM call somewhere in the product Built by people who don't necessarily have engineering or data infrastructure backgrounds – 63% of vibe coding users are non-developers This guide is written with that profile in mind: you want analytics that's quick to set up, doesn't require a tracking plan, scales as you grow, and doesn't break your bank before you have revenue. Why do vibe-coded apps need analytics at all? Because vibe coding makes shipping cheap, but it doesn't make decisions for you . You can prompt your way to ten features in a weekend, but only data tells you which one users actually care about. As products get cheaper to build, the gap between "I have an MVP" and "I have a product people pay for" is more the product learning curve rather than coding speed. Analytics is also how you...

Pointed an AI at our solved IT-help Slack threads to write Confluence pages. 3 months in. Wanted to share an experiment that's been working better than I expected. 200 person org. JSM, Confluence KB, IT team of 2. Maybe 700 Confluence articles for IT and ops, accumulated over a few years. Usual problem: docs are bad because writing them is the boring last step of every ticket. So we tried something different. The team was never going to write better docs in any reliable way (the task is too easy to defer). We pointed an AI at our solved Slack support threads to draft pages from them. Whoever resolves an issue in our IT-help channel reacts with an emoji (we use ✅), the AI grabs the thread and drafts a Confluence article into a review queue. One of us reviews/edits/publishes (or kills) the drafts the next morning. 3 months in: 48 drafts, 30 published, 12 killed, 6 in queue. Search hit rate on tier-1 questions roughly doubled in our internal sampling. Doc-writing time across the team dropped to ~15 min/week of review. Drafts are decent when the thread was short and the resolution was clean. Long troubleshoot-3-things-at-once threads produce drafts nobody can use. We throw out anything where the AI couldnt tell who was the tech vs the user, or anything that confidently contradicts an existing Confluence page (resolving that is harder than just writing the page fresh). Anyone else tried this kind of setup? Curious whether Rovo can be wired from Confluence side, or if you need something Slack-native to catch the thread before context drifts. submitted by /u/ninjapapi [link] [comments]

Multiple Site/instances questions submitted by /u/PlasticSupport9822 [link] [comments]

Anyone actually solved automating HRMS-driven access approval in Jira working on something for a client and hitting a wall they want HRMS (org chart/manager hierarchy) connected to JSM so that when a user requests access, system finds their manager automatically, pings them for approval, and provisions on approval. native tools and basic IdP sync aren't cutting it without ugly custom scripts anyone doing this successfully? what's your stack submitted by /u/Trick-Succotash-6278 [link] [comments]

PostHog network interceptor is stripping CSRF headers and breaking routes Has anyone faced issues with PostHog’s network/fetch interceptor modifying request headers? After enabling PostHog, our POST requests started failing because CSRF headers were getting stripped/trimmed from outgoing requests. The same routes work fine when PostHog is disabled. submitted by /u/FomexSystems [link] [comments]

Api connection I’m feeling like an idiot because i cannot get VS code to connect to jira using api token. Does it have to be scoped? Is the ‘Atlassian: Jira, Rovo Dev, bitbucket ‘ the wrong extension? Its asking for email separate from token so i assume i don’t need to concatenate those? All I’m getting is a 401 & i don’t see whats wrong. I can use oauth & get in but that won’t do the tasks i want. submitted by /u/Ride4fun [link] [comments]

Is Trello Down In Gulf Region?? submitted by /u/Green-Illustrator986 [link] [comments]

Been building this for a while — finally put together a quick (super sped‑up) demo of OdinFlow, my Jira automation tool. Wanted to show a bunch of features without making a long video, so everything is fast‑paced. Create triggers → add actions → save → done. No scripts, no coding, just visual automation. If you work with Jira and want to automate repetitive stuff, I’d love feedback from anyone who tries it out. OdinFlow is live on the Atlassian Marketplace. Marketplace Link: OdinFlow | Atlassian Marketplace Website Link: https://deadvikingsoftware.com submitted by /u/Ok_Strength4594 [link] [comments]

The do's and don'ts of minimum viable product marketing PostHog is a much bigger company than when I joined in 2021 as the first product marketer – but while we've more than 10xed since then, the product marketing team has only 4xed. Yet we still somehow ship more launches per marketer per month than most companies do in a year. This is possible because our marketers, like our engineers, emphasize shipping over collaboration . I call this minimum viable product marketing , which is kind of a joke but also kind of not. Minimum viable product marketing isn't a clear set of rules. It's an anti-framework informed by years in teams that followed the advice of thought-leaders and that just ended up with a reputation as PowerPoint pushers. Here's how it works for product launches at PostHog. What we focus on There's no central process that defines how we think about products before we launch them. Instead, we're tactical: what are we actually going to do ? At PostHog, that means a launch checklist. We have a template – a recent example of it in action is the PostHog Code launch plan – but it's a starting point, not a destination. We change the plan for every launch because every launch is different. Still, there are things I feel strongly about us doing for every launch... Write an announcement. Your announcement may span multiple channels, like blogposts or social posts . Personally, I start with the email because it's the most intrusive to the user. That means it should be the shortest and that forces clarity. If the email is clear, the blog post will be. If it isn't, neither will be. This is the hard bit, so I've written more about how I personally approach this below. Spoiler: It's basically just creative writing. Write the title first. If you can't write the title, you don't know what you're announcing. The title is the only part of the post most readers will see, so it needs to be right. I find it helps not to spend time perfecting it: just get a vomit draft out quickly and go from there. Writing is rewriting, after all. ...

[ Removed by Reddit ] [ Removed by Reddit on account of violating the content policy . ] submitted by /u/aloo_man2 [link] [comments]

Are there plans to support simple on/off (boolean) feature flag experiments in PostHog? Hey everyone, I’ve been using PostHog for some time and really enjoy the product overall. One thing I keep running into though — experiments are currently built around multivariate testing, but sometimes I just want to run a simple A/B test where the feature flag is either on or off, without defining named variants. Basically: control group sees the flag as false, test group sees it as true. Clean and simple, and covers most our use cases, also simpler to implement. We had something similar in GrowthBook. Is this on the roadmap? Or is there already a way to do this that I’m missing? Would love to hear from the PostHog team or anyone who’s worked around this. Thanks! submitted by /u/ggevorg [link] [comments]

Jira based TCM tool recs? Hey guys - my organization is current evaluating jira-based test case management systems and we are deciding between xray and zephyr. Any preferences on one over the other? Also curious about how their AI features compared with one another and whether you found them helpful (sembiq vs bearq). Thanks in advance! submitted by /u/miffy_02 [link] [comments]

How to see the average time in status for Jira issues? submitted by /u/SaaSJet [link] [comments]

Multiple Atlassian services are experiencing issues Can we please chill on whatever we've been doing lately? Multiple down time events isn't okay given how much money companies pay you for service submitted by /u/Capital_Wave5937 [link] [comments]

Multiple Atlassian services are experiencing issues Can we please chill on whatever we've been doing lately? Multiple down time events isn't okay given how much money companies pay you for service submitted by /u/Capital_Wave5937 [link] [comments]

Unable to log in to Bitbucket / Atlassian account — production is down, need urgent help Hi Atlassian team/community, We are facing an urgent issue: we are unable to log in to our Atlassian/Bitbucket account, and because of this our production recovery/deployment is blocked. We cannot even access Bitbucket to raise or manage the issue properly. This is impacting production, so we need urgent guidance on how to contact Atlassian emergency support or get login access restored. Details: Product: Bitbucket Cloud Issue: Unable to log in to Atlassian/Bitbucket Impact: Production is down / deployment blocked Can someone please guide us on the fastest way to reach Atlassian support when we cannot log in? submitted by /u/TopContact6575 [link] [comments]

Is atlassian down? Not able to access jira cloud and confluence for the past few minutes? Is everyone facing this issue? submitted by /u/Prize-Comfortable767 [link] [comments]

Personal Access Tokens? Probably a super edge use case here but has anyone noticed that their Personal Access Tokens have been removed from the profile page (Data Centre) in the latest LTS release, specifically Jira v10.3.21? I’m also wondering whether this could possibly be related to the use of the Kantega app. I was operating just fine until now; where a delta between 10.3.19 and 10.3.21 appears to have removed the ability to create and use PATs. I can’t see anything in the release notes regarding this so casting it out to a wider audience to see if anyone else if having a similar issue. My only other thought is a similar historic issue with poor communication from Atlassian when deploying with Helm charts, they added an option to remove the ability to add apps from the Manage Apps page and didn’t communicate it. Wouldn’t surprise me if this is a similar thing. A feature change that wasn’t communicated properly. submitted by /u/samuel-stephens [link] [comments]

Can you automatically link Jira tasks created through confluence? I want to set up a workflow where a Task in Jira are made through topics in Confluence. For example, I want to make a page with a list of bugs in Confluence. Each bug is made into a Jira work item (by selecting the text -> 'Jira work item'), so we have a list of all the bugs with explanation, and developers working in Jira can easily add stuff there, and go back to from Jira work items for info. When I made a task that way, it didn't look like it was linked back to the Confluence line it was made from, and vice versa the line didn't have a link to the jira work item. Is there way to make this happen? We're on the free plan, and I want to make this as quick and easy for my team as possible. submitted by /u/R_Pelleboer [link] [comments]

Rovo deflection at 4 months: 18% on a 1200-person org. Expected 35%. What are we missing? Rolled out Rovo across our 1200-person org for IT helpdesk in January. The deck pitched 35-40% tier-1 deflection by month 4. We are at 18. What we tried: rebuilt the Confluence KB with the Rovo template (helped marginally). Added agents for the top 6 ticket categories (categorization got better, deflection didn't move). Narrowed scope per agent and tuned search relevance. Opened a partner ticket, got referred to professional services which felt like a concession. Stuff i think we got wrong: KB is written like documentation. real tickets are phrased like i cant get into the thing anything multi-step (group adds, conditional access) opens a ticket anyway slack handoff to a human is rough enough that users DM IT directly a chunk of what shows as deflection was stuff our scripted flows already handled Anyone here actually hitting 30+ in a similar sized org? Curious what your config looks like, what categories you gave up on, and whether the slack experience is working or your users route around it. Would rather know we're running it wrong than write a renewal rec to leadership. submitted by /u/Major-Language8609 [link] [comments]

JSM alternatives, given Atlassian's stance for AI training? JSM has already proven to be a severe PITA for client management. You can't really control the client experience, etc... and now this requirement forcing orgs to either upgrade to Enterprise (not currently cost-aligned), or put us in violation of client MSAs and our own internal policies around AI training, the juice is quickly loosing its worth relative to the squeeze. Thoughts appreciated! submitted by /u/Routine-Kangaroo2 [link] [comments]

Top Not-Right-Wingers doom over migrants flooding Europe and white people becoming replaced submitted by /u/EffectiveRock3795 [link] [comments]

Help With My Master’s Thesis (5 min) I’m finishing my Master’s thesis and running a survey about online brand communities. If you’ve ever used Atlassian or Salesforce communities, I’d really appreciate your input: 🔗 https://forms.gle/BXuwfnQyNXNrcjoMA Thank you! submitted by /u/CupWhole4138 [link] [comments]

Free Gantt for Jira on the Marketplace — actually free up to 10 users, no time limit The Atlassian Marketplace has 7 paid Gantt plugins. None of them have a free tier above 10 users. Most start at $1.50/user/month from user 1, billed monthly to your Atlassian invoice. For a 5-person team that's $90/month. For a 20-person team, $360. For a planning chart. Most teams either pay it or live without and keep guessing. There's a third option as of last week. What you'll see when you open it The whole project on one screen: - Spreadsheet on the left (key, summary, assignee, dates), Gantt on the right - Drag a bar to reschedule — the change writes back to Jira instantly - The bars on the critical path are red. The one causing your slip is the one with everything red downstream of it - Saved a baseline two weeks ago? Ghost bars overlay the chart so you can see the drift, task by task - Drag from the end of one bar to the start of another → dependency arrow, auto-detects FS / SS / FF / SF - One click → "Standup mode": filters to active sprint, hides done, flat view, day zoom, sorted by assignee. Run your standup off it. It runs entirely inside Atlassian Forge. No external server, no data leaving your tenant, no credit card to install, no trial expiry. Pricing - Free for everyone right now (paid launch is Aug 1, 2026) - Free FOREVER for teams up to 10 users — not a trial, the actual free tier - $1.20/user/month for 11–100 users (about a third of BigGantt) - Scales down to $0.04/user at enterprise headcount Try it without installing Live screenshots from a real Jira project (no install, no login): [link in first comment] 2-minute screen recording of the auto-scheduling and critical path in action: [link in first comment] If it looks right, the Marketplace install is 30 seconds. Forge deletes everything if you uninstall — there's nothing to clean up. Honest stuff - It just shipped. Zero reviews on the Marketplace yet. Be the first to leave an honest one (good or bad) - 3.15.1 has the resource-leveling UI in place but the backend isn't wired yet — that lands next...

[ Removed by Reddit ] [ Removed by Reddit on account of violating the content policy . ] submitted by /u/loopiq_28 [link] [comments]

Posthog not catching some users that Google Analytics is Anyone with experience using both GA & Posthog come across an issue where GA shows some people, but Posthog misses them? I do have GA initialized from both the cloudflare head and react app, so I’m aware some double counting happens. But I often see users show up in only one source which is probably the head. Any idea why the analytics within the app sometimes don’t fire? Also thinking of adding Posthog to my head as well, but don’t want to double count the data. Would it be possible to automatically identify and merge both the head and inside react instances? submitted by /u/G952 [link] [comments]

But is it any good? The front end site for posthog is AWFUL and it raises a serious question about whether the underlying product is actually any good or whether it's equally cluttered and confusing. Concerned because I'm looking for something to give me clear insights. Gut check says this just isn't it. Push through the UX because it's awesome underneath or go somewhere else because it's more of the same at the product level? submitted by /u/Reythia [link] [comments]

Bugs and issues with Confluence Database Hey there, I'm trying to familiarize myself with Confluence Cloud and I have been making a quick and dirty database. However, I'm running into a series of bugs that are annoying and slow my work down significantly. When clicking on a cell and typing, the 1st letter is always ignored. Typing in a cell that isn't fully activated seems to activate it with the 1st key press, THEN it starts to intake data. Is this intended behaviour? Why? More critical than #1 is that about 20-30% of the time, when typing in text fields, after tabbing away from the active cell, the text will disappear and be lost. I have yet to find a real pattern to this but I am still learning. Sometimes, when moving around the database and editing text in a few places, the blue "Editing" box on the cells get stuck on. Upon reloading or exiting the database and entering again, the database has moved and overwritten cells seemingly at random. I am running on the latest Chrome version and have no idea why these are happening. They are causing me pretty significant setbacks during my work. Does anyone have insight? Thanks! submitted by /u/Fiishman [link] [comments]

Can posthog clearly tell me from where did site visitors came from? I just hate Google Analytics. Unbelievable that for the past two decades I still can't easily understand from where the current visitors of my website are coming from. submitted by /u/Wave-in-Kanagawa [link] [comments]

The best MCP servers for developers at startups There are tens of thousands of MCP servers in the wild as of writing this article – Glama lists over 22,000, PulseMCP tracks 14,000+, and mcp.so and other registries add thousands more. Plugins and connectors have let you use different tools inside an editor for years. The leap with MCP is composability : an agent can pull a Linear issue, find the related PR in GitHub, check the deploy in Vercel, and verify the fix in PostHog in a single prompt . A handful of well-chosen MCP servers, composed thoughtfully, will get you further than a stack full of them. Below are the workflows we see startup teams run most often, and the servers that compose well together for each. Best MCP servers, by workflow Debugging in production Something's wrong. Maybe you're seeing error spikes or a slow endpoint; maybe your CS team is getting flooded with complaints about a buggy feature. You want to find the cause without leaving your editor. The loop: pull error traces → check recent deploys → query logs and analytics → reproduce locally → fix. The servers: PostHog MCP pulls error tracking, session replays of affected users, recent feature flag changes, and anomalies in product analytics, all from one connector. Replaces 4-5 separate dashboards. A deployment platform MCP ( Vercel , Render , or Cloudflare ) lets the agent check what shipped recently and read live logs. GitHub MCP jumps from a stack trace to the code that caused it, finds the PR that introduced the bug, and opens a fix. Filesystem MCP lets the agent edit your local code directly when you're ready to ship. A real prompt example for this loop: "Using PostHog and the Vercel MCP, pull the top error traces for /checkout from yesterday afternoon, find the deploy that went out around 3pm, and use GitHub to show me the diff." Shipping code You're writing code and want the agent to help you scaffold it, open the PR, watch the deploy, and confirm nothing broke. The loop: write code → open PR → check deploy status → verify it didn't b...

JSM vs CSM Has anyone switched from JSM to CSM, or successfully been working with JSM/CSM/Jira? Would appreciate hearing wins/losses to adopt. For example, we wouldn't sacrifice capabilities with SLAs, automation capabilities, and connectivity with Jira Software and dashboards. I am also concerned that adding CSM would be like adding JPD to our portfolio- a "team-managed"-esque way of managing spaces, aka lost oversight and centralization of things like custom fields leading to a lot of potential for junk and more complex searching. submitted by /u/JiraKarma [link] [comments]

The final day of team 26. Ooh Hello Alexis Ohanian. ( co founder of Reddit) How was your day 2. Share your thoughts now. submitted by /u/AmbitiousYudi1991 [link] [comments]

Rovo for JSM at scale, where does the API surface actually break down? Atlassian admin at a 1400 person org, 4 years on JSM, just past 6 months on Rovo. Most of the basic Rovo features work fine but were hitting walls when we try to extend it to anything custom past their out-of-box workflows. Specifically. The Rovo API for custom AI agents is REST-only with no streaming, so anything needing intermediate updates during multi-step workflows falls back to polling. Rate limits on the Rovo APIs hit way before our JSM API rate limits, especially on read-heavy operations. The webhook infrastructure for Rovo events is documented but the retry behavior isnt explicit, which makes reliable integrations frustrating to build. Has anyone actually built deep custom workflows on Rovo and gotten past these walls? Or is this just where Rovo currently sits and you have to architect around the limits? Genuinely asking because we may need to make a build vs replace decision in the next quarter. submitted by /u/akuchil420 [link] [comments]

Think you can find 4 hidden groups of 4 related words? Puzzle by u/Professional-Shoe-86? submitted by /u/EffectiveRock3795 [link] [comments]

Experiences with Atlassian Cloud - how high is the ROI? Dear Sir or Madam, We are currently in the planning phase for our move to the cloud. Within this organisation, we use the Jira and Confluence applications. We would also like to look into the cloud solution. I would like to draw your attention to the key question, which is directed at those who have already migrated. I would appreciate your feedback on your experiences with the migration. In your view, which core functionalities are crucial for using the cloud? In this context, we should ask ourselves what business impact the use of the cloud has compared to the on-premises version. We should also look at the key features that make a difference to you. We have also looked at Rovo, but at the moment I am still lacking detailed use cases that really bothered me about the DC. That is why it would be important for me that the cloud version makes a positive impression on me. I am looking forward for your responses. submitted by /u/Practical_Credit2420 [link] [comments]

4,063 errors closed without a human opening PostHog – here's what we learned Last month, our customers deployed AI agents to PostHog projects to try and solve 6,124 errors in their products. They resolved 4,063 issues. They suppressed 1,751 more. They routed 310 to the right team. Almost none of those agents opened the PostHog UI to do it. The numbers track with our bet on self-driving products – the more agents understand your codebase and your data, the more of the routine work they take off your plate, and triage is one of the first places that shift becomes visible. We were obviously interested to find out what types of problems users were addressing with the MCP. Here's what we found. Agents do three different jobs in your error backlog We grouped 30 days of MCP-driven error triage actions, and they sorted cleanly into three buckets: Resolve a.k.a. "We fixed it." This is the headline number. Over 4,000 issues were marked as done by an agent after either generating a fix, confirming a fix had shipped, or tracing the root cause and concluding the issue had already self-resolved. Suppress a.k.a. "Stop showing me this." About 1,750 issues marked as known noise – third-party SDK warnings, expected exceptions, the canonical browser garbage you've seen a thousand times ( Script error. , ResizeObserver loop completed... , Object Not Found Matching Id ). Agents are quietly maintaining the noise filter you keep meaning to maintain yourself. Route a.k.a. "Wrong owner." Over 300 issues reassigned to a different person or team – sometimes alongside a freshly-created Linear ticket so the new owner has a place to track the work. Three different outcomes, three different reasons users invoked the tool. Our initial perception of the agent doing only triage was too coarse. What actually happened is more like "the agent does whatever needs to be done and then marks the issue accordingly." What we learned A few things stood out from watching agent-driven triage in the wild. Smaller, sharper tools win When we first shipped MCP support for error tracking, er...

The stuff nobody tells you about startup marketing Every few weeks, a founder emails me some version of the same thing: “We’re getting early traction. How do we start doing marketing?” Most advice is written for people at companies that already know what they’re doing. You don’t. That’s fine. I didn’t either when I joined PostHog in 2020, and somehow I’m still here! This is everything about marketing that I’d tell the Charles who doesn’t know anything in 2020. 1. You do have experience People who contact me think marketing is a thing they haven’t started yet. I used to think the same, but it turns out a bunch of things we were already doing at PostHog was marketing. We just didn’t think of it like that. Our launch post on Hacker News? Marketing. An unusually deep employee handbook on a weird-looking website? Also marketing. Posting what is basically your diary to your company website? Marketingggggg! Marketing is simply closing the gap between what you’re building and the people who’d care about it. Our handbook is a good example: James and Tim knew early users might doubt there was a real company behind PostHog, so they published all internal processes and values on the main website to build credibility fast. So no, you don’t need a fancy strategy, brand guidelines, a content calendar, or a paid budget. Just figure out what your audience finds interesting and talk about it. Plus, when you treat marketing this way, your brand will be that much more authentic. 2. Depth-first > breadth-first So you have something working. Yay. The instinct at this point is to write a list of 5-10 other channels to try in the hope that one of them also works. This is a great way to end up, six months later, with a pile of stuff that didn’t work and no idea why. It’s much better to do more of the thing that’s already showing signs of life. If you went mildly viral on LinkedIn, keep posting every day. If a podcast appearance converted, email another hundred. If a blog post got picked up, write the next one. And the next one. That’s what ...

HELP! ATLASSIAN DELETED OUR WHOLE SITE We've been working on a project for more than 2 years. We took a little break to focus on some other stuff and didn't do any updates on it (like 5/6 months). Now we can't access our site anymore and it says it has been deleted. There was more than 15.000 hours of code in there.. We lost everything. They just deleted our site without a warning? Anything we can do to get it back?? submitted by /u/No-Support-5219 [link] [comments]

Wired Claude into PostHog to do an end-to-end audit + build cohorts Spent yesterday connecting Claude to our PostHog instance. Workflow: Fed it our PRD It audited the existing setup Planned cohorts + dashboards Built the analytical layer it could build (queries, cohort definitions, dashboard tiles) Wrote an engineering handoff doc for the parts only the eng team can do (tracking plan changes, instrumentation gaps) Reported the whole thing as a gap report PRD coverage went 15% → 80% in one session Outputs after one session: 42 cohorts 8 dashboards 30 workflows queued The thing I didn't expect: half the events I "needed to add" were already firing — just nested inside other event payloads. Nobody on the team had connected them. Claude did. Anyone else doing this? Curious what your MCP / API setup looks like. submitted by /u/stephaneboghossian [link] [comments]

How was Your Day 1 At Team 26? Share you thoughts. submitted by /u/AmbitiousYudi1991 [link] [comments]

PostHog POC at work: A bit overwhelmed As the title suggests, I was tasked by my manager to do a POC ( proof-of-concept ) on collecting analytics for our web app. I was able to set PostHog up for our project but working with it has been a bit overwhelming. For context, our app is a NextJS based web app. We expect around 2.5k to 3k MAU. The AI setup wizard did tag a few elements for capture but I want to understand how do I decide which elements to tag to get useful data and customise the dashboard for said useful data. These have been the overwhelming parts. I understand it's project specific but how do I go about it. Any guides I could follow. P.S: I am new to this team and really want to make a good impression so any help would be really appreciated /\ . Plus it would give me confidence boost that I can do these kinds of projects. submitted by /u/the_consoler [link] [comments]

Advice Request: HR Screening Prep for Sr. Solution Consultant, Cloud Hello, I have an HR screening for the above mentioned role in a couple days. I was hoping others that have gone through the process and are currently at Atlassian might be able to share a few tips. Most everything I've been able to find is related to coding interviews, so would appreciate any advice. Thank you in advance! submitted by /u/edenri [link] [comments]

Disabling provisioning notifications Hey all, We're doing a Atlassian implementation and we're running into challenge when provisioning JSM customer licensing to our users. When we provision a user they receive a notification that they've been invited to join JSM by the administrator who triggered the provisioning. 'John Smith invited you to join them in Jira Service Management' This hasn't been an issue thus far, as we've been slowing onboarding agents across a couple different units where folks were aware of what was happening. With our customer provisioning/portal access however it gets a bit messy. Our customer group is a little over 100k users, and well, sending out that invite notification to that many users is no bueno. My understanding is that there is no ability to suppress this notification directly from the Atlassian side based on: https://jira.atlassian.com/browse/JSDCLOUD-15729 Has anyone else run into this? A potential workaround? We're going to run some tests today with different processes, like provisioning an empty entra group and then associating the users to the entra group after the fact. Many thanks. submitted by /u/seaefjaye [link] [comments]

Years of experience? For Google SWE III (L4) and Microsoft SDE II roles, job descriptions usually say “2+ years of experience.” I currently have 2.4 years of experience , and I’m trying to understand how this is interpreted in practice. What’s the actual experience range most candidates get hired at? Does “2+ years” realistically mean closer to 3+ years ? Has anyone here gotten interviews/offers with ~2–2.5 years , or is that rare? submitted by /u/Expensive_Regular203 [link] [comments]

PostHog Code and the self-driving product Today, PostHog Code enters beta. It's a desktop app that runs coding agents on top of your product data. The obvious stuff ships itself. The tricky stuff shows up as a prioritized to-do list for you to steer. We built it to chase one idea: self-driving product development. How we're defining self-driving A self-driving product can prompt itself. It understands your codebase, your data, and your users. It proposes and ships work on its own, inside of guardrails you set. The self in self-driving isn't autonomy from the engineer. It's autonomy from user instruction as the starting point. A self-driving product puts put the 1% gains on cruise control: the bugs, UX issues, paper cuts, and conversion tweaks. The things that drain engineering hours, but rarely need strategic input. This is where product data does the heavy lifting. In a typical week, PostHog customers generate 100,000+ failed queries and ~1.5 million new error tracking issues. Each one is a signal an agent could act on. Acting on signals takes more than writing code. Claude Code, Codex, and others already do that part well. To make them self-driving, we add 5 things on top: tools, skills, signals, memory, and evaluation . Our AI engineering handbook goes deep on each. Here's the short version. Anatomy of a self-driving product 1. Tools – what the agent can do Tools are small, specific actions the agent can take. At PostHog we treat them as atomic capabilities – things like create_insight or read_taxonomy . The latter does a lot of heavy lifting. It lets the agent check what events and properties actually exist before it writes a query or an instrumentation PR. 2. Skills – how to get the job done If tools are the fork and knife, skills are the recipe. A skill ties tools, docs, and rules into a playbook. You can see the gap in our own data: docs-search is the most-called tool on PostHog's MCP server, at ~28K calls a month. PostHog Code has skills for the workflows we see most – instrumenting events, auditing...

Loom Mobile Force Sign Up Hi everyone, I think this is a legal issue and wanting more people to report this. Loom by Atlassian is forcing users to sign up for a free account on mobile. My take is, at least 90% of people are accessing Loom on their phones. I'm selling digital products and use Loom to give a how to guide/overview. I could NOT BELIEVE Loom was asking users to create an account despite desktop not needing it. This is a total tactic and honestly, gross. Loom playing this off as if it's a feature they cannot do just yet?? Please vote and report this: https://jira.atlassian.com/browse/LOOM-121 Does anyone have other recommendations for accounts? Thank you! submitted by /u/Pure_Banana_8009 [link] [comments]

I created mkdocs2confluence — a CLI tool that compiles MkDocs to native Confluence storage format [looking for feedback] Why I'm a docs-as-code advocate — if it's not in git, it doesn't exist. Confluence is great for visibility, but the native Confluence workflow doesn't really work for me, as a software architect and developer. If you maintain docs in MkDocs and also need them in Confluence, as it's the company developer and documentation portal. You know the pain: copy-paste, reformatting, drift between the two. I got tired of it and built mkdocs2confluence. There already exist numerous markdown to Confluence converters, and I have tried most of them, but none of them really suited me and the goal of having as much of the MkDocs markdown compiled into native Confluence content as possible while maintaining a native look and feel, is the challenge. What it does It's a compiler/transpiler, not an HTML converter. Every Markdown construct is mapped to the equivalent native Confluence macro or element — admonitions become info/tip/warning macros, Mermaid diagrams are rendered as PNGs via Kroki, internal links become native Confluence page links, and YAML front matter becomes a queryable Page Properties macro. Pages look and behave like hand-authored Confluence content, not pasted HTML. Install pip install mkdocs2confluence Try it with preview mode # Preview what a page compiles to (no Confluence account needed) mk2conf preview --config mkdocs.yml --page index.md --html --out /tmp/preview.html Publish for real # Publish everything in your nav: export CONFLUENCE_API_TOKEN=your_token mk2conf publish --config mkdocs.yml Current maturity and help needed I use it daily on my own MkDocs site. It's not battle-tested across every MkDocs setup — that's exactly why I'm posting. MkDocs Markdown comes in a lot of flavors and I'll never catch every edge case on my own. If you try it and something breaks, I would really appreciate your feedback.. - 📦 PyPI: https://pypi.org/project/mkdocs2confluence/ - 🐙 GitHub: https://github.com/jeckyl2010/mkdocs2confluence Note: The code base is written by AI, but I ...

All Set For Teams 26 submitted by /u/AmbitiousYudi1991 [link] [comments]

RAG over Confluence in JSM workflows. what i learned trying to make stale docs useful My boss told me to make our Confluence content useful for IT request answering. Spent like 6 weeks trying to do it and it failed. Tbh the premise was reasonable: we have a couple thousand IT runbook articles in Confluence, the team writes them well, surely we can layer something AI on top and have it answer the dumb questions for us. What I learned that I think is worth sharing: First the obvious. Confluence content is f***ing messier than you think! Out-of-date pages, duplicates, half-finished drafts that nobody archived, articles written for one team that get found by another, etc. Pulling it through any AI layer requires either a serious clean-up first or some kind of dating/owner metadata so the AI can prefer fresh content. We did the cleanup, took a month. 2- a lot of the actual employee questions arent in Confluence at all. A) because my company is cheap and hasn't given everyone a Jira/Confluence seat yet, B) people just don't use it. Theyre in the threads. Slack threads, email threads, jira ticket comments. The stuff that actually solved a problem at 4am six weeks ago is in someones DM with a senior engineer, not in a wiki. So a Confluence-only RAG layer hits the easy questions and misses the gnarly ones. If you are about to do this, do the Confluence audit first. Your future self will thank you! :) (btw, for us specifically, my boss ultimately agreed that my salary is more valuable than trying to automate something that there are pretty good tools out there for already. There are at least like 3-4 good ones. We chose Risotto because my boss liked how snazzy it is. IT did bridge the gap between slack threads and Confluence content together. It doesnt fix is the underlying problem which is Confluence messiness. but its another layer over a problem the org still needs to deal with eventually.) submitted by /u/Tasty-Win219 [link] [comments]

Major outage right now Major outage across most of their apps. Possible cyberattack? submitted by /u/Agitated-Crab-3445 [link] [comments]

Why attacking your competitors online is dumb Let's say a competitor copies your feature. They lift your positioning, write a misleading comparison post, or just take a cheap shot at you on X. And let's say you're 'right' – they actually did the thing, and it's actually annoying. Still don't do it. This isn't a moral argument about keeping it classy. I just think public competitor attacks never work, and there are much better places to put the energy. You're only talking to your own fans When you attack a competitor publicly, you reach two audiences: your fans, who already agree with you, and their fans, who will immediately get defensive. Your fans cheer. Their fans roll their eyes. The people actually evaluating their options – the ones who matter – are unlikely to switch to you because of a zinger of a tweet. You're also, the moment you go on the offensive, opening yourself up to scrutiny. We're not perfect and we've made loads of mistakes! Whatever you're accusing a competitor of, like copying something, overstating a claim, or making a bad call, there's a good chance you've done some version of it too, and someone will find it. If you're open source, this gets even more awkward. PostHog is built on the work of many others, and we think others should be able to build on ours too. Attacking a competitor for being "inspired" by us kinda misses the point of open source. You're doing their marketing, with your audience PostHog has more users than most of our direct competitors. When we publicly name a competitor, we're lending them our audience – introducing thousands of developers to a product and telling them it's worth being upset about. So you fire off the tweet, they get more traction than they would have otherwise, their team screenshots it for the all-hands, and you've spent your energy on this instead of on your product. Most people didn't see the thing you're upset about If you're in a competitive market, you're probably monitoring it closely. Social listening, Google Alerts, the Slack channel where so...

Free Chrome extension that tracks Bitbucket Cloud PRs in the background — no tab required Builder here, being upfront. Free, no backend, no sign-up. Most of you are probably here for Jira/Confluence, but if your team also lives in Bitbucket Cloud, this might save you some time. Bitbucket has noticeably less third-party tooling than GitHub or GitLab, and the things devs ask for ("did my pipeline pass yet?", "what needs my review?", "any unresolved comments?") usually mean keeping a tab open and refreshing. So I built PR Radar — a Chrome extension that polls the Bitbucket API in the background and surfaces: - Pipeline status per PR (filtered by source branch, like the UI does) - Inline comment counts - Reviewer participation ("hasReviewed" from participants state) - Diff stats from the paginated diffstat API - Desktop notification + sound the moment a pipeline passes/fails — no tab needed - Urgency triage chips (review requested, changes requested, stale, etc.) - Merge from the popup with confirmation Honest caveats: Bitbucket's API doesn't expose draft state or merge conflicts, so the extension can't show those. Everything else works as well as the API allows. Uses the new /2.0/user/workspaces endpoint (the deprecated /workspaces is sunset). Auth uses an Atlassian API token (Basic auth, email:token) with read scopes for user/workspace/repository/pullrequest and write for pullrequest. Token stays in chrome.storage.local on your device — never leaves your browser, no telemetry. Also supports GitHub and GitLab if your team spans platforms. Genuine feedback welcome, especially from anyone hitting weird edge cases on Bitbucket's API. Link: https://chromewebstore.google.com/detail/pr-radar-%E2%80%93-github-gitlab/hkombgibegjffiadmekpiabdakkoidmh submitted by /u/Better_Ad6110 [link] [comments]

My Atlassian free site was deactivated in error I was told by an Atlassian support member that free sites are deleted due to inactivity after 40 days. I know that I made an edit on one of my pages 23 days ago. Yet, my Confluence site is gone. Being a free member, I no longer have the option on the Contact Us page to open a ticket. I can only "Ask the Community", which will be no help to me. Anyone know how I can contact them? This site was my backup. My laptop crapped out and I was going to the site to gather the data I already lost. submitted by /u/sporwancher [link] [comments]

We thought our Jira cycle time was stable… until we looked at it week by week For a long time we tracked just one number in Jira — average cycle time. It looked… fine. Pretty stable. But at the same time: some issues started taking way longer than expected delivery felt less predictable planning got harder sprint after sprint At first we thought it's just random noise. Then we tried looking at the same metric as a weekly trend instead of a single value — and it completely changed the picture. What we saw: a slow upward drift over several weeks (completely invisible in the average) occasional spikes that explained “bad sprints” periods where flow was stable vs clearly unstable https://preview.redd.it/ppnfp9qqmiyg1.png?width=1983&format=png&auto=webp&s=d3c426d9f33211b1c6d73c0e4aaa1f3255959d49 Basically, nothing “broke” suddenly — it was drifting the whole time. We ended up building a small dashboard gadget for this because we couldn’t find a simple way to see it in Jira natively. Not sure if we just missed something though. Curious how others handle this: 👉 Do you track cycle time (or similar metrics) as a trend over time? 👉 Or do you mostly rely on single snapshot values? If anyone’s doing something similar in Jira, would love to hear how you set it up. Happy to share what we built too. submitted by /u/KneeStriking3866 [link] [comments]

ChatGPT + Product Discovery submitted by /u/vuckele [link] [comments]

PostHog's next chapter Building PostHog has been living the dream. We got into YC. We pivoted a bunch. We found product-market fit, onboarded hundreds of thousands of customers, and revenue grew fast. Best of all, it's been fun. We found a way of working we really like (by design, I may add!), and we've grown by listening to users, and doing the right thing by them. They talked about it and more users showed up. It was predictable. Then AI happened. Slowly at first, then all of a sudden, and nothing felt predictable anymore. The turning point for me came last summer. I spent six weeks travelling in California, Hawaii, and Canada, spending lots of time with my family and only a little time working. One evening, having just put the kids to sleep, I started reading The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future by Keach Hagey. In it, Hagey describes why Sutskever decided to co-found OpenAI. He believed the brain was a physical system that could, in principle, be recreated. It's just a thing, right? We can engineer a thing! This got me thinking about the gap between the capabilities of an LLM and the human brain, and how AI might shape what we build next. The obvious limitations of LLMs I'm hardly breaking new ground with these observations, but they bear repeating because they matter: LLMs have no innate goal setting. They mostly predict what text to spit out in response to a prompt. Skills are starting to change this, but it's nascent and primitive. An LLM without a prompt has no purpose. AI is spiky. Models are incredibly powerful in many ways, but my 2-year-old is still better able to interact with the real world than any robot. He is rounded. He can do the basics of everything, whereas LLMs completely lack certain capabilities. LLMs don't self-improve. You can fine tune them for specific use cases, but there's no organic improvement. Some disagree and argue ever expanding context windows offset this, but the model weights themselves are fixed unless you proactively d...

The 5 best real-time analytics platforms for developers, compared "How many people are on my site right now? What are they up to?" A real-time analytics platform lets you watch your product as it's being used – users currently online, pages they're on, the event that just fired three seconds ago, the referrer that's suddenly spiking... Nothing beats a live dashboard when you're launching on Hacker News or running a flash sale. This guide compares the best real-time analytics tools for developers, what each one does well, and how to pick the right one for you. What features do you need in a real-time analytics platform? At a minimum, a real-time analytics platform should include: A live dashboard of active users, pageviews, top pages, and referrers A real-time event stream or activity feed Geographic and device breakdown of current visitors Auto-refreshing data (every few seconds, ideally) The best real-time analytics tools go further with: Live event data that's also queryable later – so "what happened during the launch spike" is still answerable next week Session replay integration – click a live user and watch what they're actually doing Identified users, not just anonymous pageviews – see which specific customer is on your pricing page right now Alerts on live metrics – get pinged when traffic spikes, signups stall, or errors jump Shareable or embeddable live views – TV-mode dashboards for war rooms or office displays What's the best real-time analytics platform for developers? 1. PostHog PostHog Web Analytics gives you a live dashboard showing who's on your site right now, what pages they're visiting, where they're coming from, and – because users are identified – which specific customers are currently active. The real-time dashboard is currently in alpha. What makes PostHog different from a standalone analytics tool is that it's a multi-tool developer platform . The same events powering your live view are also available across product analytics , session replay , feature flags , experiments , error tracking , LLM observabilit...