id stringlengths 11 11 | created timestamp[s]date 2026-01-01 00:00:00 2026-01-01 00:00:00 | topic stringclasses 14 values | task_type stringclasses 10 values | difficulty stringclasses 3 values | instruction stringlengths 189 248 | input stringclasses 1 value | output stringclasses 9 values | reasoning_steps listlengths 0 5 | metadata dict | hash stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|
train_46400 | 2026-01-01T00:00:00 | Agentic coding systems (plan→edit→test→reflect) | design | expert | Task: design
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: expert
Target language: Java
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Java",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"documentation",
"tooling"
],
"moe_experts": [
"data_curation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | c4e76ee168185313eb639c94ae45ef50dfafa798 | |
train_46401 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | agent_loop | advanced | Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: C#
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "C#",
"developer_needs": [
"ci_integration",
"documentation",
"tooling",
"security_gates"
],
"moe_experts": [
"agentic_systems_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | ed1aa7f501d57db81279566c861d697023bf3cbd | |
train_46402 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | failure_analysis | expert | Task: failure_analysis
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: Java
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [] | {
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"security_gates",
"tooling"
],
"moe_experts": [
"data_curation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 23f150093184637cf60f4de287b50e8501c4f051 | |
train_46403 | 2026-01-01T00:00:00 | Secure code generation and policy gates | agent_loop | intermediate | Task: agent_loop
Topic: Secure code generation and policy gates
Difficulty: intermediate
Target language: Python
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Python",
"developer_needs": [
"reproducibility",
"documentation",
"evaluation_metrics",
"cost_latency_tradeoffs"
],
"moe_experts": [
"data_curation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 196ef74efb445ba7d1b526b7328e0f5e82ab99ed | |
train_46404 | 2026-01-01T00:00:00 | Agentic coding systems (plan→edit→test→reflect) | eval | advanced | Task: eval
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
Target language: Java
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Eval:
pass@k, time-to-green, regressions, diff size
| [] | {
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"governance",
"reproducibility",
"cost_latency_tradeoffs"
],
"moe_experts": [
"agentic_systems_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 7203cc3cef03f179953ce5762232fac758da566c | |
train_46405 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | code | expert | Task: code
Topic: Extended context and repo-scale understanding
Difficulty: expert
Target language: Bash
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Bash",
"developer_needs": [
"reproducibility",
"ci_integration",
"cost_latency_tradeoffs",
"tests_are_truth"
],
"moe_experts": [
"governance_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 941c2aac652783ca05ae1e8de5d437513532cf4e | |
train_46406 | 2026-01-01T00:00:00 | Latency, cost, and reliability optimization | patch_diff | intermediate | Task: patch_diff
Topic: Latency, cost, and reliability optimization
Difficulty: intermediate
Target language: Bash
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "Bash",
"developer_needs": [
"governance",
"auditability",
"reproducibility",
"tooling"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 31b2bae43143e686aea1c2ca0409d3723e103b87 | |
train_46407 | 2026-01-01T00:00:00 | Reasoning-first coding models and tunable deliberation | compare | expert | Task: compare
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: expert
Target language: Java
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "Java",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"tests_are_truth",
"cost_latency_tradeoffs"
],
"moe_experts": [
"performance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | fc407cfdb23e61f29468f0192a2397b01059dd95 | |
train_46408 | 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: Bash
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Eval:
pass@k, time-to-green, regressions, diff size
| [] | {
"target_language": "Bash",
"developer_needs": [
"evaluation_metrics",
"documentation",
"tests_are_truth",
"reproducibility"
],
"moe_experts": [
"data_curation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 50f449d6f9eb41588c6afeca595d36e98735a6ee | |
train_46409 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | compare | intermediate | Task: compare
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: Go
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "Go",
"developer_needs": [
"reproducibility",
"tests_are_truth",
"evaluation_metrics",
"repo_scale_reasoning"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 648230042ff698ded340c930140a9d7025774b6f | |
train_46410 | 2026-01-01T00:00:00 | Secure code generation and policy gates | review | expert | Task: review
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: SQL
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [] | {
"target_language": "SQL",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"cost_latency_tradeoffs",
"evaluation_metrics"
],
"moe_experts": [
"governance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | f2dcc32d3defbe8d98f118d74360172068ba61b1 | |
train_46411 | 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: C#
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [] | {
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"repo_scale_reasoning",
"governance",
"auditability"
],
"moe_experts": [
"governance_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | ff9011851087891713931bb00b9ab426b96422b1 | |
train_46412 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | patch_diff | intermediate | Task: patch_diff
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: Java
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Java",
"developer_needs": [
"auditability",
"evaluation_metrics",
"reproducibility",
"cost_latency_tradeoffs"
],
"moe_experts": [
"performance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 262b351f86dab7ef01a823c62f9958a97f479b28 | |
train_46413 | 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: Java
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [] | {
"target_language": "Java",
"developer_needs": [
"documentation",
"evaluation_metrics",
"ci_integration",
"repo_scale_reasoning"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 1c165027dae0f0b7a8ee27bb379cb49cb9ebe132 | |
train_46414 | 2026-01-01T00:00:00 | Secure code generation and policy gates | patch_diff | expert | Task: patch_diff
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: Python
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "Python",
"developer_needs": [
"tests_are_truth",
"reproducibility",
"cost_latency_tradeoffs",
"repo_scale_reasoning"
],
"moe_experts": [
"data_curation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 1a1c47f5942f6bc61f2b1d1b4943d1aea62a46ff | |
train_46415 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | review | expert | Task: review
Topic: Self-improving agents and feedback loops
Difficulty: expert
Target language: Bash
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Bash",
"developer_needs": [
"auditability",
"tooling",
"security_gates",
"ci_integration"
],
"moe_experts": [
"performance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 704bb005731101e7369696ba87f47553af503f7e | |
train_46416 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | data_pipeline | intermediate | Task: data_pipeline
Topic: Self-improving agents and feedback loops
Difficulty: intermediate
Target language: Rust
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Rust",
"developer_needs": [
"evaluation_metrics",
"reproducibility",
"auditability",
"repo_scale_reasoning"
],
"moe_experts": [
"evaluation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e2902daa1b4dcc91436e83a8fbb3afb5e9de4866 | |
train_46417 | 2026-01-01T00:00:00 | Latency, cost, and reliability optimization | code | intermediate | Task: code
Topic: Latency, cost, and reliability optimization
Difficulty: intermediate
Target language: Python
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Scaffold:
```python
def agent_loop(plan, edit, test, issue, max_iters=4):
history = []
p = plan(issue)
for _ in range(max_iters):
patch = edit(issue, p)
ok, report = test(patch)
history.append({"plan": p, "ok": ok})
if ok:
return patch, history
p = p + " | refine"
return patch, history
```
| [] | {
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"governance",
"tooling"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 0d8fe929e9d185977c6671a8c8687f5313e0a369 | |
train_46418 | 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: Python
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Scaffold:
```python
def agent_loop(plan, edit, test, issue, max_iters=4):
history = []
p = plan(issue)
for _ in range(max_iters):
patch = edit(issue, p)
ok, report = test(patch)
history.append({"plan": p, "ok": ok})
if ok:
return patch, history
p = p + " | refine"
return patch, history
```
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Python",
"developer_needs": [
"auditability",
"reproducibility",
"cost_latency_tradeoffs",
"tests_are_truth"
],
"moe_experts": [
"data_curation_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | ee11923a4737b2c062dd2c1ef26a3a8864e2ee0b | |
train_46419 | 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: Go
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Eval:
pass@k, time-to-green, regressions, diff size
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"reproducibility",
"ci_integration",
"tests_are_truth"
],
"moe_experts": [
"security_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | fd6a9259fb897eddceb362d34f6efcedcbd0646d | |
train_46420 | 2026-01-01T00:00:00 | Reasoning-first coding models and tunable deliberation | patch_diff | advanced | Task: patch_diff
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: advanced
Target language: TypeScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "TypeScript",
"developer_needs": [
"auditability",
"tests_are_truth",
"ci_integration",
"documentation"
],
"moe_experts": [
"agentic_systems_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 6a712f43dd1a65061cddd123a2865769f40b8346 | |
train_46421 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | design | advanced | Task: design
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: Rust
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Rust",
"developer_needs": [
"tooling",
"evaluation_metrics",
"repo_scale_reasoning",
"ci_integration"
],
"moe_experts": [
"data_curation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 0d7b8335485eaf4a36ad53df86a3253aaa624794 | |
train_46422 | 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: Bash
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Eval:
pass@k, time-to-green, regressions, diff size
| [] | {
"target_language": "Bash",
"developer_needs": [
"ci_integration",
"documentation",
"evaluation_metrics",
"repo_scale_reasoning"
],
"moe_experts": [
"data_curation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 3b8ca3694f100ec11ad7ab964f74cc3b94fd30d8 | |
train_46423 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | code | advanced | Task: code
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: JavaScript
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"security_gates",
"reproducibility",
"tooling"
],
"moe_experts": [
"data_curation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 738e59870d9cec94390676c2fc93e1a423b97622 | |
train_46424 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | agent_loop | expert | Task: agent_loop
Topic: Extended context and repo-scale understanding
Difficulty: expert
Target language: Go
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "Go",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"governance",
"evaluation_metrics"
],
"moe_experts": [
"security_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 028587454e1e70fc03f16ebd05c31f831bede196 | |
train_46425 | 2026-01-01T00:00:00 | Secure code generation and policy gates | patch_diff | expert | Task: patch_diff
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: TypeScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "TypeScript",
"developer_needs": [
"auditability",
"security_gates",
"reproducibility",
"tests_are_truth"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 42ad6bba4f2414071248bae9c8a0e8a3ee64b180 | |
train_46426 | 2026-01-01T00:00:00 | Agentic coding systems (plan→edit→test→reflect) | compare | expert | Task: compare
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: expert
Target language: Go
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Go",
"developer_needs": [
"auditability",
"evaluation_metrics",
"documentation",
"ci_integration"
],
"moe_experts": [
"data_curation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | f911eb2fb55cb04c3c7caf7af5ba9fa031d2d3ff | |
train_46427 | 2026-01-01T00:00:00 | Tool calling, sandboxes, and CI integration | patch_diff | intermediate | Task: patch_diff
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
Target language: JavaScript
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "JavaScript",
"developer_needs": [
"auditability",
"ci_integration",
"security_gates",
"tests_are_truth"
],
"moe_experts": [
"security_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 4c75496836e82413bbb0edae9888a6f99c499ba3 | |
train_46428 | 2026-01-01T00:00:00 | Multimodal dev workflows (docs, diagrams, traces) | eval | expert | Task: eval
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: expert
Target language: TypeScript
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Eval:
pass@k, time-to-green, regressions, diff size
| [] | {
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"tests_are_truth",
"reproducibility",
"cost_latency_tradeoffs"
],
"moe_experts": [
"evaluation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 0c57f739f3c0a7c357e43b0afc06b9831f8e2306 | |
train_46429 | 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: Bash
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Bash",
"developer_needs": [
"security_gates",
"repo_scale_reasoning",
"auditability",
"ci_integration"
],
"moe_experts": [
"evaluation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e73f1618ed7561e8d53af404bf4785dfdb4db797 | |
train_46430 | 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: Bash
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "Bash",
"developer_needs": [
"tooling",
"evaluation_metrics",
"ci_integration",
"reproducibility"
],
"moe_experts": [
"data_curation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 61066ebeeaf48860a9b36159f1a8329be22e0d21 | |
train_46431 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | compare | intermediate | Task: compare
Topic: Mixture-of-Experts (MoE) for code
Difficulty: intermediate
Target language: Rust
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Rust",
"developer_needs": [
"documentation",
"governance",
"auditability",
"tests_are_truth"
],
"moe_experts": [
"evaluation_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 1f8a1dbbaba54dddbad007bb2466ee34536c6d2e | |
train_46432 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | compare | advanced | Task: compare
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: Go
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "Go",
"developer_needs": [
"tooling",
"tests_are_truth",
"evaluation_metrics",
"security_gates"
],
"moe_experts": [
"coding_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | b4ab948815c9d6e152a8f15319849ea126035e9d | |
train_46433 | 2026-01-01T00:00:00 | Tool calling, sandboxes, and CI integration | eval | expert | Task: eval
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: Python
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Eval:
pass@k, time-to-green, regressions, diff size
| [] | {
"target_language": "Python",
"developer_needs": [
"documentation",
"auditability",
"reproducibility",
"tooling"
],
"moe_experts": [
"governance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 41b92495c66e5021d19fa6fb15f9a226017bec42 | |
train_46434 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | code | intermediate | Task: code
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: C#
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "C#",
"developer_needs": [
"reproducibility",
"documentation",
"governance",
"tests_are_truth"
],
"moe_experts": [
"governance_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | b2cacd75e20657e429ee4dec3bb6638ba7a5bbe2 | |
train_46435 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | review | expert | Task: review
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: Rust
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [] | {
"target_language": "Rust",
"developer_needs": [
"repo_scale_reasoning",
"evaluation_metrics",
"cost_latency_tradeoffs",
"tooling"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 2d079af68b948c24f7d283cb3e5bab4e2946d24e | |
train_46436 | 2026-01-01T00:00:00 | Latency, cost, and reliability optimization | review | advanced | Task: review
Topic: Latency, cost, and reliability optimization
Difficulty: advanced
Target language: Java
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [] | {
"target_language": "Java",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"tests_are_truth",
"ci_integration"
],
"moe_experts": [
"data_curation_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | c6e65d8391f361c5556d501bbfe96dd42c763750 | |
train_46437 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | data_pipeline | advanced | Task: data_pipeline
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: Java
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [] | {
"target_language": "Java",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"documentation",
"governance"
],
"moe_experts": [
"evaluation_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | c78897e1f0014015e0213b46182bd7ca759bd7f3 | |
train_46438 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | patch_diff | expert | Task: patch_diff
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: Go
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "Go",
"developer_needs": [
"reproducibility",
"tests_are_truth",
"governance",
"auditability"
],
"moe_experts": [
"agentic_systems_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 1792a90830716f1873cf0297874af708ce6d93de | |
train_46439 | 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: Bash
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Bash",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"governance",
"auditability"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | ee280604651a9d250d4c6249396a957814215a59 | |
train_46440 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | failure_analysis | advanced | Task: failure_analysis
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: SQL
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "SQL",
"developer_needs": [
"documentation",
"governance",
"tests_are_truth",
"reproducibility"
],
"moe_experts": [
"data_curation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 6d84587e58f206bb969094dce3afed5bd5acdfe9 | |
train_46441 | 2026-01-01T00:00:00 | Latency, cost, and reliability optimization | data_pipeline | intermediate | Task: data_pipeline
Topic: Latency, cost, and reliability optimization
Difficulty: intermediate
Target language: TypeScript
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [] | {
"target_language": "TypeScript",
"developer_needs": [
"governance",
"security_gates",
"evaluation_metrics",
"tooling"
],
"moe_experts": [
"governance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | edbb0e1ef7481088d67c065d8557e2f11b7b249e | |
train_46442 | 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: Go
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "Go",
"developer_needs": [
"ci_integration",
"tooling",
"auditability",
"cost_latency_tradeoffs"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 92a6b00788a1988bd778236ff39a45ad5ec52557 | |
train_46443 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | agent_loop | intermediate | Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: Bash
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Bash",
"developer_needs": [
"tests_are_truth",
"documentation",
"repo_scale_reasoning",
"security_gates"
],
"moe_experts": [
"agentic_systems_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e678393b446d6979b1dce6f3a05f7051f5db3cb6 | |
train_46444 | 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: C#
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "C#",
"developer_needs": [
"governance",
"tooling",
"security_gates",
"evaluation_metrics"
],
"moe_experts": [
"agentic_systems_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 35c3f258213264d0fb9a8e22d9e1ab5f0efc419e | |
train_46445 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | patch_diff | advanced | Task: patch_diff
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: JavaScript
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"ci_integration",
"evaluation_metrics"
],
"moe_experts": [
"security_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | cfae7e09df4e92acf2f6c1b6e1a034ad7514fc25 | |
train_46446 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | design | intermediate | Task: design
Topic: Mixture-of-Experts (MoE) for code
Difficulty: intermediate
Target language: Java
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Java",
"developer_needs": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"security_gates",
"tests_are_truth"
],
"moe_experts": [
"coding_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | c98c1443db7fdaae2956f01687510bd09354de42 | |
train_46447 | 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: JavaScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"auditability",
"evaluation_metrics",
"cost_latency_tradeoffs"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 68085678c5dc3be9b15d5276a28963e3c79f67c2 | |
train_46448 | 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: TypeScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"auditability",
"ci_integration",
"tests_are_truth"
],
"moe_experts": [
"coding_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 73cf9767179918833811b6390e15a92ac85fcd26 | |
train_46449 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | patch_diff | advanced | Task: patch_diff
Topic: Model merging, distillation, and continued pretraining
Difficulty: advanced
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "Python",
"developer_needs": [
"tooling",
"evaluation_metrics",
"repo_scale_reasoning",
"governance"
],
"moe_experts": [
"performance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 5ea155024e26b1c85c7b4e35bd7459575c2735b7 | |
train_46450 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | agent_loop | advanced | Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: SQL
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "SQL",
"developer_needs": [
"repo_scale_reasoning",
"documentation",
"cost_latency_tradeoffs",
"security_gates"
],
"moe_experts": [
"data_curation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 4c6625fc857f18e99e1d6c178acd7311d0f4d8ca | |
train_46451 | 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: JavaScript
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "JavaScript",
"developer_needs": [
"tooling",
"governance",
"auditability",
"tests_are_truth"
],
"moe_experts": [
"security_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 270bb1d3976f9e31589497e5dce1dd8751a82818 | |
train_46452 | 2026-01-01T00:00:00 | Multimodal dev workflows (docs, diagrams, traces) | explain | intermediate | Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: intermediate
Target language: SQL
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"documentation",
"repo_scale_reasoning"
],
"moe_experts": [
"security_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | bb411324b0d265e5780e540c79c8ff41f0cc6ec9 | |
train_46453 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | data_pipeline | advanced | Task: data_pipeline
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: Java
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [] | {
"target_language": "Java",
"developer_needs": [
"auditability",
"governance",
"reproducibility",
"evaluation_metrics"
],
"moe_experts": [
"agentic_systems_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 77d4ff7be59b38e1d883411227fc1f19cd574ed1 | |
train_46454 | 2026-01-01T00:00:00 | Tool calling, sandboxes, and CI integration | review | intermediate | Task: review
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
Target language: C#
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "C#",
"developer_needs": [
"tests_are_truth",
"reproducibility",
"governance",
"documentation"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | cd393c150e7d04de8d4318787d478aee5fc068d3 | |
train_46455 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | explain | expert | Task: explain
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: C#
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "C#",
"developer_needs": [
"tests_are_truth",
"ci_integration",
"reproducibility",
"cost_latency_tradeoffs"
],
"moe_experts": [
"security_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | ba3299fdfa840d59556ea10ecf02bcc0345f0625 | |
train_46456 | 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: Java
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [] | {
"target_language": "Java",
"developer_needs": [
"evaluation_metrics",
"tooling",
"governance",
"auditability"
],
"moe_experts": [
"coding_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 66d48c0f064a6f81ffd4e1896acaba4db619469a | |
train_46457 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | patch_diff | expert | Task: patch_diff
Topic: Extended context and repo-scale understanding
Difficulty: expert
Target language: Java
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "Java",
"developer_needs": [
"governance",
"security_gates",
"auditability",
"evaluation_metrics"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 7b97a24b4266586e8b5d032c89485350fa332783 | |
train_46458 | 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: Python
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Python",
"developer_needs": [
"documentation",
"governance",
"security_gates",
"repo_scale_reasoning"
],
"moe_experts": [
"governance_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 661de9065f792bcf0509468650f163f62eba53b7 | |
train_46459 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | agent_loop | advanced | Task: agent_loop
Topic: Self-improving agents and feedback loops
Difficulty: advanced
Target language: JavaScript
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "JavaScript",
"developer_needs": [
"tooling",
"auditability",
"documentation",
"security_gates"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | aedcc472a66373e528806de6810cce5b74d77965 | |
train_46460 | 2026-01-01T00:00:00 | Reasoning-first coding models and tunable deliberation | data_pipeline | expert | Task: data_pipeline
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: expert
Target language: C#
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "C#",
"developer_needs": [
"repo_scale_reasoning",
"governance",
"cost_latency_tradeoffs",
"auditability"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | ed1c7c0d8e9a60a59114aeaa7097e6fce4d8ccb1 | |
train_46461 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | eval | expert | Task: eval
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: Bash
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Eval:
pass@k, time-to-green, regressions, diff size
| [] | {
"target_language": "Bash",
"developer_needs": [
"evaluation_metrics",
"documentation",
"security_gates",
"ci_integration"
],
"moe_experts": [
"governance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 6af4c52dd12b9f64e34b5e67599b2c8906879791 | |
train_46462 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | agent_loop | intermediate | Task: agent_loop
Topic: Self-improving agents and feedback loops
Difficulty: intermediate
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "Python",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"tests_are_truth",
"repo_scale_reasoning"
],
"moe_experts": [
"security_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 689f6e288216779816983d7ed9a291783ba35d7b | |
train_46463 | 2026-01-01T00:00:00 | SWE-bench style real-repo evaluation | design | advanced | Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: SQL
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "SQL",
"developer_needs": [
"auditability",
"tests_are_truth",
"reproducibility",
"ci_integration"
],
"moe_experts": [
"agentic_systems_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 9fe8bd4c0b2fd3a82dc1f1a2d0bbc59b3e321222 | |
train_46464 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | agent_loop | advanced | Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"auditability",
"reproducibility"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 5aba9c280f2a3fad0d7266b70a5eac977d5b0004 | |
train_46465 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | review | expert | Task: review
Topic: Self-improving agents and feedback loops
Difficulty: expert
Target language: Rust
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Rust",
"developer_needs": [
"governance",
"evaluation_metrics",
"cost_latency_tradeoffs",
"security_gates"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 4a4e204dab9c46764c20349862d20a119d5ef347 | |
train_46466 | 2026-01-01T00:00:00 | Latency, cost, and reliability optimization | failure_analysis | expert | Task: failure_analysis
Topic: Latency, cost, and reliability optimization
Difficulty: expert
Target language: Go
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [] | {
"target_language": "Go",
"developer_needs": [
"ci_integration",
"auditability",
"cost_latency_tradeoffs",
"tests_are_truth"
],
"moe_experts": [
"coding_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | f9b47b9af42f23688097c83c8cee5831480e3d55 | |
train_46467 | 2026-01-01T00:00:00 | Reasoning-first coding models and tunable deliberation | review | expert | Task: review
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: expert
Target language: Go
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Go",
"developer_needs": [
"cost_latency_tradeoffs",
"governance",
"repo_scale_reasoning",
"auditability"
],
"moe_experts": [
"data_curation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | b264c661a517c1635a375d608ef0b61e8e56ef4f | |
train_46468 | 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: TypeScript
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "TypeScript",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"reproducibility",
"auditability"
],
"moe_experts": [
"data_curation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 19dd3677ab80704a8b7ea56a8c56c7bbbaa435ef | |
train_46469 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | agent_loop | intermediate | Task: agent_loop
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: C#
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "C#",
"developer_needs": [
"cost_latency_tradeoffs",
"ci_integration",
"evaluation_metrics",
"documentation"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 2e8332d245b1dd046fb9333dd6106ce099b6e087 | |
train_46470 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | compare | intermediate | Task: compare
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: TypeScript
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "TypeScript",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"governance",
"auditability"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 28a03a9fbb18acb3b0aace8eaeda3ddbe1bfb186 | |
train_46471 | 2026-01-01T00:00:00 | Dataset curation pipelines (filter, dedupe, quality) | design | advanced | Task: design
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: Go
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Go",
"developer_needs": [
"governance",
"tooling",
"security_gates",
"auditability"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 3fab3f21cf7eac765b84bdcfa3fbc934032729cb | |
train_46472 | 2026-01-01T00:00:00 | Agentic coding systems (plan→edit→test→reflect) | agent_loop | intermediate | Task: agent_loop
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: intermediate
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "Python",
"developer_needs": [
"tooling",
"cost_latency_tradeoffs",
"governance",
"reproducibility"
],
"moe_experts": [
"governance_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 25f983f0e5a4093b86735811f60ae0e7484c422d | |
train_46473 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | explain | intermediate | Task: explain
Topic: Self-improving agents and feedback loops
Difficulty: intermediate
Target language: SQL
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"tooling",
"auditability"
],
"moe_experts": [
"governance_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | ea11daaec00571dd191b656a1b4555ce1782d2f8 | |
train_46474 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | failure_analysis | expert | Task: failure_analysis
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
Target language: C#
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "C#",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"tooling",
"evaluation_metrics"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | dc69c5fd5cfe84a83c134ee18fea0975846f0b07 | |
train_46475 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | failure_analysis | expert | Task: failure_analysis
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
Target language: Bash
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Bash",
"developer_needs": [
"governance",
"auditability",
"cost_latency_tradeoffs",
"ci_integration"
],
"moe_experts": [
"agentic_systems_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e58b2c47d19dccf02ebe98992994c44f342121f3 | |
train_46476 | 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: JavaScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "JavaScript",
"developer_needs": [
"auditability",
"cost_latency_tradeoffs",
"security_gates",
"governance"
],
"moe_experts": [
"data_curation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | b994cedd70e0b935ae58528ee4a141014f88309b | |
train_46477 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | failure_analysis | advanced | Task: failure_analysis
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Java
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [] | {
"target_language": "Java",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"ci_integration",
"repo_scale_reasoning"
],
"moe_experts": [
"governance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 2a107e456f1c10908be529b2b4a68c8ca85432a2 | |
train_46478 | 2026-01-01T00:00:00 | Latency, cost, and reliability optimization | failure_analysis | intermediate | Task: failure_analysis
Topic: Latency, cost, and reliability optimization
Difficulty: intermediate
Target language: Python
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"auditability",
"cost_latency_tradeoffs",
"tests_are_truth"
],
"moe_experts": [
"evaluation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e9492808c8825ff19f183cfffa2af785b37185ec | |
train_46479 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | design | intermediate | Task: design
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: SQL
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "SQL",
"developer_needs": [
"tests_are_truth",
"documentation",
"governance",
"ci_integration"
],
"moe_experts": [
"governance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 4ac8a8df5aa8659606bcd94700d25400c1a290f2 | |
train_46480 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | compare | intermediate | Task: compare
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: JavaScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "JavaScript",
"developer_needs": [
"governance",
"security_gates",
"repo_scale_reasoning",
"cost_latency_tradeoffs"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 144ed43f4dfb22253303006022df3144c5c05a77 | |
train_46481 | 2026-01-01T00:00:00 | Agentic coding systems (plan→edit→test→reflect) | agent_loop | advanced | Task: agent_loop
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
Target language: Go
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "Go",
"developer_needs": [
"cost_latency_tradeoffs",
"tooling",
"evaluation_metrics",
"repo_scale_reasoning"
],
"moe_experts": [
"security_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 1323900fd0d2d5ab37ab2b80eff46d315d6ded76 | |
train_46482 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | review | advanced | Task: review
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Python",
"developer_needs": [
"ci_integration",
"reproducibility",
"tooling",
"evaluation_metrics"
],
"moe_experts": [
"data_curation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e3fb5189fe3ccf266b0a665423cac84eab509797 | |
train_46483 | 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: JavaScript
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "JavaScript",
"developer_needs": [
"cost_latency_tradeoffs",
"reproducibility",
"tooling",
"repo_scale_reasoning"
],
"moe_experts": [
"coding_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 94bd31124501164e37273230d8a45851f6b60c46 | |
train_46484 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | agent_loop | intermediate | Task: agent_loop
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: C#
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [] | {
"target_language": "C#",
"developer_needs": [
"tooling",
"security_gates",
"evaluation_metrics",
"documentation"
],
"moe_experts": [
"data_curation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 2c36e46e8398d28df87da9343d8964bfb9090158 | |
train_46485 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | compare | expert | Task: compare
Topic: Self-improving agents and feedback loops
Difficulty: expert
Target language: C#
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "C#",
"developer_needs": [
"security_gates",
"governance",
"tooling",
"evaluation_metrics"
],
"moe_experts": [
"governance_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | c1d1e10ef17e57ae88f7297b3cbcbf8cac7d35bb | |
train_46486 | 2026-01-01T00:00:00 | Latency, cost, and reliability optimization | failure_analysis | intermediate | Task: failure_analysis
Topic: Latency, cost, and reliability optimization
Difficulty: intermediate
Target language: Java
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [] | {
"target_language": "Java",
"developer_needs": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"reproducibility",
"ci_integration"
],
"moe_experts": [
"data_curation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 255b185fc9f3720c7e950f719c173740a940ce70 | |
train_46487 | 2026-01-01T00:00:00 | Model merging, distillation, and continued pretraining | patch_diff | expert | Task: patch_diff
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: C#
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "C#",
"developer_needs": [
"ci_integration",
"security_gates",
"documentation",
"tooling"
],
"moe_experts": [
"governance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 016a8df867549d7005d299c033ea5409b45c8de6 | |
train_46488 | 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: Rust
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Rust",
"developer_needs": [
"security_gates",
"repo_scale_reasoning",
"tests_are_truth",
"governance"
],
"moe_experts": [
"coding_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e3bd48be3ec349ab7ccdd68a532bbd2aa75b9bcd | |
train_46489 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | patch_diff | expert | Task: patch_diff
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
Target language: C#
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [] | {
"target_language": "C#",
"developer_needs": [
"reproducibility",
"documentation",
"tooling",
"repo_scale_reasoning"
],
"moe_experts": [
"evaluation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | fcd7a6a2991d14aa21e5bd3fd430a6636447dd7a | |
train_46490 | 2026-01-01T00:00:00 | Self-improving agents and feedback loops | failure_analysis | expert | Task: failure_analysis
Topic: Self-improving agents and feedback loops
Difficulty: expert
Target language: Go
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
| [] | {
"target_language": "Go",
"developer_needs": [
"auditability",
"cost_latency_tradeoffs",
"reproducibility",
"security_gates"
],
"moe_experts": [
"performance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 9e4103bfc0585fb48f26c1e95ddefd37129b6b6c | |
train_46491 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | patch_diff | intermediate | Task: patch_diff
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: Java
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Patch (diff-style):
```diff
- if x == 0:
- return 1/x
+ if x == 0:
+ raise ValueError('division by zero')
```
Acceptance:
- Tests pass
- No new regressions
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Java",
"developer_needs": [
"ci_integration",
"evaluation_metrics",
"tooling",
"governance"
],
"moe_experts": [
"data_curation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 250d6e03e2f97a7f18b115dacfe54e93b6420cce | |
train_46492 | 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: TypeScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Loop: Plan → Edit → Test → Reflect → Human gate
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "TypeScript",
"developer_needs": [
"repo_scale_reasoning",
"reproducibility",
"cost_latency_tradeoffs",
"ci_integration"
],
"moe_experts": [
"coding_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | e2c8184320c871ba452fcda115e328ea942bef36 | |
train_46493 | 2026-01-01T00:00:00 | Dataset curation pipelines (filter, dedupe, quality) | compare | intermediate | Task: compare
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: TypeScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Compare: capability, cost, latency, reliability
| [] | {
"target_language": "TypeScript",
"developer_needs": [
"cost_latency_tradeoffs",
"documentation",
"repo_scale_reasoning",
"ci_integration"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 215b0a8e0393adbbda41529b4f09ce711862686a | |
train_46494 | 2026-01-01T00:00:00 | Dataset curation pipelines (filter, dedupe, quality) | data_pipeline | expert | Task: data_pipeline
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
Target language: TypeScript
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "TypeScript",
"developer_needs": [
"documentation",
"governance",
"security_gates",
"auditability"
],
"moe_experts": [
"performance_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 62b6a5e234c205b287efbd58a80390f4373d7bf9 | |
train_46495 | 2026-01-01T00:00:00 | Governance, provenance, and licensing for code data | review | intermediate | Task: review
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
Target language: TypeScript
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Review: correctness, security, performance, governance
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "TypeScript",
"developer_needs": [
"auditability",
"repo_scale_reasoning",
"ci_integration",
"evaluation_metrics"
],
"moe_experts": [
"data_curation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 2c61d2f6b64992bb03c322b3dd618818164ed25c | |
train_46496 | 2026-01-01T00:00:00 | Extended context and repo-scale understanding | design | intermediate | Task: design
Topic: Extended context and repo-scale understanding
Difficulty: intermediate
Target language: Bash
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [] | {
"target_language": "Bash",
"developer_needs": [
"documentation",
"tooling",
"tests_are_truth",
"cost_latency_tradeoffs"
],
"moe_experts": [
"agentic_systems_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | 001df8d625420b32aaba69ce9b2248774ebe543d | |
train_46497 | 2026-01-01T00:00:00 | Mixture-of-Experts (MoE) for code | explain | advanced | Task: explain
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: SQL
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "SQL",
"developer_needs": [
"auditability",
"security_gates",
"repo_scale_reasoning",
"evaluation_metrics"
],
"moe_experts": [
"governance_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | dc60306c05c2612a794766eaf7356a95ea7717a1 | |
train_46498 | 2026-01-01T00:00:00 | Code-specialized model families and sizing tradeoffs | explain | intermediate | Task: explain
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "Python",
"developer_needs": [
"security_gates",
"auditability",
"tests_are_truth",
"governance"
],
"moe_experts": [
"coding_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | d7076693ac4530d82345cdfece7cd2c7ca7965c3 | |
train_46499 | 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: C#
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts. | Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outperform single-pass generation.
Design with risks, metrics, acceptance criteria
| [
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] | {
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"cost_latency_tradeoffs",
"tooling",
"reproducibility"
],
"moe_experts": [
"agentic_systems_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
} | f1cd3297e9de2e0a13bb0a2a4d93e14b5190e4ae |
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