Inside 5 Engineering Teams Using Modern Stacks to Ship Fast and Smart

“There’s more than one way to ship.” 

This isn’t an idiom, but it should be for developers. 

The actual idiom “there’s more than one way to skin a cat” expresses the idea that there are many ways to get the end result that you want. And when it comes to shipping fast while maintaining quality, the following five engineers all have their own tried and true methods for achieving success. 

 

 

ThousandEyes, a part of Cisco, provides visibility into digital experiences delivered over the internet. 

 

What’s your rule for releasing fast without chaos — and what KPI proves it?

To release quickly without chaos, it’s essential to establish automated testing and real-time alerting at the individual service level. As our services grow more complex, predicting every possible interaction in advance becomes unmanageable. Instead, each service should define clear health metrics — such as error rates, response times and availability — and continuously monitor these indicators. By injecting continuous baseline synthetic traffic (independent of real users), we can proactively detect degradation or failures before they impact customers, even for transitive dependencies. Fast, automated rollback mechanisms further reduce risk, enabling confident, rapid releases. The KPI I use to prove this out is mean time to recovery.

 

What standard or metric defines “quality” in your toolchain?

For me, “quality” in a toolchain is defined by architectural simplicity and the ability to reason about system behavior. I apply a concept similar to cyclomatic complexity — not just to code, but to system architecture as a whole. By modeling services and their interactions as a graph, I assess the impact of potential outages or degradations, not only for each service but also for downstream dependencies. Each connection in this graph represents more than just up/down status; it includes metrics like latency, retry behavior and resource saturation. For example, Bufferbloat illustrates how local optimizations (like buffer sizes) can have unexpected system-wide effects. High architectural complexity makes systems brittle and harder to maintain, while simplicity enhances reliability. Ultimately, quality is reflected in how easily we can understand, reason about and operate the system.

 

Share one recent adoption and its measurable impact.

Recently, I’ve expanded my use of AI beyond code assistance to non-coding tasks, particularly for technical document review. By leveraging AI to summarize and research referenced technologies, I significantly reduce the time spent on background research and ramp-up, allowing me to focus on the critical parts of the proposal. Additionally, I use AI to summarize chat discussions, as well as to serve as a first-pass editor for my own writing. Summarization and grading are strengths of current LLMs, so I’m finding it very effective to take on a collaborative approach to using AI in that respect. While integration across all tools is still developing, the measurable impact has been a noticeable increase in productivity and efficiency — often reducing document review and summarization time by 25 to 30 percent. I’m optimistic about further productivity gains as AI tools mature.

 

 

Apex Fintech Solutions provides the tools and services that enable hundreds of clients to launch, scale, and support digital investing for tens of millions of end investors. The company provides essential infrastructure and a comprehensive ecosystem of cloud-based products to enable and streamline trading, wealth management, cost basis, tax reporting, and, through its subsidiary Apex Clearing™, custody and clearing.

 

What’s your rule for releasing fast without chaos — and what KPI proves it?

Our architecture philosophy is “design for failure, optimize for recovery.” Fast releases without chaos require cloud-native patterns baked into every layer of our platform. This isn’t just a motto  — it’s enforced through our “zero-trust deployment architecture.” Every service must demonstrate it can handle downstream failures, upstream timeouts and resource constraints before it touches production. We enable engineers to ship quickly by using tools like Bitbucket Pipelines for CI/CD, ArgoCD for GitOps driven deployments and terraform with open policy agent policies for consistent, compliant infrastructure changes. We track deployment frequency above 50 daily releases, change failure rate below 10 percent and mean time to recovery under 30 minutes, as our proving metrics for fast releases without chaos. These three must trend positively together since you can’t optimize one without the others declining if your architecture isn’t truly resilient.

 

What standard or metric defines “quality” in your toolchain?

Quality in our cloud platform is defined by architectural consistency and operational excellence at scale. Our quality standard is “cloud-native compliance” — every component must be containerized, observable, scalable and stateless. 

 

Share one recent adoption and its measurable impact.

A recent adoption has been using Claude as a co-pilot for our engineers. They now rely on it daily for tasks like creating CI/CD pipeline snippets, generating Terraform modules’ test cases and even troubleshooting cryptic error messages across GCP, AWS and Kubernetes.

 

Instead of being a novelty, Claude has become a day-to-day accelerant — helping the team spend less time on boilerplate and more time on building reliable, cost-optimized platforms. We don’t use AI for the wow factor; we use it to save hours on the unglamorous but essential engineering work.

 

 

Parsec Automation, LLC (Parsec) is a provider of manufacturing operations management software.

 

What’s your rule for releasing fast without chaos — and what KPI proves it?

When it comes to technology releases — whether it is an incremental update or a major version release — one thing we keep in mind is that speed for the sake of speed tends to create more problems than value. The pressure to act, execute and deliver is not unique to software providers; it is a paradigm shared by any business that wishes to remain competitive in today’s crowded marketplace. However, we operate under the “smooth is fast and fast is smooth” mantra, which is to say — we believe it is worth taking extra time to deliver a polished product that provides a seamless experience for the end user. As for KPIs? One need look no further than what users of our platform are saying. In the recent InfoTech report, 100 percent of surveyed respondents said they plan on renewing their TrakSYS subscriptions. An overwhelming majority report being happy with how our tools work and the results they help unlock.

 

What standard or metric defines “quality” in your toolchain?

Quality Management is a focal point for many of our customers. Likewise, when we are working on product development, our teams go to great lengths to stress test and build functionality that will deliver maximal, non-disruptive value to the end user on day one. Practically speaking, this can look as simple as ensuring that the platform enables people to use the charts and reporting visuals they prefer. It can mean ensuring that text on screen can be colored and oriented in any way a user may want to see it. Or, on a more technical level, it can look like creating quality of life improvements — like a back button — that don’t “jump off the page,” but absolutely streamline the use of the tech. Whatever it is, big or small, we endeavor to ensure things in our platform work in all the ways and scenarios our users would expect them to.

 

Share one recent adoption and its measurable impact.

Like the calculator, AI is becoming integrated into daily life. GenAI is — in many ways — overtaking what used to be outsourced to search engines. While we will be talking about this more later in the year, AI has already begun making its way into our platform. TrakSYS IQ Assistant — functionality that will be widely released toward the end of the year — utilizes natural language queries to surface contextualized insights and even generate dynamic visuals on demand. It seamlessly integrates data across systems for a single source of truth, enabling fast, confident decision-making. This, alongside upcoming platform updates, supports our mission of empowering shop floor workers and making the management of manufacturing operations as simple as possible.

 

 

Hiro is a company that creates developer tools for Stacks, a network that enables apps and smart contracts for Bitcoin.

 

What’s your rule for releasing fast without chaos — and what KPI proves it?

All members of the team must be aligned on the same strategy and vision for the product we’re building. This applies to engineering, design, people ops and management. Once all of us put our minds together and agree on the level of quality we must deliver, prioritizing work and getting things done becomes easy because the team has conviction in what we’re doing. That is what creates a fast and reliable release schedule. A KPI that proves this is the low turnaround time for any issue or feature request reported to us by our partners and users of our dev tools.

 

What standard or metric defines “quality” in your toolchain?

For us, quality means delivering the data our dev clients need quickly, correctly and in a way that is easy for them to consume. Our goal is to empower developers to build their apps on the Stacks network without them having to worry about running infrastructure services that get in the way of their business.

 

Share one recent adoption and its measurable impact.

AI tools have definitely made a positive impact in our workflows. We’ve used these tools extensively to assist in code, design and infrastructure tasks and have made us much more productive and faster when shipping new releases and building new features. We believe AI is not a replacement for great engineers but an amplifier of their work. It makes us all much more productive.

 

 Dropbox is a cloud-based service that provides file storage, syncing and sharing for individuals and businesses. 

 

What’s your rule for releasing fast without chaos — and what KPI proves it?

Building Dropbox Dash taught us that releasing fast only works when evaluation is built in. We treat every change, from prompts to retrievers to model settings, with the same rigor as production code. Each pull request runs about 150 canonical queries, judged automatically in under ten minutes. Metrics like source F1 (≥ 0.85) and latency (p95 ≤ 5 s) keep us accountable. This structure enables fast, confident releases across a platform trusted by more than 700 million users.

 

What standard or metric defines “quality” in your toolchain?

At Dropbox, quality is measurable, versioned, and enforced. Every change is scored across Boolean gates like “Citations present?”, scalar budgets such as source F1 and latency, and rubric scores for tone, clarity, and formatting. We use LLMs as judges, guided by calibrated rubrics that check factual accuracy and context alignment. The results feed shared dashboards so quality stays visible, repeatable, and reliable across Dropbox’s global infrastructure.

 

Share one recent adoption and its measurable impact.

One of our most impactful adoptions in building Dropbox Dash has been using LLMs to evaluate LLMs. Instead of static BLEU or ROUGE scores, we build judge models that grade factual accuracy, citation correctness, and clarity. This automation keeps evaluation continuous and scalable. Each change is tested and verified before release, backed by rigorous datasets, actionable metrics, and automated gates. It’s how Dropbox ships experiences quickly and safely at global scale.