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2026-01-01 00:00:00
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train_00200
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
code
|
intermediate
|
Task: code
Topic: Multimodal dev workflows (docs, diagrams, traces)
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": [
"repo_scale_reasoning",
"reproducibility",
"documentation"
]
}
|
|
train_00201
| 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: 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": [
"ci_integration",
"documentation",
"evaluation_metrics"
]
}
|
|
train_00202
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
agent_loop
|
expert
|
Task: agent_loop
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"repo_scale_reasoning",
"cost_latency_tradeoffs"
]
}
|
|
train_00203
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
eval
|
advanced
|
Task: eval
Topic: Extended context and repo-scale understanding
Difficulty: advanced
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": [
"tooling",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_00204
| 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: 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": [
"documentation",
"cost_latency_tradeoffs",
"reproducibility"
]
}
|
|
train_00205
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
explain
|
expert
|
Task: explain
Topic: Reasoning-first coding models and tunable deliberation
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"security_gates"
]
}
|
|
train_00206
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
compare
|
advanced
|
Task: compare
Topic: Extended context and repo-scale understanding
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"governance",
"documentation"
]
}
|
|
train_00207
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Go",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"ci_integration"
]
}
|
|
train_00208
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
review
|
intermediate
|
Task: review
Topic: Tool calling, sandboxes, and CI integration
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Python",
"developer_needs": [
"documentation",
"security_gates",
"cost_latency_tradeoffs"
]
}
|
|
train_00209
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
compare
|
expert
|
Task: compare
Topic: Extended context and repo-scale understanding
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Go",
"developer_needs": [
"documentation",
"ci_integration",
"tooling"
]
}
|
|
train_00210
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Secure code generation and policy gates
Difficulty: advanced
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"tooling",
"evaluation_metrics"
]
}
|
|
train_00211
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: SWE-bench style real-repo evaluation
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Go",
"developer_needs": [
"repo_scale_reasoning",
"ci_integration",
"documentation"
]
}
|
|
train_00212
| 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: 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": [
"tooling",
"governance",
"documentation"
]
}
|
|
train_00213
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
data_pipeline
|
intermediate
|
Task: data_pipeline
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_00214
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
foundation
|
Task: design
Topic: Governance, provenance, and licensing for code data
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": [
"security_gates",
"tooling",
"governance"
]
}
|
|
train_00215
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
review
|
foundation
|
Task: review
Topic: Secure code generation and policy gates
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.
Review: correctness, security, performance, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"governance",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_00216
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
review
|
expert
|
Task: review
Topic: Multimodal dev workflows (docs, diagrams, traces)
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": [
"ci_integration",
"security_gates",
"tests_are_truth"
]
}
|
|
train_00217
| 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: 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": [
"cost_latency_tradeoffs",
"security_gates",
"documentation"
]
}
|
|
train_00218
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
explain
|
intermediate
|
Task: explain
Topic: Secure code generation and policy gates
Difficulty: intermediate
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"ci_integration",
"documentation",
"tooling"
]
}
|
|
train_00219
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Bash",
"developer_needs": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"documentation"
]
}
|
|
train_00220
| 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: 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",
"evaluation_metrics",
"ci_integration"
]
}
|
|
train_00221
| 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: 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",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_00222
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
compare
|
foundation
|
Task: compare
Topic: Reasoning-first coding models and tunable deliberation
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Bash",
"developer_needs": [
"documentation",
"evaluation_metrics",
"governance"
]
}
|
|
train_00223
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
review
|
foundation
|
Task: review
Topic: Secure code generation and policy gates
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.
Review: correctness, security, performance, governance
|
{
"target_language": "C#",
"developer_needs": [
"ci_integration",
"reproducibility",
"governance"
]
}
|
|
train_00224
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
eval
|
expert
|
Task: eval
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "JavaScript",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
}
|
|
train_00225
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: intermediate
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": [
"tests_are_truth",
"governance",
"documentation"
]
}
|
|
train_00226
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "SQL",
"developer_needs": [
"ci_integration",
"governance",
"evaluation_metrics"
]
}
|
|
train_00227
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: expert
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": [
"tests_are_truth",
"repo_scale_reasoning",
"evaluation_metrics"
]
}
|
|
train_00228
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
review
|
advanced
|
Task: review
Topic: Extended context and repo-scale understanding
Difficulty: advanced
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Python",
"developer_needs": [
"security_gates",
"tooling",
"tests_are_truth"
]
}
|
|
train_00229
| 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: 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": [
"documentation",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_00230
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
code
|
foundation
|
Task: code
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
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": [
"tooling",
"documentation",
"reproducibility"
]
}
|
|
train_00231
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Code-specialized model families and sizing tradeoffs
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"reproducibility",
"documentation",
"repo_scale_reasoning"
]
}
|
|
train_00232
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
eval
|
foundation
|
Task: eval
Topic: Code-specialized model families and sizing tradeoffs
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Java",
"developer_needs": [
"reproducibility",
"documentation",
"governance"
]
}
|
|
train_00233
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
review
|
advanced
|
Task: review
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: advanced
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Rust",
"developer_needs": [
"governance",
"reproducibility",
"evaluation_metrics"
]
}
|
|
train_00234
| 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: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"tests_are_truth",
"ci_integration"
]
}
|
|
train_00235
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
explain
|
foundation
|
Task: explain
Topic: Secure code generation and policy gates
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_00236
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
review
|
foundation
|
Task: review
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Review: correctness, security, performance, governance
|
{
"target_language": "C#",
"developer_needs": [
"security_gates",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_00237
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
code
|
intermediate
|
Task: code
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
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": [
"tests_are_truth",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_00238
| 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: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_00239
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
eval
|
advanced
|
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: advanced
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": [
"tooling",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_00240
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
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": [
"reproducibility",
"security_gates",
"cost_latency_tradeoffs"
]
}
|
|
train_00241
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Bash",
"developer_needs": [
"tooling",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_00242
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"reproducibility"
]
}
|
|
train_00243
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
compare
|
foundation
|
Task: compare
Topic: Extended context and repo-scale understanding
Difficulty: foundation
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",
"ci_integration"
]
}
|
|
train_00244
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "TypeScript",
"developer_needs": [
"tests_are_truth",
"repo_scale_reasoning",
"evaluation_metrics"
]
}
|
|
train_00245
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
intermediate
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
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": [
"governance",
"tests_are_truth",
"reproducibility"
]
}
|
|
train_00246
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
compare
|
expert
|
Task: compare
Topic: SWE-bench style real-repo evaluation
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "SQL",
"developer_needs": [
"tests_are_truth",
"documentation",
"governance"
]
}
|
|
train_00247
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
review
|
foundation
|
Task: review
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Review: correctness, security, performance, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"tests_are_truth",
"repo_scale_reasoning",
"security_gates"
]
}
|
|
train_00248
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
foundation
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
Difficulty: foundation
Target language: Python
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": "Python",
"developer_needs": [
"tests_are_truth",
"tooling",
"security_gates"
]
}
|
|
train_00249
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
code
|
intermediate
|
Task: code
Topic: Governance, provenance, and licensing for code data
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.
Reference scaffold:
```python
def agent_loop(plan, edit, test, issue, max_iters=3):
history = []
p = plan(issue)
for _ in range(max_iters):
patch = edit(issue, p)
ok, report = test(patch)
history.append({"plan": p, "passed": ok, "report": report[:200]})
if ok:
return patch, history
p = p + " | refine from failures"
return patch, history
```
Operational notes: sandbox, pinned deps, human gate.
|
{
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"documentation"
]
}
|
|
train_00250
| 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: 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": [
"tooling",
"evaluation_metrics",
"ci_integration"
]
}
|
|
train_00251
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
foundation
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Bash",
"developer_needs": [
"repo_scale_reasoning",
"tooling",
"documentation"
]
}
|
|
train_00252
| 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: 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": "Python",
"developer_needs": [
"security_gates",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_00253
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
compare
|
intermediate
|
Task: compare
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Bash",
"developer_needs": [
"security_gates",
"tests_are_truth",
"tooling"
]
}
|
|
train_00254
| 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: 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": [
"reproducibility",
"tests_are_truth",
"tooling"
]
}
|
|
train_00255
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
expert
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Reference scaffold:
```python
def agent_loop(plan, edit, test, issue, max_iters=3):
history = []
p = plan(issue)
for _ in range(max_iters):
patch = edit(issue, p)
ok, report = test(patch)
history.append({"plan": p, "passed": ok, "report": report[:200]})
if ok:
return patch, history
p = p + " | refine from failures"
return patch, history
```
Operational notes: sandbox, pinned deps, human gate.
|
{
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"governance",
"tests_are_truth"
]
}
|
|
train_00256
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
review
|
expert
|
Task: review
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Bash",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_00257
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
eval
|
intermediate
|
Task: eval
Topic: Model merging, distillation, and continued pretraining
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Go",
"developer_needs": [
"governance",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_00258
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Bash",
"developer_needs": [
"tooling",
"repo_scale_reasoning",
"governance"
]
}
|
|
train_00259
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
foundation
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
Target language: Python
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": "Python",
"developer_needs": [
"tooling",
"reproducibility",
"governance"
]
}
|
|
train_00260
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Bash",
"developer_needs": [
"security_gates",
"documentation",
"repo_scale_reasoning"
]
}
|
|
train_00261
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
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": [
"tests_are_truth",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_00262
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
expert
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
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",
"security_gates",
"evaluation_metrics"
]
}
|
|
train_00263
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Tool calling, sandboxes, and CI integration
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "TypeScript",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"tests_are_truth"
]
}
|
|
train_00264
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
design
|
expert
|
Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: expert
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"cost_latency_tradeoffs",
"evaluation_metrics",
"governance"
]
}
|
|
train_00265
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Extended context and repo-scale understanding
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Bash",
"developer_needs": [
"tooling",
"reproducibility",
"security_gates"
]
}
|
|
train_00266
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Governance, provenance, and licensing for code data
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Rust",
"developer_needs": [
"documentation",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_00267
| 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: 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": [
"security_gates",
"ci_integration",
"reproducibility"
]
}
|
|
train_00268
| 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: 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",
"repo_scale_reasoning",
"reproducibility"
]
}
|
|
train_00269
| 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: 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": [
"reproducibility",
"repo_scale_reasoning",
"tests_are_truth"
]
}
|
|
train_00270
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Mixture-of-Experts (MoE) for code
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Rust",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"reproducibility"
]
}
|
|
train_00271
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
code
|
advanced
|
Task: code
Topic: Code-specialized model families and sizing tradeoffs
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.
Reference scaffold:
```python
def agent_loop(plan, edit, test, issue, max_iters=3):
history = []
p = plan(issue)
for _ in range(max_iters):
patch = edit(issue, p)
ok, report = test(patch)
history.append({"plan": p, "passed": ok, "report": report[:200]})
if ok:
return patch, history
p = p + " | refine from failures"
return patch, history
```
Operational notes: sandbox, pinned deps, human gate.
|
{
"target_language": "Python",
"developer_needs": [
"tests_are_truth",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_00272
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
design
|
intermediate
|
Task: design
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"tooling",
"repo_scale_reasoning",
"security_gates"
]
}
|
|
train_00273
| 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: 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",
"cost_latency_tradeoffs",
"ci_integration"
]
}
|
|
train_00274
| 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: 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": [
"evaluation_metrics",
"tests_are_truth",
"governance"
]
}
|
|
train_00275
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
expert
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"security_gates"
]
}
|
|
train_00276
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
eval
|
advanced
|
Task: eval
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_00277
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: intermediate
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Python",
"developer_needs": [
"governance",
"reproducibility",
"evaluation_metrics"
]
}
|
|
train_00278
| 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: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"documentation",
"governance",
"tests_are_truth"
]
}
|
|
train_00279
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
eval
|
foundation
|
Task: eval
Topic: Extended context and repo-scale understanding
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": [
"tooling",
"ci_integration",
"repo_scale_reasoning"
]
}
|
|
train_00280
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
design
|
advanced
|
Task: design
Topic: Reasoning-first coding models and tunable deliberation
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"governance",
"cost_latency_tradeoffs",
"repo_scale_reasoning"
]
}
|
|
train_00281
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
advanced
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
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": [
"evaluation_metrics",
"governance",
"documentation"
]
}
|
|
train_00282
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
review
|
foundation
|
Task: review
Topic: SWE-bench style real-repo evaluation
Difficulty: foundation
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.
Review: correctness, security, performance, governance
|
{
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_00283
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: intermediate
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "TypeScript",
"developer_needs": [
"documentation",
"reproducibility",
"evaluation_metrics"
]
}
|
|
train_00284
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "TypeScript",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"reproducibility"
]
}
|
|
train_00285
| 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: 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": [
"ci_integration",
"security_gates",
"governance"
]
}
|
|
train_00286
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Code-specialized model families and sizing tradeoffs
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "JavaScript",
"developer_needs": [
"ci_integration",
"governance",
"documentation"
]
}
|
|
train_00287
| 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: 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": [
"documentation",
"governance",
"reproducibility"
]
}
|
|
train_00288
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"repo_scale_reasoning",
"ci_integration",
"documentation"
]
}
|
|
train_00289
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
expert
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"repo_scale_reasoning",
"documentation",
"governance"
]
}
|
|
train_00290
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
code
|
expert
|
Task: code
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"documentation"
]
}
|
|
train_00291
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
review
|
advanced
|
Task: review
Topic: Reasoning-first coding models and tunable deliberation
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"tooling",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_00292
| 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: 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": [
"ci_integration",
"reproducibility",
"tooling"
]
}
|
|
train_00293
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
review
|
expert
|
Task: review
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"documentation",
"repo_scale_reasoning"
]
}
|
|
train_00294
| 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: 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": [
"tooling",
"ci_integration",
"documentation"
]
}
|
|
train_00295
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "C#",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_00296
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
design
|
expert
|
Task: design
Topic: Reasoning-first coding models and tunable deliberation
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"documentation",
"evaluation_metrics"
]
}
|
|
train_00297
| 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: 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": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_00298
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"documentation",
"governance"
]
}
|
|
train_00299
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
compare
|
foundation
|
Task: compare
Topic: Reasoning-first coding models and tunable deliberation
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "SQL",
"developer_needs": [
"tests_are_truth",
"tooling",
"reproducibility"
]
}
|
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