id stringlengths 11 11 | created timestamp[s]date 2026-01-01 00:00:00 2026-01-01 00:00:00 | topic stringclasses 12 values | task_type stringclasses 8 values | difficulty stringclasses 4 values | instruction stringlengths 201 264 | input stringclasses 1 value | output stringclasses 7 values | metadata dict |
|---|---|---|---|---|---|---|---|---|
train_09700 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | eval | expert | Task: eval
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: Bash
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "Bash",
"developer_needs": [
"reproducibility",
"tooling",
"security_gates"
]
} | |
train_09701 | 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: 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": [
"tests_are_truth",
"tooling",
"reproducibility"
]
} | |
train_09702 | 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: 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": [
"security_gates",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
} | |
train_09703 | 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
| {
"target_language": "C#",
"developer_needs": [
"reproducibility",
"security_gates",
"repo_scale_reasoning"
]
} | |
train_09704 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | code | foundation | Task: code
Topic: SWE-bench style real-repo evaluation
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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "JavaScript",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"documentation"
]
} | |
train_09705 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | explain | expert | Task: explain
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: Rust
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "Rust",
"developer_needs": [
"tooling",
"security_gates",
"ci_integration"
]
} | |
train_09706 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | explain | intermediate | Task: explain
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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "JavaScript",
"developer_needs": [
"documentation",
"tests_are_truth",
"evaluation_metrics"
]
} | |
train_09707 | 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: 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": "C#",
"developer_needs": [
"repo_scale_reasoning",
"tooling",
"documentation"
]
} | |
train_09708 | 2026-01-01T00:00:00 | Tool calling, sandboxes, and CI integration | explain | intermediate | Task: explain
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
Target language: 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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "Python",
"developer_needs": [
"documentation",
"security_gates",
"evaluation_metrics"
]
} | |
train_09709 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | explain | advanced | Task: explain
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: SQL
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"tooling",
"repo_scale_reasoning"
]
} | |
train_09710 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | agent_loop | intermediate | Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
| {
"target_language": "Bash",
"developer_needs": [
"ci_integration",
"repo_scale_reasoning",
"security_gates"
]
} | |
train_09711 | 2026-01-01T00:00:00 | Dataset curation pipelines (filter, dedupe, quality) | compare | intermediate | Task: compare
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"tests_are_truth"
]
} | |
train_09712 | 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: 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": [
"ci_integration",
"governance",
"tooling"
]
} | |
train_09713 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | review | intermediate | Task: review
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: TypeScript
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": "TypeScript",
"developer_needs": [
"ci_integration",
"governance",
"tooling"
]
} | |
train_09714 | 2026-01-01T00:00:00 | Reasoning-first coding models and tunable deliberation | code | advanced | Task: code
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: advanced
Target language: 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": [
"ci_integration",
"reproducibility",
"governance"
]
} | |
train_09715 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | review | advanced | Task: review
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: JavaScript
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
| {
"target_language": "JavaScript",
"developer_needs": [
"tests_are_truth",
"ci_integration",
"tooling"
]
} | |
train_09716 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | data_pipeline | advanced | Task: data_pipeline
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: Rust
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
| {
"target_language": "Rust",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"documentation"
]
} | |
train_09717 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | design | expert | Task: design
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": [
"repo_scale_reasoning",
"security_gates",
"tooling"
]
} | |
train_09718 | 2026-01-01T00:00:00 | Reasoning-first coding models and tunable deliberation | code | foundation | Task: code
Topic: Reasoning-first coding models and tunable deliberation
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.
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": [
"reproducibility",
"evaluation_metrics",
"security_gates"
]
} | |
train_09719 | 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: 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": [
"tests_are_truth",
"documentation",
"evaluation_metrics"
]
} | |
train_09720 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | code | foundation | Task: code
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: foundation
Target language: Go
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "Go",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"repo_scale_reasoning"
]
} | |
train_09721 | 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: 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": [
"documentation",
"tests_are_truth",
"tooling"
]
} | |
train_09722 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | explain | intermediate | Task: explain
Topic: Model merging, distillation, and continued pretraining
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": [
"security_gates",
"reproducibility",
"repo_scale_reasoning"
]
} | |
train_09723 | 2026-01-01T00:00:00 | Multimodal dev workflows (docs, diagrams, traces) | eval | advanced | Task: eval
Topic: Multimodal dev workflows (docs, diagrams, traces)
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",
"repo_scale_reasoning",
"governance"
]
} | |
train_09724 | 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: 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",
"security_gates",
"repo_scale_reasoning"
]
} | |
train_09725 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | compare | advanced | Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: TypeScript
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": "TypeScript",
"developer_needs": [
"cost_latency_tradeoffs",
"security_gates",
"tooling"
]
} | |
train_09726 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | data_pipeline | intermediate | Task: data_pipeline
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: 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": [
"governance",
"reproducibility",
"ci_integration"
]
} | |
train_09727 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | compare | intermediate | Task: compare
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: Go
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "Go",
"developer_needs": [
"tests_are_truth",
"tooling",
"governance"
]
} | |
train_09728 | 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: 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",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
} | |
train_09729 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | eval | advanced | Task: eval
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "TypeScript",
"developer_needs": [
"ci_integration",
"documentation",
"evaluation_metrics"
]
} | |
train_09730 | 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: 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.
Review: correctness, security, performance, governance
| {
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"reproducibility",
"security_gates"
]
} | |
train_09731 | 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "JavaScript",
"developer_needs": [
"documentation",
"governance",
"security_gates"
]
} | |
train_09732 | 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: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
| {
"target_language": "SQL",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
} | |
train_09733 | 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: 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.
Review: correctness, security, performance, governance
| {
"target_language": "C#",
"developer_needs": [
"documentation",
"security_gates",
"cost_latency_tradeoffs"
]
} | |
train_09734 | 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: 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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "SQL",
"developer_needs": [
"tests_are_truth",
"documentation",
"reproducibility"
]
} | |
train_09735 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | data_pipeline | intermediate | Task: data_pipeline
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: Java
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
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_09736 | 2026-01-01T00:00:00 | Reasoning-first coding models and tunable deliberation | explain | foundation | Task: explain
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: foundation
Target language: C#
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"governance",
"tooling"
]
} | |
train_09737 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | data_pipeline | intermediate | Task: data_pipeline
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: Python
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
| {
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"tooling",
"documentation"
]
} | |
train_09738 | 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: 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": [
"evaluation_metrics",
"ci_integration",
"governance"
]
} | |
train_09739 | 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: JavaScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "JavaScript",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"tooling"
]
} | |
train_09740 | 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: 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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "Java",
"developer_needs": [
"tests_are_truth",
"ci_integration",
"cost_latency_tradeoffs"
]
} | |
train_09741 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | compare | advanced | Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: JavaScript
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"tooling",
"ci_integration"
]
} | |
train_09742 | 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: TypeScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
| {
"target_language": "TypeScript",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"tests_are_truth"
]
} | |
train_09743 | 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "Python",
"developer_needs": [
"ci_integration",
"documentation",
"evaluation_metrics"
]
} | |
train_09744 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | explain | foundation | Task: explain
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: foundation
Target language: 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": [
"ci_integration",
"reproducibility",
"governance"
]
} | |
train_09745 | 2026-01-01T00:00:00 | Secure code generation and policy gates | eval | expert | Task: eval
Topic: Secure code generation and policy gates
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"security_gates"
]
} | |
train_09746 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | explain | intermediate | Task: explain
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
Target language: 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",
"governance"
]
} | |
train_09747 | 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: 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": [
"tooling",
"evaluation_metrics",
"tests_are_truth"
]
} | |
train_09748 | 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
| {
"target_language": "Rust",
"developer_needs": [
"security_gates",
"governance",
"evaluation_metrics"
]
} | |
train_09749 | 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: 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": [
"tooling",
"ci_integration",
"evaluation_metrics"
]
} | |
train_09750 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | explain | foundation | Task: explain
Topic: SWE-bench style real-repo evaluation
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": [
"evaluation_metrics",
"security_gates",
"repo_scale_reasoning"
]
} | |
train_09751 | 2026-01-01T00:00:00 | Dataset curation pipelines (filter, dedupe, quality) | agent_loop | advanced | Task: agent_loop
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"cost_latency_tradeoffs",
"ci_integration",
"reproducibility"
]
} | |
train_09752 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | compare | foundation | Task: compare
Topic: SWE-bench style real-repo evaluation
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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "SQL",
"developer_needs": [
"cost_latency_tradeoffs",
"evaluation_metrics",
"governance"
]
} | |
train_09753 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | design | foundation | Task: design
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": [
"documentation",
"tests_are_truth",
"ci_integration"
]
} | |
train_09754 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | compare | advanced | Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: Rust
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "Rust",
"developer_needs": [
"security_gates",
"ci_integration",
"tooling"
]
} | |
train_09755 | 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"reproducibility"
]
} | |
train_09756 | 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: 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": "Rust",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"documentation"
]
} | |
train_09757 | 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: 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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "Python",
"developer_needs": [
"governance",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
} | |
train_09758 | 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: 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": [
"tooling",
"evaluation_metrics",
"tests_are_truth"
]
} | |
train_09759 | 2026-01-01T00:00:00 | Multimodal dev workflows (docs, diagrams, traces) | code | foundation | Task: code
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": [
"tooling",
"evaluation_metrics",
"governance"
]
} | |
train_09760 | 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: 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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "TypeScript",
"developer_needs": [
"repo_scale_reasoning",
"evaluation_metrics",
"reproducibility"
]
} | |
train_09761 | 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
| {
"target_language": "Rust",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"governance"
]
} | |
train_09762 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | compare | intermediate | Task: compare
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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "Java",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"tooling"
]
} | |
train_09763 | 2026-01-01T00:00:00 | Tool calling, sandboxes, and CI integration | review | expert | Task: review
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: 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.
Review: correctness, security, performance, governance
| {
"target_language": "SQL",
"developer_needs": [
"tests_are_truth",
"security_gates",
"governance"
]
} | |
train_09764 | 2026-01-01T00:00:00 | Multimodal dev workflows (docs, diagrams, traces) | code | advanced | Task: code
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: 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",
"tooling",
"ci_integration"
]
} | |
train_09765 | 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: 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.
Review: correctness, security, performance, governance
| {
"target_language": "C#",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"security_gates"
]
} | |
train_09766 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | design | intermediate | Task: design
Topic: Mixture-of-Experts (MoE) for code
Difficulty: intermediate
Target language: 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",
"reproducibility",
"evaluation_metrics"
]
} | |
train_09767 | 2026-01-01T00:00:00 | Secure code generation and policy gates | code | intermediate | Task: code
Topic: Secure code generation and policy gates
Difficulty: intermediate
Target language: 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",
"cost_latency_tradeoffs",
"ci_integration"
]
} | |
train_09768 | 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: 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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"security_gates"
]
} | |
train_09769 | 2026-01-01T00:00:00 | Dataset curation pipelines (filter, dedupe, quality) | agent_loop | intermediate | Task: agent_loop
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
| {
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"documentation",
"repo_scale_reasoning"
]
} | |
train_09770 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | data_pipeline | foundation | Task: data_pipeline
Topic: Model merging, distillation, and continued pretraining
Difficulty: foundation
Target language: 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": [
"evaluation_metrics",
"reproducibility",
"tooling"
]
} | |
train_09771 | 2026-01-01T00:00:00 | Multimodal dev workflows (docs, diagrams, traces) | design | intermediate | Task: design
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "Python",
"developer_needs": [
"governance",
"documentation",
"repo_scale_reasoning"
]
} | |
train_09772 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | review | foundation | Task: review
Topic: Extended context and repo-scale understanding
Difficulty: foundation
Target language: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
| {
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"repo_scale_reasoning"
]
} | |
train_09773 | 2026-01-01T00:00:00 | Agentic coding systems (plan→edit→test→reflect) | design | intermediate | Task: design
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: intermediate
Target language: JavaScript
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"evaluation_metrics"
]
} | |
train_09774 | 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: 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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "Python",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"repo_scale_reasoning"
]
} | |
train_09775 | 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
| {
"target_language": "Rust",
"developer_needs": [
"ci_integration",
"reproducibility",
"documentation"
]
} | |
train_09776 | 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: 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": [
"repo_scale_reasoning",
"evaluation_metrics",
"documentation"
]
} | |
train_09777 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | review | intermediate | Task: review
Topic: Mixture-of-Experts (MoE) for code
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.
Review: correctness, security, performance, governance
| {
"target_language": "TypeScript",
"developer_needs": [
"ci_integration",
"governance",
"security_gates"
]
} | |
train_09778 | 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: 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": "SQL",
"developer_needs": [
"security_gates",
"tooling",
"tests_are_truth"
]
} | |
train_09779 | 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: 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": [
"documentation",
"governance",
"repo_scale_reasoning"
]
} | |
train_09780 | 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: 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": "Rust",
"developer_needs": [
"ci_integration",
"documentation",
"tooling"
]
} | |
train_09781 | 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: 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": [
"documentation",
"reproducibility",
"ci_integration"
]
} | |
train_09782 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | design | foundation | Task: design
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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "JavaScript",
"developer_needs": [
"tests_are_truth",
"governance",
"documentation"
]
} | |
train_09783 | 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: 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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "Python",
"developer_needs": [
"ci_integration",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
} | |
train_09784 | 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: Rust
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts. | Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "Rust",
"developer_needs": [
"security_gates",
"repo_scale_reasoning",
"tooling"
]
} | |
train_09785 | 2026-01-01T00:00:00 | Multimodal dev workflows (docs, diagrams, traces) | compare | intermediate | Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
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": [
"governance",
"reproducibility",
"documentation"
]
} | |
train_09786 | 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
| {
"target_language": "Python",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"repo_scale_reasoning"
]
} | |
train_09787 | 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: 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": [
"evaluation_metrics",
"governance",
"reproducibility"
]
} | |
train_09788 | 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: 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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"reproducibility",
"ci_integration"
]
} | |
train_09789 | 2026-01-01T00:00:00 | Dataset curation pipelines (filter, dedupe, quality) | compare | expert | Task: compare
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "SQL",
"developer_needs": [
"ci_integration",
"evaluation_metrics",
"governance"
]
} | |
train_09790 | 2026-01-01T00:00:00 | Tool calling, sandboxes, and CI integration | code | intermediate | Task: code
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
Target language: 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": [
"governance",
"tests_are_truth",
"tooling"
]
} | |
train_09791 | 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: 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.
Review: correctness, security, performance, governance
| {
"target_language": "Bash",
"developer_needs": [
"tooling",
"tests_are_truth",
"ci_integration"
]
} | |
train_09792 | 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: 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": [
"reproducibility",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
} | |
train_09793 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | code | advanced | Task: code
Topic: Extended context and repo-scale understanding
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.
Design guidance with risks, metrics, acceptance criteria
| {
"target_language": "C#",
"developer_needs": [
"governance",
"repo_scale_reasoning",
"evaluation_metrics"
]
} | |
train_09794 | 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: 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.
Review: correctness, security, performance, governance
| {
"target_language": "C#",
"developer_needs": [
"reproducibility",
"tooling",
"documentation"
]
} | |
train_09795 | 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": [
"tests_are_truth",
"documentation",
"repo_scale_reasoning"
]
} | |
train_09796 | 2026-01-01T00:00:00 | Agentic coding systems (plan→edit→test→reflect) | compare | expert | Task: compare
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Compare: capability, cost, latency, reliability, governance
| {
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"governance",
"documentation"
]
} | |
train_09797 | 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: 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": [
"repo_scale_reasoning",
"security_gates",
"cost_latency_tradeoffs"
]
} | |
train_09798 | 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
| {
"target_language": "Rust",
"developer_needs": [
"reproducibility",
"tooling",
"cost_latency_tradeoffs"
]
} | |
train_09799 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | review | advanced | Task: review
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: 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.
Review: correctness, security, performance, governance
| {
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"reproducibility"
]
} |
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