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timestamp[s]date 2026-01-01 00:00:00
2026-01-01 00:00:00
| topic
stringclasses 12
values | task_type
stringclasses 8
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stringlengths 201
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| input
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values | metadata
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|---|---|---|---|---|---|---|---|---|
train_03800
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
design
|
expert
|
Task: design
Topic: Extended context and repo-scale understanding
Difficulty: expert
Target language: Java
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Java",
"developer_needs": [
"documentation",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_03801
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
advanced
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: 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": [
"repo_scale_reasoning",
"evaluation_metrics",
"governance"
]
}
|
|
train_03802
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
explain
|
intermediate
|
Task: explain
Topic: Code-specialized model families and sizing tradeoffs
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": [
"governance",
"repo_scale_reasoning",
"evaluation_metrics"
]
}
|
|
train_03803
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
explain
|
expert
|
Task: explain
Topic: Extended context and repo-scale understanding
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"tests_are_truth"
]
}
|
|
train_03804
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
intermediate
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Java",
"developer_needs": [
"tooling",
"governance",
"cost_latency_tradeoffs"
]
}
|
|
train_03805
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
expert
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
Target language: SQL
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"governance",
"reproducibility",
"tooling"
]
}
|
|
train_03806
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
design
|
advanced
|
Task: design
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: SQL
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"ci_integration"
]
}
|
|
train_03807
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: intermediate
Target language: Rust
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Rust",
"developer_needs": [
"evaluation_metrics",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_03808
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: SQL
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "SQL",
"developer_needs": [
"documentation",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_03809
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
foundation
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: foundation
Target language: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "TypeScript",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_03810
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
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": [
"documentation",
"governance",
"reproducibility"
]
}
|
|
train_03811
| 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: Java
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_03812
| 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: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"tooling",
"repo_scale_reasoning",
"evaluation_metrics"
]
}
|
|
train_03813
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
design
|
intermediate
|
Task: design
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": [
"cost_latency_tradeoffs",
"ci_integration",
"evaluation_metrics"
]
}
|
|
train_03814
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
compare
|
advanced
|
Task: compare
Topic: Secure code generation and policy gates
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": [
"tests_are_truth",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_03815
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
design
|
expert
|
Task: design
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
Target language: Rust
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"governance",
"tooling",
"tests_are_truth"
]
}
|
|
train_03816
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
Target language: 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": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"tooling"
]
}
|
|
train_03817
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: JavaScript
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "JavaScript",
"developer_needs": [
"documentation",
"governance",
"cost_latency_tradeoffs"
]
}
|
|
train_03818
| 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: 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": [
"documentation",
"reproducibility",
"tests_are_truth"
]
}
|
|
train_03819
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
foundation
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
Target language: 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": [
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_03820
| 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: SQL
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "SQL",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_03821
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
advanced
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: Java
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"reproducibility",
"governance"
]
}
|
|
train_03822
| 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: Rust
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Rust",
"developer_needs": [
"evaluation_metrics",
"governance",
"tests_are_truth"
]
}
|
|
train_03823
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
compare
|
intermediate
|
Task: compare
Topic: Mixture-of-Experts (MoE) for code
Difficulty: intermediate
Target language: 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": [
"tests_are_truth",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_03824
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
review
|
foundation
|
Task: review
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
Target language: SQL
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "SQL",
"developer_needs": [
"governance",
"tooling",
"repo_scale_reasoning"
]
}
|
|
train_03825
| 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: 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": [
"security_gates",
"reproducibility",
"governance"
]
}
|
|
train_03826
| 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: 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": [
"tests_are_truth",
"documentation",
"tooling"
]
}
|
|
train_03827
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
eval
|
foundation
|
Task: eval
Topic: SWE-bench style real-repo evaluation
Difficulty: foundation
Target language: 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": [
"documentation",
"governance",
"cost_latency_tradeoffs"
]
}
|
|
train_03828
| 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: 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",
"ci_integration",
"repo_scale_reasoning"
]
}
|
|
train_03829
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: foundation
Target language: Bash
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Bash",
"developer_needs": [
"governance",
"reproducibility",
"evaluation_metrics"
]
}
|
|
train_03830
| 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": [
"cost_latency_tradeoffs",
"tests_are_truth",
"documentation"
]
}
|
|
train_03831
| 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: 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": [
"reproducibility",
"tests_are_truth",
"documentation"
]
}
|
|
train_03832
| 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: 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": [
"ci_integration",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_03833
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
Target language: Java
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"governance",
"tooling"
]
}
|
|
train_03834
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
compare
|
intermediate
|
Task: compare
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"repo_scale_reasoning",
"tests_are_truth"
]
}
|
|
train_03835
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
expert
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
Target language: JavaScript
Context: 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",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_03836
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Java",
"developer_needs": [
"governance",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_03837
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: expert
Target language: Go
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"reproducibility"
]
}
|
|
train_03838
| 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: 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",
"evaluation_metrics",
"governance"
]
}
|
|
train_03839
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Bash",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_03840
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
review
|
expert
|
Task: review
Topic: SWE-bench style real-repo evaluation
Difficulty: expert
Target language: 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": [
"ci_integration",
"documentation",
"governance"
]
}
|
|
train_03841
| 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: 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": [
"ci_integration",
"reproducibility",
"security_gates"
]
}
|
|
train_03842
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
explain
|
expert
|
Task: explain
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: C#
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"documentation",
"ci_integration"
]
}
|
|
train_03843
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
design
|
advanced
|
Task: design
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: SQL
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"tooling",
"repo_scale_reasoning"
]
}
|
|
train_03844
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Secure code generation and policy gates
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "TypeScript",
"developer_needs": [
"governance",
"tests_are_truth",
"evaluation_metrics"
]
}
|
|
train_03845
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"repo_scale_reasoning",
"tests_are_truth"
]
}
|
|
train_03846
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
intermediate
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: 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": [
"security_gates",
"tooling",
"ci_integration"
]
}
|
|
train_03847
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
design
|
expert
|
Task: design
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
Target language: 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": [
"tooling",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_03848
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
design
|
intermediate
|
Task: design
Topic: Code-specialized model families and sizing tradeoffs
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": [
"ci_integration",
"repo_scale_reasoning",
"tests_are_truth"
]
}
|
|
train_03849
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: Java
Context: 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": "Java",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"evaluation_metrics"
]
}
|
|
train_03850
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
advanced
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: C#
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"tooling",
"tests_are_truth",
"documentation"
]
}
|
|
train_03851
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
design
|
advanced
|
Task: design
Topic: Mixture-of-Experts (MoE) for code
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",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
}
|
|
train_03852
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
review
|
foundation
|
Task: review
Topic: Tool calling, sandboxes, and CI integration
Difficulty: foundation
Target language: 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": [
"tests_are_truth",
"governance",
"reproducibility"
]
}
|
|
train_03853
| 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: Bash
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Bash",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_03854
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
design
|
intermediate
|
Task: design
Topic: Secure code generation and policy gates
Difficulty: intermediate
Target language: C#
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"documentation",
"repo_scale_reasoning",
"evaluation_metrics"
]
}
|
|
train_03855
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
eval
|
advanced
|
Task: eval
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: 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": [
"repo_scale_reasoning",
"tooling",
"documentation"
]
}
|
|
train_03856
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
explain
|
advanced
|
Task: explain
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"ci_integration",
"evaluation_metrics"
]
}
|
|
train_03857
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
code
|
intermediate
|
Task: code
Topic: Agentic coding systems (plan→edit→test→reflect)
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"governance",
"tooling",
"tests_are_truth"
]
}
|
|
train_03858
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "TypeScript",
"developer_needs": [
"tooling",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_03859
| 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: Java
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"tooling",
"security_gates"
]
}
|
|
train_03860
| 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: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"ci_integration",
"reproducibility"
]
}
|
|
train_03861
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
design
|
advanced
|
Task: design
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: C#
Context: 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": [
"reproducibility",
"governance",
"documentation"
]
}
|
|
train_03862
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
review
|
expert
|
Task: review
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Rust",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"tooling"
]
}
|
|
train_03863
| 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: 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": [
"reproducibility",
"security_gates",
"tooling"
]
}
|
|
train_03864
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
foundation
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
Target language: Java
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Java",
"developer_needs": [
"documentation",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_03865
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"governance",
"documentation"
]
}
|
|
train_03866
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"repo_scale_reasoning",
"tooling",
"ci_integration"
]
}
|
|
train_03867
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
eval
|
expert
|
Task: eval
Topic: SWE-bench style real-repo evaluation
Difficulty: expert
Target language: Python
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Python",
"developer_needs": [
"security_gates",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_03868
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
code
|
intermediate
|
Task: code
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: Bash
Context: 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": [
"documentation",
"reproducibility",
"tooling"
]
}
|
|
train_03869
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
compare
|
intermediate
|
Task: compare
Topic: Secure code generation and policy gates
Difficulty: intermediate
Target language: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Rust",
"developer_needs": [
"reproducibility",
"governance",
"ci_integration"
]
}
|
|
train_03870
| 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: Java
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Java",
"developer_needs": [
"governance",
"repo_scale_reasoning",
"tooling"
]
}
|
|
train_03871
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
eval
|
intermediate
|
Task: eval
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"governance",
"security_gates"
]
}
|
|
train_03872
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
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.
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",
"repo_scale_reasoning"
]
}
|
|
train_03873
| 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: 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": [
"documentation",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_03874
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
design
|
advanced
|
Task: design
Topic: Code-specialized model families and sizing tradeoffs
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": [
"documentation",
"tooling",
"security_gates"
]
}
|
|
train_03875
| 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: 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",
"evaluation_metrics",
"tooling"
]
}
|
|
train_03876
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
eval
|
expert
|
Task: eval
Topic: Extended context and repo-scale understanding
Difficulty: expert
Target language: Go
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Go",
"developer_needs": [
"repo_scale_reasoning",
"tooling",
"reproducibility"
]
}
|
|
train_03877
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
explain
|
advanced
|
Task: explain
Topic: Extended context and repo-scale understanding
Difficulty: advanced
Target language: TypeScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"governance"
]
}
|
|
train_03878
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: Bash
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Bash",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"security_gates"
]
}
|
|
train_03879
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"tooling",
"governance"
]
}
|
|
train_03880
| 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: 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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Python",
"developer_needs": [
"repo_scale_reasoning",
"ci_integration",
"security_gates"
]
}
|
|
train_03881
| 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: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"documentation",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_03882
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
intermediate
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Python",
"developer_needs": [
"governance",
"tooling",
"ci_integration"
]
}
|
|
train_03883
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Go",
"developer_needs": [
"documentation",
"reproducibility",
"tests_are_truth"
]
}
|
|
train_03884
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
agent_loop
|
foundation
|
Task: agent_loop
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"governance",
"ci_integration",
"repo_scale_reasoning"
]
}
|
|
train_03885
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
code
|
intermediate
|
Task: code
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: Go
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Go",
"developer_needs": [
"documentation",
"security_gates",
"governance"
]
}
|
|
train_03886
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
design
|
expert
|
Task: design
Topic: Secure code generation and policy gates
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
}
|
|
train_03887
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
compare
|
advanced
|
Task: compare
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: TypeScript
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"ci_integration",
"governance"
]
}
|
|
train_03888
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: SQL
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_03889
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
compare
|
advanced
|
Task: compare
Topic: Secure code generation and policy gates
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Rust",
"developer_needs": [
"ci_integration",
"repo_scale_reasoning",
"security_gates"
]
}
|
|
train_03890
| 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: 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": [
"tooling",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_03891
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: C#
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "C#",
"developer_needs": [
"documentation",
"evaluation_metrics",
"tooling"
]
}
|
|
train_03892
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
design
|
intermediate
|
Task: design
Topic: Model merging, distillation, and continued pretraining
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": [
"ci_integration",
"reproducibility",
"documentation"
]
}
|
|
train_03893
| 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": [
"documentation",
"evaluation_metrics",
"security_gates"
]
}
|
|
train_03894
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
compare
|
expert
|
Task: compare
Topic: SWE-bench style real-repo evaluation
Difficulty: expert
Target language: Go
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Go",
"developer_needs": [
"governance",
"repo_scale_reasoning",
"tests_are_truth"
]
}
|
|
train_03895
| 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: Go
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Go",
"developer_needs": [
"documentation",
"tooling",
"ci_integration"
]
}
|
|
train_03896
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: JavaScript
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "JavaScript",
"developer_needs": [
"documentation",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_03897
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
compare
|
advanced
|
Task: compare
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: Java
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Java",
"developer_needs": [
"governance",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_03898
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
explain
|
advanced
|
Task: explain
Topic: SWE-bench style real-repo evaluation
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": [
"reproducibility",
"ci_integration",
"repo_scale_reasoning"
]
}
|
|
train_03899
| 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: 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": [
"ci_integration",
"security_gates",
"tests_are_truth"
]
}
|
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