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timestamp[s]date 2026-01-01 00:00:00
2026-01-01 00:00:00
| topic
stringclasses 14
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stringclasses 10
values | difficulty
stringclasses 3
values | instruction
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|---|---|---|---|---|---|---|---|---|---|---|
train_01200
| 2026-01-01T00:00:00
|
Self-improving agents and feedback loops
|
review
|
intermediate
|
Task: review
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.
Review: correctness, security, performance, governance
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "SQL",
"developer_needs": [
"repo_scale_reasoning",
"ci_integration",
"auditability",
"security_gates"
],
"moe_experts": [
"governance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
81fa390faeb47f215ce3820566122ffbeff20d51
|
|
train_01201
| 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: 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.
Eval:
pass@k, time-to-green, regressions, diff size
|
[] |
{
"target_language": "Python",
"developer_needs": [
"ci_integration",
"auditability",
"documentation",
"tests_are_truth"
],
"moe_experts": [
"coding_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
ef96c06b568f12dc72a6ca5eec4e81085e595f80
|
|
train_01202
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
patch_diff
|
expert
|
Task: patch_diff
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: Bash
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": "Bash",
"developer_needs": [
"reproducibility",
"evaluation_metrics",
"tooling",
"governance"
],
"moe_experts": [
"security_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
7db75282fc9df12cd5db6a75e0ad9eef7310b73d
|
|
train_01203
| 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: Go
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": "Go",
"developer_needs": [
"security_gates",
"ci_integration",
"documentation",
"repo_scale_reasoning"
],
"moe_experts": [
"agentic_systems_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
c91d6172e1d30d408e3e814dbef7e1634f2bf7e3
|
|
train_01204
| 2026-01-01T00:00:00
|
Self-improving agents and feedback loops
|
data_pipeline
|
advanced
|
Task: data_pipeline
Topic: Self-improving agents and feedback loops
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.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
|
[] |
{
"target_language": "Python",
"developer_needs": [
"reproducibility",
"documentation",
"tooling",
"evaluation_metrics"
],
"moe_experts": [
"evaluation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
0494692566269aa93554b35ab32160c99769bbbe
|
|
train_01205
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
explain
|
intermediate
|
Task: explain
Topic: Mixture-of-Experts (MoE) for code
Difficulty: intermediate
Target language: 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.
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": [
"governance",
"tests_are_truth",
"evaluation_metrics",
"security_gates"
],
"moe_experts": [
"coding_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
648109f794e88e9c5f19979c7ca497bc93ffd107
|
|
train_01206
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
intermediate
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: 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.
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": "Go",
"developer_needs": [
"auditability",
"repo_scale_reasoning",
"tests_are_truth",
"security_gates"
],
"moe_experts": [
"security_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
16d40bf08934fbc619e0cc0cc7995f3ad436e077
|
|
train_01207
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
intermediate
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: intermediate
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.
Loop: Plan → Edit → Test → Reflect → Human gate
|
[] |
{
"target_language": "C#",
"developer_needs": [
"tooling",
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"tests_are_truth"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
da00025ae2559e848962d68d66637c135a942db8
|
|
train_01208
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Latency, cost, and reliability optimization
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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "Python",
"developer_needs": [
"cost_latency_tradeoffs",
"ci_integration",
"auditability",
"reproducibility"
],
"moe_experts": [
"data_curation_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
fad51c712826ecc3aaba0510caf5374bd882ce21
|
|
train_01209
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
review
|
intermediate
|
Task: review
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: TypeScript
Context: 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.
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": [
"repo_scale_reasoning",
"tooling",
"evaluation_metrics",
"tests_are_truth"
],
"moe_experts": [
"data_curation_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
22d8023af9ff7ad1656ec76a6132525aaa286534
|
|
train_01210
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
compare
|
expert
|
Task: compare
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
Target language: 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.
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": [
"tooling",
"reproducibility",
"ci_integration",
"documentation"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
825ed42826a2d98700b74b1da6df182d6251d8ed
|
|
train_01211
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
compare
|
advanced
|
Task: compare
Topic: Latency, cost, and reliability optimization
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.
Compare: capability, cost, latency, reliability
|
[] |
{
"target_language": "SQL",
"developer_needs": [
"evaluation_metrics",
"tooling",
"ci_integration",
"cost_latency_tradeoffs"
],
"moe_experts": [
"agentic_systems_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
63dad6d62580aa32a5e9eec08fb3127ed7be95a0
|
|
train_01212
| 2026-01-01T00:00:00
|
Extended context and repo-scale understanding
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Extended context and repo-scale understanding
Difficulty: advanced
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.
Loop: Plan → Edit → Test → Reflect → Human gate
|
[] |
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"evaluation_metrics",
"repo_scale_reasoning",
"auditability"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
a7c33f9521ef005d58cfb18669e536c1097d7cd4
|
|
train_01213
| 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: 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": [
"security_gates",
"auditability",
"ci_integration",
"tests_are_truth"
],
"moe_experts": [
"evaluation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
f5bf8c73f7c276cad46d3a22e8f67304e38c0852
|
|
train_01214
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: 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.
Loop: Plan → Edit → Test → Reflect → Human gate
|
[] |
{
"target_language": "Java",
"developer_needs": [
"cost_latency_tradeoffs",
"governance",
"tooling",
"ci_integration"
],
"moe_experts": [
"data_curation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
27b64f9d96c50a81e7ac05fa0bb9b8e5810e3545
|
|
train_01215
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
compare
|
advanced
|
Task: compare
Topic: Latency, cost, and reliability optimization
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.
Compare: capability, cost, latency, reliability
|
[] |
{
"target_language": "C#",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"security_gates",
"cost_latency_tradeoffs"
],
"moe_experts": [
"evaluation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
e00387664a0f5a2de9af5860d2f5267b9c80a4a9
|
|
train_01216
| 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: 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.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
|
[] |
{
"target_language": "Python",
"developer_needs": [
"documentation",
"tests_are_truth",
"evaluation_metrics",
"ci_integration"
],
"moe_experts": [
"governance_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
7dccb81321ac184de58a49fd820bc561d2de7323
|
|
train_01217
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
patch_diff
|
intermediate
|
Task: patch_diff
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
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": [
"security_gates",
"tooling",
"reproducibility",
"auditability"
],
"moe_experts": [
"data_curation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
113049b74ac122ee8f84a0e2aff49f59179b5cd2
|
|
train_01218
| 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: Python
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": "Python",
"developer_needs": [
"ci_integration",
"evaluation_metrics",
"cost_latency_tradeoffs",
"tests_are_truth"
],
"moe_experts": [
"evaluation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
0e3e366c9f872de913c06258071f11143b5b4197
|
|
train_01219
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Latency, cost, and reliability optimization
Difficulty: expert
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.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
|
[] |
{
"target_language": "TypeScript",
"developer_needs": [
"repo_scale_reasoning",
"governance",
"tests_are_truth",
"ci_integration"
],
"moe_experts": [
"coding_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
296cf58a21944f81f4e2e35e0419cf8f8f3bf8c3
|
|
train_01220
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
advanced
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
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.
Review: correctness, security, performance, governance
|
[] |
{
"target_language": "Java",
"developer_needs": [
"tests_are_truth",
"evaluation_metrics",
"tooling",
"governance"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
4e89ceec56154563183f75288ad86bacba097687
|
|
train_01221
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
explain
|
advanced
|
Task: explain
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
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.
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",
"cost_latency_tradeoffs",
"governance",
"tooling"
],
"moe_experts": [
"governance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
7f6ef3264ff809aadc1017f610e7e428be5a8596
|
|
train_01222
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
explain
|
expert
|
Task: explain
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: expert
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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "Java",
"developer_needs": [
"documentation",
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"security_gates"
],
"moe_experts": [
"security_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
cd7faf1113c7bb0842672471e378c2c4af23302b
|
|
train_01223
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
eval
|
intermediate
|
Task: eval
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
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.
Eval:
pass@k, time-to-green, regressions, diff size
|
[] |
{
"target_language": "C#",
"developer_needs": [
"documentation",
"cost_latency_tradeoffs",
"evaluation_metrics",
"repo_scale_reasoning"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
edc6cfcc4cc8ac7a6fb43bf90a61fa0cdf04e289
|
|
train_01224
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
expert
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
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
|
[] |
{
"target_language": "Rust",
"developer_needs": [
"governance",
"tooling",
"ci_integration",
"security_gates"
],
"moe_experts": [
"security_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
7ad18da331d1b2b76e64a3f7b36e642bbd5a3dab
|
|
train_01225
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
code
|
intermediate
|
Task: code
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: Python
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.
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": [
"documentation",
"security_gates",
"evaluation_metrics",
"repo_scale_reasoning"
],
"moe_experts": [
"performance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
84e24afa8da546964c8bbdba0043fb446e87161d
|
|
train_01226
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
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.
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": "Rust",
"developer_needs": [
"tooling",
"repo_scale_reasoning",
"ci_integration",
"evaluation_metrics"
],
"moe_experts": [
"agentic_systems_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
b2e4c4953e4c2ac7282b284ce434beb85cf61c3b
|
|
train_01227
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
design
|
intermediate
|
Task: design
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.
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": [
"tooling",
"reproducibility",
"security_gates",
"ci_integration"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
87e39b05d6506fd6dbb920befe2bb668caeee720
|
|
train_01228
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
eval
|
expert
|
Task: eval
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: TypeScript
Context: 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": "TypeScript",
"developer_needs": [
"reproducibility",
"auditability",
"evaluation_metrics",
"cost_latency_tradeoffs"
],
"moe_experts": [
"evaluation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
ba4082313618d9803402fdc3355a6526894851ec
|
|
train_01229
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
data_pipeline
|
intermediate
|
Task: data_pipeline
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: SQL
Context: 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": "SQL",
"developer_needs": [
"ci_integration",
"security_gates",
"repo_scale_reasoning",
"tooling"
],
"moe_experts": [
"governance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
fcf6ef9260dee6c58e9378b177472f60d9b49a85
|
|
train_01230
| 2026-01-01T00:00:00
|
Self-improving agents and feedback loops
|
design
|
advanced
|
Task: design
Topic: Self-improving agents and feedback loops
Difficulty: advanced
Target language: Python
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": "Python",
"developer_needs": [
"governance",
"evaluation_metrics",
"repo_scale_reasoning",
"reproducibility"
],
"moe_experts": [
"coding_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
62e13a982f1f7d6d08e12d529725ead9881ad31c
|
|
train_01231
| 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: 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.
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": "Java",
"developer_needs": [
"evaluation_metrics",
"tooling",
"reproducibility",
"ci_integration"
],
"moe_experts": [
"coding_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
43c177cf4315c8c3791c336fa7093ac1a4afcc51
|
|
train_01232
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
eval
|
expert
|
Task: eval
Topic: Latency, cost, and reliability optimization
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.
Eval:
pass@k, time-to-green, regressions, diff size
|
[] |
{
"target_language": "TypeScript",
"developer_needs": [
"security_gates",
"cost_latency_tradeoffs",
"evaluation_metrics",
"governance"
],
"moe_experts": [
"governance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
8f75193d1bd21d4419b6995d38305967e60240c5
|
|
train_01233
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
design
|
expert
|
Task: design
Topic: Tool calling, sandboxes, and CI integration
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.
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": [
"cost_latency_tradeoffs",
"reproducibility",
"documentation",
"repo_scale_reasoning"
],
"moe_experts": [
"performance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
ed44f3dea80594cdd74bd5e08a5c58051b398c2b
|
|
train_01234
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
eval
|
advanced
|
Task: eval
Topic: Model merging, distillation, and continued pretraining
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": [
"auditability",
"tests_are_truth",
"repo_scale_reasoning",
"reproducibility"
],
"moe_experts": [
"coding_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
a074681ac2bf99203a7e3c9301f6ca0e7b155b7a
|
|
train_01235
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
eval
|
expert
|
Task: eval
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
Target language: Go
Context: 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": "Go",
"developer_needs": [
"repo_scale_reasoning",
"ci_integration",
"evaluation_metrics",
"tests_are_truth"
],
"moe_experts": [
"data_curation_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
13235d30df688c85c1d7931a322ea460113622bc
|
|
train_01236
| 2026-01-01T00:00:00
|
Self-improving agents and feedback loops
|
failure_analysis
|
advanced
|
Task: failure_analysis
Topic: Self-improving agents and feedback loops
Difficulty: advanced
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.
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": "Rust",
"developer_needs": [
"cost_latency_tradeoffs",
"security_gates",
"repo_scale_reasoning",
"evaluation_metrics"
],
"moe_experts": [
"evaluation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
619c15a21fb87ea58d6b8ba8b9990d3109d498cc
|
|
train_01237
| 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: 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": "Go",
"developer_needs": [
"ci_integration",
"reproducibility",
"security_gates",
"cost_latency_tradeoffs"
],
"moe_experts": [
"performance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
e94d7b95a8b8ea610e050f9ed2713da6644a70df
|
|
train_01238
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
code
|
intermediate
|
Task: code
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: intermediate
Target language: 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.
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": "TypeScript",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"repo_scale_reasoning",
"security_gates"
],
"moe_experts": [
"security_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
8f47300c0275be440be73995bb9314dd1f53a08c
|
|
train_01239
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
code
|
intermediate
|
Task: code
Topic: Latency, cost, and reliability optimization
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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "JavaScript",
"developer_needs": [
"security_gates",
"tooling",
"tests_are_truth",
"auditability"
],
"moe_experts": [
"evaluation_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
bdac62d34b33586e6bef83213c0535d14c712a84
|
|
train_01240
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
explain
|
advanced
|
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: advanced
Target language: C#
Context: 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": [
"reproducibility",
"evaluation_metrics",
"tooling",
"repo_scale_reasoning"
],
"moe_experts": [
"agentic_systems_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
f6e03f96ec6757c184b9ff0b6ac758abb9403b95
|
|
train_01241
| 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: 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.
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": [
"documentation",
"cost_latency_tradeoffs",
"security_gates",
"governance"
],
"moe_experts": [
"evaluation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
b164d053c3b8c7b0fe4566ab4e8cc01fae237283
|
|
train_01242
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
intermediate
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
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.
Review: correctness, security, performance, governance
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"documentation",
"tests_are_truth",
"security_gates"
],
"moe_experts": [
"evaluation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
c03e99acb2b9e8afe3af2c4e886454df655b2d43
|
|
train_01243
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
code
|
advanced
|
Task: code
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "C#",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"tooling",
"documentation"
],
"moe_experts": [
"data_curation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
c92b64139db7ebd648739e1b7976a753b732b864
|
|
train_01244
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
design
|
intermediate
|
Task: design
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
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.
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": [
"tooling",
"documentation",
"evaluation_metrics",
"security_gates"
],
"moe_experts": [
"evaluation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
baa758de35f8d3d6bd413d0f2e258382fbd58315
|
|
train_01245
| 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: 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": [
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"evaluation_metrics",
"tests_are_truth"
],
"moe_experts": [
"governance_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
6bf35917f94b8e647014d60d2542a336651bf58f
|
|
train_01246
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
explain
|
expert
|
Task: explain
Topic: Tool calling, sandboxes, and CI integration
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
|
[] |
{
"target_language": "C#",
"developer_needs": [
"documentation",
"tooling",
"governance",
"cost_latency_tradeoffs"
],
"moe_experts": [
"performance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
3467ad3ed3bfaedf01e1f07fd00d47a07b73b580
|
|
train_01247
| 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: 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": [
"evaluation_metrics",
"ci_integration",
"tooling",
"security_gates"
],
"moe_experts": [
"agentic_systems_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
663414870eca80f8f6b6c851993f4543fa066dc4
|
|
train_01248
| 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: 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.
Compare: capability, cost, latency, reliability
|
[] |
{
"target_language": "Bash",
"developer_needs": [
"governance",
"security_gates",
"repo_scale_reasoning",
"auditability"
],
"moe_experts": [
"performance_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
b2a46b01076e970840fdccb3e99d06ccadb2abb4
|
|
train_01249
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
advanced
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
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.
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": [
"ci_integration",
"security_gates",
"evaluation_metrics",
"tests_are_truth"
],
"moe_experts": [
"data_curation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
dcf7c65073ef3773f0942de01ff4d9080987c9e6
|
|
train_01250
| 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: 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.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
|
[] |
{
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"repo_scale_reasoning",
"security_gates",
"auditability"
],
"moe_experts": [
"governance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
4c047e725551bb29efc77cd10c918b7d1b48f6ca
|
|
train_01251
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
data_pipeline
|
advanced
|
Task: data_pipeline
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.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
|
[] |
{
"target_language": "Bash",
"developer_needs": [
"auditability",
"ci_integration",
"cost_latency_tradeoffs",
"documentation"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
ebe837aecdeb80408f3cde68fe83284608975250
|
|
train_01252
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
code
|
intermediate
|
Task: code
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "Rust",
"developer_needs": [
"security_gates",
"evaluation_metrics",
"governance",
"tooling"
],
"moe_experts": [
"coding_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
c1b81a65c29fb5e812544bb41b48dffcd242e818
|
|
train_01253
| 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: 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.
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": "Rust",
"developer_needs": [
"tooling",
"ci_integration",
"documentation",
"governance"
],
"moe_experts": [
"performance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
f60736714a81662d163f2b7d6c313fa1bada285b
|
|
train_01254
| 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: 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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "TypeScript",
"developer_needs": [
"auditability",
"ci_integration",
"governance",
"documentation"
],
"moe_experts": [
"coding_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
62cb8b9a9bbd185da8f87869018dc95dea179223
|
|
train_01255
| 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: 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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "SQL",
"developer_needs": [
"reproducibility",
"cost_latency_tradeoffs",
"repo_scale_reasoning",
"evaluation_metrics"
],
"moe_experts": [
"evaluation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
f381f43d40fda4dbe816db7cb78a7707952ff585
|
|
train_01256
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Latency, cost, and reliability optimization
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
|
[] |
{
"target_language": "C#",
"developer_needs": [
"ci_integration",
"documentation",
"evaluation_metrics",
"governance"
],
"moe_experts": [
"agentic_systems_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
9c6ab1c933f623a1aee23759c63952ec1a8679f9
|
|
train_01257
| 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: 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.
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": [
"documentation",
"reproducibility",
"tests_are_truth",
"governance"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
da93adc0e65c1328dda5803e7e453171d4b9e468
|
|
train_01258
| 2026-01-01T00:00:00
|
Self-improving agents and feedback loops
|
data_pipeline
|
expert
|
Task: data_pipeline
Topic: Self-improving agents and feedback loops
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.
Pipeline:
Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
|
[] |
{
"target_language": "Go",
"developer_needs": [
"governance",
"cost_latency_tradeoffs",
"tests_are_truth",
"reproducibility"
],
"moe_experts": [
"coding_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
0d7a04e92b446d6a1d3d350e3af137f51f6b7898
|
|
train_01259
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
eval
|
advanced
|
Task: eval
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Go
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": "Go",
"developer_needs": [
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"tooling",
"tests_are_truth"
],
"moe_experts": [
"governance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
7a4c225fa7b2099ec4f3d15a8b7a012671181942
|
|
train_01260
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
explain
|
expert
|
Task: explain
Topic: Tool calling, sandboxes, and CI integration
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.
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": [
"cost_latency_tradeoffs",
"documentation",
"governance",
"ci_integration"
],
"moe_experts": [
"performance_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
1c9e1515bd2c1e2596491460aca7083b66d20b18
|
|
train_01261
| 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: Python
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.
Eval:
pass@k, time-to-green, regressions, diff size
|
[] |
{
"target_language": "Python",
"developer_needs": [
"ci_integration",
"governance",
"repo_scale_reasoning",
"evaluation_metrics"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
95e6cf5a69b45016cf703ee9145ee6644f9b6b95
|
|
train_01262
| 2026-01-01T00:00:00
|
SWE-bench style real-repo evaluation
|
review
|
advanced
|
Task: review
Topic: SWE-bench style real-repo evaluation
Difficulty: advanced
Target language: 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.
Review: correctness, security, performance, governance
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "JavaScript",
"developer_needs": [
"reproducibility",
"repo_scale_reasoning",
"documentation",
"security_gates"
],
"moe_experts": [
"data_curation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
d2fcad05413a410076bae7e518b15feb9f86e759
|
|
train_01263
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
design
|
expert
|
Task: design
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: Bash
Context: 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": "Bash",
"developer_needs": [
"documentation",
"repo_scale_reasoning",
"security_gates",
"tests_are_truth"
],
"moe_experts": [
"performance_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
e295c5c8b42bc5ad707ad35b817c35ddbfc34c39
|
|
train_01264
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
code
|
intermediate
|
Task: code
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
Target language: Python
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.
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": [
"ci_integration",
"reproducibility",
"cost_latency_tradeoffs",
"governance"
],
"moe_experts": [
"evaluation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
50703b4d8de8d87e3f42b87aecb92b2c763faea7
|
|
train_01265
| 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: 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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "Go",
"developer_needs": [
"evaluation_metrics",
"tooling",
"repo_scale_reasoning",
"governance"
],
"moe_experts": [
"governance_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
33b525619799a54dca8e64ad4fa839350dec7d81
|
|
train_01266
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: 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": [
"security_gates",
"ci_integration",
"auditability",
"documentation"
],
"moe_experts": [
"coding_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
b466e88631179c8698383129bab478d78d7d5d4b
|
|
train_01267
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
design
|
intermediate
|
Task: design
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: 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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "Rust",
"developer_needs": [
"auditability",
"security_gates",
"tooling",
"ci_integration"
],
"moe_experts": [
"governance_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
b8b2a1fd9eca68baec40ec31ee4b176573e66c74
|
|
train_01268
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
expert
|
Task: design
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
Target language: JavaScript
Context: 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": "JavaScript",
"developer_needs": [
"reproducibility",
"auditability",
"governance",
"tooling"
],
"moe_experts": [
"security_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
c14e3ac652b5cf1a2d447d0cb524dcdd1c84e770
|
|
train_01269
| 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: 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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "SQL",
"developer_needs": [
"tooling",
"tests_are_truth",
"auditability",
"cost_latency_tradeoffs"
],
"moe_experts": [
"governance_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
89f49ca5663d457a8c068ea0cff91202b048b537
|
|
train_01270
| 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: 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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "Go",
"developer_needs": [
"repo_scale_reasoning",
"ci_integration",
"cost_latency_tradeoffs",
"evaluation_metrics"
],
"moe_experts": [
"security_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
d7bdd59e449838399f5c72c7ae3eaa3597b62df7
|
|
train_01271
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
patch_diff
|
advanced
|
Task: patch_diff
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: advanced
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.
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": "Rust",
"developer_needs": [
"repo_scale_reasoning",
"security_gates",
"documentation",
"cost_latency_tradeoffs"
],
"moe_experts": [
"security_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
ef2327849b30a0c40e50fb2900cc7555338f69bf
|
|
train_01272
| 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: 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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "Java",
"developer_needs": [
"ci_integration",
"cost_latency_tradeoffs",
"evaluation_metrics",
"security_gates"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
8dc28ea0a978f3ca2270102756571b52213c2a26
|
|
train_01273
| 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: 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.
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": "Python",
"developer_needs": [
"tests_are_truth",
"governance",
"ci_integration",
"repo_scale_reasoning"
],
"moe_experts": [
"evaluation_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
842db9aa8f32f89ba9d78de4f6d714713e068273
|
|
train_01274
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
design
|
intermediate
|
Task: design
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
Target language: Bash
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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "Bash",
"developer_needs": [
"evaluation_metrics",
"documentation",
"tests_are_truth",
"cost_latency_tradeoffs"
],
"moe_experts": [
"data_curation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
42a17cbe15f840612a0387d2bcdbba4e9fbcfb9d
|
|
train_01275
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
review
|
expert
|
Task: review
Topic: Mixture-of-Experts (MoE) for code
Difficulty: expert
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.
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": [
"tooling",
"reproducibility",
"cost_latency_tradeoffs",
"documentation"
],
"moe_experts": [
"evaluation_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
f40db5a87779abe48be3c4a4676c91482da32f90
|
|
train_01276
| 2026-01-01T00:00:00
|
Multimodal dev workflows (docs, diagrams, traces)
|
explain
|
advanced
|
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: advanced
Target language: Java
Context: 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": [
"evaluation_metrics",
"governance",
"auditability",
"repo_scale_reasoning"
],
"moe_experts": [
"security_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
905ce2261772d7f2029fea94179f642912b9d8c6
|
|
train_01277
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
patch_diff
|
advanced
|
Task: patch_diff
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Bash
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": "Bash",
"developer_needs": [
"security_gates",
"auditability",
"cost_latency_tradeoffs",
"tooling"
],
"moe_experts": [
"agentic_systems_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
04e85f773bdb6f1f8f670db24b3cf459afb4bdf3
|
|
train_01278
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
code
|
advanced
|
Task: code
Topic: Secure code generation and policy gates
Difficulty: advanced
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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "TypeScript",
"developer_needs": [
"auditability",
"documentation",
"cost_latency_tradeoffs",
"reproducibility"
],
"moe_experts": [
"governance_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
b526a452a286377b2f3401a96adee9e5650981d1
|
|
train_01279
| 2026-01-01T00:00:00
|
Mixture-of-Experts (MoE) for code
|
agent_loop
|
advanced
|
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: 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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "Go",
"developer_needs": [
"ci_integration",
"tests_are_truth",
"reproducibility",
"documentation"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
23847aec4837cdae28d64bab74fe4ae4ec526569
|
|
train_01280
| 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: 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": [
"ci_integration",
"documentation",
"tests_are_truth",
"governance"
],
"moe_experts": [
"agentic_systems_expert",
"evaluation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
6da05d5131a84307ea9846f23a51569129bc9639
|
|
train_01281
| 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: 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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "TypeScript",
"developer_needs": [
"documentation",
"ci_integration",
"governance",
"cost_latency_tradeoffs"
],
"moe_experts": [
"coding_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
a7a4a77f75bd29a7b07995fa6c904679d518b250
|
|
train_01282
| 2026-01-01T00:00:00
|
Code-specialized model families and sizing tradeoffs
|
review
|
advanced
|
Task: review
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "Rust",
"developer_needs": [
"security_gates",
"governance",
"documentation",
"tooling"
],
"moe_experts": [
"security_expert",
"performance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
df42cdf29c1ddd9fcd6c63d38f50ce3f9bac39d2
|
|
train_01283
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
code
|
intermediate
|
Task: code
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
Target language: 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
|
[
"Identify failing behavior via tests",
"Minimize change surface",
"Apply patch",
"Run targeted + full suite",
"Verify no regressions"
] |
{
"target_language": "Go",
"developer_needs": [
"repo_scale_reasoning",
"cost_latency_tradeoffs",
"tooling",
"tests_are_truth"
],
"moe_experts": [
"coding_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
3f962232e2a76e250c576a1fdd9c72bd72337550
|
|
train_01284
| 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: 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
|
[] |
{
"target_language": "C#",
"developer_needs": [
"cost_latency_tradeoffs",
"tests_are_truth",
"security_gates",
"repo_scale_reasoning"
],
"moe_experts": [
"evaluation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
c0cf8dbba184c70066ddb781bbb700aee5d18610
|
|
train_01285
| 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: 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": [
"documentation",
"ci_integration",
"cost_latency_tradeoffs",
"reproducibility"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
7164581da1bc8d5fbf89380adc362b83508e3fe2
|
|
train_01286
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
eval
|
advanced
|
Task: eval
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
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.
Eval:
pass@k, time-to-green, regressions, diff size
|
[] |
{
"target_language": "Python",
"developer_needs": [
"evaluation_metrics",
"ci_integration",
"reproducibility",
"documentation"
],
"moe_experts": [
"evaluation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
1f6de1c84518bb75e129872905b7ba59fa644d3b
|
|
train_01287
| 2026-01-01T00:00:00
|
Agentic coding systems (plan→edit→test→reflect)
|
failure_analysis
|
expert
|
Task: failure_analysis
Topic: Agentic coding systems (plan→edit→test→reflect)
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": [
"ci_integration",
"reproducibility",
"tests_are_truth",
"tooling"
],
"moe_experts": [
"evaluation_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
7db859105d14e92a2bebbfac862d17fabf3d148f
|
|
train_01288
| 2026-01-01T00:00:00
|
Secure code generation and policy gates
|
review
|
intermediate
|
Task: review
Topic: Secure code generation and policy gates
Difficulty: intermediate
Target language: Go
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": "Go",
"developer_needs": [
"tooling",
"repo_scale_reasoning",
"evaluation_metrics",
"documentation"
],
"moe_experts": [
"agentic_systems_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
395bd542f6a8a0d5bdcf532fbddc415da9d9683f
|
|
train_01289
| 2026-01-01T00:00:00
|
Self-improving agents and feedback loops
|
explain
|
expert
|
Task: explain
Topic: Self-improving agents and feedback loops
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": [
"cost_latency_tradeoffs",
"auditability",
"tooling",
"security_gates"
],
"moe_experts": [
"agentic_systems_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
79cbb50b146db0dd4c76f675201087b01c45a38e
|
|
train_01290
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Latency, cost, and reliability optimization
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.
Loop: Plan → Edit → Test → Reflect → Human gate
|
[] |
{
"target_language": "Bash",
"developer_needs": [
"auditability",
"tooling",
"ci_integration",
"reproducibility"
],
"moe_experts": [
"security_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
357289dedfa6831c79c6292d23e8bae32d210085
|
|
train_01291
| 2026-01-01T00:00:00
|
Governance, provenance, and licensing for code data
|
eval
|
expert
|
Task: eval
Topic: Governance, provenance, and licensing for code data
Difficulty: expert
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.
Eval:
pass@k, time-to-green, regressions, diff size
|
[] |
{
"target_language": "Java",
"developer_needs": [
"governance",
"security_gates",
"reproducibility",
"documentation"
],
"moe_experts": [
"evaluation_expert",
"security_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
fb9d559536b913b11700d7a27a44cbebb3592adc
|
|
train_01292
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
failure_analysis
|
expert
|
Task: failure_analysis
Topic: Tool calling, sandboxes, and CI integration
Difficulty: expert
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": [
"ci_integration",
"governance",
"documentation",
"auditability"
],
"moe_experts": [
"coding_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
ba4043f142b04739e983d894b5767a5396f0af87
|
|
train_01293
| 2026-01-01T00:00:00
|
Model merging, distillation, and continued pretraining
|
failure_analysis
|
expert
|
Task: failure_analysis
Topic: Model merging, distillation, and continued pretraining
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.
Failure:
- Initial patch broke edge case
Reflection:
- Missing zero-input guard
Correction:
- Add explicit validation + test
|
[] |
{
"target_language": "C#",
"developer_needs": [
"evaluation_metrics",
"security_gates",
"governance",
"reproducibility"
],
"moe_experts": [
"agentic_systems_expert",
"coding_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
4fbd732ed9a6805c754f6551e49aff347d2e4f27
|
|
train_01294
| 2026-01-01T00:00:00
|
Dataset curation pipelines (filter, dedupe, quality)
|
patch_diff
|
expert
|
Task: patch_diff
Topic: Dataset curation pipelines (filter, dedupe, quality)
Difficulty: expert
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.
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": "TypeScript",
"developer_needs": [
"reproducibility",
"documentation",
"tests_are_truth",
"governance"
],
"moe_experts": [
"performance_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
ade62b09f7d773cba99b676b883f3d63b3c886d4
|
|
train_01295
| 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: 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": [
"auditability",
"tooling",
"tests_are_truth",
"security_gates"
],
"moe_experts": [
"agentic_systems_expert",
"governance_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
b9b1bfe7ee93c766bf72adbebf0adff70ef9692b
|
|
train_01296
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
eval
|
intermediate
|
Task: eval
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.
Eval:
pass@k, time-to-green, regressions, diff size
|
[] |
{
"target_language": "Bash",
"developer_needs": [
"repo_scale_reasoning",
"tests_are_truth",
"governance",
"ci_integration"
],
"moe_experts": [
"security_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
6700303413640188721295bc1899d12ee5aeb954
|
|
train_01297
| 2026-01-01T00:00:00
|
Reasoning-first coding models and tunable deliberation
|
explain
|
advanced
|
Task: explain
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: advanced
Target language: 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.
Design with risks, metrics, acceptance criteria
|
[] |
{
"target_language": "C#",
"developer_needs": [
"tooling",
"auditability",
"security_gates",
"ci_integration"
],
"moe_experts": [
"evaluation_expert",
"agentic_systems_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
cc76bfd73d0798278e1b76b4f593706f6fc64103
|
|
train_01298
| 2026-01-01T00:00:00
|
Latency, cost, and reliability optimization
|
agent_loop
|
expert
|
Task: agent_loop
Topic: Latency, cost, and reliability optimization
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.
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": [
"security_gates",
"governance",
"evaluation_metrics",
"cost_latency_tradeoffs"
],
"moe_experts": [
"coding_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
357289dedfa6831c79c6292d23e8bae32d210085
|
|
train_01299
| 2026-01-01T00:00:00
|
Tool calling, sandboxes, and CI integration
|
failure_analysis
|
intermediate
|
Task: failure_analysis
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
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.
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": "JavaScript",
"developer_needs": [
"security_gates",
"repo_scale_reasoning",
"tooling",
"cost_latency_tradeoffs"
],
"moe_experts": [
"agentic_systems_expert",
"data_curation_expert"
],
"governance": {
"audit_required": true,
"tests_required": true
}
}
|
df7c8b851938df8ed2070d33a70ea7b234e8f214
|
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