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_30400
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
failure_analysis
intermediate
Task: failure_analysis Topic: Multimodal dev workflows (docs, diagrams, traces) 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "SQL", "developer_needs": [ "tests_are_truth", "cost_latency_tradeoffs", "governance", "documentation" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ff85c39f5fcce20411d75328e74d6611213a07b7
train_30401
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: 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
[]
{ "target_language": "JavaScript", "developer_needs": [ "documentation", "security_gates", "ci_integration", "tests_are_truth" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6d31d68fc5663a4e8c1451ffaee704e404661709
train_30402
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
data_pipeline
expert
Task: data_pipeline Topic: Multimodal dev workflows (docs, diagrams, traces) 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. 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": "SQL", "developer_needs": [ "tests_are_truth", "documentation", "tooling", "governance" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
cc98cba0e8783e0e38cb9da3fb4369fa4c88038f
train_30403
2026-01-01T00:00:00
Self-improving agents and feedback loops
compare
advanced
Task: compare Topic: Self-improving agents and feedback loops 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. Compare: capability, cost, latency, reliability
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Python", "developer_needs": [ "auditability", "security_gates", "evaluation_metrics", "reproducibility" ], "moe_experts": [ "governance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a122310c146114cabd6c37c5ef81f6c84b5ff0b3
train_30404
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
patch_diff
intermediate
Task: patch_diff Topic: Mixture-of-Experts (MoE) for code 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": [ "tests_are_truth", "repo_scale_reasoning", "cost_latency_tradeoffs", "ci_integration" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2ca410e5c81e7ae7912d15d01d838e0ee541e0a0
train_30405
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
design
expert
Task: design Topic: Reasoning-first coding models and tunable deliberation 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "C#", "developer_needs": [ "cost_latency_tradeoffs", "repo_scale_reasoning", "reproducibility", "tooling" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
79f4e3a828c03d4b9af03dde54a3ee87ffd606f1
train_30406
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: 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
[]
{ "target_language": "Rust", "developer_needs": [ "reproducibility", "auditability", "documentation", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5d5e95eafe4cdfcdc369a052aeefd2483dcfb8c8
train_30407
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: 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": "TypeScript", "developer_needs": [ "cost_latency_tradeoffs", "security_gates", "ci_integration", "auditability" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
cb2fc62311db13f84c4f2ff2f9679c4ff5433ba2
train_30408
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: 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. 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": "SQL", "developer_needs": [ "governance", "documentation", "repo_scale_reasoning", "ci_integration" ], "moe_experts": [ "data_curation_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
970431286f0083848a424eb3dbb2d80c1cf870d6
train_30409
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
agent_loop
expert
Task: agent_loop Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: expert Target language: 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. 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": [ "security_gates", "evaluation_metrics", "ci_integration", "tooling" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b608634be04a17337913f4f80f5aa505b9d0432a
train_30410
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: 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. 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": "C#", "developer_needs": [ "documentation", "auditability", "repo_scale_reasoning", "evaluation_metrics" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e61824c4b98f4518e9bd913b4a09ce3883f0efad
train_30411
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
compare
intermediate
Task: compare Topic: Multimodal dev workflows (docs, diagrams, traces) 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "C#", "developer_needs": [ "governance", "reproducibility", "tests_are_truth", "documentation" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1a0960e7e240d93684fcb891349bb29fefba49fb
train_30412
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: 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "SQL", "developer_needs": [ "evaluation_metrics", "tests_are_truth", "repo_scale_reasoning", "governance" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
70a7cd6b8a42353aadf9d89ca753c88aa61e5e51
train_30413
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: 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
[ "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", "security_gates", "tooling", "auditability" ], "moe_experts": [ "coding_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b9729e283a8930aa99b0ac5133ca6b78d6b0d7dd
train_30414
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: 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. Review: correctness, security, performance, governance
[]
{ "target_language": "C#", "developer_needs": [ "evaluation_metrics", "tests_are_truth", "ci_integration", "security_gates" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
c1a57b97529ff0aa96e152049b448d02198cdba6
train_30415
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
agent_loop
expert
Task: agent_loop Topic: Code-specialized model families and sizing tradeoffs Difficulty: expert Target language: 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. Loop: Plan → Edit → Test → Reflect → Human gate
[]
{ "target_language": "SQL", "developer_needs": [ "tests_are_truth", "auditability", "repo_scale_reasoning", "governance" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
739c510011625d6ad0c068e738289443425b0cf1
train_30416
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
design
advanced
Task: design Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: advanced Target language: SQL Context: 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": [ "reproducibility", "governance", "tooling", "cost_latency_tradeoffs" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bbb8b72d4d663925867be7c3100a307061f9b204
train_30417
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
failure_analysis
intermediate
Task: failure_analysis Topic: SWE-bench style real-repo evaluation 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "Python", "developer_needs": [ "tests_are_truth", "documentation", "repo_scale_reasoning", "tooling" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
28bd68ca16324f00515312881ffa1f036196c17c
train_30418
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: 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
[]
{ "target_language": "C#", "developer_needs": [ "security_gates", "auditability", "governance", "reproducibility" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8258a50b8f1dae2ce1c77c38aad0305145c2f9fc
train_30419
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
failure_analysis
advanced
Task: failure_analysis Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced 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. 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": "TypeScript", "developer_needs": [ "governance", "tooling", "auditability", "ci_integration" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
efa548106269a0252b424851bfd1baf21c57080f
train_30420
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
compare
advanced
Task: compare Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: advanced Target language: 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Python", "developer_needs": [ "repo_scale_reasoning", "documentation", "tests_are_truth", "security_gates" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b45b793a83fe4ccb1148c2daa71ec1b63162d7b6
train_30421
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: 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. 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": [ "tests_are_truth", "documentation", "repo_scale_reasoning", "security_gates" ], "moe_experts": [ "security_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
939ef8fde879a86440959e863f743b4e031cc4a9
train_30422
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
design
expert
Task: design Topic: Mixture-of-Experts (MoE) for code Difficulty: expert Target language: SQL Context: 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": [ "reproducibility", "governance", "security_gates", "evaluation_metrics" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e183fe22439075b02dd35e4533fd571bc6f10a73
train_30423
2026-01-01T00:00:00
Latency, cost, and reliability optimization
eval
expert
Task: eval Topic: Latency, cost, and reliability optimization Difficulty: expert 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. 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": "JavaScript", "developer_needs": [ "repo_scale_reasoning", "tooling", "ci_integration", "cost_latency_tradeoffs" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a1b7288e871d2c69fb2ba1f3ff3fb45478bb2d5f
train_30424
2026-01-01T00:00:00
Secure code generation and policy gates
data_pipeline
expert
Task: data_pipeline Topic: Secure code generation and policy gates Difficulty: expert Target language: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Go", "developer_needs": [ "cost_latency_tradeoffs", "security_gates", "tooling", "ci_integration" ], "moe_experts": [ "coding_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6caa921d04a9a1a712d8e1a3a68eb7abf405c89c
train_30425
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: 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": "TypeScript", "developer_needs": [ "reproducibility", "auditability", "ci_integration", "evaluation_metrics" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
7f096ff6f817cb45cb904ec5d71cb40eecb6a261
train_30426
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: 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 ```
[]
{ "target_language": "Python", "developer_needs": [ "tooling", "auditability", "repo_scale_reasoning", "reproducibility" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
70253284ea296b08a7b1f90df660e7c8531a462c
train_30427
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: 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
[]
{ "target_language": "Bash", "developer_needs": [ "auditability", "reproducibility", "ci_integration", "tests_are_truth" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
baca4b616d74e848de766b7b886ccb67a296576f
train_30428
2026-01-01T00:00:00
Extended context and repo-scale understanding
patch_diff
advanced
Task: patch_diff Topic: Extended context and repo-scale understanding 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. 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": [ "tooling", "repo_scale_reasoning", "governance", "auditability" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3330cd2c1cef68aa01a9f6f92c3f8d17b435b3f9
train_30429
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: 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": [ "repo_scale_reasoning", "documentation", "auditability", "governance" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
af56908bc2927aad8c937dea036f37cf59a3ae7d
train_30430
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
data_pipeline
intermediate
Task: data_pipeline Topic: Code-specialized model families and sizing tradeoffs Difficulty: intermediate Target language: 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. 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": [ "reproducibility", "cost_latency_tradeoffs", "repo_scale_reasoning", "auditability" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
7012674feb551b02e89eedcb2fbd64d7b5b152c4
train_30431
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
agent_loop
intermediate
Task: agent_loop Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: intermediate Target language: 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
[]
{ "target_language": "Python", "developer_needs": [ "governance", "auditability", "documentation", "tooling" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
98905552fa64ed883f10dae5376ac86485af1fc0
train_30432
2026-01-01T00:00:00
Self-improving agents and feedback loops
agent_loop
expert
Task: agent_loop Topic: Self-improving agents and feedback loops 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. Loop: Plan → Edit → Test → Reflect → Human gate
[]
{ "target_language": "TypeScript", "developer_needs": [ "repo_scale_reasoning", "governance", "cost_latency_tradeoffs", "tests_are_truth" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
885f74cadefdf639d586bb17bf494ac5f65de064
train_30433
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: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "evaluation_metrics", "auditability", "documentation", "governance" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
7639325a30eb01fb385c2554c37305821a2be074
train_30434
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
compare
advanced
Task: compare Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: advanced Target language: 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
[ "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", "documentation", "security_gates", "tooling" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5a83455abeb4f5021f645b8cc881a1af9ec08842
train_30435
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
failure_analysis
expert
Task: failure_analysis Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: expert 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "JavaScript", "developer_needs": [ "cost_latency_tradeoffs", "reproducibility", "tests_are_truth", "ci_integration" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ce8ed1a8e1744af4c552b752fa2332f1b654ee9a
train_30436
2026-01-01T00:00:00
Latency, cost, and reliability optimization
design
intermediate
Task: design Topic: Latency, cost, and reliability optimization 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
[]
{ "target_language": "Go", "developer_needs": [ "ci_integration", "tests_are_truth", "auditability", "governance" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
4a5d828961aa819570aa57a0b81670cb85d87e43
train_30437
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
explain
advanced
Task: explain Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: 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": [ "evaluation_metrics", "auditability", "cost_latency_tradeoffs", "tests_are_truth" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
26e9ac969a073ca6be22e8e916beaa4c4449ecca
train_30438
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
explain
intermediate
Task: explain Topic: Tool calling, sandboxes, and CI integration 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "C#", "developer_needs": [ "reproducibility", "security_gates", "evaluation_metrics", "tooling" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f25eb2e6a984defd0eda1368759dac3bd5bf2bff
train_30439
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: 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. 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": [ "reproducibility", "repo_scale_reasoning", "ci_integration", "tooling" ], "moe_experts": [ "coding_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
d7abc592e759cdbabe7849351af296020556de1c
train_30440
2026-01-01T00:00:00
Secure code generation and policy gates
data_pipeline
intermediate
Task: data_pipeline Topic: Secure code generation and policy gates Difficulty: intermediate Target language: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Rust", "developer_needs": [ "tooling", "ci_integration", "repo_scale_reasoning", "security_gates" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bb0a1d9c2ed1552b804af14e81838cfd6e64e8af
train_30441
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
failure_analysis
advanced
Task: failure_analysis Topic: Tool calling, sandboxes, and CI integration 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "Go", "developer_needs": [ "documentation", "repo_scale_reasoning", "security_gates", "evaluation_metrics" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
955779a2b5702b430bbe2a9b1d8af68b85a74b5a
train_30442
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
data_pipeline
intermediate
Task: data_pipeline Topic: Governance, provenance, and licensing for code data Difficulty: intermediate 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. 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": "Bash", "developer_needs": [ "evaluation_metrics", "auditability", "reproducibility", "documentation" ], "moe_experts": [ "evaluation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2f2c565622d679ac9b3e63d3fe453c8445c9a2a4
train_30443
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
agent_loop
intermediate
Task: agent_loop Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: 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": [ "evaluation_metrics", "cost_latency_tradeoffs", "tooling", "reproducibility" ], "moe_experts": [ "coding_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
22c9eb2ad44997a3807cdf0da1904bdd4e7b63a8
train_30444
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
explain
intermediate
Task: explain Topic: Tool calling, sandboxes, and CI integration 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "TypeScript", "developer_needs": [ "ci_integration", "documentation", "security_gates", "cost_latency_tradeoffs" ], "moe_experts": [ "governance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2675ef3bda42d345643a9a797597405cc41ad440
train_30445
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: 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": [ "evaluation_metrics", "governance", "ci_integration", "documentation" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
83a90439d1e5cd1fce24a4a1693fc1ed0ca42396
train_30446
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
eval
intermediate
Task: eval Topic: Multimodal dev workflows (docs, diagrams, traces) 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "C#", "developer_needs": [ "cost_latency_tradeoffs", "repo_scale_reasoning", "documentation", "governance" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
43f8736406b2352a7d507f9ed14af5d7dd34bc01
train_30447
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
explain
intermediate
Task: explain Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: 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": [ "repo_scale_reasoning", "security_gates", "cost_latency_tradeoffs", "auditability" ], "moe_experts": [ "coding_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
82a38fb9fb3aacb8907882576eeff808abc89b16
train_30448
2026-01-01T00:00:00
Self-improving agents and feedback loops
eval
advanced
Task: eval Topic: Self-improving agents and feedback loops 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "Bash", "developer_needs": [ "ci_integration", "repo_scale_reasoning", "tests_are_truth", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
93c2c7b761bd4d1df084641f1d5904c4c9b3804d
train_30449
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
review
advanced
Task: review Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: advanced Target language: 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. Review: correctness, security, performance, governance
[]
{ "target_language": "Rust", "developer_needs": [ "reproducibility", "tests_are_truth", "governance", "documentation" ], "moe_experts": [ "governance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
099c97f6176f0a0b2105f9d5575c535a765ef9b1
train_30450
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: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "documentation", "evaluation_metrics", "auditability", "governance" ], "moe_experts": [ "evaluation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
135fda1d52408849838a249375627f9da769f2dd
train_30451
2026-01-01T00:00:00
Latency, cost, and reliability optimization
data_pipeline
advanced
Task: data_pipeline Topic: Latency, cost, and reliability optimization 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "C#", "developer_needs": [ "ci_integration", "reproducibility", "tooling", "repo_scale_reasoning" ], "moe_experts": [ "governance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3ab5d5d633ceb6f6d05ca57f4d4a5b91427c373a
train_30452
2026-01-01T00:00:00
Extended context and repo-scale understanding
explain
advanced
Task: explain Topic: Extended context and repo-scale understanding Difficulty: advanced Target language: TypeScript Context: 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": [ "ci_integration", "tooling", "repo_scale_reasoning", "evaluation_metrics" ], "moe_experts": [ "evaluation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
7935f4b064118e62a7e75d63ad5eb35d19b94c32
train_30453
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: 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
[]
{ "target_language": "JavaScript", "developer_needs": [ "repo_scale_reasoning", "auditability", "reproducibility", "governance" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
712d842e70f4228f32ec962375e79ede3bad5932
train_30454
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: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "SQL", "developer_needs": [ "evaluation_metrics", "tests_are_truth", "auditability", "governance" ], "moe_experts": [ "governance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
cc6c5adf1df28ae0b5dc363fe7eea8187a2e46e2
train_30455
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: 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. 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": "SQL", "developer_needs": [ "tooling", "evaluation_metrics", "repo_scale_reasoning", "documentation" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
323cff49a68529b48fed8dc76de263951d40d344
train_30456
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
review
expert
Task: review Topic: Dataset curation pipelines (filter, dedupe, quality) 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
[]
{ "target_language": "Rust", "developer_needs": [ "governance", "documentation", "cost_latency_tradeoffs", "evaluation_metrics" ], "moe_experts": [ "security_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3b2953bb6cbf456b0e1a9ccf795c04439d5d85b9
train_30457
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: 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. 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": [ "repo_scale_reasoning", "governance", "auditability", "ci_integration" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
253b999e4f81d76f717bcc6521e9988b8e238918
train_30458
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
review
advanced
Task: review Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced Target language: 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. Review: correctness, security, performance, governance
[]
{ "target_language": "C#", "developer_needs": [ "cost_latency_tradeoffs", "tooling", "security_gates", "ci_integration" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
70719c8ab84bf8a84365728035004ad7373ed0c3
train_30459
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
review
intermediate
Task: review Topic: Reasoning-first coding models and tunable deliberation Difficulty: intermediate Target language: 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. Review: correctness, security, performance, governance
[]
{ "target_language": "Bash", "developer_needs": [ "security_gates", "cost_latency_tradeoffs", "repo_scale_reasoning", "documentation" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8b4f36793dce867cd1085786fd67ddf3cf7158f8
train_30460
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
design
expert
Task: design Topic: Model merging, distillation, and continued pretraining Difficulty: expert Target language: 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": [ "ci_integration", "evaluation_metrics", "reproducibility", "tests_are_truth" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
20b0a6f94102390dbd8a256c2a6568816c20f4b5
train_30461
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: 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", "ci_integration", "tooling", "documentation" ], "moe_experts": [ "security_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ec269ecf28a893834f7b228ccd2b3353521be186
train_30462
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: 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": [ "auditability", "reproducibility", "documentation", "governance" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
acc4f570e322ef62f6d0cff06232f56f081f5461
train_30463
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: 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": "C#", "developer_needs": [ "tests_are_truth", "governance", "tooling", "reproducibility" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fb871a716f5b5c43978c48941244f4fa880b2351
train_30464
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: 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Python", "developer_needs": [ "auditability", "reproducibility", "repo_scale_reasoning", "tests_are_truth" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
11b4ef8944e905960f6a7d1c99b0ea9c110f6cfa
train_30465
2026-01-01T00:00:00
Latency, cost, and reliability optimization
code
expert
Task: code Topic: Latency, cost, and reliability optimization 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "SQL", "developer_needs": [ "reproducibility", "tooling", "documentation", "auditability" ], "moe_experts": [ "security_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
13381b03849540884ab727b6d34f4ef97d541550
train_30466
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
agent_loop
intermediate
Task: agent_loop Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: 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. Loop: Plan → Edit → Test → Reflect → Human gate
[]
{ "target_language": "JavaScript", "developer_needs": [ "tests_are_truth", "tooling", "cost_latency_tradeoffs", "security_gates" ], "moe_experts": [ "data_curation_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5549d8f132d3d38fd2b7073e36cdf3c55382410a
train_30467
2026-01-01T00:00:00
Self-improving agents and feedback loops
eval
expert
Task: eval Topic: Self-improving agents and feedback loops 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "Java", "developer_needs": [ "documentation", "tooling", "ci_integration", "repo_scale_reasoning" ], "moe_experts": [ "agentic_systems_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1563357e66c2480dae2522844ccaed364f69c1d0
train_30468
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: 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": [ "tests_are_truth", "security_gates", "governance", "tooling" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1c6ed7af229fbea693d7dcb0eb3f98cf2b03a909
train_30469
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: 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Rust", "developer_needs": [ "reproducibility", "documentation", "ci_integration", "security_gates" ], "moe_experts": [ "governance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f1af06da8ccdb0c7aa2f0a8dca6b81b263a8fdde
train_30470
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
code
advanced
Task: code Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: advanced Target language: 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": [ "security_gates", "evaluation_metrics", "documentation", "tooling" ], "moe_experts": [ "evaluation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
34910bbc31a666a9e7023d780bbee03f1157b2ff
train_30471
2026-01-01T00:00:00
Self-improving agents and feedback loops
eval
advanced
Task: eval Topic: Self-improving agents and feedback loops 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "TypeScript", "developer_needs": [ "governance", "cost_latency_tradeoffs", "ci_integration", "evaluation_metrics" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a3ee21fc98941bcf7f2d286b070d73da8c58d8ef
train_30472
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: 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", "governance", "tests_are_truth", "reproducibility" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3c1676ec5579cf4e63ffcd466b7efa8f5af9cb59
train_30473
2026-01-01T00:00:00
Self-improving agents and feedback loops
patch_diff
advanced
Task: patch_diff Topic: Self-improving agents and feedback loops Difficulty: advanced 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. 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": [ "ci_integration", "tests_are_truth", "security_gates", "governance" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6876a48ae77bf552b3c34d380d8cac72b2baf67b
train_30474
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
agent_loop
advanced
Task: agent_loop Topic: Model merging, distillation, and continued pretraining Difficulty: advanced Target language: 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
[]
{ "target_language": "C#", "developer_needs": [ "auditability", "documentation", "security_gates", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
476cab57c000bf9f3cf9c438c2655fc01fa035cb
train_30475
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
explain
expert
Task: explain Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: expert Target language: 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", "security_gates", "repo_scale_reasoning", "reproducibility" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
07cad43701b999da8538a7506ada6893088df76c
train_30476
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: 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": [ "cost_latency_tradeoffs", "reproducibility", "auditability", "documentation" ], "moe_experts": [ "data_curation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3c3861761b82a5e17b359f51af8da33caa754893
train_30477
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
review
expert
Task: review Topic: Dataset curation pipelines (filter, dedupe, quality) 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. 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": [ "evaluation_metrics", "reproducibility", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bc57c254f3ce85c6a88e5110f85e9d6e29c33ade
train_30478
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: 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": [ "documentation", "cost_latency_tradeoffs", "reproducibility", "repo_scale_reasoning" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1aac9b74558bda44839e44275a2ead9a36f4d2a0
train_30479
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: 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": [ "tests_are_truth", "security_gates", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "coding_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
945cabf830331e77fd56455296b9b66a1ccbfd39
train_30480
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
expert
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: expert Target language: 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": [ "governance", "tooling", "repo_scale_reasoning", "security_gates" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6018ab7ed8b6d22636f72f6592cd3b5167280d2d
train_30481
2026-01-01T00:00:00
Secure code generation and policy gates
explain
intermediate
Task: explain Topic: Secure code generation and policy gates 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "C#", "developer_needs": [ "cost_latency_tradeoffs", "tooling", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5414e832e91f17d4de854d03198e4c412fcf9eca
train_30482
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
explain
intermediate
Task: explain Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: intermediate Target language: 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": [ "reproducibility", "governance", "auditability", "tests_are_truth" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
51f0e6b16be5e50b4ea4314838a3e10713dc97ef
train_30483
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
compare
expert
Task: compare 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "C#", "developer_needs": [ "reproducibility", "governance", "ci_integration", "cost_latency_tradeoffs" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2db0dcd85df7c7b61f579d7bab001be9af0e675f
train_30484
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: 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. Review: correctness, security, performance, governance
[]
{ "target_language": "JavaScript", "developer_needs": [ "tests_are_truth", "repo_scale_reasoning", "governance", "tooling" ], "moe_experts": [ "agentic_systems_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
31e89c5e3a590b00753cc42741d2ff24a31394df
train_30485
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
compare
expert
Task: compare Topic: SWE-bench style real-repo evaluation Difficulty: expert Target language: 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. Compare: capability, cost, latency, reliability
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "evaluation_metrics", "governance", "tests_are_truth", "tooling" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5dafbd3f36642b2576cdfcaa621bc55a02259298
train_30486
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: 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. 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": "Java", "developer_needs": [ "evaluation_metrics", "reproducibility", "ci_integration", "auditability" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ff283c98d3d8a8d3bd1665f6e0eacef5e8b265c5
train_30487
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
patch_diff
intermediate
Task: patch_diff Topic: Agentic coding systems (plan→edit→test→reflect) 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. 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": [ "repo_scale_reasoning", "ci_integration", "evaluation_metrics", "tests_are_truth" ], "moe_experts": [ "security_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1e72d3d6ec7610b38cd83b450832edc93d3c0d2e
train_30488
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
failure_analysis
advanced
Task: failure_analysis Topic: Tool calling, sandboxes, and CI integration 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "TypeScript", "developer_needs": [ "ci_integration", "tests_are_truth", "documentation", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
0859c12eedbe20c6dc8c66f082c1273548283f4d
train_30489
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: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "SQL", "developer_needs": [ "evaluation_metrics", "governance", "reproducibility", "tooling" ], "moe_experts": [ "coding_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5b66d5f6aa4118d387c9a4baea4681459471f00f
train_30490
2026-01-01T00:00:00
Secure code generation and policy gates
compare
advanced
Task: compare Topic: Secure code generation and policy gates Difficulty: advanced Target language: 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": [ "repo_scale_reasoning", "security_gates", "tooling", "evaluation_metrics" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5756608c4df62181cb8c4c5454b5c2d02c98e2dd
train_30491
2026-01-01T00:00:00
Extended context and repo-scale understanding
patch_diff
advanced
Task: patch_diff Topic: Extended context and repo-scale understanding 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. 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": "SQL", "developer_needs": [ "tests_are_truth", "tooling", "security_gates", "repo_scale_reasoning" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
d69ec9441d573bb42e577274e6b7f66fac9f5dc5
train_30492
2026-01-01T00:00:00
Secure code generation and policy gates
agent_loop
advanced
Task: agent_loop Topic: Secure code generation and policy gates Difficulty: advanced Target language: SQL 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": "SQL", "developer_needs": [ "security_gates", "tests_are_truth", "auditability", "ci_integration" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
9b9e903b2d5f84b9412b000b8f44e79e7d5702f1
train_30493
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
code
intermediate
Task: code Topic: SWE-bench style real-repo evaluation Difficulty: intermediate Target language: 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": [ "tests_are_truth", "ci_integration", "tooling", "documentation" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3d63f3693054699a2109df4bae64a68d6921126d
train_30494
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
compare
advanced
Task: compare Topic: Model merging, distillation, and continued pretraining Difficulty: advanced Target language: 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. Compare: capability, cost, latency, reliability
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Java", "developer_needs": [ "auditability", "repo_scale_reasoning", "security_gates", "governance" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
762d7511047cbfffae46949873985dcecfbe13a3
train_30495
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
expert
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: expert Target language: 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
[]
{ "target_language": "Go", "developer_needs": [ "governance", "repo_scale_reasoning", "tests_are_truth", "ci_integration" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2323b12110dee14f3e0370e774e9f2de3013ea60
train_30496
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: 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
[]
{ "target_language": "TypeScript", "developer_needs": [ "auditability", "evaluation_metrics", "documentation", "security_gates" ], "moe_experts": [ "security_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
79519693627592bd839edb810bc210fd8e119fb1
train_30497
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
review
advanced
Task: review Topic: Mixture-of-Experts (MoE) for code 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. Review: correctness, security, performance, governance
[]
{ "target_language": "Go", "developer_needs": [ "tests_are_truth", "evaluation_metrics", "security_gates", "documentation" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2acd4b754ccabde730e5d2ceca6f92e16783042f
train_30498
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
data_pipeline
advanced
Task: data_pipeline Topic: Governance, provenance, and licensing for code data Difficulty: advanced 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Python", "developer_needs": [ "tooling", "cost_latency_tradeoffs", "repo_scale_reasoning", "evaluation_metrics" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e4bcba771c2fbd42d5e91633ae426ecddea34473
train_30499
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: 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": "Bash", "developer_needs": [ "repo_scale_reasoning", "cost_latency_tradeoffs", "security_gates", "tests_are_truth" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6f0484c5b96cfda55e152709e397162f7148a6d8