id
stringlengths 11
11
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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
values | difficulty
stringclasses 4
values | instruction
stringlengths 201
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| input
stringclasses 1
value | output
stringclasses 7
values | metadata
dict |
|---|---|---|---|---|---|---|---|---|
train_00100
| 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: 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",
"ci_integration"
]
}
|
|
train_00101
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Rust",
"developer_needs": [
"reproducibility",
"repo_scale_reasoning",
"ci_integration"
]
}
|
|
train_00102
| 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: 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": [
"ci_integration",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_00103
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
advanced
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
Difficulty: advanced
Target language: Rust
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Rust",
"developer_needs": [
"documentation",
"tests_are_truth",
"ci_integration"
]
}
|
|
train_00104
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
eval
|
advanced
|
Task: eval
Topic: Tool calling, sandboxes, and CI integration
Difficulty: advanced
Target language: Rust
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Rust",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"security_gates"
]
}
|
|
train_00105
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: Rust
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Rust",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_00106
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
eval
|
intermediate
|
Task: eval
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
Target language: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Rust",
"developer_needs": [
"tooling",
"governance",
"ci_integration"
]
}
|
|
train_00107
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
review
|
advanced
|
Task: review
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Rust",
"developer_needs": [
"reproducibility",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_00108
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
eval
|
advanced
|
Task: eval
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: C#
Context: 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": "C#",
"developer_needs": [
"evaluation_metrics",
"tooling",
"governance"
]
}
|
|
train_00109
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
code
|
advanced
|
Task: code
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
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": [
"tooling",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_00110
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
review
|
intermediate
|
Task: review
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: C#
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "C#",
"developer_needs": [
"security_gates",
"cost_latency_tradeoffs",
"evaluation_metrics"
]
}
|
|
train_00111
| 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": [
"governance",
"security_gates",
"cost_latency_tradeoffs"
]
}
|
|
train_00112
| 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: Java
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"documentation",
"ci_integration"
]
}
|
|
train_00113
| 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: 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",
"security_gates",
"ci_integration"
]
}
|
|
train_00114
| 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: 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": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"ci_integration"
]
}
|
|
train_00115
| 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: Rust
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Rust",
"developer_needs": [
"tests_are_truth",
"tooling",
"cost_latency_tradeoffs"
]
}
|
|
train_00116
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
compare
|
foundation
|
Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: foundation
Target language: 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": [
"tests_are_truth",
"repo_scale_reasoning",
"ci_integration"
]
}
|
|
train_00117
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
advanced
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
Difficulty: advanced
Target language: Python
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Python",
"developer_needs": [
"repo_scale_reasoning",
"tooling",
"documentation"
]
}
|
|
train_00118
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
code
|
advanced
|
Task: code
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
Target language: TypeScript
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"tooling",
"tests_are_truth",
"governance"
]
}
|
|
train_00119
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
code
|
foundation
|
Task: code
Topic: Tool calling, sandboxes, and CI integration
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.
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": [
"repo_scale_reasoning",
"security_gates",
"tooling"
]
}
|
|
train_00120
| 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: Rust
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"tests_are_truth",
"ci_integration",
"documentation"
]
}
|
|
train_00121
| 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: Java
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Java",
"developer_needs": [
"documentation",
"ci_integration",
"reproducibility"
]
}
|
|
train_00122
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
review
|
advanced
|
Task: review
Topic: Dataset curation pipelines (filter, dedupe, quality)
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"tooling"
]
}
|
|
train_00123
| 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: Python
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Python",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"repo_scale_reasoning"
]
}
|
|
train_00124
| 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: 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": [
"governance",
"tests_are_truth",
"tooling"
]
}
|
|
train_00125
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
compare
|
intermediate
|
Task: compare
Topic: Governance, provenance, and licensing for code data
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "SQL",
"developer_needs": [
"security_gates",
"governance",
"evaluation_metrics"
]
}
|
|
train_00126
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
review
|
foundation
|
Task: review
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: foundation
Target language: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_00127
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
review
|
foundation
|
Task: review
Topic: Mixture-of-Experts (MoE) for code
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"reproducibility",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_00128
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Rust",
"developer_needs": [
"ci_integration",
"security_gates",
"governance"
]
}
|
|
train_00129
| 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: 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": [
"governance",
"repo_scale_reasoning",
"reproducibility"
]
}
|
|
train_00130
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
design
|
advanced
|
Task: design
Topic: Tool calling, sandboxes, and CI integration
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": [
"ci_integration",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_00131
| 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: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Python",
"developer_needs": [
"reproducibility",
"tests_are_truth",
"tooling"
]
}
|
|
train_00132
| 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: 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",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_00133
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
explain
|
expert
|
Task: explain
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
Target language: C#
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "C#",
"developer_needs": [
"cost_latency_tradeoffs",
"ci_integration",
"repo_scale_reasoning"
]
}
|
|
train_00134
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
compare
|
intermediate
|
Task: compare
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
Target language: TypeScript
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"cost_latency_tradeoffs",
"ci_integration"
]
}
|
|
train_00135
| 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: 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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"governance",
"tooling",
"tests_are_truth"
]
}
|
|
train_00136
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
code
|
expert
|
Task: code
Topic: Secure code generation and policy gates
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Go",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"ci_integration"
]
}
|
|
train_00137
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
design
|
foundation
|
Task: design
Topic: Secure code generation and policy gates
Difficulty: foundation
Target language: Rust
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Rust",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_00138
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
Target language: JavaScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_00139
| 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: 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": [
"documentation",
"tests_are_truth",
"security_gates"
]
}
|
|
train_00140
| 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: 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": [
"repo_scale_reasoning",
"tests_are_truth",
"cost_latency_tradeoffs"
]
}
|
|
train_00141
| 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: 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": [
"tooling",
"tests_are_truth",
"security_gates"
]
}
|
|
train_00142
| 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: TypeScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"ci_integration",
"evaluation_metrics",
"repo_scale_reasoning"
]
}
|
|
train_00143
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
eval
|
expert
|
Task: eval
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: JavaScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "JavaScript",
"developer_needs": [
"evaluation_metrics",
"reproducibility",
"ci_integration"
]
}
|
|
train_00144
| 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: 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": [
"documentation",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_00145
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: 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": [
"repo_scale_reasoning",
"governance",
"documentation"
]
}
|
|
train_00146
| 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: 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": [
"security_gates",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_00147
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
design
|
advanced
|
Task: design
Topic: Tool calling, sandboxes, and CI integration
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": [
"governance",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_00148
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
compare
|
intermediate
|
Task: compare
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: 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": [
"governance",
"reproducibility",
"ci_integration"
]
}
|
|
train_00149
| 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: Rust
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Rust",
"developer_needs": [
"documentation",
"governance",
"ci_integration"
]
}
|
|
train_00150
| 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: Go
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "Go",
"developer_needs": [
"tooling",
"reproducibility",
"repo_scale_reasoning"
]
}
|
|
train_00151
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
compare
|
foundation
|
Task: compare
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: foundation
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": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"reproducibility"
]
}
|
|
train_00152
| 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: 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": [
"reproducibility",
"security_gates",
"tests_are_truth"
]
}
|
|
train_00153
| 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: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"tooling",
"reproducibility"
]
}
|
|
train_00154
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
review
|
advanced
|
Task: review
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Java",
"developer_needs": [
"reproducibility",
"security_gates",
"cost_latency_tradeoffs"
]
}
|
|
train_00155
| 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: JavaScript
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"evaluation_metrics",
"reproducibility"
]
}
|
|
train_00156
| 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: 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": [
"cost_latency_tradeoffs",
"ci_integration",
"security_gates"
]
}
|
|
train_00157
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
compare
|
expert
|
Task: compare
Topic: Extended context and repo-scale understanding
Difficulty: expert
Target language: SQL
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "SQL",
"developer_needs": [
"governance",
"ci_integration",
"tests_are_truth"
]
}
|
|
train_00158
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
eval
|
foundation
|
Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: foundation
Target language: Bash
Context: 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": "Bash",
"developer_needs": [
"governance",
"ci_integration",
"evaluation_metrics"
]
}
|
|
train_00159
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
compare
|
expert
|
Task: compare
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: JavaScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"ci_integration",
"governance",
"documentation"
]
}
|
|
train_00160
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: advanced
Target language: Bash
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Bash",
"developer_needs": [
"reproducibility",
"tooling",
"documentation"
]
}
|
|
train_00161
| 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: 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": [
"repo_scale_reasoning",
"governance",
"cost_latency_tradeoffs"
]
}
|
|
train_00162
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
foundation
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: foundation
Target language: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"tooling",
"documentation",
"tests_are_truth"
]
}
|
|
train_00163
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
code
|
foundation
|
Task: code
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.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "SQL",
"developer_needs": [
"governance",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_00164
| 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: 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": [
"tooling",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_00165
| 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: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Python",
"developer_needs": [
"tests_are_truth",
"governance",
"repo_scale_reasoning"
]
}
|
|
train_00166
| 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: Java
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "Java",
"developer_needs": [
"tooling",
"evaluation_metrics",
"governance"
]
}
|
|
train_00167
| 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: JavaScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"ci_integration",
"cost_latency_tradeoffs"
]
}
|
|
train_00168
| 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: 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": [
"governance",
"cost_latency_tradeoffs",
"documentation"
]
}
|
|
train_00169
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
review
|
advanced
|
Task: review
Topic: Tool calling, sandboxes, and CI integration
Difficulty: advanced
Target language: JavaScript
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"tooling"
]
}
|
|
train_00170
| 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: 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": [
"evaluation_metrics",
"ci_integration",
"reproducibility"
]
}
|
|
train_00171
| 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: 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",
"tooling",
"reproducibility"
]
}
|
|
train_00172
| 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: 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": [
"tooling",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_00173
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
compare
|
intermediate
|
Task: compare
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: 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": [
"ci_integration",
"tooling",
"tests_are_truth"
]
}
|
|
train_00174
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
advanced
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: TypeScript
Context: 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": "TypeScript",
"developer_needs": [
"tooling",
"documentation",
"reproducibility"
]
}
|
|
train_00175
| 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: 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": [
"governance",
"evaluation_metrics",
"tests_are_truth"
]
}
|
|
train_00176
| 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: 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.
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": [
"tooling",
"governance",
"documentation"
]
}
|
|
train_00177
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "C#",
"developer_needs": [
"governance",
"documentation",
"security_gates"
]
}
|
|
train_00178
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
review
|
intermediate
|
Task: review
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: Python
Context: Integrate an LLM agent into CI for a large monorepo.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Review: correctness, security, performance, governance
|
{
"target_language": "Python",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"tests_are_truth"
]
}
|
|
train_00179
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
explain
|
advanced
|
Task: explain
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: SQL
Context: 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": [
"ci_integration",
"documentation",
"cost_latency_tradeoffs"
]
}
|
|
train_00180
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: C#
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"tooling"
]
}
|
|
train_00181
| 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: 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": "Go",
"developer_needs": [
"tooling",
"evaluation_metrics",
"security_gates"
]
}
|
|
train_00182
| 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: Rust
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Rust",
"developer_needs": [
"cost_latency_tradeoffs",
"tooling",
"tests_are_truth"
]
}
|
|
train_00183
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Extended context and repo-scale understanding
Difficulty: advanced
Target language: 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",
"reproducibility"
]
}
|
|
train_00184
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
agent_loop
|
foundation
|
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: foundation
Target language: 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": [
"documentation",
"governance",
"reproducibility"
]
}
|
|
train_00185
| 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: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"tests_are_truth",
"governance"
]
}
|
|
train_00186
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: TypeScript
Context: Design a data pipeline for continued pretraining with auditability.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Agent loop: Plan → Edit (diff) → Test → Reflect → Human gate
|
{
"target_language": "TypeScript",
"developer_needs": [
"tooling",
"documentation",
"ci_integration"
]
}
|
|
train_00187
| 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: JavaScript
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"tests_are_truth",
"governance",
"cost_latency_tradeoffs"
]
}
|
|
train_00188
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
design
|
expert
|
Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: expert
Target language: 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",
"security_gates",
"repo_scale_reasoning"
]
}
|
|
train_00189
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
advanced
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "JavaScript",
"developer_needs": [
"tests_are_truth",
"security_gates",
"tooling"
]
}
|
|
train_00190
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
code
|
expert
|
Task: code
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: 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",
"security_gates",
"governance"
]
}
|
|
train_00191
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
explain
|
intermediate
|
Task: explain
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: intermediate
Target language: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Python",
"developer_needs": [
"governance",
"repo_scale_reasoning",
"documentation"
]
}
|
|
train_00192
| 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: 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.
Review: correctness, security, performance, governance
|
{
"target_language": "TypeScript",
"developer_needs": [
"tests_are_truth",
"documentation",
"governance"
]
}
|
|
train_00193
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
expert
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: expert
Target language: TypeScript
Context: Evaluate two coding models for internal rollout under strict governance.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "TypeScript",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"ci_integration"
]
}
|
|
train_00194
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: 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.
Pipeline:
1) Ingest
2) Normalize
3) Filter
4) Dedupe
5) Quality score
6) Sample
7) Audit
|
{
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"tests_are_truth",
"reproducibility"
]
}
|
|
train_00195
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
eval
|
foundation
|
Task: eval
Topic: Secure code generation and policy gates
Difficulty: foundation
Target language: 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.
Eval:
- Tasks: real issues
- Metrics: pass@k, time-to-green
- Gates: lint/security
|
{
"target_language": "Bash",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"security_gates"
]
}
|
|
train_00196
| 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: Python
Context: Fix a failing issue with tests as the oracle and produce a safe patch.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Python",
"developer_needs": [
"governance",
"documentation",
"ci_integration"
]
}
|
|
train_00197
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
advanced
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
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.
Compare: capability, cost, latency, reliability, governance
|
{
"target_language": "Go",
"developer_needs": [
"repo_scale_reasoning",
"evaluation_metrics",
"cost_latency_tradeoffs"
]
}
|
|
train_00198
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
code
|
expert
|
Task: code
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: Bash
Context: Create an eval harness that reflects real developer workflows.
Deliver production-grade guidance or artifacts.
|
Key facts:
- Modern AI coding emphasizes correctness via tests, agentic loops, and real-repo evaluation.
- Reasoning-first and MoE approaches improve capability-per-compute when paired with tools.
Design guidance with risks, metrics, acceptance criteria
|
{
"target_language": "Bash",
"developer_needs": [
"tooling",
"documentation",
"governance"
]
}
|
|
train_00199
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
review
|
advanced
|
Task: review
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.
Review: correctness, security, performance, governance
|
{
"target_language": "Bash",
"developer_needs": [
"governance",
"tooling",
"tests_are_truth"
]
}
|
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