Jody Bailey

Who needs self-service? In this article, Conduktor’s VP of Engineering, Matt Searle, talks about his own experiences attempting to balance developer autonomy alongside platform stability. His key insight? Govern Kafka, or risk havoc. Read his latest blog—and learn more about how Conduktor can help you implement self-service with guardrails. https://lnkd.in/ejWTRctu

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Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

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🧢 Platform engineers aren’t just building tools. They’re building products. At PlatformCon 2025, our CTO and co-founder Ganesh Datta made the case for why platform teams need to think like product managers. His talk covers: ✔️ How to design for adoption, not just availability ✔️ Why golden paths are only valuable if they’re opinionated ✔️ What product management failure modes look like in platform teams ✔️ The metrics that actually show you’re making an impact This is how great platforms get built. Don’t miss it. 🎥 Watch the full session → https://lnkd.in/guE__kYB

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What does it take to build a successful engineering organization? 🚀 It’s not just strategy decks or scaling headcount. It’s the messy, behind-the-scenes work—building clarity when there’s chaos, balancing autonomy with alignment, and constantly evolving your architecture and culture as you grow. I’ve spent the last few years learning (and re-learning) this. So, I wrote a 3-part blog series to share some nuggets. If you're leading (or hoping to lead) at scale, I hope there's something useful in here for you. Would love your thoughts, and even more—your own lessons 🙂 #engineering #leadership #lessonslearned

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Spotify's engineering team built a background coding agent with Claude Agent SDK that handles complex migrations across their massive codebase. Engineers describe what they need in plain text—via Slack, even—and the agent takes it from there, running tests and opening pull requests. They're now merging 650+ agent-generated PRs into production each month. Read more about how they built it, and what else Claude is enabling at Spotify: https://lnkd.in/gVhivXu6

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Often when speaking about autonomy we forget the essential element that needs to accompany it, accountability. Often software teams are held accountable for decisions they didn’t make. If you have team accountable for a system, they should have autonomy in making decisions about it too.

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Under the hood, Coder Prebuilt Workspaces consist of a pool of pre‑warmed infrastructure that developers (or agents) can instantly claim to start coding. Because Coder infrastructure is defined in Terraform, you can pre-build anything from VMs to Dev Containers, or even s3 buckets and so developers can "claim" one in seconds. Since our customers have thousands of active developers, the additional cost of prebuilt infrastructure is quite low. If you have 1000 active users/workspaces, maintaining a pool of 3 "warm" prebuilt workspaces instances increase your infra cost by 0.3% but can saves hours of "developer waiting time" when provisioning infrastructure. Learn more about pre-builds (Premium) in the Coder docs: https://lnkd.in/grFh4pBC

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Custom software is not a feature upgrade. It is an operating decision. When software is built around your workflows, three things change: • Scale stops being constrained by tools • Security becomes architectural, not reactive • ROI compounds instead of leaking through licenses Off-the-shelf products optimize for averages. Custom software optimizes for you. Hiteshi builds systems aligned to business logic, not templates. #CustomSoftware #EnterpriseTech #DigitalTransformation #Hiteshi

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💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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Ever feel like your toolchain is running you instead of the other way around? Most teams don’t set out to create tool sprawl - it just happens. A tool for testing. Another for CI/CD. A few more for monitoring, feature flags, and compliance. Before long, you’re managing 10 dashboards, 5 logins, and a whole lot of context switching. The result? Slower delivery, higher overhead, and less visibility. What we’re hearing from customers is clear: “We don’t want fewer tools — we just need them to work together.” That’s exactly what CloudBees Unify helps with. 🧩 A control plane that connects Jenkins, GitHub Actions, GitLab, Gerrit, and more - without forcing change. 👀 Unified visibility, compliance, and traceability across every pipeline. 🚀 Less toil, fewer silos, and modernization without disruption. Tool sprawl doesn’t need to end in consolidation - it ends when your tools finally work in harmony. 💬 What’s the worst “tool sprawl” scenario you’ve lived (or inherited)? I’d love to hear how your team is tackling it today. Also, go and check out our guide: https://lnkd.in/gCZ-__U4

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For growing tech teams, traditional infrastructure can feel like walking a tightrope, one misstep and things break, users churn, and teams scramble. Container orchestration changes that story. It brings reliability you don’t have to babysit, performance that flexes with real-time demand, and the freedom to scale without stress. Developers get to focus on what they do best i.e. shipping great code instead of duct-taping deployments. It isn’t just a tech upgrade. It’s how modern teams stay fast, stable, and fearless. Ready to build like that? Visit www.intagleo.com #Kubernetes #DevOps #CloudNative #DeveloperExperience #IntagleoSystems 

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🎙️ What’s really happening inside your engineering org? In this episode of the Mob Mentality Show, we sit down with Philip Borlin, Director of Engineering and advocate for lean thinking, mobbing, and team capability building, to uncover how Gemba Walks, smaller batch sizes, and healthy team nudges reveal the actual state of your tech organization—not just what reports say. https://lnkd.in/gjxEZYF8 https://lnkd.in/gzV9SMRx We explore how leaders can stop flying blind and start leading based on facts from the field. 🔍 Topics Covered: ✅ Gemba Walks (Japanese term meaning “go to the real place”): - Why your assumptions about how work gets done are probably wrong - How spending even one hour a week in the mob or at the code level changes everything - The myth of managing solely through middle managers - Why high-fidelity information beats filtered reporting - Remote-friendly adaptations: mobbing, Lean coffees, and async insight gathering ✅ Mobbing (also known as ensemble programming): - How mobbing surfaces capability gaps and builds shared understanding - Growing capabilities without enforcing rigid standards - Real stories of capability fire drills, single points of failure, and org fragility - “Low and slow” growth as the only sustainable path to true skill development? ✅ Fixing Batch Size and WIP (Work In Progress): - How large batches lead to delivery waste, delays, and bugs - The surprising power of reducing ticket size to unlock flow - Socratic coaching at stand-ups to improve team work slicing - Giving permission to drop non-priority work and focus only on what matters ✅ Building a Learning Culture: - Why capability resilience > retaining every team member forever - Using “nudges” and peer pressure the right way - Investing in bright spots without ignoring skeptics - Cultivating environments where psychological safety and growth feed off each other 💡 Whether you’re a Director of Engineering, Tech Lead, Agile Coach, or Software Engineer, this episode gives you practical ways to lead with clarity, scale team capability, and build resilience into your org’s DNA. Huge thanks to Dave Adsit for recommending that Phil come on the show! Chris Lucian #SoftwareEngineering #EngineeringLeadership #TechLeadership #GrowEngineeringTeams #BuildBetterTeams #LeanSoftwareDevelopment #FlowEfficiency #WorkInProgress #ContinuousDelivery #LearningCulture #KaizenCulture #CapabilityBuilding #OrganizationalResilience #TeamCapability #GembaWalk #ManagementByWalkingAround #Mobbing #TeamCollaboration #GoSeeThinkDo #SystemsThinking #AgileLeadership #RightPeopleOnTheBus #PsychologicalSafety #RemoteCulture

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Standard RAG falls apart on aggregative queries. Things like "average ARR for companies with >1k employees" require reasoning across hundreds of docs, and vector retrieval just can't handle that reliably. We built Structured-RAG to fix this: - induces a schema from your corpus at ingestion - maps every doc to structured SQL records - translates queries to SQL at inference 👉 Also releasing two new aggregative QA benchmarks with the paper: https:// arxiv.org/abs/2511.08505

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🎯 Running inference workloads on Kubernetes? I just wrote a deep dive on Gateway API Inference Extension and how it works. The Gateway API Inference Extension introduces two new Custom Resource Definitions (CRDs): InferenceModel and InferencePool. By using these two new resources, along with the endpoint selector, your L7 routing infrastructure becomes an “Inference Gateway” enabling you to self-host GenAI/LLMs with a “model-as-a-service” mindset. https://lnkd.in/g_eFqwUd #ai #llm #genai #inference

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Today we released Rootly's largest open-source project to date: On-Call Health. It helps engineering teams spot risk early by pulling signals from tools responders are using: Rootly, PagerDuty, GitHub, Slack, Linear and Jira. These signals feed into a risk score that tracks trends over time, relative to each person's own baseline. We built this alongside engineering teams at Brex and Weights & Biases - their feedback shaped everything from the scoring to the integrations we prioritized. Try it now at https://oncallhealth.ai/ – fully open source and free to use.

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