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EKR-Software_Architecture-C5665466AD17
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engineering_knowledge_record
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feature_implementation
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[{"operation": "Plan", "content": "Planning feature_implementation for Software_Architecture with 2 sprints [Ref: decentralized governance: Microservices decentralize governance so each service can choose the best technology stack for its r]", "confidence": 3, "timestamp": "2026-07-16T16:33:59.977660+00:00"}, {"operati...
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[{"type": "observation", "content": "Engineering challenge in Technical Debt", "source": "Software_Architecture engineering scenario"}, {"type": "measurement", "content": "Engineering challenge in Technical Debt", "source": "Software_Architecture engineering scenario"}]
["KA-Software_Architecture-1782", "KA-Software_Architecture-9566", "KA-Software_Architecture-2684"]
{}
{}
EKR-Software_Architecture-C5665466AD17
engineering_knowledge_record
feature_implementation_Software_Architecture
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EKR-Testing-A5A2B1B728CC
incident_response_Testing
engineering_knowledge_record
Testing
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incident_response
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[{"operation": "Detect", "content": "Detected anomaly in Testing \\u2014 Kubernetes node failure takes down critical pods [Ref: testing for reliability: Testing for reliability includes unit tests, integration tests, load tests, and disaster recovery dr]", "confidence": 3, "timestamp": "2026-07-16T16:33:59.977660+00:00...
{}
{}
[{"type": "observation", "content": "No pod anti-affinity and single AZ deployment", "source": "Testing engineering scenario"}, {"type": "measurement", "content": "No pod anti-affinity and single AZ deployment", "source": "Testing engineering scenario"}]
{}
{}
{}
EKR-Testing-A5A2B1B728CC
engineering_knowledge_record
incident_response_Testing
1.0.0
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EKR-Performance-A21A4C5C139C
debugging_session_Performance
engineering_knowledge_record
Performance
3
3
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debugging_session
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[{"operation": "Reproduce", "content": "Reproduced issue consistently in staging environment [Ref: CDN: Content Delivery Networks (CDNs) cache content at edge servers close to users, reducing latency and ]", "confidence": 3, "timestamp": "2026-07-16T16:33:59.977660+00:00"}, {"operation": "Isolate", "content": "Isolated...
{}
{}
[{"type": "observation", "content": "Engineering challenge in Concurrency", "source": "Performance engineering scenario"}, {"type": "measurement", "content": "Engineering challenge in Concurrency", "source": "Performance engineering scenario"}, {"type": "log", "content": "Engineering challenge in Concurrency", "source"...
["KA-Performance-6045", "KA-Performance-7743", "KA-Performance-2456", "KA-Performance-2790"]
{}
{}
EKR-Performance-A21A4C5C139C
engineering_knowledge_record
debugging_session_Performance
1.0.0
discovery
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EKR-Algorithms-ECF8F88404C4
architecture_decision_Algorithms
engineering_knowledge_record
Algorithms
4
4
3
architecture_decision
70
[{"operation": "Research", "content": "Researching approaches for Engineering challenge in Dynamic Programming", "confidence": 3, "timestamp": "2026-07-16T16:33:59.977660+00:00"}, {"operation": "Analyze", "content": "Analysis reveals bottleneck in Graph Algorithms component", "confidence": 3, "timestamp": "2026-07-16T1...
[{"decision": "Use PostgreSQL over MongoDB", "context": "Context: architecture_decision in Algorithms", "alternatives": "[\"MongoDB\", \"Cassandra\"]", "outcome": "Strong consistency needs and complex joins required"}, {"decision": "Adopt gRPC for inter-service communication", "context": "Context: architecture_decision...
{}
[{"type": "observation", "content": "Engineering challenge in Dynamic Programming", "source": "Algorithms engineering scenario"}, {"type": "measurement", "content": "Engineering challenge in Dynamic Programming", "source": "Algorithms engineering scenario"}, {"type": "log", "content": "Engineering challenge in Dynamic ...
["KA-Algorithms-7252", "KA-Algorithms-8159", "KA-Algorithms-4823", "KA-Algorithms-4662", "KA-Algorithms-5244"]
{}
{}
EKR-Algorithms-ECF8F88404C4
engineering_knowledge_record
architecture_decision_Algorithms
1.0.0
discovery
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EKR-Human_Factors-990F2632F1F7
architecture_decision_Human_Factors
engineering_knowledge_record
Human_Factors
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architecture_decision
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[{"operation": "Research", "content": "Researching approaches for Engineering challenge in Collaboration", "confidence": 3, "timestamp": "2026-07-16T16:33:59.977660+00:00"}, {"operation": "Analyze", "content": "Analysis reveals bottleneck in Communication component", "confidence": 3, "timestamp": "2026-07-16T16:33:59.9...
[{"decision": "Implement CQRS pattern", "context": "Context: architecture_decision in Human_Factors", "alternatives": "[\"CRUD\", \"Event Sourcing\"]", "outcome": "Read/write workloads have different scaling needs"}, {"decision": "Use PostgreSQL over MongoDB", "context": "Context: architecture_decision in Human_Factors...
{}
[{"type": "observation", "content": "Engineering challenge in Collaboration", "source": "Human_Factors engineering scenario"}, {"type": "measurement", "content": "Engineering challenge in Collaboration", "source": "Human_Factors engineering scenario"}]
["KA-Human_Factors-1756", "KA-Human_Factors-9510"]
{}
{}
EKR-Human_Factors-990F2632F1F7
engineering_knowledge_record
architecture_decision_Human_Factors
1.0.0
discovery
2026-07-16T16:33:59.977660+00:00
2026-07-16T16:33:59.977660+00:00
discovery
8
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EKR-Networking-528057672DE4
documentation_Networking
engineering_knowledge_record
Networking
1
2
3
documentation
72
[{"operation": "Audit", "content": "Engineering step Audit for documentation in Networking [Ref: circuit breaker: The Circuit Breaker pattern prevents cascading failures by detecting when a downstream service is fa]", "confidence": 3, "timestamp": "2026-07-16T16:33:59.981507+00:00"}, {"operation": "Outline", "content":...
{}
{}
[{"type": "observation", "content": "Engineering challenge in gRPC", "source": "Networking engineering scenario"}]
["KA-Networking-9984", "KA-Networking-7664", "KA-Networking-2080", "KA-Networking-6396", "KA-Networking-2457"]
{}
{}
EKR-Networking-528057672DE4
engineering_knowledge_record
documentation_Networking
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EKR-AI_Engineering-B51E47B20FB1
performance_optimization_AI_Engineering
engineering_knowledge_record
AI_Engineering
3
3
3
performance_optimization
73
[{"operation": "Measure", "content": "Measured CPU, baseline 960ms [Ref: AI observability: AI systems require specialized observability: tracking embeddings drift, hallucination rates, token ]", "confidence": 3, "timestamp": "2026-07-16T16:33:59.981507+00:00"}, {"operation": "Analyze", "content": "Analysis reveals bott...
{}
{}
[{"type": "observation", "content": "Thundering herd on cache expiry", "source": "AI_Engineering engineering scenario"}, {"type": "measurement", "content": "Thundering herd on cache expiry", "source": "AI_Engineering engineering scenario"}, {"type": "log", "content": "Thundering herd on cache expiry", "source": "AI_Eng...
{}
{}
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EKR-AI_Engineering-B51E47B20FB1
engineering_knowledge_record
performance_optimization_AI_Engineering
1.0.0
discovery
2026-07-16T16:33:59.981507+00:00
2026-07-16T16:33:59.981507+00:00
discovery
7
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EKR-Foundations-E134E6021BD1
technical_debt_Foundations
engineering_knowledge_record
Foundations
3
3
3
technical_debt
74
[{"operation": "Identify", "content": "Identified code smell in Probability", "confidence": 3, "timestamp": "2026-07-16T16:33:59.981507+00:00"}, {"operation": "Quantify", "content": "Engineering step Quantify for technical_debt in Foundations [Ref: trade-off thinking: Engineering is about trade-offs: consistency vs ava...
{}
{}
[{"type": "observation", "content": "Engineering challenge in Logic", "source": "Foundations engineering scenario"}, {"type": "measurement", "content": "Engineering challenge in Logic", "source": "Foundations engineering scenario"}]
{}
{}
{}
EKR-Foundations-E134E6021BD1
engineering_knowledge_record
technical_debt_Foundations
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EKR-Networking-7F605D1C99BA
bug_fix_Networking
engineering_knowledge_record
Networking
2
3
3
bug_fix
75
[{"operation": "Observe", "content": "In Networking, observed Memory leak in cache layer [Ref: retry with backoff: Clients making RPCs must implement exponential backoff with jitter for retries to prevent retry stor]", "confidence": 3, "timestamp": "2026-07-16T16:33:59.981507+00:00"}, {"operation": "Diagnose", "content...
{}
{}
[{"type": "observation", "content": "Evicted entries not garbage collected", "source": "Networking engineering scenario"}, {"type": "measurement", "content": "Evicted entries not garbage collected", "source": "Networking engineering scenario"}, {"type": "log", "content": "Evicted entries not garbage collected", "source...
["KA-Networking-8308"]
{}
{}
EKR-Networking-7F605D1C99BA
engineering_knowledge_record
bug_fix_Networking
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EKR-Software_Architecture-5847226658E1
feature_implementation_Software_Architecture
engineering_knowledge_record
Software_Architecture
3
2
3
feature_implementation
76
[{"operation": "Plan", "content": "Planning feature_implementation for Software_Architecture with 2 sprints [Ref: bulkhead pattern: The Bulkhead pattern isolates system components into pools so failure in one pool does not cascade, ]", "confidence": 3, "timestamp": "2026-07-16T16:33:59.981507+00:00"}, {"operation": "De...
{}
{}
[{"type": "observation", "content": "Engineering challenge in Technical Debt", "source": "Software_Architecture engineering scenario"}, {"type": "measurement", "content": "Engineering challenge in Technical Debt", "source": "Software_Architecture engineering scenario"}]
{}
{}
{}
EKR-Software_Architecture-5847226658E1
engineering_knowledge_record
feature_implementation_Software_Architecture
1.0.0
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End of preview. Expand in Data Studio

TOON Dataset v0.4 — 1M Engineering Knowledge Records

A structured engineering knowledge dataset for training autonomous coding models. Each record (EKR) captures an engineering episode with reasoning chains, decisions, tradeoffs, and evidence.

Data Format

This dataset is available in two formats:

1. Parquet (recommended for ML)

  • dataset_1m.parquet — 279 MB
  • 28 flat columns, loadable via datasets library
  • List fields (reasoning, decisions, etc.) serialized as JSON strings

2. TOON (native format)

  • dataset_1m.toon — 2.54 GB
  • Structured indented format preserving full hierarchy
  • Requires TOON parser for loading

Quick Start

from datasets import load_dataset

ds = load_dataset("Manthanbro1/Coding", split="train")
print(ds[0]["reasoning"])  # JSON string of reasoning steps

Data Fields

Column Type Description
id string Unique EKR identifier
name string Human-readable name
type string Record type
domain string Engineering domain (Networking, Cloud, Security, etc.)
difficulty int Difficulty level (0-6)
quality_score int Quality level (2-5 Q2/Q3/Q4)
confidence int Confidence level
episode_type string Type of engineering episode
throughput_step int Generation throughput step
reasoning JSON string List of reasoning steps (operation, content, confidence)
decisions JSON string List of decisions with context and alternatives
tradeoffs JSON string List of tradeoffs considered
evidence JSON string List of evidence items
knowledge_atoms JSON string References to knowledge atoms
tags JSON string Record tags
properties JSON string Additional properties
metadata_id string Metadata record ID
metadata_object_type string Metadata object type
metadata_object_name string Metadata object name
metadata_version string Metadata version
metadata_lifecycle_stage string Metadata lifecycle stage
metadata_created_at string Metadata creation timestamp
metadata_updated_at string Metadata update timestamp
lifecycle_stage string Record lifecycle stage
reasoning_count int Number of reasoning steps
decision_count int Number of decisions
evidence_count int Number of evidence items
atom_count int Number of knowledge atom references

Quality Distribution

Quality Count Percentage
Q2 144,138 14.4%
Q3 740,140 74.0%
Q4 115,722 11.6%

Domains (15 total)

Distributed_Systems, Software_Architecture, Production_Engineering, Databases, Networking, DevOps, Security, Performance, Testing, Cloud, Systems, AI_Engineering, Human_Factors, Foundations, Algorithms

12 Episode Types

bug_fix, code_review, design_discussion, documentation, feature_implementation, incident_response, performance_optimization, refactoring, research, rollback, security_review, technical_debt

Building

Dataset generated by TOON Dataset Compiler.

git clone https://github.com/ManthanGadiya/Dataset.git
cd Dataset
pip install -r requirements.txt
py -3.12 scripts/generate_production.py

License

See LICENSE in the source repository.

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