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train_10100
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
compare
intermediate
Task: compare Topic: Code-specialized model families and sizing tradeoffs Difficulty: intermediate Target language: Java Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Java", "developer_needs": [ "tests_are_truth", "tooling", "reproducibility", "evaluation_metrics" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b3671cee2db56b6d1a9475de2de4bf8bcb98c136
train_10101
2026-01-01T00:00:00
Self-improving agents and feedback loops
patch_diff
intermediate
Task: patch_diff Topic: Self-improving agents and feedback loops 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. 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": [ "tests_are_truth", "repo_scale_reasoning", "tooling", "security_gates" ], "moe_experts": [ "evaluation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2f24e9fac3822a5995730dbc4107533f83ab1dc3
train_10102
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: 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. 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": [ "tests_are_truth", "reproducibility", "evaluation_metrics", "auditability" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a7c547f6608ecb3663afaa2fdb5ef1fc59fbff21
train_10103
2026-01-01T00:00:00
Latency, cost, and reliability optimization
eval
intermediate
Task: eval 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. 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": [ "evaluation_metrics", "tests_are_truth", "documentation", "security_gates" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5f8991f6510d681425ac31277cef23a37e1d6a94
train_10104
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
explain
advanced
Task: explain Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced Target language: Go Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Go", "developer_needs": [ "documentation", "evaluation_metrics", "repo_scale_reasoning", "cost_latency_tradeoffs" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bb7d66befaa9163cd0e6df596a9375f049953c3b
train_10105
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
eval
expert
Task: eval Topic: Tool calling, sandboxes, and CI integration Difficulty: expert Target language: 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": [ "ci_integration", "documentation", "evaluation_metrics", "tooling" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5a7b3aa92dd62a4ead28b7bc3c9240c25344b3eb
train_10106
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
design
advanced
Task: design Topic: Reasoning-first coding models and tunable deliberation 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": [ "repo_scale_reasoning", "ci_integration", "reproducibility", "documentation" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fbb53c4faa101df3ff5af92df7b5369f1ce3ef34
train_10107
2026-01-01T00:00:00
Secure code generation and policy gates
explain
expert
Task: explain Topic: Secure code generation and policy gates Difficulty: expert 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": [ "governance", "tests_are_truth", "repo_scale_reasoning", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
341184d4d020c552f1860e762339df6399a41d1a
train_10108
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
review
advanced
Task: review Topic: Model merging, distillation, and continued pretraining Difficulty: advanced Target language: 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. 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": [ "reproducibility", "tests_are_truth", "governance", "cost_latency_tradeoffs" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
9149e7a0502d8a0626e7461feb1d5934f24615bc
train_10109
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: Java Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Java", "developer_needs": [ "auditability", "ci_integration", "cost_latency_tradeoffs", "security_gates" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2564163c542d34646faaa2fe4e90e539ea799399
train_10110
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: Python Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Scaffold: ```python def agent_loop(plan, edit, test, issue, max_iters=4): history = [] p = plan(issue) for _ in range(max_iters): patch = edit(issue, p) ok, report = test(patch) history.append({"plan": p, "ok": ok}) if ok: return patch, history p = p + " | refine" return patch, history ```
[]
{ "target_language": "Python", "developer_needs": [ "tooling", "cost_latency_tradeoffs", "governance", "auditability" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
cda0093a46aa332ddf54bfd6d4699919d6b0cc41
train_10111
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
eval
intermediate
Task: eval Topic: Code-specialized model families and sizing tradeoffs 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "JavaScript", "developer_needs": [ "cost_latency_tradeoffs", "ci_integration", "governance", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ab5e984b5bf152d06fc91ad8c944fc67cf5ba69c
train_10112
2026-01-01T00:00:00
Self-improving agents and feedback loops
failure_analysis
intermediate
Task: failure_analysis Topic: Self-improving agents and feedback loops Difficulty: intermediate Target language: Go Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. 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": "Go", "developer_needs": [ "governance", "auditability", "security_gates", "reproducibility" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
cf7b38408227212f9a4bf566588e721165cf7343
train_10113
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
code
expert
Task: code Topic: Code-specialized model families and sizing tradeoffs Difficulty: expert Target language: 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Rust", "developer_needs": [ "tests_are_truth", "tooling", "cost_latency_tradeoffs", "evaluation_metrics" ], "moe_experts": [ "evaluation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3f3d1e226b773390c1c5060dd3209a2124f92088
train_10114
2026-01-01T00:00:00
Extended context and repo-scale understanding
compare
intermediate
Task: compare Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: TypeScript Context: 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": [ "cost_latency_tradeoffs", "auditability", "ci_integration", "tests_are_truth" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
872ca6ed01a5dfd8aa0452ae07651eb6190a652f
train_10115
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: 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": [ "tooling", "evaluation_metrics", "reproducibility", "ci_integration" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5b750d4049ddbd28337ac74acfe7c4e92b03cbc5
train_10116
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: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "C#", "developer_needs": [ "security_gates", "cost_latency_tradeoffs", "governance", "ci_integration" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5245c44820611622330bfc2f59f24381ee16791b
train_10117
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: 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. Review: correctness, security, performance, governance
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Java", "developer_needs": [ "tests_are_truth", "repo_scale_reasoning", "governance", "evaluation_metrics" ], "moe_experts": [ "data_curation_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
121921391d5dc13e66edda547af330e269f14a07
train_10118
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: 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": [ "tooling", "auditability", "evaluation_metrics", "ci_integration" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
895992cd899766db8973f3889171cc8dd5dd9772
train_10119
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
design
advanced
Task: design Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: 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": [ "tooling", "auditability", "security_gates", "governance" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
53b256c53d05e666131c3b93e1fb857a05136598
train_10120
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: 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
[]
{ "target_language": "SQL", "developer_needs": [ "auditability", "documentation", "governance", "repo_scale_reasoning" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
9466ddf7c0b3e5e7fb0ecf536cbe5e6676f4f0c9
train_10121
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: 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", "security_gates", "repo_scale_reasoning", "documentation" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a59f1862eb73d24a4cd42816884a590890866802
train_10122
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: 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. 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": [ "auditability", "documentation", "evaluation_metrics", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
74a92bbe1c3fe994807ec0572076fb15eb875440
train_10123
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
patch_diff
expert
Task: patch_diff Topic: Governance, provenance, and licensing for code data 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. 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": [ "ci_integration", "governance", "cost_latency_tradeoffs", "repo_scale_reasoning" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
63a0519174144756f33d5d936db4ac5e057c06a7
train_10124
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: 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": "Go", "developer_needs": [ "tooling", "evaluation_metrics", "repo_scale_reasoning", "documentation" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
af1f789b0d4b82ee595753d8583c77e656cc6c96
train_10125
2026-01-01T00:00:00
Latency, cost, and reliability optimization
patch_diff
expert
Task: patch_diff Topic: Latency, cost, and reliability optimization 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. 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": [ "documentation", "tooling", "tests_are_truth", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b54b13d176f21b68cb72c43e522520db10c6a3fd
train_10126
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
design
advanced
Task: design Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: Python Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Python", "developer_needs": [ "reproducibility", "repo_scale_reasoning", "tooling", "evaluation_metrics" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fd06a59dc9445b512857c9e9922c05f57f984205
train_10127
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
patch_diff
advanced
Task: patch_diff Topic: Mixture-of-Experts (MoE) for code 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": [ "tooling", "security_gates", "cost_latency_tradeoffs", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
179287bc14e9233c057ad5706f440ad22da7c744
train_10128
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
agent_loop
intermediate
Task: agent_loop Topic: Tool calling, sandboxes, and CI integration 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. Loop: Plan → Edit → Test → Reflect → Human gate
[]
{ "target_language": "SQL", "developer_needs": [ "documentation", "repo_scale_reasoning", "tests_are_truth", "governance" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
af7ce911cd079db4d68cefe4a9f5207366dd6b4f
train_10129
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
compare
intermediate
Task: compare Topic: Mixture-of-Experts (MoE) for code Difficulty: intermediate Target language: 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "SQL", "developer_needs": [ "evaluation_metrics", "tests_are_truth", "documentation", "reproducibility" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1f9641c4153b30d314fc22ffd8f653ea9e8a01da
train_10130
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
eval
intermediate
Task: eval Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: intermediate Target language: 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "TypeScript", "developer_needs": [ "repo_scale_reasoning", "reproducibility", "evaluation_metrics", "tooling" ], "moe_experts": [ "evaluation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2d494c31fde97957bf47c12d80b6f891dff57bd9
train_10131
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
patch_diff
intermediate
Task: patch_diff Topic: Model merging, distillation, and continued pretraining 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. 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": [ "governance", "repo_scale_reasoning", "reproducibility", "evaluation_metrics" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
da662655b9a2ce01ac448c1682d3084eac374130
train_10132
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: 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
[]
{ "target_language": "JavaScript", "developer_needs": [ "governance", "ci_integration", "tooling", "documentation" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5c492df3f2f7532e3d8201ab1c2d87a3fd23b9b7
train_10133
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
review
intermediate
Task: review Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: intermediate Target language: SQL Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Review: correctness, security, performance, governance
[]
{ "target_language": "SQL", "developer_needs": [ "tooling", "reproducibility", "auditability", "documentation" ], "moe_experts": [ "data_curation_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
70917a23d42b94b147137f94e40965fbf2ff3b17
train_10134
2026-01-01T00:00:00
Latency, cost, and reliability optimization
design
advanced
Task: design Topic: Latency, cost, and reliability optimization 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Java", "developer_needs": [ "documentation", "governance", "auditability", "reproducibility" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1b8a68184df6741f884e413fc7cac30635e64351
train_10135
2026-01-01T00:00:00
Extended context and repo-scale understanding
code
advanced
Task: code Topic: Extended context and repo-scale understanding Difficulty: advanced Target language: TypeScript Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "TypeScript", "developer_needs": [ "repo_scale_reasoning", "tooling", "evaluation_metrics", "cost_latency_tradeoffs" ], "moe_experts": [ "governance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
25fc3166ef5e14907d2e7b1caa8bc500c8c0e2f9
train_10136
2026-01-01T00:00:00
Secure code generation and policy gates
review
expert
Task: review Topic: Secure code generation and policy gates Difficulty: expert Target language: SQL Context: 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": "SQL", "developer_needs": [ "cost_latency_tradeoffs", "reproducibility", "tests_are_truth", "tooling" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
7fa36afeb62e9deddd8cccf36ba0def7f317576c
train_10137
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: 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": [ "reproducibility", "evaluation_metrics", "cost_latency_tradeoffs", "governance" ], "moe_experts": [ "governance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f177894c3ad4cdcc334c29c98ef0b8738ffff4bd
train_10138
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: 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Bash", "developer_needs": [ "ci_integration", "governance", "tests_are_truth", "security_gates" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
d25babcf3f47f26e78a40edff362fbeee8b2c319
train_10139
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: 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": [ "tooling", "governance", "auditability", "security_gates" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8ae83f84c63a2c152a5149fff8cf22830889b3e7
train_10140
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
review
advanced
Task: review Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: advanced Target language: Rust Context: 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": "Rust", "developer_needs": [ "ci_integration", "repo_scale_reasoning", "governance", "cost_latency_tradeoffs" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
7213e460b52074750eeb73425f57a59330d69022
train_10141
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
compare
expert
Task: compare Topic: Reasoning-first coding models and tunable deliberation Difficulty: expert Target language: 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Python", "developer_needs": [ "documentation", "tooling", "reproducibility", "ci_integration" ], "moe_experts": [ "coding_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e86f732d8d677d1022034b58c77390195a5d0b19
train_10142
2026-01-01T00:00:00
Self-improving agents and feedback loops
explain
advanced
Task: explain Topic: Self-improving agents and feedback loops 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. 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", "tooling", "evaluation_metrics", "auditability" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
db00b9aefadfb0af8078b182afa0e72f1a4639e8
train_10143
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: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Go", "developer_needs": [ "documentation", "repo_scale_reasoning", "auditability", "tooling" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e894d5598736d416c7e91de7d607ce867c196e6c
train_10144
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: 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": [ "tests_are_truth", "ci_integration", "auditability", "tooling" ], "moe_experts": [ "evaluation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6699cd379927a2bd40e25862d52a88bd7242425d
train_10145
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
data_pipeline
expert
Task: data_pipeline Topic: Agentic coding systems (plan→edit→test→reflect) 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Rust", "developer_needs": [ "security_gates", "reproducibility", "auditability", "tooling" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bfaf866577e7e73d0962ae0d0e03ff70bd5c9ac7
train_10146
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: 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "JavaScript", "developer_needs": [ "tests_are_truth", "auditability", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5a6288970b4dfe00e2657d8cd67792925a6572b6
train_10147
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: 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": [ "security_gates", "reproducibility", "documentation", "evaluation_metrics" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2c97e69915e68ea604aa41bdb856c4b6f1070e78
train_10148
2026-01-01T00:00:00
Secure code generation and policy gates
review
advanced
Task: review Topic: Secure code generation and policy gates 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. 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": [ "tests_are_truth", "reproducibility", "tooling", "ci_integration" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fa949f6799d94773ff16ec92ccbc3f1b47ceeb25
train_10149
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: 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. 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": [ "evaluation_metrics", "reproducibility", "governance", "security_gates" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
abf8caff00ef9339ab0d1975b0d71472a2ac3e57
train_10150
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: 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": [ "evaluation_metrics", "tests_are_truth", "security_gates", "governance" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1111f5e2abdcad816d06c7da042d0b855ae01cc1
train_10151
2026-01-01T00:00:00
Extended context and repo-scale understanding
explain
expert
Task: explain Topic: Extended context and repo-scale understanding Difficulty: expert Target language: 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. 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": [ "evaluation_metrics", "tooling", "cost_latency_tradeoffs", "security_gates" ], "moe_experts": [ "data_curation_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
d34176664be2afe15fcce3861a6becb8e6ef30db
train_10152
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: 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. 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": [ "tooling", "cost_latency_tradeoffs", "auditability", "repo_scale_reasoning" ], "moe_experts": [ "coding_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bef596fc5405740086f0b45f3457bcef5eb0802a
train_10153
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
code
expert
Task: code Topic: SWE-bench style real-repo evaluation Difficulty: expert Target language: Rust Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Rust", "developer_needs": [ "reproducibility", "auditability", "evaluation_metrics", "tooling" ], "moe_experts": [ "evaluation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fa60b8205d1046cc8ea28b40da21aa3e9a647792
train_10154
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
data_pipeline
advanced
Task: data_pipeline Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: JavaScript Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "JavaScript", "developer_needs": [ "ci_integration", "documentation", "repo_scale_reasoning", "evaluation_metrics" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
afecf56309acd34a4bb91cad96abffbab8e31d42
train_10155
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
agent_loop
intermediate
Task: agent_loop Topic: Reasoning-first coding models and tunable deliberation Difficulty: intermediate Target language: 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. Loop: Plan → Edit → Test → Reflect → Human gate
[]
{ "target_language": "Java", "developer_needs": [ "reproducibility", "evaluation_metrics", "repo_scale_reasoning", "governance" ], "moe_experts": [ "governance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
cc4cb2e4475f2adae71f66617de91d87c58fe8c7
train_10156
2026-01-01T00:00:00
Extended context and repo-scale understanding
eval
advanced
Task: eval Topic: Extended context and repo-scale understanding Difficulty: advanced Target language: 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. 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": "Rust", "developer_needs": [ "repo_scale_reasoning", "evaluation_metrics", "tests_are_truth", "cost_latency_tradeoffs" ], "moe_experts": [ "coding_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
20d10197250ae320a558c3dd8c0a70009e666ab3
train_10157
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: 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": [ "cost_latency_tradeoffs", "security_gates", "tooling", "documentation" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f585728dca7be63143ab851823ccc1ac6074eb56
train_10158
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
failure_analysis
expert
Task: failure_analysis Topic: Reasoning-first coding models and tunable deliberation 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Bash", "developer_needs": [ "evaluation_metrics", "reproducibility", "cost_latency_tradeoffs", "ci_integration" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
54ad60b1c888915a58c44d1f747c0ebb8b137e42
train_10159
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: 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": "Java", "developer_needs": [ "security_gates", "documentation", "auditability", "reproducibility" ], "moe_experts": [ "coding_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
88f567a1fea656883b89d1e2690ba1b99d618b35
train_10160
2026-01-01T00:00:00
Latency, cost, and reliability optimization
compare
expert
Task: compare Topic: Latency, cost, and reliability optimization 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "C#", "developer_needs": [ "documentation", "auditability", "repo_scale_reasoning", "governance" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bfefbecd002949773711b35bdd9ca9a38bb4a046
train_10161
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: 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. Compare: capability, cost, latency, reliability
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Go", "developer_needs": [ "documentation", "governance", "ci_integration", "security_gates" ], "moe_experts": [ "evaluation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
209a44ce144613b436cb53cc7ccaa73ce072c8ec
train_10162
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
explain
intermediate
Task: explain Topic: Code-specialized model families and sizing tradeoffs Difficulty: intermediate Target language: 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": [ "repo_scale_reasoning", "tests_are_truth", "auditability", "reproducibility" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
c1aa356f7e7c47bb97ce09d2387bab56ea03c63c
train_10163
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: 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Go", "developer_needs": [ "documentation", "repo_scale_reasoning", "tooling", "ci_integration" ], "moe_experts": [ "coding_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1717eed285faa02e565d7a0e07578351834ac33f
train_10164
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: 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
[]
{ "target_language": "Python", "developer_needs": [ "ci_integration", "reproducibility", "evaluation_metrics", "security_gates" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
76195036db6e14ea133f89df86075d1a3d9d68d5
train_10165
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
design
advanced
Task: design Topic: Model merging, distillation, and continued pretraining 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Python", "developer_needs": [ "documentation", "reproducibility", "governance", "security_gates" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
7ec2600af037c7f4460a1e436cc2db76855c14c8
train_10166
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
eval
advanced
Task: eval Topic: Mixture-of-Experts (MoE) for code Difficulty: advanced Target language: 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "Python", "developer_needs": [ "ci_integration", "governance", "tests_are_truth", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
d559bf3f91286992d55d6679105f32afcdbd13b4
train_10167
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: 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "SQL", "developer_needs": [ "auditability", "governance", "ci_integration", "cost_latency_tradeoffs" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6d88a57541160b0a009c6cca72a5ffce40d3cca3
train_10168
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
data_pipeline
advanced
Task: data_pipeline Topic: Multimodal dev workflows (docs, diagrams, traces) 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": [ "governance", "tests_are_truth", "repo_scale_reasoning", "tooling" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3c57bac22934e537a6a60cc93b0c9f5855f41cc9
train_10169
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: 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": [ "cost_latency_tradeoffs", "auditability", "tests_are_truth", "tooling" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6929a0633f00a5774a47d94f2e84e3c1258a1c6e
train_10170
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: 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
[ "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", "tests_are_truth", "cost_latency_tradeoffs", "auditability" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b88aed6fffd7ce4d4e285c947aee69add2a556e6
train_10171
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: 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 ```
[]
{ "target_language": "Python", "developer_needs": [ "tooling", "reproducibility", "repo_scale_reasoning", "tests_are_truth" ], "moe_experts": [ "performance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
453c6647ae67b106a693a1a022911e6b2bd0ad9e
train_10172
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
failure_analysis
advanced
Task: failure_analysis Topic: Mixture-of-Experts (MoE) for code 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Bash", "developer_needs": [ "cost_latency_tradeoffs", "auditability", "security_gates", "reproducibility" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b4892d94505c78cac9d1440cef5d3b148e04084f
train_10173
2026-01-01T00:00:00
Self-improving agents and feedback loops
failure_analysis
intermediate
Task: failure_analysis Topic: Self-improving agents and feedback loops 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. 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": [ "tests_are_truth", "evaluation_metrics", "ci_integration", "governance" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a8f5c4fe5fa9aeb26505ea433c52eff3d00d0d23
train_10174
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
compare
expert
Task: compare Topic: Reasoning-first coding models and tunable deliberation Difficulty: expert Target language: 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": [ "documentation", "tests_are_truth", "evaluation_metrics", "security_gates" ], "moe_experts": [ "governance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
eb506d9fed3cfda6180e6439700552033a9e29b6
train_10175
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": [ "tests_are_truth", "evaluation_metrics", "security_gates", "reproducibility" ], "moe_experts": [ "security_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
81fa390faeb47f215ce3820566122ffbeff20d51
train_10176
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
agent_loop
advanced
Task: agent_loop Topic: Tool calling, sandboxes, and CI integration 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. 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": [ "repo_scale_reasoning", "documentation", "governance", "security_gates" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2391b2e489bb54f5c2009b8bb87f54f6513cee49
train_10177
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
eval
advanced
Task: eval Topic: Mixture-of-Experts (MoE) for code Difficulty: advanced Target language: 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": [ "tests_are_truth", "repo_scale_reasoning", "documentation", "reproducibility" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bea19cacd1e534211c724927369db9842bed2c22
train_10178
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: 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. 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": "JavaScript", "developer_needs": [ "evaluation_metrics", "governance", "documentation", "tests_are_truth" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
711f4c8f6d590ad1b34bf338238de011ee88baa0
train_10179
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
agent_loop
advanced
Task: agent_loop Topic: Tool calling, sandboxes, and CI integration 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. 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": [ "evaluation_metrics", "tests_are_truth", "cost_latency_tradeoffs", "repo_scale_reasoning" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b3300b958cc3dae8f6001cfc9a14262e0e999862
train_10180
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
explain
expert
Task: explain Topic: Model merging, distillation, and continued pretraining Difficulty: expert Target language: Bash Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Bash", "developer_needs": [ "documentation", "cost_latency_tradeoffs", "security_gates", "tests_are_truth" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
cb1cf62ce85b77364d632fcef9fe9a3712c674a5
train_10181
2026-01-01T00:00:00
Latency, cost, and reliability optimization
eval
intermediate
Task: eval Topic: Latency, cost, and reliability optimization 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "Go", "developer_needs": [ "tests_are_truth", "security_gates", "cost_latency_tradeoffs", "evaluation_metrics" ], "moe_experts": [ "coding_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
07a69f61710db9e665370538306ea6ae62220b31
train_10182
2026-01-01T00:00:00
Secure code generation and policy gates
explain
expert
Task: explain Topic: Secure code generation and policy gates 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. 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": "Rust", "developer_needs": [ "tooling", "documentation", "tests_are_truth", "cost_latency_tradeoffs" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a36b1db22b40348b4c85b3ea7f972b4dba13278a
train_10183
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: 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. 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": "Java", "developer_needs": [ "repo_scale_reasoning", "tests_are_truth", "ci_integration", "tooling" ], "moe_experts": [ "governance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
288371d5c3d08b43ed100d40932501bf4ed194ff
train_10184
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: 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": "Bash", "developer_needs": [ "evaluation_metrics", "documentation", "auditability", "security_gates" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
570fe076ccbcf7fd3361adc52a612633bcf0745e
train_10185
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
agent_loop
expert
Task: agent_loop Topic: Reasoning-first coding models and tunable deliberation Difficulty: expert Target language: 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. Loop: Plan → Edit → Test → Reflect → Human gate
[]
{ "target_language": "Python", "developer_needs": [ "tests_are_truth", "cost_latency_tradeoffs", "ci_integration", "evaluation_metrics" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8e84758ee125f7a3fb40781060b064a698dfb545
train_10186
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
data_pipeline
expert
Task: data_pipeline 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "JavaScript", "developer_needs": [ "repo_scale_reasoning", "cost_latency_tradeoffs", "reproducibility", "tests_are_truth" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
446d52e40a60968652f70686e919dec41db0e427
train_10187
2026-01-01T00:00:00
Extended context and repo-scale understanding
failure_analysis
advanced
Task: failure_analysis Topic: Extended context and repo-scale understanding Difficulty: advanced Target language: Java Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "Java", "developer_needs": [ "evaluation_metrics", "documentation", "reproducibility", "auditability" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
072ceccf970e7af468a6fbafefa9a50665eceaf3
train_10188
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
patch_diff
expert
Task: patch_diff Topic: Model merging, distillation, and continued pretraining Difficulty: expert Target language: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "reproducibility", "governance", "auditability", "evaluation_metrics" ], "moe_experts": [ "governance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
0f4ab023b1787c56d7084af0ba96a43440aab2aa
train_10189
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
compare
intermediate
Task: compare Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: 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": [ "tooling", "security_gates", "ci_integration", "tests_are_truth" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f48cb39e0dba2e45b8216fce58250cf7a5437406
train_10190
2026-01-01T00:00:00
Extended context and repo-scale understanding
patch_diff
expert
Task: patch_diff Topic: Extended context and repo-scale understanding Difficulty: expert Target language: Java Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Patch (diff-style): ```diff - if x == 0: - return 1/x + if x == 0: + raise ValueError('division by zero') ``` Acceptance: - Tests pass - No new regressions
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Java", "developer_needs": [ "security_gates", "cost_latency_tradeoffs", "reproducibility", "repo_scale_reasoning" ], "moe_experts": [ "data_curation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e21472c8fccd7af485f89ae3913cf5e6c1ad7c92
train_10191
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: 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
[]
{ "target_language": "Python", "developer_needs": [ "documentation", "repo_scale_reasoning", "security_gates", "tests_are_truth" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
08021b03d2768e03e3f4a67b9abdac50997e87cd
train_10192
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
explain
intermediate
Task: explain Topic: Reasoning-first coding models and tunable deliberation 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Rust", "developer_needs": [ "evaluation_metrics", "cost_latency_tradeoffs", "tooling", "auditability" ], "moe_experts": [ "evaluation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
79a45351d5f72c84af239e6a19c0a09573be7a8a
train_10193
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
code
advanced
Task: code Topic: Reasoning-first coding models and tunable deliberation 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Bash", "developer_needs": [ "repo_scale_reasoning", "security_gates", "documentation", "ci_integration" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
4aa0627b25054b52750d651fccbce655cf591a5b
train_10194
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: 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "C#", "developer_needs": [ "cost_latency_tradeoffs", "governance", "evaluation_metrics", "reproducibility" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f59a8c63de6f708f206cccadfc810343cb1a09f0
train_10195
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
design
intermediate
Task: design Topic: Model merging, distillation, and continued pretraining 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Java", "developer_needs": [ "documentation", "tooling", "cost_latency_tradeoffs", "ci_integration" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8bf413822fc67489bab6b02b80f8a836b03c464f
train_10196
2026-01-01T00:00:00
Self-improving agents and feedback loops
code
advanced
Task: code Topic: Self-improving agents and feedback loops 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Java", "developer_needs": [ "ci_integration", "auditability", "repo_scale_reasoning", "documentation" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b8bce8e2faceefb423e542bc342e5813387d7fe7
train_10197
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: 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. 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": "C#", "developer_needs": [ "governance", "documentation", "tests_are_truth", "evaluation_metrics" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
17a00d77213653cf9648f10a4ed8d5d52784efc4
train_10198
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: 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", "ci_integration", "auditability", "documentation" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8468fb4c4e7f31afc0a1a53b9ee0be57c1676bd8
train_10199
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: 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": [ "governance", "repo_scale_reasoning", "documentation", "security_gates" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
141975cd86c423e4dbbd6b716acd335b2847cc9d