Jeromy Evans

💡 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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Really proud of the work Harry Pierson and the team have done in releasing DBOS Java today! DBOS Java brings lightweight durable workflows to Java. Workflows make your program durable by checkpointing its state in Postgres. If your program ever fails, when it restarts all your workflows will automatically resume from the last completed step. Workflows are useful for solving many problems, such as: - Orchestrating business processes so they seamlessly recover from any failure - Operating an AI agent, or anything that connects to an unreliable or non-deterministic API. - Running reliable background jobs with no timeouts. - Processing incoming events (e.g. from Kafka) exactly once What’s unique about DBOS workflows is that they’re implemented entirely in a lightweight Postgres-backed library. All you have to do to use DBOS is install the library into your application and register workflows and steps. As your program runs, the library checkpoints your workflows and steps in Postgres. If your program ever fails, when it restarts it uses those checkpoints to resume all your workflows from the last completed step. There are no other dependencies you have to manage, no separate workflow orchestrator–just your program and Postgres. The big advantage of this approach is that you can incrementally add DBOS to any Java application–it’s just a library. You can add workflows to your application without rearchitecting it or adopting any new infrastructure. It works out of the box with frameworks like Spring. Also, because it’s all Postgres-backed, you get all the tooling you’re familiar with: backups, GUIs, CLI tools–it all just works. You can use Supabase, RDS, Neon, or any other Postgres provider. Check it out on GitHub, try it out, and give us a star!

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"APIs are the connective tissue that allow an organization to thrive." Nearly every company I speak with is navigating the same challenge: modernizing their API landscape while preparing for AI-driven systems. The biggest brands want clear guidance on how to govern, scale, and future-proof their APIs as AI becomes more central to how software is built and operated. This conversation with Matthew Gray, Head of Channel Sales at Postman, does a great job exploring what that journey looks like in practice and why APIs sit at the center of it. Full discussion: https://lnkd.in/g8qUX9dt

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99% success rate on a site we’d never seen before. Not bad for launch 🚀 Developers build on Deck to interact with websites that have no APIs, reading or writing data wherever users need it. What’s remarkable is the quality and consistency, even across new, unseen systems. Auth Agents are redefining what “integration” means. Check our guides if you're ready to start building: https://lnkd.in/eG4h6aMS

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It's a big day at Honeycomb! We're announcing major advancements in Honeycomb Telemetry Pipeline that unlock a new era of observability—one without tradeoffs. 🔥 Over the past several years, conversations around observability have revolved around challenging tradeoffs: retention versus fidelity, availability versus speed—and always, the constant pressure of cost. With the rise of OpenTelemetry shifting telemetry ownership from vendors to engineering teams, it was no surprise to see telemetry pipelines quickly emerge as essential infrastructure. Yet—as standalone solutions—these pipelines reinforce divisions between the producers and consumes of telemetry data (but this time, more toxically, often within the same organization). Honeycomb is committed to a different approach: deeply integrating telemetry pipelines into the observability experience itself. ✨Enhance✨ is the first step toward this vision, eliminating the traditional tradeoffs in data management. Teams now retain easy, cost effective access to all their telemetry, even (especially! 💥) the bits they didn’t expect to matter. By aligning telemetry data management directly with data usage patterns, we're able to foster positive feedback loops across engineering teams and lay the groundwork for a more integrated, insightful, and collaborative future. Welcome to an era of true lossless observability. It's gonna be exciting. 🚀 Learn more from our press release: https://lnkd.in/dqgCZDyy

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Great piece by Emilia David on the LangChain open source integration ecosystem, the power of LangGraph, and our newly released LangGraph Platform Helps put everything you see us working in perspective Link: https://lnkd.in/ghajS7zx

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🚀 Excited to share that today Vantage is launching a native integration with Vercel! This gives customers the ability to see their Vercel usage and costs in Vantage alongside all other providers they use to run their infrastructure. All the same great Vantage features now work for Vercel costs: cost allocation, anomaly detection, budgeting and more. I've known Guillermo Rauch for over a decade at this point and have followed the Vercel story closely - it's been amazing to see all the work the company has done over the years. Vercel has also won the hearts and minds of millions of developers. Vercel has done things TheRight™ way by shipping a FinOps Foundation FOCUS-compatible endpoint to allow their customers to easily export cost and usage data. If you're a Vercel customer, this integration is now included in the Vantage integration library by default. Login to the Vantage console to integrate Vercel or read the full blog post with all the details in the link below.

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The future of GTM isn’t headcount. It’s infrastructure. You don’t need more SDRs. You need better systems. The companies that are scaling right now aren’t hiring faster. They’re building smarter. → Enriched data flows → Instant routing logic → Agent-led follow-up → Continuous learning loops This is the shift from manual ops to GTM engineering. From reacting to events, to designing for them. It’s not just automation, it’s autonomy. The sooner you invest in this foundation, the sooner your reps stop chasing forms and start chasing revenue.

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CPTO Revolution: Why SaaS Companies Are Merging Product and Tech Leadership I’ve lived the pain of siloed leadership and the inefficiencies it creates many times. And I’ve also seen how tightly integrated product and tech can transform not just delivery, but outcomes. Traditional org charts are no longer keeping up. The most competitive SaaS companies aren’t hiring separate CTOs and CPOs anymore - they’re bringing both roles under one roof with CPTOs. This isn’t about headcount optimization. It’s about reducing friction and moving faster in a world where complexity is climbing and customer expectations evolve by the week. Here’s the core issue: when product and engineering leadership are separate, you’re always playing a game of telephone. Product defines what’s needed. Engineering estimates and negotiates. Product pushes. Engineering pushes back. Handoffs everywhere. Every step is a delay. A CPTO owns the entire journey - from insight to shipped product. One person accountable. One vision. One continuous flow. And it works. Companies running this model are seeing up to 40% faster go-to-market. Less tech debt. Clearer priorities. Higher team velocity. What Actually Changes 1. Aligned Metrics You stop optimizing for isolated outcomes. Product isn’t chasing adoption while engineering shields uptime. Both roll into shared business goals. 2. AI Gets Strategic With unified leadership, AI isn’t a bolt-on. It becomes core to how you design, build, and validate products - through data, feedback loops, and automation. 3. Developer Experience Matters Platform engineering gets the attention it deserves. Internal tools align with product delivery, and suddenly teams aren’t waiting - they’re shipping. UX maturity, platform architecture, technical scalability - all of it becomes a single continuum. You’re not just improving “process”; you’re aligning every decision to what users need and what’s feasible to build. The CPTO Skillset This isn’t a hybrid role - it’s an integrated one. You need the technical depth to guide architecture and the product judgment to say no to overengineering. You talk to engineers about latency and reliability, and to the board about market traction and differentiation. As AI accelerates software development, what matters is orchestration—of systems, teams, and strategy. You need to see the whole board. Cloud infra spend is growing ~10% yearly, mostly due to AI. That spend needs direction. CPTOs are the ones who can match innovation speed with platform efficiency. Companies clinging to the old CTO/CPO split are playing a slower game. Integration is no longer optional - it’s a competitive advantage. So—what’s keeping your org from making the leap?

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Drawing from my experiences at TrueCar and numerous discussions with engineering leaders, I did some research to really quantify the real cost of static staging ecosystems. Traditional staging environments come with significant hidden costs - in cloud infrastructure spend, developer productivity, release velocity, and headcount allocation. I know how ephemeral environments can address these issues, cutting down costs and enhancing the speed and reliability of software delivery. This piece is a distillation of hands-on experience and insights from across the industry, aimed at helping you refine your development lifecycle and save you real money and time. Learn more: https://lnkd.in/gjNBTWcT

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There's been a whole rash of posts from observability vendors on this site, arguing over whether their product is cheaper than someone else's product, how costs gets calculated or forecasted, whether on prem (sorry, "BYOC") is cheaper or hosted is cheaper, and on and on. Here's what I think: it doesn't fucking matter. Or rather: it only matters if observability is a cost center to you, an infrastructure tool, for operational feedback loops. If you are using observability primarily to investigate customer complaints or reliability concerns, then your feedback loops are long, laggy, and lossy. Use the three pillars, and cut costs aggressively. But if you're using observability as a *development* tool, then you should be treating it like an investment, not a cost center. The twin mandate of observability is external and internal: lean into your business differentiators for all they're worth, and accelerate the speed of product development, until you're developing at the speed of AI. If you're getting a 30% return on a particular investment, are you looking to reduce that investment? No. You're saying "how much can I invest until I reach the point of diminishing returns?" If your observability isn't an investment, get better observability.

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Attending RailsConf and talking to folks, my biggest realization is just how expansive Ruby on Rails’ usage is, just that it doesn’t get spoken about publicly as often as other frameworks / technologies. Rails just seems to be silently chugging along in many companies’ tech stacks for more than a decade while happily helping the company scale. If there was one piece of tech I wish was actually hyped up way more than it is, it would be Rails, for how much productivity gains it brings to the table.

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How can you make sure orchestrating agents across many frameworks doesn't cost you a fortune? Today, Caelum Forder and I look into agent sessions from Coral Protocol.

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