I work as an engineer at Bitboundaire, and for us, releases are not “events.” They’re just part of the everyday noise.

We don’t wait for a big Friday release. We don’t stack changes for an “end of the month deploy.” Products evolve every day, founders change direction every day, and users absolutely do not plan their lives around our release calendar. If we care about them, we have to keep up.

That’s why we built a way of working where deploying 24/7 is normal, safe, and frankly a bit boring. It’s not based on heroics or YOLO culture. It’s based on structure, discipline, and one simple idea:

We truly care about the people who depend on what we ship.

Everything else is just making that care concrete.

Slicing Work So Deploys Stay Small

The first thing that makes 24/7 deploys possible is how we shape work. If you start with giant tickets, you end with scary deploys. It’s that simple.

I try to avoid tasks like “Implement signup flow.” A task like that usually hides dozens of concerns: UI, routing, validations, error states, external integrations, analytics, observability and so on. Put all of that into one PR and you get long-lived branches, painful reviews and deployment anxiety.

Instead, we slice flows into small, independent pieces.

Backend slices
  • Signup endpoint contract
    ⁠ . Request/response formats, status codes, error model
    ⁠ . Shared understanding with frontend
  • signup domain rules
    ⁠ . Business rules, security checks, rate limiting if needed
  • signup persistence layer
    ⁠ . Database integration, migrations, repository logic
  • signup observability
    ⁠ . ⁠Logs, traces, metrics for signup events and failures
Frontend slices
  • Signup UI components
    ⁠ . ⁠Inputs, buttons, error messages, loaders
    ⁠ . No backend yet, just UI primitives
  • Signup page layout
    ⁠ . Page structure, routing, layout
  • Signup api integration
    ⁠ . ⁠Hooking the page to the backend
    ⁠ . Handling the happy path end-to-end
  • Signup error and edge cases
    ⁠ . API failure states, network issues, validation errors

Each slice has a narrow scope and a clear definition of done. That keeps the blast radius small for every deploy. It’s not just architecture aesthetics; it’s a form of respect for production and for the users who never asked to be part of our experiments.

⁠Code and Tests Move Together

There is one rule we enforce pretty hard: we don’t push tests to “later.”

For each small chunk of work, code and tests move together. If the implementation is “ready” but tests are not, the task is not ready. Depending on the change, that might mean a few focused unit tests for pure logic, integration tests for components that talk to databases or external services, or E2E tests for critical user journeys.

In practice, this gives us three big benefits:

  • Every task gets its own safety net
  • Regression creep is a lot smaller
  • The system’s expected behavior is documented in executable form

We’re not chasing a coverage percentage just to feel good. We’re refusing to outsource bug discovery to our users.

For me, this is a very direct way to live the “we truly care” motto: quality is not optional, and it’s not something we’ll maybe add if we have time.

Why Tests Feel “Slow” (And Why They’re Actually How You Go Fast)

I get why a lot of people resist tests. If your mental model of delivery is “I just need to make this work and push it,” then writing tests looks like extra work that slows everything down. You see the time it takes to write the test, but you don’t see the time you’re about to burn in the future debugging something you broke on a Friday.

What actually happens without tests is this: every new change carries the full cost of fear. You touch one file, and suddenly you’re guessing what else might break. You spend more time manually clicking through flows, more time staring at logs, more time asking “is it safe to deploy this?” The first delivery might look fast, but every delivery after that gets slower, riskier and more stressful.

With tests, you pay a small, explicit cost up front to avoid a massive, hidden cost later. Once a feature has solid tests around it, I can change it next week or next month with way more confidence. CI becomes a safety net instead of a checkbox. Deploys stop being arguments and start being routine. Over time, tests don’t just protect quality; they compound speed by letting you move without re-doing the same manual validation over and over.

So yes, writing tests can make a single task look slower in isolation. But if you care about shipping all the time, not just “this one time,” tests are the only way you actually get faster as the system grows.

You can really see the difference when you compare this with teams that rely on a separate “test team” to do manual regression. On paper, it looks efficient: developers “move fast,” and a group of testers “ensures quality.” In reality, you’ve just moved the cost to the end of the pipeline and multiplied it. Every change now waits in a queue. A full regression cycle can take hours/days. Devs are idle or context-switching while they wait. Testers spend their time repeating the same click paths over and over instead of actually exploring edge cases or improving quality.

From a money perspective, it’s even worse. The company is paying multiple salaries for humans to simulate what a test suite could do in minutes on cheap compute. Every release becomes a project, not an operation. The more features you add, the more manual work you create, and the more people you need just to keep the lights on. It feels like growth, but it’s really drag.

With developer-owned automated tests, the cost curve flips. You invest a bit more time per change at the beginning, and then let machines run your regression portfolio thousands of times for almost free. Humans stop acting like slow, expensive CI servers and start doing higher-leverage work: risk analysis, product thinking, exploratory testing where automation doesn’t reach. In terms of both speed and money, tests aren’t the tax. They’re the only way the system scales without burning everyone out or hiring an army of people to click buttons all day.

Our CI/CD Pipeline: Turning Culture into Automation

All of this would be theater if the pipeline didn’t enforce it.

In most projects, the stack looks roughly like this: Git workflows, AWS CDK/Terraform for infrastructure, and a staging environment that mirrors production as closely as possible. The tools matter less than how the flow is structured.

A common path I work with looks like this:

  • I start on a local branch, focused on one small, clear change.
  • When it’s ready, I open a PR into staging. This is the first gate before anything gets close to production.
  • CI kicks in on that PR: linting, type checks, unit tests, integration tests, and CDK diff or other infra checks. If something breaks, it breaks early.

Once the PR is approved and merged into staging, the code is deployed to the staging environment, which mirrors production: same infrastructure shape, same services, realistic data patterns. After deployment, we run E2E tests there for key flows.

If everything behaves correctly, we open a PR from staging into main. At that point we’re basically saying “this is production-ready.” Branch main is always deployable, and production deploys are small and predictable, not a massive “release event.”

Because staging mirrors production, we’re not guessing how things will behave in prod. We respect production enough to make sure it’s never the first place we test something “for real.”

Mirror of Production: Practicing Before It Matters

The mirror of production is what keeps production boring for us.

Our staging environment isn’t a playground with random settings. It’s intentionally close to production: same topology (services, queues, databases, networking), same deployment process, same observability stack.

We always deploy there first. We run E2E tests there. Sometimes we let internal users or stakeholders try features behind internal flags before exposing anything to real customers.

By the time we promote a change to production, the code, artifacts and execution paths have already run in a realistic environment. We’re not hoping. We already watched it work.

If you truly care about stability, you invest in a place where you can practice the hard parts before real users feel them. That’s exactly what staging is for us

Feature Flags: Shipping Without Releasing

Feature flags are another core part of this setup. They let us separate deploying from releasing.

We rely heavily on tools like PostHog to manage our flags and rollouts.

A common pattern is to dark-launch a feature: we deploy the code to production, but expose it only to internal roles or a small cohort. We can then test behavior, data flows and observability in the real environment with minimal risk.

From there, we gradually roll out: first to the team, then to a small slice of users, then to a bigger segment, and only at the end to everyone. If logs, metrics or user feedback show something off, we can flip the flag off quickly instead of rolling back a full deployment.

Because flags let us ship incomplete pieces safely, main can stay deployable even while a feature is still under active development. That’s how we maintain a high shipping frequency without turning real users into beta testers by surprise.

How We Behave to Make 24/7 Deploys Real

None of these functions if the team behaves like they’re still living in a big-bang release world.

Inside Bitboundaire, there are some expectations that we take seriously.

We prefer small PRs by default. Many small changes are easier to review, easier to reason about, and much easier to roll back than one giant diff. Huge PRs are usually a smell that something upstream went wrong in how the work was sliced.

We keep code reviews fast. Reviews are part of the job, not an optional extra. Slow reviews create long-lived branches and brittle deploys. If we want 24/7 deploys, we need fast feedback loops, and that includes feedback between engineers.

We avoid long-lived feature branches. The longer a branch sits, the more the world moves under it, and the more painful it is to merge. Short-lived branches that often integrate with staging keep risk under control.

We expect engineers to own features end-to-end: the implementation, the tests, the dashboards and the metrics that show whether the thing actually works in production. And when incidents happen, we focus on learning: what needs to change in tests, alerts or process, so this doesn’t surprise us again?

That’s where “we truly care” shows up internally. It’s not a slogan on the wall. It’s visible in the constraints we accept as a team.

Why Founders Should Care About 24/7 Deploys

This whole setup is not just an engineering hobby. It changes how the business behaves.

For founders, 24/7 deploys mean ideas can be validated much faster. The gap between “I think this will help our users” and “we have real data from production” gets much smaller. Instead of betting on one big release, we can make many small bets, adjust and double down on what actually works.

Because each deploy is small, the risk per change is low. Experiments become less scary. You don’t need a giant “release day” to show progress to customers or investors. Progress becomes continuous.

This also keeps the product closer to reality. Feedback from users and metrics from production come back into the roadmap quickly, instead of getting stuck behind a release train. What’s running in production is never too far from what the team has in their heads.

From my perspective, this is one of the clearest ways we prove to founders that we truly care: we don’t just ship features, we build a machine that lets their product adapt safely whenever the business needs to move.

Why We’re Not Afraid of Friday 5 PM

In a lot of teams, deploying on Friday at 5 PM is a running joke for “we’re going to regret this.”

For us, it’s just another deploy. Not because we’re reckless, but because everything around it is designed to keep it boring:

  • The change set is small
  • The code already ran in a mirror of production
  • E2E tests have validated key flows
  • Feature flags give us a fast kill switch
  • Observability tells us quickly if something is off
  • Rollback is simple and well-practiced

We also keep an eye on error rates, latency and key funnel steps in near real-time, so we usually know within minutes whether a deployment is healthy or not.

We still respect people’s time zones and on-call schedules. The point is not to show off. The point is to reach a level of confidence where “we deployed” does not automatically mean “prepare for a crisis.”

We care about our teammates’ weekends and our users’ experience at the same time. The way we structure work and deploys is designed to protect both.

Closing: Moving Fast Because We Care Enough to Make It Safe

Deploying all the time is not the real goal. Deploying safely, respectfully and consistently is.

For me at Bitboundaire, that translates into a few concrete habits:

  • Shape work into small slices that are easy to reason about and reverse
  • Always ship code together with its tests
  • Validate changes in a mirror of production before real users see them
  • Use feature flags to separate deploy from release
  • Keep the culture optimized for small, safe, frequent changes

That’s how we turn “we truly care” into something visible: less downtime, faster feedback, less stress for engineers and a product that actually keeps pace with the ambitions of its founders and the needs of its users.

In the end, what I’m really trying to optimize for is simple: small, boring, reliable deploys that respect the people on both sides of the screen.