Saad Rehmani

💡 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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Two of AWS’ Sr. Principal Engineers, Nicholas Matsakis and Marc Bowes, take us inside the development of Aurora DSQL in a fascinating new blog. The post shows how the team scaled write operations without two-phase commit, overcame garbage collection hurdles, and ultimately embraced Rust for both data and control planes. What I find particularly interesting is how the team's journey with Rust evolved. They started cautiously, using it for a single component, and ended up rewriting almost everything in Rust. It's a reminder that sometimes the hard choices - like adopting a new language - can unlock tremendous value. The full post is well worth your time. It's not just about database internals, but also about team dynamics, a culture of learning (and questioning established approaches), and making difficult architectural decisions. Read it here: https://lnkd.in/eMhxBu9e Now, go build!

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NEXTGEN INFRA STARTUPS MUST PLAY OFFENSE Historically, software infrastructure / middleware startups have played DEFENSE well. They have built deep, defensible technology moats over a period of time. The founders have been deeply technical, have sold to other developers, and have been content to not have a strong public-facing brand. However, this playbook (and archetype) must change. Infra founders and startups must play OFFENSE and become more dynamic and front footed. Why? First, foundation models are adding more and more infastructural components and nibbling away at what was traditionally considered infrastructure. No company can now comfortably sit and own a part of the stack. It will inevitably erode. Second, end customers are deciding or opining on what infrastructure to use. Guess what? Customers of infra products are no longer just career engineers but ordinary people (including VCs!) Infrastructure now has a PLG motion. Having a customer-facing brand is no longer optional. Finally, it’s possible (or even probable) that your company might need to fundamentally change its business model, and not be an infrastructure company any more. And in this crucible moment, you need to have the muscle and the ability to seize the opportunity and go for it. Consider OpenAI launching ChatGPT or Vercel launching v0. They could never have managed it with a legacy infrastructure mindset. Check out Groq’s homepage - it looks more like a vibecoding startup than a chip company. To summarize, infrastructure startups need to evolve. They must start thinking about the end customers, become much more public-facing with their brand, and develop the muscle to build customer-facing (not just developer facing) products. The archetype of infrastructure founders is changing. They cannot just be technical. That’s tablestakes. They must build a deep understanding of their customers’ customers, be superb at product development, marketing, storytelling and brand building, and be ready to move up the stack at any time. In other words, they must be able to play OFFENSE. That’s what greatness means in infra companies today.

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Amazon Web Services (AWS) just launched general availability of new R8gn instances - though the way that I found out about them wasn't from the official Amazon blog post, it was from the new automated newsletter for EC2instances.info. EC2instances.info is regularly rebuilding itself every few hours and now also checks for two things: 1) new instance types and 2) any pricing changes with existing instance types. You can sign up to receive email alerts about these two changes as often as the site finds them. If you're curious about getting the notifications sent your way to see what Amazon is launching, especially leading up to AWS re:Invent, I included the link for how to set this up in the comments below.

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Should you avoid the em dash (—)? Some argue it signals AI-generated text. I disagree. Used sparingly, it’s a helpful tool — adding pause, emphasis, or flow to your writing. The real issue is overuse or fully outsourcing writing to AI without care. If you want to insert it: Mac: Option + Shift + Hyphen iOS: Long press the hyphen key. Write like a human — em dash and all.

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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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Full delegation to autonomous teams is not a silver bullet. Sometimes, it’s just abdication. For the past 20 years, I’ve seen a recurring pattern: Leaders delegating too much responsibility, too early, under the guise of "empowerment." The result isn’t autonomy. It’s confusion. We need to stop treating leadership tools like they fit every situation. You cannot use the same approach for a Day 1 junior as you do for a 10-year veteran. This is where Situational Leadership comes in, a leadership method that's too underutilized. Situational leadership method requires you to assess the readiness of the individual or team you're leading: * Brand new on the job? Newcomers can rarely handle full delegation effectively. Instead leaders shouldn't shy back from suggesting direction and follow up frequently. * Fully ready and capable? High control is demotivating. A too directive style then becomes ineffective Even seasoned experts are helped by direction on a new assignment. Giving clear instructions to a senior hire isn't micromanagement; it’s alignment. It prevents slow onboarding and misaligned expectations. Don’t be afraid to be directive when the situation calls for it. But don’t get stuck there. What's your experience from overusing any of the leadership styles? What do you think about this subject? There are 4 styles: Directive, Coaching, Supporting, and Delegating. Notice that "Abdicating" isn't on that list.

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An agent has no consistent definition. You can think of an agent as an autonomous or semi-autonomous system that can take actions on behalf of the user in a given environment, state, and make decisions or take actions that can accomplish certain tasks along a given time-frame, either by calling tools, dynamically selecting the next action to take or use a deterministic "workflow" (systems where LLMs and tools are orchestrated through predefined code paths). Using an Agent framework that gives you the ability to built systems in both, a reliable and a dynamic way can accelerate your agent development journey using agent abstractions. This means building Agentic systems that can reliably call tools, store memory (episodic/semantic/procedural), have comprehensive logging and observability, human in the loop workflows and the ability to build various multi-agent patterns flexibly based on your use case. A successful Agentic system in production is usually a combination of both, dynamic and predictable/reliable multi-agent systems. Strands Agents SDK gives you exactly these capabilities by treating each “agent” as a combination of a foundation model plus a suite of tools. You define a prompt and register your tools (decorated functions) in code, then Strands handles reasoning→planning→tool-execution cycles, local testing, and cloud deployment (ECS, Fargate, Lambda, EC2), along with support for all other agent abstractions provided above. Excited to share a hedge-fund analyst multi agent system: This uses the newest Anthropic's Claude 4 Sonnet/Opus that powers the Lead Analyst Agent, routing incoming queries to specialized sub-agents for fundamental, technical, and market analyses. Each specialist is wrapped as a callable tool (using the “agents-as-tools” multi-agent pattern), so the orchestrator never has to implement domain logic itself and can handoff the task to an agent as a tool. For sensitive operations (insider lookups), we utilize a HITL approval step that halts execution until a human grants consent. We also use meta-tooling that enables the Lead Analyst to generate, load, and invoke new custom tools at runtime—whether it’s a portfolio beta calculator or a pricer—without redeployment. Strands also embeds observability (Langfuse) and OpenTelemetry tracing so you can trace reasoning events, tool invocations, errors, and end-to-end workflows in real time. View more information on the code implementation here: https://lnkd.in/gJmwVyGi Code implementation: https://lnkd.in/gzTtJvJq Thanks to 🏄‍♂️ Cagatay Cali for being a reviewer/collaborator on this! Feel free to try it out and reach out with any questions/ideas. #aws #agenticAI #strands #agents #generativeAI

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Cloud cost uncertainty isn’t a fluke; it’s by design. Cloud providers profit from complexity: 💸 Granular billing 💸 Shifting discount programs 💸 Opaque pricing for AI workloads 💸 Exploding SaaS and shadow IT The result? Engineering and FinOps leaders are left scrambling to explain unpredictable cloud bills while innovation can’t slow down. Traditional dashboards and static budgets won’t save you. You need adaptive cloud cost management that embeds cost awareness into engineering workflows, detects anomalies early, and drives shared accountability. We break it all down in our latest post, including 5 steps to start leading through the chaos. Read it here: https://lnkd.in/ge4jadxr #FinOps #CloudCost #EngineeringLeadership #CloudComputing #AI #SaaS #Ternary

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Platform engineering is the future 🚀 Developers are at the core of everything we do at PagerDuty 💚 Our latest collaboration with Spotify for Backstage brings PagerDuty’s industry-leading Incident Management directly into Backstage, the open platform reimagining developer experience. With the new PagerDuty Plugin for Backstage, devs can see incidents, on-call context, and service ownership right where they already work : Less dilution ; More focus. Let’s go 🎉 Read more 👇 https://lnkd.in/gnsq8Nr9

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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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