Datasets:
WithinUsAI
/
Genesis_AI_Code_10k

Dataset card Data Studio Files
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2026-01-01 00:00:00
2026-01-01 00:00:00
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train_06100
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
code
expert
Task: code Topic: SWE-bench style real-repo evaluation Difficulty: expert Target language: JavaScript Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "cost_latency_tradeoffs", "tooling", "evaluation_metrics" ] }
train_06101
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
data_pipeline
expert
Task: data_pipeline Topic: SWE-bench style real-repo evaluation Difficulty: expert Target language: Go Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "Go", "developer_needs": [ "documentation", "governance", "repo_scale_reasoning" ] }
train_06102
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
eval
expert
Task: eval Topic: Governance, provenance, and licensing for code data Difficulty: expert Target language: Rust Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "Rust", "developer_needs": [ "security_gates", "evaluation_metrics", "ci_integration" ] }
train_06103
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
design
advanced
Task: design Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced Target language: Java Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "repo_scale_reasoning", "ci_integration", "security_gates" ] }
train_06104
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
eval
expert
Task: eval Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: expert Target language: SQL Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "SQL", "developer_needs": [ "tests_are_truth", "cost_latency_tradeoffs", "tooling" ] }
train_06105
2026-01-01T00:00:00
Extended context and repo-scale understanding
eval
intermediate
Task: eval Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: SQL Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "SQL", "developer_needs": [ "documentation", "ci_integration", "evaluation_metrics" ] }
train_06106
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
design
foundation
Task: design Topic: Tool calling, sandboxes, and CI integration Difficulty: foundation Target language: C# Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "C#", "developer_needs": [ "ci_integration", "tests_are_truth", "documentation" ] }
train_06107
2026-01-01T00:00:00
Extended context and repo-scale understanding
design
foundation
Task: design Topic: Extended context and repo-scale understanding Difficulty: foundation Target language: C# Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "C#", "developer_needs": [ "evaluation_metrics", "tooling", "governance" ] }
train_06108
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
review
intermediate
Task: review Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: intermediate Target language: Rust Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "Rust", "developer_needs": [ "ci_integration", "repo_scale_reasoning", "reproducibility" ] }
train_06109
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
design
advanced
Task: design Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: Java Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "governance", "reproducibility", "security_gates" ] }
train_06110
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
data_pipeline
expert
Task: data_pipeline Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: expert Target language: Python Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "Python", "developer_needs": [ "governance", "repo_scale_reasoning", "security_gates" ] }
train_06111
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
foundation
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: foundation Target language: Rust Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Rust", "developer_needs": [ "tooling", "security_gates", "tests_are_truth" ] }
train_06112
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
code
expert
Task: code Topic: Reasoning-first coding models and tunable deliberation Difficulty: expert Target language: Go Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Go", "developer_needs": [ "tooling", "tests_are_truth", "security_gates" ] }
train_06113
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
code
expert
Task: code Topic: Tool calling, sandboxes, and CI integration Difficulty: expert Target language: Java Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "evaluation_metrics", "cost_latency_tradeoffs", "tests_are_truth" ] }
train_06114
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
data_pipeline
expert
Task: data_pipeline Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: expert Target language: SQL Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "SQL", "developer_needs": [ "governance", "tooling", "ci_integration" ] }
train_06115
2026-01-01T00:00:00
Secure code generation and policy gates
compare
intermediate
Task: compare Topic: Secure code generation and policy gates Difficulty: intermediate Target language: Python Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "Python", "developer_needs": [ "governance", "tooling", "tests_are_truth" ] }
train_06116
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
data_pipeline
advanced
Task: data_pipeline Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: Go Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "Go", "developer_needs": [ "cost_latency_tradeoffs", "security_gates", "documentation" ] }
train_06117
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
agent_loop
expert
Task: agent_loop Topic: Model merging, distillation, and continued pretraining Difficulty: expert Target language: Bash Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "Bash", "developer_needs": [ "governance", "security_gates", "reproducibility" ] }
train_06118
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
agent_loop
foundation
Task: agent_loop Topic: Governance, provenance, and licensing for code data Difficulty: foundation Target language: Go Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "Go", "developer_needs": [ "tooling", "reproducibility", "evaluation_metrics" ] }
train_06119
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
data_pipeline
foundation
Task: data_pipeline Topic: Model merging, distillation, and continued pretraining Difficulty: foundation Target language: Bash Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "Bash", "developer_needs": [ "tooling", "cost_latency_tradeoffs", "reproducibility" ] }
train_06120
2026-01-01T00:00:00
Secure code generation and policy gates
code
intermediate
Task: code Topic: Secure code generation and policy gates Difficulty: intermediate Target language: Bash Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Bash", "developer_needs": [ "tests_are_truth", "reproducibility", "cost_latency_tradeoffs" ] }
train_06121
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
data_pipeline
expert
Task: data_pipeline Topic: Tool calling, sandboxes, and CI integration Difficulty: expert Target language: SQL Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "SQL", "developer_needs": [ "reproducibility", "cost_latency_tradeoffs", "repo_scale_reasoning" ] }
train_06122
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
code
advanced
Task: code Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: advanced Target language: JavaScript Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "governance", "documentation", "tooling" ] }
train_06123
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
eval
foundation
Task: eval Topic: SWE-bench style real-repo evaluation Difficulty: foundation Target language: Go Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "Go", "developer_needs": [ "repo_scale_reasoning", "evaluation_metrics", "documentation" ] }
train_06124
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
review
intermediate
Task: review Topic: SWE-bench style real-repo evaluation Difficulty: intermediate Target language: Bash Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "Bash", "developer_needs": [ "tests_are_truth", "reproducibility", "repo_scale_reasoning" ] }
train_06125
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
agent_loop
advanced
Task: agent_loop Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: JavaScript Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "JavaScript", "developer_needs": [ "tooling", "governance", "cost_latency_tradeoffs" ] }
train_06126
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
design
intermediate
Task: design Topic: Mixture-of-Experts (MoE) for code Difficulty: intermediate Target language: JavaScript Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "reproducibility", "governance", "tooling" ] }
train_06127
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
eval
expert
Task: eval Topic: Reasoning-first coding models and tunable deliberation Difficulty: expert Target language: Bash Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "Bash", "developer_needs": [ "reproducibility", "repo_scale_reasoning", "tests_are_truth" ] }
train_06128
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
compare
expert
Task: compare Topic: Model merging, distillation, and continued pretraining Difficulty: expert Target language: JavaScript Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "JavaScript", "developer_needs": [ "evaluation_metrics", "reproducibility", "repo_scale_reasoning" ] }
train_06129
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
design
expert
Task: design Topic: Model merging, distillation, and continued pretraining Difficulty: expert Target language: Go Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Go", "developer_needs": [ "ci_integration", "governance", "documentation" ] }
train_06130
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
design
foundation
Task: design Topic: Tool calling, sandboxes, and CI integration Difficulty: foundation Target language: Java Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "documentation", "evaluation_metrics", "repo_scale_reasoning" ] }
train_06131
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
compare
intermediate
Task: compare Topic: Mixture-of-Experts (MoE) for code Difficulty: intermediate Target language: SQL Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "SQL", "developer_needs": [ "cost_latency_tradeoffs", "repo_scale_reasoning", "ci_integration" ] }
train_06132
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
code
advanced
Task: code Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: advanced Target language: Java Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "security_gates", "repo_scale_reasoning", "evaluation_metrics" ] }
train_06133
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
code
intermediate
Task: code Topic: SWE-bench style real-repo evaluation Difficulty: intermediate Target language: Go Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Go", "developer_needs": [ "tooling", "security_gates", "governance" ] }
train_06134
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
data_pipeline
intermediate
Task: data_pipeline Topic: Reasoning-first coding models and tunable deliberation Difficulty: intermediate Target language: Rust Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "Rust", "developer_needs": [ "governance", "ci_integration", "reproducibility" ] }
train_06135
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
eval
expert
Task: eval Topic: Governance, provenance, and licensing for code data Difficulty: expert Target language: Java Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "Java", "developer_needs": [ "documentation", "ci_integration", "tooling" ] }
train_06136
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
compare
intermediate
Task: compare Topic: Model merging, distillation, and continued pretraining Difficulty: intermediate Target language: Java Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "Java", "developer_needs": [ "evaluation_metrics", "security_gates", "governance" ] }
train_06137
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
data_pipeline
intermediate
Task: data_pipeline Topic: Model merging, distillation, and continued pretraining Difficulty: intermediate Target language: C# Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "C#", "developer_needs": [ "documentation", "tooling", "reproducibility" ] }
train_06138
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
explain
expert
Task: explain Topic: SWE-bench style real-repo evaluation Difficulty: expert Target language: TypeScript Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "TypeScript", "developer_needs": [ "governance", "ci_integration", "repo_scale_reasoning" ] }
train_06139
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
review
foundation
Task: review Topic: Model merging, distillation, and continued pretraining Difficulty: foundation Target language: SQL Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "SQL", "developer_needs": [ "reproducibility", "ci_integration", "repo_scale_reasoning" ] }
train_06140
2026-01-01T00:00:00
Extended context and repo-scale understanding
code
intermediate
Task: code Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: SQL Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "SQL", "developer_needs": [ "reproducibility", "tests_are_truth", "repo_scale_reasoning" ] }
train_06141
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
review
expert
Task: review Topic: Tool calling, sandboxes, and CI integration Difficulty: expert Target language: Python Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "Python", "developer_needs": [ "repo_scale_reasoning", "documentation", "reproducibility" ] }
train_06142
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
eval
intermediate
Task: eval Topic: Mixture-of-Experts (MoE) for code Difficulty: intermediate Target language: JavaScript Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "JavaScript", "developer_needs": [ "governance", "tests_are_truth", "evaluation_metrics" ] }
train_06143
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
explain
intermediate
Task: explain Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: intermediate Target language: Java Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "ci_integration", "evaluation_metrics", "governance" ] }
train_06144
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
explain
intermediate
Task: explain Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: intermediate Target language: Java Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "cost_latency_tradeoffs", "tests_are_truth", "ci_integration" ] }
train_06145
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
explain
intermediate
Task: explain Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: SQL Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "SQL", "developer_needs": [ "cost_latency_tradeoffs", "tests_are_truth", "reproducibility" ] }
train_06146
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
foundation
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: foundation Target language: Python Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Python", "developer_needs": [ "ci_integration", "repo_scale_reasoning", "tests_are_truth" ] }
train_06147
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
design
advanced
Task: design Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced Target language: Java Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "reproducibility", "tests_are_truth", "repo_scale_reasoning" ] }
train_06148
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
eval
advanced
Task: eval Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced Target language: C# Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "C#", "developer_needs": [ "evaluation_metrics", "governance", "tooling" ] }
train_06149
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
explain
foundation
Task: explain Topic: Tool calling, sandboxes, and CI integration Difficulty: foundation Target language: Bash Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Bash", "developer_needs": [ "evaluation_metrics", "governance", "reproducibility" ] }
train_06150
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
eval
foundation
Task: eval Topic: Mixture-of-Experts (MoE) for code Difficulty: foundation Target language: SQL Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "SQL", "developer_needs": [ "evaluation_metrics", "reproducibility", "tooling" ] }
train_06151
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
agent_loop
advanced
Task: agent_loop Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: Go Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "Go", "developer_needs": [ "tooling", "governance", "reproducibility" ] }
train_06152
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
agent_loop
advanced
Task: agent_loop Topic: Governance, provenance, and licensing for code data Difficulty: advanced Target language: Python Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "Python", "developer_needs": [ "cost_latency_tradeoffs", "documentation", "repo_scale_reasoning" ] }
train_06153
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
review
advanced
Task: review Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: JavaScript Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "JavaScript", "developer_needs": [ "ci_integration", "documentation", "tooling" ] }
train_06154
2026-01-01T00:00:00
Extended context and repo-scale understanding
review
expert
Task: review Topic: Extended context and repo-scale understanding Difficulty: expert Target language: C# Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "C#", "developer_needs": [ "evaluation_metrics", "cost_latency_tradeoffs", "security_gates" ] }
train_06155
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
explain
expert
Task: explain Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: expert Target language: Bash Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Bash", "developer_needs": [ "evaluation_metrics", "documentation", "ci_integration" ] }
train_06156
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
agent_loop
foundation
Task: agent_loop Topic: Governance, provenance, and licensing for code data Difficulty: foundation Target language: SQL Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "SQL", "developer_needs": [ "tooling", "security_gates", "repo_scale_reasoning" ] }
train_06157
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
review
expert
Task: review Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: expert Target language: TypeScript Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "TypeScript", "developer_needs": [ "tests_are_truth", "cost_latency_tradeoffs", "documentation" ] }
train_06158
2026-01-01T00:00:00
Extended context and repo-scale understanding
design
intermediate
Task: design Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: JavaScript Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "ci_integration", "tests_are_truth", "documentation" ] }
train_06159
2026-01-01T00:00:00
Extended context and repo-scale understanding
explain
intermediate
Task: explain Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: Rust Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Rust", "developer_needs": [ "repo_scale_reasoning", "security_gates", "tooling" ] }
train_06160
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
code
advanced
Task: code Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: advanced Target language: C# Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "C#", "developer_needs": [ "tooling", "ci_integration", "security_gates" ] }
train_06161
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
advanced
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: Go Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Go", "developer_needs": [ "ci_integration", "governance", "security_gates" ] }
train_06162
2026-01-01T00:00:00
Extended context and repo-scale understanding
design
expert
Task: design Topic: Extended context and repo-scale understanding Difficulty: expert Target language: Go Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Go", "developer_needs": [ "ci_integration", "security_gates", "governance" ] }
train_06163
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
code
foundation
Task: code Topic: Tool calling, sandboxes, and CI integration Difficulty: foundation Target language: SQL Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "SQL", "developer_needs": [ "cost_latency_tradeoffs", "ci_integration", "security_gates" ] }
train_06164
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
data_pipeline
advanced
Task: data_pipeline Topic: Mixture-of-Experts (MoE) for code Difficulty: advanced Target language: Go Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "Go", "developer_needs": [ "reproducibility", "evaluation_metrics", "tooling" ] }
train_06165
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
code
advanced
Task: code Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: Bash Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Bash", "developer_needs": [ "security_gates", "repo_scale_reasoning", "ci_integration" ] }
train_06166
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
code
foundation
Task: code Topic: Governance, provenance, and licensing for code data Difficulty: foundation Target language: SQL Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "SQL", "developer_needs": [ "documentation", "security_gates", "repo_scale_reasoning" ] }
train_06167
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
review
intermediate
Task: review Topic: Reasoning-first coding models and tunable deliberation Difficulty: intermediate Target language: Go Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "Go", "developer_needs": [ "ci_integration", "tests_are_truth", "tooling" ] }
train_06168
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
design
advanced
Task: design Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced Target language: SQL Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "SQL", "developer_needs": [ "governance", "tooling", "evaluation_metrics" ] }
train_06169
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
data_pipeline
advanced
Task: data_pipeline Topic: Mixture-of-Experts (MoE) for code Difficulty: advanced Target language: JavaScript Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "JavaScript", "developer_needs": [ "security_gates", "cost_latency_tradeoffs", "tests_are_truth" ] }
train_06170
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
code
advanced
Task: code Topic: Reasoning-first coding models and tunable deliberation Difficulty: advanced Target language: JavaScript Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "tooling", "documentation", "repo_scale_reasoning" ] }
train_06171
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
compare
expert
Task: compare Topic: Reasoning-first coding models and tunable deliberation Difficulty: expert Target language: Java Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "Java", "developer_needs": [ "governance", "repo_scale_reasoning", "cost_latency_tradeoffs" ] }
train_06172
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
review
intermediate
Task: review Topic: Reasoning-first coding models and tunable deliberation Difficulty: intermediate Target language: Go Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "Go", "developer_needs": [ "evaluation_metrics", "security_gates", "reproducibility" ] }
train_06173
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
code
foundation
Task: code Topic: Mixture-of-Experts (MoE) for code Difficulty: foundation Target language: Java Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Java", "developer_needs": [ "security_gates", "governance", "tooling" ] }
train_06174
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
explain
foundation
Task: explain Topic: Model merging, distillation, and continued pretraining Difficulty: foundation Target language: Bash Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Bash", "developer_needs": [ "governance", "tests_are_truth", "tooling" ] }
train_06175
2026-01-01T00:00:00
Extended context and repo-scale understanding
agent_loop
intermediate
Task: agent_loop Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: Rust Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "Rust", "developer_needs": [ "repo_scale_reasoning", "evaluation_metrics", "reproducibility" ] }
train_06176
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
compare
foundation
Task: compare Topic: Model merging, distillation, and continued pretraining Difficulty: foundation Target language: JavaScript Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "JavaScript", "developer_needs": [ "tooling", "repo_scale_reasoning", "documentation" ] }
train_06177
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
design
expert
Task: design Topic: Tool calling, sandboxes, and CI integration Difficulty: expert Target language: Go Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Go", "developer_needs": [ "governance", "tooling", "ci_integration" ] }
train_06178
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
advanced
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: JavaScript Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "tooling", "security_gates", "repo_scale_reasoning" ] }
train_06179
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
compare
expert
Task: compare Topic: Code-specialized model families and sizing tradeoffs Difficulty: expert Target language: Rust Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "Rust", "developer_needs": [ "reproducibility", "cost_latency_tradeoffs", "security_gates" ] }
train_06180
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
code
intermediate
Task: code Topic: SWE-bench style real-repo evaluation Difficulty: intermediate Target language: Go Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Go", "developer_needs": [ "ci_integration", "tests_are_truth", "governance" ] }
train_06181
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
code
intermediate
Task: code Topic: Tool calling, sandboxes, and CI integration Difficulty: intermediate Target language: Rust Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "Rust", "developer_needs": [ "documentation", "ci_integration", "tests_are_truth" ] }
train_06182
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
data_pipeline
advanced
Task: data_pipeline Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: advanced Target language: Go Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "Go", "developer_needs": [ "documentation", "reproducibility", "governance" ] }
train_06183
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
data_pipeline
expert
Task: data_pipeline Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: expert Target language: C# Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "C#", "developer_needs": [ "tests_are_truth", "governance", "repo_scale_reasoning" ] }
train_06184
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
compare
foundation
Task: compare Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: foundation Target language: C# Context: Integrate an LLM agent into CI for a large monorepo. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Compare: capability, cost, latency, reliability, governance
{ "target_language": "C#", "developer_needs": [ "ci_integration", "tooling", "security_gates" ] }
train_06185
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
review
advanced
Task: review Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: Rust Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "Rust", "developer_needs": [ "governance", "ci_integration", "cost_latency_tradeoffs" ] }
train_06186
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
explain
intermediate
Task: explain Topic: Tool calling, sandboxes, and CI integration Difficulty: intermediate Target language: JavaScript Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "ci_integration", "cost_latency_tradeoffs", "documentation" ] }
train_06187
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
agent_loop
intermediate
Task: agent_loop Topic: Tool calling, sandboxes, and CI integration Difficulty: intermediate Target language: C# Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "C#", "developer_needs": [ "governance", "ci_integration", "documentation" ] }
train_06188
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
review
expert
Task: review Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: expert Target language: TypeScript Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "TypeScript", "developer_needs": [ "security_gates", "tests_are_truth", "evaluation_metrics" ] }
train_06189
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
review
expert
Task: review Topic: SWE-bench style real-repo evaluation Difficulty: expert Target language: Java Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "Java", "developer_needs": [ "security_gates", "evaluation_metrics", "documentation" ] }
train_06190
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
data_pipeline
intermediate
Task: data_pipeline Topic: Code-specialized model families and sizing tradeoffs Difficulty: intermediate Target language: C# Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "C#", "developer_needs": [ "reproducibility", "governance", "tooling" ] }
train_06191
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
review
foundation
Task: review Topic: Governance, provenance, and licensing for code data Difficulty: foundation Target language: TypeScript Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Review: correctness, security, performance, governance
{ "target_language": "TypeScript", "developer_needs": [ "tooling", "ci_integration", "repo_scale_reasoning" ] }
train_06192
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
data_pipeline
advanced
Task: data_pipeline Topic: Reasoning-first coding models and tunable deliberation Difficulty: advanced Target language: SQL Context: Evaluate two coding models for internal rollout under strict governance. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Pipeline: 1) Ingest 2) Normalize 3) Filter 4) Dedupe 5) Quality score 6) Sample 7) Audit
{ "target_language": "SQL", "developer_needs": [ "cost_latency_tradeoffs", "documentation", "tests_are_truth" ] }
train_06193
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
explain
advanced
Task: explain Topic: Governance, provenance, and licensing for code data Difficulty: advanced Target language: SQL Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "SQL", "developer_needs": [ "tooling", "tests_are_truth", "security_gates" ] }
train_06194
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
agent_loop
advanced
Task: agent_loop Topic: Mixture-of-Experts (MoE) for code Difficulty: advanced Target language: Java Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
{ "target_language": "Java", "developer_needs": [ "cost_latency_tradeoffs", "reproducibility", "documentation" ] }
train_06195
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
expert
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: expert Target language: JavaScript Context: Design a data pipeline for continued pretraining with auditability. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "JavaScript", "developer_needs": [ "documentation", "reproducibility", "ci_integration" ] }
train_06196
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
eval
advanced
Task: eval Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: advanced Target language: C# Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "C#", "developer_needs": [ "evaluation_metrics", "documentation", "cost_latency_tradeoffs" ] }
train_06197
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
design
intermediate
Task: design Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: C# Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "C#", "developer_needs": [ "tests_are_truth", "governance", "security_gates" ] }
train_06198
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
eval
intermediate
Task: eval Topic: Tool calling, sandboxes, and CI integration Difficulty: intermediate Target language: Bash Context: Fix a failing issue with tests as the oracle and produce a safe patch. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Eval: - Tasks: real issues - Metrics: pass@k, time-to-green - Gates: lint/security
{ "target_language": "Bash", "developer_needs": [ "reproducibility", "tooling", "tests_are_truth" ] }
train_06199
2026-01-01T00:00:00
Secure code generation and policy gates
code
foundation
Task: code Topic: Secure code generation and policy gates Difficulty: foundation Target language: SQL Context: Create an eval harness that reflects real developer workflows. Deliver production-grade guidance or artifacts.
Key facts: - Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation. - Reasoning-first and MoE approaches improve capability-per-compute when paired with tools. Design guidance with risks, metrics, acceptance criteria
{ "target_language": "SQL", "developer_needs": [ "cost_latency_tradeoffs", "tooling", "governance" ] }