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timestamp[s]date 2026-01-01 00:00:00
2026-01-01 00:00:00
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| input
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values | metadata
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|---|---|---|---|---|---|---|---|---|
train_09200
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
advanced
|
Task: design
Topic: Governance, provenance, and licensing for code data
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Go",
"developer_needs": [
"ci_integration",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_09201
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
eval
|
advanced
|
Task: eval
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "C#",
"developer_needs": [
"reproducibility",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_09202
| 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: 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": [
"cost_latency_tradeoffs",
"ci_integration",
"security_gates"
]
}
|
|
train_09203
| 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: 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": [
"reproducibility",
"governance",
"documentation"
]
}
|
|
train_09204
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
compare
|
foundation
|
Task: compare
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"repo_scale_reasoning",
"ci_integration",
"reproducibility"
]
}
|
|
train_09205
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
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": [
"repo_scale_reasoning",
"security_gates",
"tests_are_truth"
]
}
|
|
train_09206
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
expert
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"governance"
]
}
|
|
train_09207
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
eval
|
intermediate
|
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "C#",
"developer_needs": [
"governance",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_09208
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "C#",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"evaluation_metrics"
]
}
|
|
train_09209
| 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: SQL
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": "SQL",
"developer_needs": [
"governance",
"security_gates",
"tests_are_truth"
]
}
|
|
train_09210
| 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: 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": [
"repo_scale_reasoning",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_09211
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: expert
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "SQL",
"developer_needs": [
"repo_scale_reasoning",
"documentation",
"ci_integration"
]
}
|
|
train_09212
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
code
|
advanced
|
Task: code
Topic: Tool calling, sandboxes, and CI integration
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": [
"ci_integration",
"governance",
"security_gates"
]
}
|
|
train_09213
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Extended context and repo-scale understanding
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": [
"tests_are_truth",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_09214
| 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: 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": [
"tooling",
"documentation",
"repo_scale_reasoning"
]
}
|
|
train_09215
| 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: 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": "Go",
"developer_needs": [
"governance",
"repo_scale_reasoning",
"tooling"
]
}
|
|
train_09216
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"tooling"
]
}
|
|
train_09217
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Extended context and repo-scale understanding
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"security_gates",
"repo_scale_reasoning",
"tests_are_truth"
]
}
|
|
train_09218
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
design
|
advanced
|
Task: design
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Java",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
}
|
|
train_09219
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
eval
|
intermediate
|
Task: eval
Topic: Secure code generation and policy gates
Difficulty: intermediate
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "TypeScript",
"developer_needs": [
"governance",
"tests_are_truth",
"security_gates"
]
}
|
|
train_09220
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "SQL",
"developer_needs": [
"governance",
"security_gates",
"tests_are_truth"
]
}
|
|
train_09221
| 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: Bash
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": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"tests_are_truth"
]
}
|
|
train_09222
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
explain
|
expert
|
Task: explain
Topic: Mixture-of-Experts (MoE) for code
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"reproducibility",
"cost_latency_tradeoffs"
]
}
|
|
train_09223
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
foundation
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"tests_are_truth"
]
}
|
|
train_09224
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"tests_are_truth",
"security_gates"
]
}
|
|
train_09225
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
explain
|
expert
|
Task: explain
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
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",
"reproducibility",
"governance"
]
}
|
|
train_09226
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
foundation
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"ci_integration",
"tooling"
]
}
|
|
train_09227
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
review
|
advanced
|
Task: review
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_09228
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
review
|
foundation
|
Task: review
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: foundation
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": [
"reproducibility",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_09229
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
expert
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"governance",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_09230
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
intermediate
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"evaluation_metrics",
"reproducibility",
"security_gates"
]
}
|
|
train_09231
| 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: 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": [
"ci_integration",
"documentation",
"repo_scale_reasoning"
]
}
|
|
train_09232
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"repo_scale_reasoning",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_09233
| 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: 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": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"ci_integration"
]
}
|
|
train_09234
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Python",
"developer_needs": [
"tooling",
"evaluation_metrics",
"documentation"
]
}
|
|
train_09235
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
agent_loop
|
expert
|
Task: agent_loop
Topic: SWE-bench style real-repo evaluation
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"ci_integration"
]
}
|
|
train_09236
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
design
|
expert
|
Task: design
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"ci_integration"
]
}
|
|
train_09237
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"tests_are_truth",
"tooling"
]
}
|
|
train_09238
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
review
|
foundation
|
Task: review
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
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": [
"cost_latency_tradeoffs",
"security_gates",
"documentation"
]
}
|
|
train_09239
| 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: 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": "Go",
"developer_needs": [
"tooling",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
}
|
|
train_09240
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
expert
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
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": [
"security_gates",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_09241
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
design
|
foundation
|
Task: design
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: foundation
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",
"tests_are_truth",
"evaluation_metrics"
]
}
|
|
train_09242
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Secure code generation and policy gates
Difficulty: expert
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": [
"tests_are_truth",
"cost_latency_tradeoffs",
"reproducibility"
]
}
|
|
train_09243
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Secure code generation and policy gates
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "SQL",
"developer_needs": [
"repo_scale_reasoning",
"documentation",
"governance"
]
}
|
|
train_09244
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
code
|
expert
|
Task: code
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"security_gates"
]
}
|
|
train_09245
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
design
|
foundation
|
Task: design
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
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": [
"repo_scale_reasoning",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_09246
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Rust",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_09247
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
eval
|
foundation
|
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "JavaScript",
"developer_needs": [
"repo_scale_reasoning",
"tests_are_truth",
"ci_integration"
]
}
|
|
train_09248
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
review
|
advanced
|
Task: review
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Review: correctness, security, performance, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_09249
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"tests_are_truth",
"cost_latency_tradeoffs",
"tooling"
]
}
|
|
train_09250
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: foundation
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": [
"evaluation_metrics",
"repo_scale_reasoning",
"security_gates"
]
}
|
|
train_09251
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
design
|
advanced
|
Task: design
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"reproducibility",
"tooling",
"security_gates"
]
}
|
|
train_09252
| 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: 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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"security_gates",
"tests_are_truth",
"reproducibility"
]
}
|
|
train_09253
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_09254
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
explain
|
intermediate
|
Task: explain
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: SQL
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": "SQL",
"developer_needs": [
"ci_integration",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_09255
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
code
|
expert
|
Task: code
Topic: Agentic coding systems (plan→edit→test→reflect)
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": [
"documentation",
"repo_scale_reasoning",
"cost_latency_tradeoffs"
]
}
|
|
train_09256
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
expert
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Bash",
"developer_needs": [
"tooling",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_09257
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
compare
|
expert
|
Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: expert
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"documentation",
"evaluation_metrics",
"governance"
]
}
|
|
train_09258
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
design
|
advanced
|
Task: design
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"documentation",
"governance"
]
}
|
|
train_09259
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
advanced
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
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": [
"tooling",
"documentation",
"governance"
]
}
|
|
train_09260
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
design
|
advanced
|
Task: design
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"security_gates",
"tooling",
"documentation"
]
}
|
|
train_09261
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
review
|
intermediate
|
Task: review
Topic: Secure code generation and policy gates
Difficulty: intermediate
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": [
"cost_latency_tradeoffs",
"ci_integration",
"security_gates"
]
}
|
|
train_09262
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
design
|
expert
|
Task: design
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: expert
Target language: Bash
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": "Bash",
"developer_needs": [
"documentation",
"governance",
"ci_integration"
]
}
|
|
train_09263
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
eval
|
intermediate
|
Task: eval
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "TypeScript",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_09264
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Extended context and repo-scale understanding
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Rust",
"developer_needs": [
"ci_integration",
"tooling",
"security_gates"
]
}
|
|
train_09265
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
foundation
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
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": [
"repo_scale_reasoning",
"reproducibility",
"governance"
]
}
|
|
train_09266
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
compare
|
expert
|
Task: compare
Topic: Mixture-of-Experts (MoE) for code
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "C#",
"developer_needs": [
"tests_are_truth",
"cost_latency_tradeoffs",
"reproducibility"
]
}
|
|
train_09267
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Extended context and repo-scale understanding
Difficulty: expert
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Go",
"developer_needs": [
"tooling",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_09268
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"tests_are_truth"
]
}
|
|
train_09269
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
code
|
expert
|
Task: code
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"tooling"
]
}
|
|
train_09270
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"reproducibility",
"cost_latency_tradeoffs"
]
}
|
|
train_09271
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
compare
|
expert
|
Task: compare
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Go",
"developer_needs": [
"tooling",
"security_gates",
"reproducibility"
]
}
|
|
train_09272
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
expert
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Go",
"developer_needs": [
"security_gates",
"repo_scale_reasoning",
"governance"
]
}
|
|
train_09273
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Tool calling, sandboxes, and CI integration
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": [
"cost_latency_tradeoffs",
"ci_integration",
"reproducibility"
]
}
|
|
train_09274
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
code
|
intermediate
|
Task: code
Topic: Code-specialized model families and sizing tradeoffs
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",
"cost_latency_tradeoffs",
"tooling"
]
}
|
|
train_09275
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
design
|
advanced
|
Task: design
Topic: Secure code generation and policy gates
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": [
"governance",
"repo_scale_reasoning",
"security_gates"
]
}
|
|
train_09276
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Tool calling, sandboxes, and CI integration
Difficulty: foundation
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",
"tooling",
"evaluation_metrics"
]
}
|
|
train_09277
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
design
|
foundation
|
Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: foundation
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"governance",
"reproducibility",
"tooling"
]
}
|
|
train_09278
| 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: 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",
"reproducibility",
"documentation"
]
}
|
|
train_09279
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: SWE-bench style real-repo evaluation
Difficulty: foundation
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_09280
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
compare
|
advanced
|
Task: compare
Topic: Mixture-of-Experts (MoE) for code
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Python",
"developer_needs": [
"reproducibility",
"tooling",
"tests_are_truth"
]
}
|
|
train_09281
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
code
|
expert
|
Task: code
Topic: Code-specialized model families and sizing tradeoffs
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",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_09282
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
design
|
expert
|
Task: design
Topic: Mixture-of-Experts (MoE) for code
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"documentation",
"tooling",
"security_gates"
]
}
|
|
train_09283
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
explain
|
foundation
|
Task: explain
Topic: Governance, provenance, and licensing for code data
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": [
"evaluation_metrics",
"security_gates",
"governance"
]
}
|
|
train_09284
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
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": [
"ci_integration",
"documentation",
"governance"
]
}
|
|
train_09285
| 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: 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": "SQL",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"evaluation_metrics"
]
}
|
|
train_09286
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
review
|
foundation
|
Task: review
Topic: Tool calling, sandboxes, and CI integration
Difficulty: foundation
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": [
"security_gates",
"governance",
"tests_are_truth"
]
}
|
|
train_09287
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"security_gates",
"evaluation_metrics"
]
}
|
|
train_09288
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: advanced
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": [
"repo_scale_reasoning",
"tests_are_truth",
"ci_integration"
]
}
|
|
train_09289
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
code
|
intermediate
|
Task: code
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"tests_are_truth",
"security_gates",
"evaluation_metrics"
]
}
|
|
train_09290
| 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: 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": [
"security_gates",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_09291
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
design
|
foundation
|
Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: foundation
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"documentation"
]
}
|
|
train_09292
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
eval
|
advanced
|
Task: eval
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_09293
| 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": [
"documentation",
"cost_latency_tradeoffs",
"reproducibility"
]
}
|
|
train_09294
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
advanced
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: SQL
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": "SQL",
"developer_needs": [
"evaluation_metrics",
"repo_scale_reasoning",
"cost_latency_tradeoffs"
]
}
|
|
train_09295
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
compare
|
advanced
|
Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "C#",
"developer_needs": [
"tooling",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_09296
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"governance",
"tooling",
"tests_are_truth"
]
}
|
|
train_09297
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Java",
"developer_needs": [
"governance",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_09298
| 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: 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": [
"repo_scale_reasoning",
"evaluation_metrics",
"governance"
]
}
|
|
train_09299
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Secure code generation and policy gates
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"governance",
"cost_latency_tradeoffs",
"ci_integration"
]
}
|
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