In this post event charts are interpreted as technical charts, diagrams showing how complex digital systems fail. The twelve houses are read as layers of modern infrastructure: Capacity, routing, background jobs, distributed clusters, and more.

This post was generated from a conversation with Chatgpt and is meant to serve as a starting point for interpreting technical charts. As more charts are added to the analysis pool, future blog post will reflect any changes.

This guide includes:

• Technical meanings for all 12 houses
• Three case studies:
  • CrowdStrike outage (2024)
  • Cloudflare bot-management outage (2025)
  • AWS outage (2025)
• A comparison of shared signatures across all three charts

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Technical House Meanings

1st House — Interface & Symptoms
What users see: entry points, login screens, visible errors, front-end “health.”

2nd House — Capacity, Limits, Resources
Storage, memory, file sizes, throughput limits, CPU [computer processing unit] quotas, thresholds.

3rd House — Communication Layer
APIs [application program interface], internal protocols, message passing, config propagation, cron scripts.

4th House — Foundational Architecture
Kernels, hypervisors, proxies, root assumptions the system is built on.

5th House — Generated Output
Logs, machine-learning scores, analytics, derived data products.

6th House — Maintenance & Background Jobs
Scheduled tasks, monitoring systems, health checks, update scripts.

7th House — External Actors & Threat Models
Attackers, outside users, third-party APIs application, competitor traffic.

8th House — Hidden Dependencies & Cascades
Clusters, replication, deep technical debt, “invisible” shared systems.

9th House — Global Scale & Governance
Regions, CDNs [content delivery network], permission models, policy, worldwide distribution.

10th House — Public Impact & Reputation
SLA [service level agreement] layer, public visibility, status pages, major operational impact.

11th House — Distributed Systems & Meshes
Microservices, container fleets, coordinated networked systems.

12th House — Hidden Bugs & Unknown Faults
Dormant defects, silent corruption, misdiagnosis, confusing symptoms.

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CrowdStrike Outage (19 July 2024)

Aquarius rising; a network-scale, infrastructure-wide event.

• 2nd house emphasis (Pisces): resource assumptions. Routine update uses capacity in an unexpected way.
• 4th house (Aries with Chiron): a wound to the foundational layer — a kernel-level crash.
• 6th house Cancer cluster: a routine update meant to “protect” the fleet becomes the source of failure.
• 11th house Capricorn: large, centrally managed distributed fleet all receiving the same flawed update.

Technical Summary:
A routine update (6th) pushed to a massive managed fleet (11th) destabilizes the core OS [OSI – open systems interconnection] layer (4th) after violating hidden capacity or safety assumptions (2nd).

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Cloudflare Bot-Management Outage (18 November 2025)

• 1st & 7th houses: Symptoms mimic an attack, causing early misdiagnosis.
• 2nd house cluster: A feature file exceeds a hard coded limit (capacity breach → system panic).
• 3rd & 6th houses: A query and recurring job generate the broken file every five minutes.
• 8th house Uranus: Hidden dependency breaks when schema permissions expose extra metadata.
• 11th house impact: A distributed global fleet receives the flawed file simultaneously.
• 12th house pattern: Good/bad file alternation creates diagnostic confusion.

Technical Summary:
A recurring job (6th) produces a config file from a shared cluster (8th). A permissions change doubles its size (2nd), exceeding a fixed feature limit and crashing the proxy across the distributed network (11th). Alternating good/bad configs cause confusion and resemble an attack (7th/12th).

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AWS Outage (20 October 2025)

• Leo rising: central provider in the spotlight; platform-level failure.
• 2nd house Virgo: over-constrained resource logic; finely tuned limits.
• 3rd & 9th houses: routing, cross-region communication, policy layers.
• 4th house Scorpio: highly complex internal control plane or identity layer.
• 6th house Capricorn: rigid, structured maintenance or control-plane job misfires.
• 11th house Gemini/Taurus MC: wide network of customers impacted through shared backbone.

Technical Summary:
A highly structured control-plane process (6th) tied to complex internal architecture (4th) creates a resource or routing disruption (2nd/3rd), which spreads across the global cloud infrastructure (9th/11th).

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Outage Patterns Across All Three Charts

1. The 2nd House Is Always Involved

Every outage begins when a resource limit, capacity threshold, or fixed boundary is crossed:

• CrowdStrike: Unsafe OS-level assumptions
• Cloudflare: Feature count limit exceeded
• AWS: Fine-grained capacity logic fails under load

2. The 6th House (Routine Jobs) Causes Non-Routine Disasters

• Endpoint update propagation (CrowdStrike)
• Recurring config-file generation job (Cloudflare)
• Control-plane or maintenance workflow (AWS)

3. 4th + 11th = Foundational Layer Meets Distributed Fleet

These outages combine a fragile base layer (4th) with a globally coordinated distributed system (11th). Once the foundation fails, the entire network feels it.

4. 8th & 12th Houses: Hidden Problems & Misdiagnosis

• Hidden schema behavior (Cloudflare)
• Deep OS-level interaction (CrowdStrike)
• Complex internal control logic (AWS)

These houses represent the parts of the system no one sees clearly until after the outage.

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Conclusion

When interpreted as technical diagrams, event charts reveal where in the digital stack the fault originates:

• CrowdStrike: kernel-level update pushed to a massive fleet
• Cloudflare: configuration file exceeding a fixed feature limit
• AWS: internal control-plane or capacity logic misfire

The main houses active in all three cases are the 2nd, 4th, 6th, and 11th — a classic signature of modern outages:

“A small change in a routine process destabilizes the foundation of a highly distributed system, crossing an unseen limit and causing a cascading failure.”

This framework can be applied to future outages to identify which layer of the system — capacity, maintenance, architecture, or distribution — is most likely involved.