honeycomb.io

Honeycomb for Log Analytics lets engineers onboard raw data and start debugging immediately, bypassing the slow, traditional “index first, then search” model. With powerful out-of-the-box query and outlier analysis capabilities, your engineers can quickly analyze logs, identify issues, and resolve them in seconds. See how it works: https://lnkd.in/geQkrEPM

By monitoring real-time logs, teams can optimize their systems, leading to smoother user experiences and reduced downtime. Turning raw logs into actionable insights requires the right tools, practices, and insights. Check out our guide on log monitoring key concepts and best practices for improving your observability. https://lnkd.in/g3nZvy5B

Duolingo and OneFootball, like many others, were burdened by excessive observability costs and limited metrics capabilities. Join us on Feb. 25th for a webinar to learn how they leveraged wide events for better results, and the steps they took to maximize insights while significantly reducing their observability costs. Register today! https://lnkd.in/gG6n7mXs

Let’s stop obsessing over 10x engineers, and let’s start obsessing over 10x teams 📈 Today I am honored to publish on Refactoring a new article by Charity Majors, CTO of honeycomb.io and one of my favorite authors. Charity wrote an incredible piece about a topic that is close to my heart: we need to challenge our industry's fixation on individual brilliance, and focus on building systems that make “normal” engineers do amazing work. Here are some takeaways from the article: ↳ 🎯 𝗧𝗲𝗮𝗺𝘀 𝗼𝘄𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲, 𝗻𝗼𝘁 𝗶𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹𝘀 — the success of software depends on collective ownership and delivery, not individual velocity. ↳ 💪 𝗚𝗿𝗲𝗮𝘁 𝗼𝗿𝗴𝘀 𝗲𝗺𝗽𝗼𝘄𝗲𝗿 𝗻𝗼𝗿𝗺𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 — truly exceptional organizations are those where regular engineers can consistently deliver great work. ↳ 🛠️ 𝗕𝘂𝗶𝗹𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗳𝗼𝗿 𝗵𝘂𝗺𝗮𝗻𝘀 — create processes that account for normal human limitations and cognitive biases, not superhuman abilities. ↳ 🌱 𝗚𝗿𝗼𝘄𝘁𝗵 𝗶𝘀 𝘁𝗵𝗲 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲 — foster an inclusive culture where everyone learns, teaches, and pushes their boundaries. ↳ 🎨 𝗛𝗶𝗿𝗲 𝗳𝗼𝗿 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 — build diverse teams based on unique capabilities rather than chasing "top talent" credentials. Link to the full article in the comments (*sigh* Linkedin algorithm)

By enhancing frontend monitoring with observability, developers and IT teams can catch and resolve problems proactively before they escalate. In our latest blog, we share the fundamentals of frontend monitoring, including what you need to know about performance measurement and strategies for staying ahead of monitoring challenges to deliver high-performing, user-centric applications. Learn more: https://lnkd.in/ghb-Nh7E

Tim O'Reilly has a post out there about the huge shift AI is making with our relationship to software engineering. Scary title. But the content is less so. Why? How many of you started with technologies you still use exactly the same way today? Even if you're a master Lisp/Clojure developer, you still have evolved, right (someday I've got to get my head around pure FP more). The post starts with the familiar progression of tech, so I'll spare you my C64 -> [scene missing] -> AI code assistance fable. We're all there with various starting lines. What encourages me from Tim's post is that he sees not a legion of talented programmers out of work because of AI, but a legion of talented programmers enabled to focus more deeply on solving business problems. And AI is going to cause a lot of new tools to be written and people to help understand what is being generated. Of course, it's very helpful if you're a React expert and you ask Cursor to create a nice Next.js application and use your favorite design engine. Tim can see the future where someone more deeply involved with the business could also participate, building starter projects / concepts, even actual running software, and then partner with a more experience engineer with both technical depth and stronger AI experience/tools who could shape it further. I know the big fear here is loss of control. The fear is real. I don't think about my video drivers anymore (well, mainly because I don't have a Surface Book 2 running Linux for example, a wonderfully bad disaster story), and in the same way, it could be possible in a few years to treat generated code a little more like that. Even right now, you can get things moving. "We need to split this application's features into two sections and secure each one to a different role".[clickity clackity, feedback loop for a few hours / days as we iterate on the ideas with the AI tool/chat/agent, and show progress to our stakeholders along the way], done. You have to be ok with small experiments, committing incrementally, being OK with rolling them back. Testing ideas out quickly (feature flags, anyone? OpenTelemetry instrumentation to prove / challenge assumptions?). Honeycomb, for example, fits quite nicely in here. But if we think that kind of work will be done automatically, well, by a completely closed loop of "I tell the AI to do it, and I get a finished product" - we're crazy. It's all about feedback loops. The human must be in there. I assume the massive mistake of "let's lay off 90% of our programmers" is about to run into the "oops, we don't have enough people to get things done anymore!" What do you think? https://lnkd.in/eANhPvax

With observability 2.0, you can derive any metric across any (number of) dimensions. That, by itself, is profound—but it’s what that enables that makes such a difference. On our blog, Erwin van der Koogh articulates just how much of a difference Honeycomb and observability 2.0 makes compared to your current way of working. Check it out: https://lnkd.in/gbf4yPqC

Observability costs should – and can – be predictable. Duolingo and OneFootball, like many others, were burdened by excessive observability costs and limited metrics capabilities. More and more companies are switching observability vendors due to unpredictable costs and the complexity of billing for logs, metrics, and APM services. Honeycomb’s wide events model and unified data store let you collect as much data as you want, with fast analysis that helps resolve issues before customers even notice—by default, at no extra cost. Live Webinar: 🛠 How to Get Your Observability Costs Under Control 📉 How Duolingo and OneFootball reined in costs while getting better answers 📅 February 25 ⏰ 9am PT | 12pm ET | 5pm GMT Join us to learn: ⚠️ Why custom metrics lead to unpredictable costs 📊 How to capture high-cardinality, high-dimensionality data for faster answers 📈 Scaling observability in line with business growth—without runaway costs Speakers: 🎙️ Martin Thwaites – Principal Developer Advocate, Honeycomb 🎙️ Bruno Costa (Garu) – Principal Site Reliability Engineer, OneFootball 🎙️ David Amin – Staff Site Reliability Engineer, Duolingo Register here: https://lnkd.in/emJB4WPX #Observability #SiteReliabilityEngineering #SRE #DevOps #CloudComputing #SoftwareEngineering #Webinar #TechTalks #EngineeringLeadership

Help your team break vendor lock-in using OpenTelemetry. Honeycomb works out of the box with your existing metrics, logs, and traces through the OpenTelemetry Collector. Check out our OpenTelemetry resources: https://lnkd.in/g6Ge-s9k

Observability Days Amsterdam is coming up on 11 February, and spots are filling fast! Register for free now: https://lnkd.in/gWuyrkDH