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timestamp[s]date 2026-01-01 00:00:00
2026-01-01 00:00:00
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|---|---|---|---|---|---|---|---|---|
train_06000
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
expert
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: expert
Target language: Go
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Go",
"developer_needs": [
"security_gates",
"documentation",
"governance"
]
}
|
|
train_06001
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
eval
|
advanced
|
Task: eval
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"ci_integration",
"governance"
]
}
|
|
train_06002
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
review
|
intermediate
|
Task: review
Topic: Reasoning-first coding models and tunable deliberation
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": [
"tooling",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_06003
| 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: 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": [
"documentation",
"reproducibility",
"cost_latency_tradeoffs"
]
}
|
|
train_06004
| 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: 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": [
"governance",
"ci_integration",
"reproducibility"
]
}
|
|
train_06005
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: Go
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Go",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"governance"
]
}
|
|
train_06006
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: 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": [
"ci_integration",
"evaluation_metrics",
"documentation"
]
}
|
|
train_06007
| 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: Bash
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"documentation"
]
}
|
|
train_06008
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "JavaScript",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"documentation"
]
}
|
|
train_06009
| 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: 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": [
"repo_scale_reasoning",
"ci_integration",
"reproducibility"
]
}
|
|
train_06010
| 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: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"documentation",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_06011
| 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: TypeScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "TypeScript",
"developer_needs": [
"tooling",
"security_gates",
"cost_latency_tradeoffs"
]
}
|
|
train_06012
| 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: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "SQL",
"developer_needs": [
"documentation",
"repo_scale_reasoning",
"tooling"
]
}
|
|
train_06013
| 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: Go
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_06014
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Tool calling, sandboxes, and CI integration
Difficulty: foundation
Target language: C#
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "C#",
"developer_needs": [
"security_gates",
"tooling",
"governance"
]
}
|
|
train_06015
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
eval
|
foundation
|
Task: eval
Topic: Governance, provenance, and licensing for code data
Difficulty: foundation
Target language: Python
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Python",
"developer_needs": [
"repo_scale_reasoning",
"documentation",
"ci_integration"
]
}
|
|
train_06016
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
intermediate
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: Rust
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"security_gates"
]
}
|
|
train_06017
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
design
|
intermediate
|
Task: design
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: intermediate
Target language: 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": [
"tooling",
"ci_integration",
"evaluation_metrics"
]
}
|
|
train_06018
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: intermediate
Target language: 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": [
"reproducibility",
"ci_integration",
"documentation"
]
}
|
|
train_06019
| 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: SQL
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"security_gates",
"documentation",
"ci_integration"
]
}
|
|
train_06020
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: intermediate
Target language: TypeScript
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "TypeScript",
"developer_needs": [
"governance",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_06021
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
expert
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
Target language: Go
Context: 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": [
"tests_are_truth",
"documentation",
"ci_integration"
]
}
|
|
train_06022
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
design
|
intermediate
|
Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: Python
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Python",
"developer_needs": [
"documentation",
"tests_are_truth",
"ci_integration"
]
}
|
|
train_06023
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
compare
|
expert
|
Task: compare
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: expert
Target language: 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",
"governance",
"tooling"
]
}
|
|
train_06024
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: intermediate
Target language: 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": [
"governance",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_06025
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
design
|
intermediate
|
Task: design
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: intermediate
Target language: 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",
"tests_are_truth",
"repo_scale_reasoning"
]
}
|
|
train_06026
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
Difficulty: foundation
Target language: Go
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Go",
"developer_needs": [
"documentation",
"governance",
"tests_are_truth"
]
}
|
|
train_06027
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
design
|
expert
|
Task: design
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: Go
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Go",
"developer_needs": [
"reproducibility",
"governance",
"documentation"
]
}
|
|
train_06028
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
eval
|
advanced
|
Task: eval
Topic: Extended context and repo-scale understanding
Difficulty: advanced
Target language: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_06029
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
design
|
expert
|
Task: design
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: JavaScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"tooling",
"ci_integration",
"reproducibility"
]
}
|
|
train_06030
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
explain
|
foundation
|
Task: explain
Topic: Extended context and repo-scale understanding
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": [
"security_gates",
"documentation",
"ci_integration"
]
}
|
|
train_06031
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Tool calling, sandboxes, and CI integration
Difficulty: foundation
Target language: C#
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "C#",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_06032
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
eval
|
intermediate
|
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: C#
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "C#",
"developer_needs": [
"reproducibility",
"governance",
"evaluation_metrics"
]
}
|
|
train_06033
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "JavaScript",
"developer_needs": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"repo_scale_reasoning"
]
}
|
|
train_06034
| 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: 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": [
"security_gates",
"governance",
"evaluation_metrics"
]
}
|
|
train_06035
| 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: 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": "Rust",
"developer_needs": [
"tooling",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_06036
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
explain
|
foundation
|
Task: explain
Topic: Secure code generation and policy gates
Difficulty: foundation
Target language: 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",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_06037
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
explain
|
foundation
|
Task: explain
Topic: Governance, provenance, and licensing for code data
Difficulty: foundation
Target language: 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",
"tests_are_truth",
"documentation"
]
}
|
|
train_06038
| 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: 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": [
"reproducibility",
"documentation",
"governance"
]
}
|
|
train_06039
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
explain
|
expert
|
Task: explain
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: Java
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Java",
"developer_needs": [
"tooling",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_06040
| 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: TypeScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"tooling",
"governance"
]
}
|
|
train_06041
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Extended context and repo-scale understanding
Difficulty: expert
Target language: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "JavaScript",
"developer_needs": [
"evaluation_metrics",
"documentation",
"ci_integration"
]
}
|
|
train_06042
| 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: Python
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Python",
"developer_needs": [
"tests_are_truth",
"governance",
"security_gates"
]
}
|
|
train_06043
| 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: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"governance",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_06044
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"reproducibility"
]
}
|
|
train_06045
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
intermediate
|
Task: design
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
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": [
"documentation",
"repo_scale_reasoning",
"tooling"
]
}
|
|
train_06046
| 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: 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",
"security_gates",
"documentation"
]
}
|
|
train_06047
| 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: Python
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Python",
"developer_needs": [
"tests_are_truth",
"tooling",
"ci_integration"
]
}
|
|
train_06048
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: expert
Target language: Rust
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Rust",
"developer_needs": [
"governance",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_06049
| 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: 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": [
"tests_are_truth",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_06050
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
review
|
foundation
|
Task: review
Topic: Secure code generation and policy gates
Difficulty: foundation
Target language: TypeScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"reproducibility",
"repo_scale_reasoning",
"governance"
]
}
|
|
train_06051
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Go",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"tests_are_truth"
]
}
|
|
train_06052
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Agentic coding systems (plan→edit→test→reflect)
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": [
"governance",
"evaluation_metrics",
"ci_integration"
]
}
|
|
train_06053
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
code
|
expert
|
Task: code
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: expert
Target language: TypeScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"tooling",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_06054
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: 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": [
"security_gates",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_06055
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Governance, provenance, and licensing for code data
Difficulty: foundation
Target language: Java
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"tooling",
"documentation",
"repo_scale_reasoning"
]
}
|
|
train_06056
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
review
|
advanced
|
Task: review
Topic: Extended context and repo-scale understanding
Difficulty: advanced
Target language: 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": [
"security_gates",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_06057
| 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: 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": [
"governance",
"repo_scale_reasoning",
"tooling"
]
}
|
|
train_06058
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
explain
|
advanced
|
Task: explain
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: 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": [
"repo_scale_reasoning",
"reproducibility",
"evaluation_metrics"
]
}
|
|
train_06059
| 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: 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": [
"tests_are_truth",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_06060
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
agent_loop
|
expert
|
Task: agent_loop
Topic: SWE-bench style real-repo evaluation
Difficulty: expert
Target language: Bash
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Bash",
"developer_needs": [
"security_gates",
"evaluation_metrics",
"governance"
]
}
|
|
train_06061
| 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: 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": [
"reproducibility",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_06062
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
eval
|
intermediate
|
Task: eval
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: Go
Context: 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": [
"reproducibility",
"ci_integration",
"tooling"
]
}
|
|
train_06063
| 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: 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": [
"governance",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_06064
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
code
|
foundation
|
Task: code
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: foundation
Target language: 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",
"ci_integration",
"reproducibility"
]
}
|
|
train_06065
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
compare
|
foundation
|
Task: compare
Topic: Secure code generation and policy gates
Difficulty: foundation
Target language: Python
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Python",
"developer_needs": [
"tooling",
"tests_are_truth",
"security_gates"
]
}
|
|
train_06066
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: foundation
Target language: 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.
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",
"evaluation_metrics"
]
}
|
|
train_06067
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
eval
|
foundation
|
Task: eval
Topic: Model merging, distillation, and continued pretraining
Difficulty: foundation
Target language: SQL
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_06068
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
compare
|
expert
|
Task: compare
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: JavaScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"ci_integration",
"governance"
]
}
|
|
train_06069
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
compare
|
advanced
|
Task: compare
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
Target language: Java
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Java",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"ci_integration"
]
}
|
|
train_06070
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
advanced
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Python",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"ci_integration"
]
}
|
|
train_06071
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
code
|
advanced
|
Task: code
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: 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",
"evaluation_metrics",
"ci_integration"
]
}
|
|
train_06072
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
compare
|
foundation
|
Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: foundation
Target language: TypeScript
Context: 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": [
"cost_latency_tradeoffs",
"ci_integration",
"reproducibility"
]
}
|
|
train_06073
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
foundation
|
Task: design
Topic: Governance, provenance, and licensing for code data
Difficulty: foundation
Target language: 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",
"documentation",
"reproducibility"
]
}
|
|
train_06074
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
explain
|
advanced
|
Task: explain
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: advanced
Target language: Python
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Python",
"developer_needs": [
"tests_are_truth",
"reproducibility",
"security_gates"
]
}
|
|
train_06075
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
compare
|
advanced
|
Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: C#
Context: 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": "C#",
"developer_needs": [
"documentation",
"repo_scale_reasoning",
"tooling"
]
}
|
|
train_06076
| 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: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"documentation"
]
}
|
|
train_06077
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: 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": [
"reproducibility",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_06078
| 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: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"ci_integration",
"documentation"
]
}
|
|
train_06079
| 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: 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": [
"ci_integration",
"reproducibility",
"documentation"
]
}
|
|
train_06080
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
code
|
foundation
|
Task: code
Topic: Secure code generation and policy gates
Difficulty: foundation
Target language: JavaScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"governance",
"tests_are_truth",
"documentation"
]
}
|
|
train_06081
| 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: SQL
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"governance",
"tests_are_truth",
"reproducibility"
]
}
|
|
train_06082
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
explain
|
foundation
|
Task: explain
Topic: Extended context and repo-scale understanding
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",
"cost_latency_tradeoffs",
"repo_scale_reasoning"
]
}
|
|
train_06083
| 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: 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": [
"security_gates",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_06084
| 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: SQL
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "SQL",
"developer_needs": [
"ci_integration",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_06085
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
data_pipeline
|
foundation
|
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
Target language: Java
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"ci_integration"
]
}
|
|
train_06086
| 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: 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": [
"tests_are_truth",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_06087
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
compare
|
advanced
|
Task: compare
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"reproducibility",
"cost_latency_tradeoffs"
]
}
|
|
train_06088
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
intermediate
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
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": [
"cost_latency_tradeoffs",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_06089
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
design
|
advanced
|
Task: design
Topic: Secure code generation and policy gates
Difficulty: advanced
Target language: 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": [
"tests_are_truth",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_06090
| 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: 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.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "C#",
"developer_needs": [
"security_gates",
"reproducibility",
"ci_integration"
]
}
|
|
train_06091
| 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: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "Bash",
"developer_needs": [
"tooling",
"reproducibility",
"tests_are_truth"
]
}
|
|
train_06092
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
review
|
foundation
|
Task: review
Topic: Governance, provenance, and licensing for code data
Difficulty: foundation
Target language: C#
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "C#",
"developer_needs": [
"ci_integration",
"documentation",
"security_gates"
]
}
|
|
train_06093
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
expert
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: 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": [
"tooling",
"reproducibility",
"governance"
]
}
|
|
train_06094
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
eval
|
intermediate
|
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: Java
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Java",
"developer_needs": [
"governance",
"tests_are_truth",
"reproducibility"
]
}
|
|
train_06095
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
advanced
|
Task: design
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: SQL
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"tests_are_truth",
"reproducibility",
"ci_integration"
]
}
|
|
train_06096
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
eval
|
foundation
|
Task: eval
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
Target language: SQL
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"repo_scale_reasoning",
"evaluation_metrics"
]
}
|
|
train_06097
| 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: 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": [
"governance",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_06098
| 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: 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": [
"reproducibility",
"repo_scale_reasoning",
"security_gates"
]
}
|
|
train_06099
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
code
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expert
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Task: code
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
Target language: Go
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
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Key facts:
- Modern AI 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
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{
"target_language": "Go",
"developer_needs": [
"documentation",
"reproducibility",
"governance"
]
}
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