An engineer who has spent eleven years eliminating every form of system failure evaluated hackathon projects that celebrate it — and discovered that the architecture of resilience and the architecture of intentional collapse share the same engineering vocabulary.
The industry measures reliability in nines. Five nines — 99.999% uptime — permits roughly five minutes of downtime per year. Achieving that number requires blue-green deployments, canary releases, automated failover, health check cascades, and infrastructure-as-code pipelines that can reconstruct entire environments from a declarative specification. Engineers who operate at this level develop a specific intuition: they can look at a system topology and identify the single points of failure before the first request hits production.
Artem Atamanchuk has spent eleven years building that intuition. As a Senior DevOps Engineer focused on highly available, automated platforms at global scale, his career has been a sustained exercise in preventing exactly the kind of behavior that System Collapse 2026 asked its participants to build. The hackathon, organized by Hackathon Raptors, challenged twenty-six teams to spend 72 hours creating software where breaking, adapting, and collapsing are features rather than failures. Atamanchuk evaluated twelve of those submissions — and found himself reaching for the same mental models he uses to prevent outages, now applied to systems that target 0% uptime as a design goal.
“The toolbox is identical,” Atamanchuk observes. “When I evaluate a production deployment for resilience, I look at feedback loops, blast radius containment, state management during failures, and recovery mechanisms. Every one of those concepts appeared in these hackathon projects — except the engineers were building them in reverse. Instead of minimizing blast radius, they were maximizing it. Instead of containing state drift, they were cultivating it.”
Self-Healing Pods and Self-Mutating Gardens
In Kubernetes, a self-healing pod detects its own failure, terminates, and respawns from a known-good container image. The process is deterministic: failure triggers restart, restart restores original state, original state resumes serving traffic. The system heals by reverting to its specification.
Chaos Garden by Bit Brains — one of two projects earning a perfect 5.00/5.00 in Atamanchuk’s evaluation — inverts this pattern entirely. In Chaos Garden, plants evolve based on player attention or the lack of it. Leave the garden unattended, and the flora mutates into something the developers describe as “beautifully alien.” Cross-pollination between mutated plants creates emergent behaviors that were never explicitly programmed. The system does not heal by reverting — it heals by transforming.
“In production, we treat configuration drift as a defect,” Atamanchuk explains. “If a running container diverges from its declared state, we kill it and restart from the image. Chaos Garden asks the opposite question: what if the drift is the value? What if the system becomes more interesting precisely because it diverged from its original specification?”
The parallel to infrastructure-as-code is striking. Tools like Terraform and Pulumi maintain a desired state and continuously reconcile reality against that declaration. Chaos Garden maintains no desired state at all. Its declaration is that the system should evolve unpredictably, and the implementation delivers on that promise through mutation mechanics that compound over time.
“The technical execution impressed me because the mutations are not cosmetic,” Atamanchuk notes. “They affect gameplay. New plant hybrids emerge from combinations the developers did not script. That is genuine emergent complexity — and emergent complexity is the hardest thing to engineer deliberately, whether you are building a garden simulation or a distributed microservices platform.”
For a DevOps engineer, the project also demonstrates a rarely acknowledged truth about long-running systems: production environments that have been running for years without redeployment are not stable. They are accumulating invisible drift — deprecated API calls, orphaned configuration, slowly diverging assumptions about the environment. Chaos Garden compresses years of infrastructure drift into minutes, making the consequences visible and, unexpectedly, beautiful.
Load Balancing Physics That Refuse to Balance
Gravity Drift Playground by HANUMAN FORCE, the second perfect-score submission at 5.00/5.00, is an interactive physics simulation where users manipulate gravity direction and strength in real time, observing how structures collapse and reform under changing forces. The developers designed it as a sandbox for chaos, collapse, and emergent behavior under non-linear physics rules.
At first glance, this is a physics toy. Through a DevOps lens, it is a load balancer stress test made tangible.
“Load balancing is about distributing force evenly across a system,” Atamanchuk explains. “When a load balancer fails or misconfigures, traffic concentrates on a subset of servers. The pattern is identical to what happens in Gravity Drift when you shift gravity toward one corner — all the objects pile up, the system becomes unresponsive in that zone, and everything else sits idle. It is a visual representation of hot-spot failure.”
The simulation’s real-time controls mirror what infrastructure engineers do during incident response: adjusting weights, redirecting traffic, attempting to rebalance a system that has already tipped into an unstable state. The difference is that in production, the engineer is working blind — monitoring dashboards, watching latency percentiles, inferring system state from telemetry. Gravity Drift makes the consequences of rebalancing decisions immediately visible.
“I have seen engineers make production incidents worse by over-correcting,” Atamanchuk says. “They see traffic concentrated on three servers, panic, and redistribute aggressively — which triggers health check failures, which removes healthy nodes from the pool, which concentrates traffic further. Gravity Drift reproduces this amplification dynamic. Pull gravity too hard in the opposite direction and the objects do not settle — they oscillate. The system never reaches equilibrium because the correction is as disruptive as the original failure.”
The project scored perfect marks across all three criteria because the instability is systemic rather than decorative. The physics are the content. Remove the gravity manipulation, and there is no project — which is the hallmark of submissions that internalized the hackathon’s theme rather than applying it as a layer.
Drift Detection in Reverse
In deployment pipelines, drift detection is a safety mechanism. Tools like AWS Config, Terraform plan, and ArgoCD continuously compare the running state of infrastructure against the declared state. When discrepancies appear — a security group modified manually, an environment variable changed outside the pipeline — the system alerts or automatically remediates. The goal is to ensure that what is running matches what was intended.
System Drift by Commit & Conquer scored 4.70/5.00 and takes this concept to its logical inversion. It is a rhythm-based memory puzzle game where the system actively drifts from its own rules. Instructions change mid-game. The sanity meter degrades based on player errors. Entropy accumulates as the player interacts, and the game adapts its behavior — not to help the player, but to destabilize them further.
“This project captures a pattern I see in incident response that nobody talks about publicly,” Atamanchuk observes. “During a prolonged outage, the runbook itself drifts. You start with a procedure, but conditions change faster than you can update the procedure. Step four assumes step three succeeded, but step three partially failed. You are now operating a system that has drifted from its own recovery plan.”
System Drift implements five escalating phases of psychological pressure, each introducing new forms of instability. Beat synchronization rewards rhythm, but the rhythm itself shifts. Instructions demand one color, then switch mid-action. The game tracks the player’s psychological responses and uses them as input to the destabilization engine.
“In SRE terminology, this is an adaptive adversary,” Atamanchuk explains. “The system is not randomly failing — it is failing in response to the operator’s actions. The better you perform, the more precisely the system targets your patterns. This is far more realistic than random failure injection. Real production incidents are not random. They exploit the specific assumptions your architecture makes.”
The project’s four-person team built a system that embodies a counterintuitive DevOps insight: the most dangerous state for a production system is not active failure. It is the moment between stability and failure, when operators believe they have control but the system has already drifted past the recovery threshold. System Drift gamifies that threshold crossing — and the player, like the on-call engineer, never sees it coming until it is too late.
Blast Radius as Architecture
Every incident response framework includes blast radius assessment. When a component fails, the first question is not “how do we fix it?” but “what else does this affect?” A database outage that affects a single internal tool has a small blast radius. A DNS failure that cascades across every service in the organization has a catastrophic one. The discipline of blast radius containment — circuit breakers, bulkheads, graceful degradation — represents decades of accumulated production wisdom.
Blast Radius by beTheNOOB, scoring 4.70/5.00, builds this concept into an interactive simulation. Users construct a service topology — servers, databases, load balancers, caches — introduce failures, and watch cascading latency and connection failures propagate through the architecture in real time. Revenue loss counters and user frustration metrics make the business impact of technical failures tangible.
“What this project gets right is the non-linearity,” Atamanchuk says. “In production, a slow database does not just make queries slower. It exhausts the connection pool on the application servers, which causes request queuing, which triggers timeout errors, which forces retry logic, which amplifies the load back onto the already-struggling database. Blast Radius simulates this amplification cascade, and it does so at a level of fidelity that surprised me for a 72-hour build.”
The simulation captures a principle that separates junior from senior infrastructure engineers: the failure point and the damage point are rarely the same component. A disk filling up on a logging server is the failure point. The damage accumulates on the application servers that block waiting for log writes to complete. Blast Radius teaches users to trace the propagation path rather than fixating on the origin.
“I evaluate this kind of thinking every day in production systems,” Atamanchuk notes. “Teams design circuit breakers around the component they expect to fail, but the circuit breaker itself becomes a failure mode when it trips too aggressively. Blast Radius does not yet model recovery mechanisms — and honestly, modeling the ways that recovery makes things worse would be a natural next step. Retry storms, thundering herds after a circuit breaker resets, auto-scaling that arrives sixty seconds too late — those recovery failures cause more outages than the original problems.”
Infrastructure Scars That Never Heal
Residual State by Glitch Permanence, scoring 4.60/5.00, addresses a reality that most infrastructure documentation ignores. Unlike systems designed to reset after failure, Residual State carries its damage forward permanently. Each collapse introduces lasting mutations that change future behavior. The system never recovers to its original state — it only adapts around its scars.
“Every significant incident leaves permanent artifacts in the system,” Atamanchuk explains. “We call them hotfixes, workarounds, temporary mitigations. They are never temporary. They become load-bearing elements of the architecture, and removing them risks breaking something that depends on the workaround without anyone knowing.”
Residual State includes a snapshot system allowing users to save and restore previous states — essentially infrastructure restore points. This mirrors checkpoint-based recovery patterns in production: EBS snapshots, database point-in-time recovery, infrastructure state files that can roll back a Terraform apply. The tension between “carry mutations forward” and “restore from clean state” is one that DevOps teams negotiate during every major incident.
“The hardest conversations I have during incident response are about rollback,” Atamanchuk says. “Rolling back sounds clean and safe, but the current state includes adaptations that were applied during previous incidents. Rolling back to a clean state might fix the current problem while reintroducing three problems that were solved by the accumulated workarounds. Residual State captures that dilemma — accept the mutations and work around them, or risk losing adaptations that are keeping the system alive.”
The project also generates an AI-produced poetic reflection after each collapse cycle. In production terms, these are post-incident retrospectives — documents that attempt to capture not just what happened and why, but what the organization learned from the failure. The best retrospectives, like Residual State’s generated reflections, acknowledge that the system will never be the same.
Runbooks for Reactors That Fight Back
The AZ-5 Protocol by Critical Operators, scoring 4.30/5.00, is named after the emergency shutdown button pressed during the Chernobyl disaster. Players manage a nuclear reactor that actively resists stability, balancing power output against cooling, pressure, and safety systems while the simulation introduces failures at unpredictable intervals.
“In DevOps, we write runbooks,” Atamanchuk observes. “Step-by-step procedures for handling known failure modes. The assumption is that if you follow the runbook, you can restore service. The AZ-5 Protocol challenges that assumption. The system does not just fail — it fights your recovery attempts. You adjust cooling, and pressure spikes. You reduce power, and the safety margins narrow. Every corrective action has side effects.”
This is the operational reality of complex systems. At scale, corrective actions are not independent operations — they are inputs to a system that responds to them. Increasing replica count to handle load also increases database connection pressure. Enabling rate limiting protects the backend but degrades the user experience. Every dial an engineer turns moves other dials that they may not be watching.
The AZ-5 Protocol implements narrow band operation — the system functions optimally only within a tight range of parameters, and any deviation triggers compounding instabilities. In infrastructure engineering, this manifests as the system that works perfectly at 70% CPU utilization but degrades nonlinearly at 85%. The relationship between load and performance is not a straight line; it is a cliff edge.
“What I found authentic about this project is the compound effect,” Atamanchuk says. “In the actual Chernobyl sequence, operators disabled multiple safety systems before the test that triggered the explosion. Each action in isolation seemed manageable. The compound effect was catastrophic. This project reproduces that dynamic — small adjustments accumulate until a threshold is crossed, and then everything goes at once. That is how real production incidents happen. Not a single dramatic failure, but a slow accumulation of small deviations that collectively push the system past its operating envelope.”
The SLA for Chaos
Across twelve evaluations, Atamanchuk identified a pattern that maps directly to how the infrastructure industry classifies system reliability. Some projects operated at what he describes as “four nines of instability” — highly controlled, deterministic failure patterns that could be predicted and characterized. Others operated closer to “two nines” — unstable in ways that were themselves unstable, producing different failure modes on each run.
“In production, we define SLAs, SLOs, and SLIs to measure reliability,” he explains. “An SLA promises 99.9% uptime. An SLO targets 99.95% internally. SLIs measure the actual error rate, latency percentile, and availability. These projects made me think about the equivalent metrics for intentional instability. How do you measure whether a system is failing well?”
The projects that scored highest in his evaluation were the ones whose instability had the most infrastructure behind it. Chaos Garden’s mutation mechanics operate through defined pathways. Gravity Drift’s physics follow deterministic rules even when the outcomes are chaotic. System Drift’s psychological destabilization adapts to player behavior through specific feedback loops, not random noise.
Projects in the lower third of his evaluations tended toward what he calls “unmonitored failure” — instability without observability. The system breaks, but there is no telemetry to understand why, no metrics to characterize how, no logs to trace the propagation path. In production, an outage without observability is the worst possible scenario. The system is down, and the engineer has no instruments to diagnose the cause.
“The best projects built monitoring into their chaos,” Atamanchuk notes. “Blast Radius has real-time latency metrics and revenue impact counters. Residual State tracks entropy correlation over time. System Drift measures sanity and entropy as explicit gauges. These are the equivalent of Prometheus dashboards and Grafana alerts — they make the system’s state legible, even when that state is deliberately unstable.”
The hackathon projects that scored highest were not the ones with the most dramatic failures — they were the ones that understood their failures most precisely.
“Zero-downtime infrastructure and intentional-downtime applications require the same engineering discipline,” Atamanchuk concludes. “The feedback loops, the state management, the blast radius analysis, the observability — it is all the same work. The only difference is the sign. In my career, I point those tools at failure and try to eliminate it. These teams pointed the same tools at failure and tried to cultivate it. The engineering quality is indistinguishable.”
System Collapse was organized by Hackathon Raptors, a Community Interest Company supporting innovation in software development. The event featured 26 teams competing across 72 hours, building systems designed to thrive on instability.
