A single Telugu character crashed every iPhone it touched. The bug, buried deep in Apple’s CoreText rendering engine, triggered a cascade failure that disabled iMessage, WhatsApp, and any application that attempted to render the character. Apple scrambled to release iOS 11.2.6, an emergency patch addressing a vulnerability that one engineer had methodically traced through the rendering pipeline and reported. That engineer was Igor Bulyga.
The instinct that led Bulyga to probe a text rendering edge case the same instinct that tells a production engineer “this system will break here, under these conditions” is exactly what he brought to evaluating 26 hackathon projects designed around intentional system failure.
System Collapse 2026, organized by Hackathon Raptors, challenged teams to build software where breaking, adapting, and collapsing are features rather than bugs. Twenty-six teams spent 72 hours constructing digital ecosystems, simulations, and games that embrace instability as a core mechanic. Bulyga, a Software Engineer at Reddit with over a decade of experience in system reliability and security analysis, evaluated twelve of those submissions.
“The difference between a bug and a feature is intent,” Bulyga explains. “At Reddit, we deal with cascading failures at scale a single misbehaving microservice can take down comment threading for millions of users. These hackathon projects are doing something interesting: they’re making that cascade the point.”
When Failure Is the Architecture
The strongest submission in Bulyga’s batch was The AZ-5 Protocol by Critical Operators a nuclear reactor simulation named after the emergency shutdown button pressed during the Chernobyl disaster. Players manage a reactor that actively resists stability, balancing power output against cooling, pressure, and safety systems while the simulation introduces failures at unpredictable intervals.
“This project understood something fundamental about real systems,” Bulyga notes. “Stability isn’t a state you achieve it’s a state you continuously maintain. The moment you stop actively managing it, entropy takes over.”
The AZ-5 Protocol implements what engineers call “narrow band operation” the system functions optimally only within a tight range of parameters, and any deviation triggers compounding instabilities. In reactor physics, this is the positive void coefficient that made Chernobyl’s RBMK design dangerous. In software engineering, it’s the production system that works perfectly at 70% capacity but degrades nonlinearly at 85%.
At Reddit, Bulyga encounters this pattern regularly. “Content ranking algorithms, real-time vote counting, comment tree rendering these are all systems that operate in narrow bands. Push beyond the band, and you don’t get graceful degradation. You get cliff-edge failures.”
The AZ-5 Protocol captures this cliff-edge dynamic with surprising fidelity for a 72-hour build. The simulation’s design decision to make failures compound rather than occur independently reflects genuine systems engineering knowledge. “In the real Chernobyl sequence, operators disabled multiple safety systems before the test that triggered the explosion,” Bulyga observes. “Each individual action seemed manageable. The compound effect was catastrophic. This project reproduces that compound dynamic small decisions accumulate until a threshold is crossed, and then everything goes at once.”
Blast Radius as a Design Principle
In incident response at companies like Reddit, “blast radius” refers to the scope of impact when a component fails. A database outage might affect only one feature (small blast radius) or cascade across the entire platform (catastrophic blast radius). The team beTheNOOB built this concept into an interactive simulation called, simply, Blast Radius.
Blast Radius transforms distributed system failure from an abstract concept into a visceral, visual experience. Users construct a service topology, introduce failures, and watch cascading latency and connection failures propagate through the system in real time.
“What makes this project interesting isn’t the visualization itself,” Bulyga says. “It’s that they’ve captured the non-obvious truth about cascading failures: the initial failure point is rarely where the damage accumulates. A slow database doesn’t kill the database it kills the application servers waiting on the database, which kills the load balancer health checks, which triggers failover, which overwhelms the secondary.”
This is the pattern Amazon documented after its 2017 S3 outage, where a single typo in a maintenance command cascaded through dependent services until a significant portion of the internet was affected. Blast Radius lets users develop intuition for these cascades without needing an actual production incident as a teaching tool.
The project scored 3.30/5.00 in Bulyga’s evaluation technically competent but limited by scope. “The simulation captures the cascade well, but it doesn’t yet model recovery,” he observes. “In production, the interesting part isn’t how systems fail we know that. The interesting part is how they attempt to heal, and how those healing mechanisms sometimes make things worse. Circuit breakers that trip too aggressively. Retry storms that amplify load. Auto-scaling that kicks in sixty seconds too late.” Blast Radius models the descent; the next step would be modeling the contested recovery.
Evolution Through Neglect
If The AZ-5 Protocol models active system management and Blast Radius models cascading failure, Chaos Garden by Bit Brains explores what happens when systems are simply abandoned. In Chaos Garden, plants evolve based on player attention or lack of it. Leave the garden unattended, and the flora mutates into something “beautifully alien,” as the developers describe it. Neglect doesn’t kill the system; it transforms it.
“This maps directly to what we see in long-running production systems,” Bulyga observes. “Nobody talks about the services that have been running for three years without a deploy. Those systems aren’t stable they’re accumulating technical debt, operating on deprecated APIs, slowly drifting from the assumptions their original developers made. They work, but they’ve mutated.”
The concept that Chaos Garden captures where inattention produces transformation rather than termination is central to a growing body of systems thinking. Google’s Site Reliability Engineering handbook describes “software rot” not as active degradation but as the environment changing around static code. A system that worked perfectly in 2023 may behave unpredictably in 2026, not because it changed, but because everything around it did.
Chaos Garden earned a 4.00/5.00 in Bulyga’s evaluation, tying for the highest score in his batch in part because its mutation mechanic wasn’t decorative. “The mutations affect gameplay,” he explains. “Cross-pollination between mutated plants creates emergent behaviors that the developers didn’t explicitly program. That’s genuine emergent complexity, not just a visual gimmick.”
For Bulyga, the project resonates with a pattern he encounters in security work. “The most dangerous vulnerabilities aren’t the ones we introduce intentionally. They’re the ones that emerge from the interaction between components that were each individually secure. Chaos Garden demonstrates this emergence safe plants combining into unpredictable hybrids in a way that makes the concept viscerally understandable.”
The Permanence of Production Scars
Residual State by Glitch Permanence takes the mutation concept further into uncomfortable territory. 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.
“This is the most realistic project in the batch, even though its creator probably doesn’t know it,” Bulyga says. “Every significant production incident leaves permanent artifacts. We call them ‘hotfixes,’ ‘workarounds,’ ‘temporary mitigations that became permanent.’ The system I work on today at Reddit is shaped by every outage it has ever survived.”
Residual State includes an AI-generated poetic reflection after each collapse cycle an artistic choice that Bulyga found technically unnecessary but conceptually resonant. “In post-incident reviews, we write retrospectives. They’re supposed to be analytical, but the best ones capture something about the emotional experience of the failure. Residual State automates that reflection.”
The project also implements a snapshot system allowing users to save and restore previous states, essentially creating system restore points. This mirrors a common production pattern: checkpoint-based recovery, where systems can roll back to a known-good state when mutations accumulate beyond tolerance.
“The tension between ‘carry scars forward’ and ‘roll back to clean state’ is one that every engineering team navigates during incidents,” Bulyga explains. “At Reddit, we have services where the configuration has been patched so many times that nobody fully understands the current state. Rolling back to a clean configuration would fix the accumulated weirdness but might also undo critical production adjustments made during past incidents. Residual State captures that dilemma do you accept the mutations, or do you risk losing adaptations that are keeping the system alive?”
The project earned a lower aggregate score of 2.70/5.00, primarily due to technical execution gaps. But Bulyga flags it as conceptually the most sophisticated. “Technical polish and conceptual depth are different axes. This team understood systems thinking at a level that some of the higher-scoring projects didn’t reach. Give them more time and the implementation would match the ideas.”
Imperfect Drift and the Paradox of Success
Berlin’s Imperfect Drift introduces a mechanic that experienced production engineers immediately recognize: success as a failure trigger. In the game, every 20 kills triggers “Failure Mode” controls corrupt, the screen rotates, momentum inverts. The better you perform, the more aggressively the system destabilizes.
At Reddit’s scale, this pattern manifests as load-induced failure. A viral post doesn’t just add load linearly it triggers recommendation algorithms that amplify the content, which drives more traffic, which triggers more recommendations. Success breeds more success until the system can no longer sustain the load. Bulyga draws the parallel explicitly: “Imperfect Drift gamifies what we call ‘thundering herd.’ The game punishes you for being good, which is exactly what happens when a production system’s own success overwhelms its capacity.”
Imperfect Drift earned 4.00/5.00 in Bulyga’s evaluation, matching The AZ-5 Protocol and Chaos Garden. The game’s implementation under 72 hours includes weapon switching, boss progression, and multiple failure mode effects (drift forces, input corruption, momentum inversion, screen rotation, sprite spin). “The scope is ambitious for the timeframe,” he notes. “More importantly, the failure modes aren’t random they’re mechanically consistent. Input corruption follows a pattern. Players can learn to operate within the chaos, which is exactly what production engineers do during incidents.”
System Sketch and the Observer’s Dilemma
System Architects’ System Sketch occupies a different category from the other submissions. Rather than creating a system that collapses, they built a tool for designing systems and then intentionally stress-testing them. Users draw distributed architectures load balancers, application servers, databases, caches and then simulate traffic that reveals how these architectures fail.
“Most of the other projects embody system collapse,” Bulyga explains. “This one teaches it. The value proposition is different: instead of experiencing failure viscerally, users understand failure architecturally.”
System Sketch implements auto-scaling and caching strategies as recovery mechanisms, allowing users to test whether their mitigation approaches actually work under load. This brings it closer to real chaos engineering tools like Netflix’s Chaos Monkey or Gremlin tools designed to verify that production systems can survive the failures they claim to handle.
“The gap between ‘our architecture handles failure’ and ‘we’ve proven our architecture handles failure’ is where most outages live,” Bulyga says. “This project puts users on the right side of that gap.”
What separates System Sketch from a basic diagramming tool is its feedback loop: users design, stress-test, observe failure, redesign. This iterative cycle mirrors the chaos engineering methodology that Netflix pioneered and that most large-scale platforms, including Reddit, now practice in some form. “We don’t just hope our systems handle failure,” Bulyga explains. “We inject failure intentionally, observe the response, and fix the gaps before users encounter them. System Sketch gives that methodology to people who don’t have a production system to break.”
The Taxonomy of Intentional Failure
Across his twelve evaluations, Bulyga identified a spectrum of approaches to the “systems that thrive on instability” theme. At one end: projects that treated instability as aesthetics screen shake, glitch effects, random visual corruption. At the other: projects that embedded instability into the system’s core logic, where collapse changes the rules rather than just the appearance.
“I started categorizing submissions by whether removing the instability mechanic would fundamentally change the project,” he explains. “For the strongest entries AZ-5 Protocol, Chaos Garden, Imperfect Drift removing the collapse mechanic removes the project. The instability isn’t decoration. It’s load-bearing architecture.”
Projects in the lower half of his evaluations tended toward what Bulyga calls “chaos wallpaper” instability layered on top of otherwise conventional applications. A to-do list that shuffles priorities. A typing game with increasing difficulty. A platformer with intermittent physics changes. “These aren’t bad projects,” he clarifies. “They’re competent implementations with instability as a feature, not as the foundation. The distinction matters because it’s the same distinction we make in production systems between ‘resilient to failure’ and ‘designed around failure.'”
The first category describes most well-engineered software: it handles errors, retries failed operations, degrades gracefully. The second category describes systems like Kubernetes’ self-healing pods, CRDTs that resolve conflicts through mathematical guarantees rather than prevention, or event-sourced architectures where state is reconstructed from a history of changes rather than maintained as a single source of truth. These systems don’t merely tolerate instability they depend on it.
What Controlled Chaos Reveals About Engineering Maturity
Evaluating twelve projects built around intentional system failure gave Bulyga a perspective that parallels his experience in production environments. The vulnerability that forced Apple’s emergency patch wasn’t discovered through routine testing it was found by an engineer who understood that text rendering systems have edge cases that standard test suites miss. The same pattern of thinking probing the boundaries where systems break separated the strongest hackathon submissions from the adequate ones.
“A system that breaks randomly isn’t interesting,” he says. “A system that breaks predictably under specific conditions and then adapts to those conditions is interesting. That’s the difference between noise and signal. The best projects in this hackathon created signals.”
The pattern holds in production engineering. Random failures are noise hardware glitches, network blips, cosmic rays flipping bits. The failures that teach engineers something are the deterministic ones: the memory leak that triggers under specific query patterns, the race condition that manifests only at exact concurrency thresholds, the cascade that requires three independent subsystems to be under load simultaneously.
What emerged from the System Collapse evaluations is a framework for thinking about failure that applies well beyond hackathons. The projects that scored highest weren’t necessarily the most technically polished they were the ones that demonstrated the deepest understanding of how real systems break. Narrow band operation. Cascading dependencies. Mutation through neglect. Success-induced overload. Permanent scarring from past incidents. These aren’t academic concepts. They’re the daily reality of running software at scale.
“System Collapse as a hackathon theme is better engineering education than most computer science curricula provide,” Bulyga concludes. “These 26 teams spent 72 hours learning what takes most engineers years of production experience to internalize: systems don’t fail in the ways you expect, and the most interesting systems are the ones that turn their failures into features.”
System Collapse 2026 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. Igor Bulyga served as a judge evaluating projects for technical execution, system design, and creativity and expression.
He is a computer engineer by day and a gamer by night. He has played many different games, like retro games like Contra, classic Mario, to AAA games. He likes to share his passion for gaming through his guides.