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_02400
| 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": [
"security_gates",
"tooling",
"repo_scale_reasoning"
]
}
|
|
train_02401
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
compare
|
intermediate
|
Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: Java
Context: 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": [
"documentation",
"security_gates",
"governance"
]
}
|
|
train_02402
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
explain
|
foundation
|
Task: explain
Topic: Model merging, distillation, and continued pretraining
Difficulty: foundation
Target language: 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": [
"tests_are_truth",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_02403
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
foundation
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
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": [
"cost_latency_tradeoffs",
"documentation",
"tooling"
]
}
|
|
train_02404
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
review
|
intermediate
|
Task: review
Topic: Model merging, distillation, and continued pretraining
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.
Review: correctness, security, performance, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"ci_integration"
]
}
|
|
train_02405
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
code
|
expert
|
Task: code
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: 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",
"repo_scale_reasoning",
"governance"
]
}
|
|
train_02406
| 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: 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": [
"repo_scale_reasoning",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_02407
| 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: 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": [
"security_gates",
"cost_latency_tradeoffs",
"documentation"
]
}
|
|
train_02408
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: Rust
Context: 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": [
"repo_scale_reasoning",
"tests_are_truth",
"security_gates"
]
}
|
|
train_02409
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Python",
"developer_needs": [
"security_gates",
"evaluation_metrics",
"ci_integration"
]
}
|
|
train_02410
| 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: 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": [
"ci_integration",
"documentation",
"tooling"
]
}
|
|
train_02411
| 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: Go
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Go",
"developer_needs": [
"security_gates",
"ci_integration",
"documentation"
]
}
|
|
train_02412
| 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: 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": [
"ci_integration",
"tooling",
"reproducibility"
]
}
|
|
train_02413
| 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: 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": [
"ci_integration",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_02414
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
compare
|
expert
|
Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: expert
Target language: JavaScript
Context: 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": [
"reproducibility",
"documentation",
"tooling"
]
}
|
|
train_02415
| 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": [
"tests_are_truth",
"documentation",
"security_gates"
]
}
|
|
train_02416
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Bash
Context: 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": "Bash",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_02417
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: foundation
Target language: Python
Context: 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": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"documentation"
]
}
|
|
train_02418
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
eval
|
expert
|
Task: eval
Topic: Mixture-of-Experts (MoE) for code
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "TypeScript",
"developer_needs": [
"documentation",
"tests_are_truth",
"tooling"
]
}
|
|
train_02419
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
explain
|
advanced
|
Task: explain
Topic: Secure code generation and policy gates
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"tooling"
]
}
|
|
train_02420
| 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: 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": [
"ci_integration",
"governance",
"reproducibility"
]
}
|
|
train_02421
| 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: C#
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "C#",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"governance"
]
}
|
|
train_02422
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Reasoning-first coding models and tunable deliberation
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",
"ci_integration",
"repo_scale_reasoning"
]
}
|
|
train_02423
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Bash",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"documentation"
]
}
|
|
train_02424
| 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: 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": "Java",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_02425
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
compare
|
foundation
|
Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
Target language: 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": [
"tests_are_truth",
"evaluation_metrics",
"security_gates"
]
}
|
|
train_02426
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
explain
|
expert
|
Task: explain
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: JavaScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"ci_integration",
"security_gates",
"evaluation_metrics"
]
}
|
|
train_02427
| 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: Bash
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_02428
| 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: 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": [
"reproducibility",
"tests_are_truth",
"governance"
]
}
|
|
train_02429
| 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: SQL
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"repo_scale_reasoning",
"evaluation_metrics"
]
}
|
|
train_02430
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: 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": [
"repo_scale_reasoning",
"documentation",
"tests_are_truth"
]
}
|
|
train_02431
| 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: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Python",
"developer_needs": [
"security_gates",
"reproducibility",
"ci_integration"
]
}
|
|
train_02432
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: 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",
"tests_are_truth",
"governance"
]
}
|
|
train_02433
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
code
|
expert
|
Task: code
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: Bash
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"governance",
"reproducibility",
"tests_are_truth"
]
}
|
|
train_02434
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
Target language: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "C#",
"developer_needs": [
"documentation",
"tooling",
"evaluation_metrics"
]
}
|
|
train_02435
| 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: 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": [
"reproducibility",
"tests_are_truth",
"tooling"
]
}
|
|
train_02436
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
design
|
expert
|
Task: design
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": [
"ci_integration",
"security_gates",
"cost_latency_tradeoffs"
]
}
|
|
train_02437
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
code
|
expert
|
Task: code
Topic: Model merging, distillation, and continued pretraining
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": [
"documentation",
"repo_scale_reasoning",
"cost_latency_tradeoffs"
]
}
|
|
train_02438
| 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: 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": [
"governance",
"ci_integration",
"reproducibility"
]
}
|
|
train_02439
| 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: 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": [
"governance",
"evaluation_metrics",
"documentation"
]
}
|
|
train_02440
| 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: 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": [
"reproducibility",
"tests_are_truth",
"security_gates"
]
}
|
|
train_02441
| 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: Rust
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Rust",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"governance"
]
}
|
|
train_02442
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "C#",
"developer_needs": [
"tests_are_truth",
"reproducibility",
"cost_latency_tradeoffs"
]
}
|
|
train_02443
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"tooling"
]
}
|
|
train_02444
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_02445
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
advanced
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: JavaScript
Context: 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": [
"security_gates",
"cost_latency_tradeoffs",
"reproducibility"
]
}
|
|
train_02446
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
code
|
expert
|
Task: code
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: 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": [
"cost_latency_tradeoffs",
"reproducibility",
"evaluation_metrics"
]
}
|
|
train_02447
| 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: JavaScript
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"documentation"
]
}
|
|
train_02448
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: 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": [
"repo_scale_reasoning",
"security_gates",
"reproducibility"
]
}
|
|
train_02449
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
design
|
advanced
|
Task: design
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
Target language: Java
Context: 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": [
"reproducibility",
"tests_are_truth",
"security_gates"
]
}
|
|
train_02450
| 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: 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": [
"documentation",
"tests_are_truth",
"evaluation_metrics"
]
}
|
|
train_02451
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
review
|
intermediate
|
Task: review
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: intermediate
Target language: C#
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "C#",
"developer_needs": [
"reproducibility",
"repo_scale_reasoning",
"cost_latency_tradeoffs"
]
}
|
|
train_02452
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
review
|
intermediate
|
Task: review
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: 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": [
"cost_latency_tradeoffs",
"tooling",
"reproducibility"
]
}
|
|
train_02453
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Bash
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"ci_integration",
"security_gates"
]
}
|
|
train_02454
| 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: 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": [
"tests_are_truth",
"governance",
"ci_integration"
]
}
|
|
train_02455
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
eval
|
expert
|
Task: eval
Topic: Mixture-of-Experts (MoE) for code
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Rust",
"developer_needs": [
"evaluation_metrics",
"reproducibility",
"documentation"
]
}
|
|
train_02456
| 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": [
"tests_are_truth",
"repo_scale_reasoning",
"reproducibility"
]
}
|
|
train_02457
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: foundation
Target language: Java
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"ci_integration",
"tooling",
"evaluation_metrics"
]
}
|
|
train_02458
| 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: 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": [
"tests_are_truth",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_02459
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Rust",
"developer_needs": [
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"ci_integration"
]
}
|
|
train_02460
| 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: Java
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Java",
"developer_needs": [
"tests_are_truth",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_02461
| 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": [
"tooling",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_02462
| 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: Rust
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"security_gates",
"evaluation_metrics",
"governance"
]
}
|
|
train_02463
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: C#
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"repo_scale_reasoning",
"ci_integration"
]
}
|
|
train_02464
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
advanced
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: 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",
"governance",
"security_gates"
]
}
|
|
train_02465
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: 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.
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_02466
| 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: 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.
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": [
"security_gates",
"governance",
"tooling"
]
}
|
|
train_02467
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
explain
|
foundation
|
Task: explain
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
Target language: TypeScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"governance",
"tests_are_truth",
"reproducibility"
]
}
|
|
train_02468
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
expert
|
Task: design
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: JavaScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"tests_are_truth",
"security_gates",
"governance"
]
}
|
|
train_02469
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: JavaScript
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_02470
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
compare
|
intermediate
|
Task: compare
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: Java
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Java",
"developer_needs": [
"tests_are_truth",
"ci_integration",
"tooling"
]
}
|
|
train_02471
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
review
|
expert
|
Task: review
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: Java
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_02472
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
eval
|
foundation
|
Task: eval
Topic: Secure code generation and policy gates
Difficulty: foundation
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Java",
"developer_needs": [
"tooling",
"reproducibility",
"documentation"
]
}
|
|
train_02473
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
explain
|
expert
|
Task: explain
Topic: Secure code generation and policy gates
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": [
"governance",
"cost_latency_tradeoffs",
"documentation"
]
}
|
|
train_02474
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
foundation
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"tooling",
"ci_integration",
"governance"
]
}
|
|
train_02475
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
code
|
expert
|
Task: code
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: 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",
"reproducibility"
]
}
|
|
train_02476
| 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: 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": [
"repo_scale_reasoning",
"tests_are_truth",
"governance"
]
}
|
|
train_02477
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Rust",
"developer_needs": [
"security_gates",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_02478
| 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: 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": [
"repo_scale_reasoning",
"documentation",
"tests_are_truth"
]
}
|
|
train_02479
| 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: 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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"evaluation_metrics",
"governance",
"security_gates"
]
}
|
|
train_02480
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
code
|
advanced
|
Task: code
Topic: Governance, provenance, and licensing for code data
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"governance"
]
}
|
|
train_02481
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
eval
|
expert
|
Task: eval
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_02482
| 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: 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": [
"ci_integration",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_02483
| 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: Go
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Go",
"developer_needs": [
"ci_integration",
"documentation",
"tooling"
]
}
|
|
train_02484
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: 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",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_02485
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
expert
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: expert
Target language: 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": [
"documentation",
"tooling",
"reproducibility"
]
}
|
|
train_02486
| 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: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "C#",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"documentation"
]
}
|
|
train_02487
| 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: 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": [
"evaluation_metrics",
"tooling",
"reproducibility"
]
}
|
|
train_02488
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
design
|
foundation
|
Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: foundation
Target language: 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": [
"tooling",
"documentation",
"tests_are_truth"
]
}
|
|
train_02489
| 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: Go
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Go",
"developer_needs": [
"ci_integration",
"security_gates",
"tooling"
]
}
|
|
train_02490
| 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: 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": [
"tooling",
"evaluation_metrics",
"governance"
]
}
|
|
train_02491
| 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: C#
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"governance",
"documentation",
"tooling"
]
}
|
|
train_02492
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
review
|
advanced
|
Task: review
Topic: Secure code generation and policy gates
Difficulty: advanced
Target language: JavaScript
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"documentation"
]
}
|
|
train_02493
| 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: 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": [
"ci_integration",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_02494
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Bash",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"security_gates"
]
}
|
|
train_02495
| 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: 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": [
"reproducibility",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_02496
| 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: 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",
"tests_are_truth",
"documentation"
]
}
|
|
train_02497
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
eval
|
expert
|
Task: eval
Topic: Mixture-of-Experts (MoE) for code
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"reproducibility",
"ci_integration"
]
}
|
|
train_02498
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_02499
| 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: 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": [
"documentation",
"evaluation_metrics",
"ci_integration"
]
}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.