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train_02200
2026-01-01T00:00:00
Secure code generation and policy gates
code
expert
Task: code Topic: Secure code generation and policy gates Difficulty: expert Target language: SQL Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "SQL", "developer_needs": [ "reproducibility", "cost_latency_tradeoffs", "ci_integration", "security_gates" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
83c35ba9bc239b65352df66df7168599f51c2f2a
train_02201
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: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Bash", "developer_needs": [ "auditability", "security_gates", "tooling", "tests_are_truth" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
03fb2111c0beefdf3aac5b3dd5e769c4280118cd
train_02202
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: 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Java", "developer_needs": [ "cost_latency_tradeoffs", "evaluation_metrics", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "coding_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b0cca1f995efcd6694e0a1ceb22c16e071e64e7f
train_02203
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: Bash Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Patch (diff-style): ```diff - if x == 0: - return 1/x + if x == 0: + raise ValueError('division by zero') ``` Acceptance: - Tests pass - No new regressions
[ "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", "tooling", "cost_latency_tradeoffs" ], "moe_experts": [ "data_curation_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
c7577e1e83a587bb9404c9bbef1183e66bb4079a
train_02204
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: TypeScript Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Loop: Plan → Edit → Test → Reflect → Human gate
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "security_gates", "reproducibility", "ci_integration", "evaluation_metrics" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
2cf3b640d28f27a3d0e1feff2db768e5d9efa6bb
train_02205
2026-01-01T00:00:00
Extended context and repo-scale understanding
design
advanced
Task: design Topic: Extended context and repo-scale understanding 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "C#", "developer_needs": [ "evaluation_metrics", "ci_integration", "governance", "tooling" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f9eb5cb84cab97ea1cd358f8c21339bcbf5fc5cd
train_02206
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
eval
expert
Task: eval Topic: SWE-bench style real-repo evaluation 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": [ "security_gates", "evaluation_metrics", "tooling", "reproducibility" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
313e38f25d8895bece5286c465f5589f6860b71c
train_02207
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
agent_loop
expert
Task: agent_loop Topic: Model merging, distillation, and continued pretraining Difficulty: expert Target language: 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. 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": [ "governance", "documentation", "tests_are_truth", "repo_scale_reasoning" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
112ff6ad8476e8e954ab8ffa67f24bf7c4a4b14b
train_02208
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
compare
expert
Task: compare Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: expert Target language: Bash Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Bash", "developer_needs": [ "tests_are_truth", "repo_scale_reasoning", "auditability", "security_gates" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
12188c90dff3e009d85b9745cb863729fb899342
train_02209
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: 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": [ "tests_are_truth", "reproducibility", "evaluation_metrics", "documentation" ], "moe_experts": [ "coding_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e648df8cdf5eb288e661db08a730d0084c84b7c4
train_02210
2026-01-01T00:00:00
Latency, cost, and reliability optimization
data_pipeline
intermediate
Task: data_pipeline Topic: Latency, cost, and reliability optimization Difficulty: intermediate Target language: Bash Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Bash", "developer_needs": [ "documentation", "security_gates", "governance", "ci_integration" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
4e1a10dabd06958be9ee99bf1d92fd5a1d8eaa9d
train_02211
2026-01-01T00:00:00
Latency, cost, and reliability optimization
code
advanced
Task: code Topic: Latency, cost, and reliability optimization Difficulty: advanced Target language: Python Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. 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", "ci_integration", "auditability", "documentation" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
99542cca70308c9466dc68c7c2a2165295f93cef
train_02212
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
compare
advanced
Task: compare Topic: Tool calling, sandboxes, and CI integration Difficulty: advanced Target language: 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Java", "developer_needs": [ "tests_are_truth", "repo_scale_reasoning", "auditability", "ci_integration" ], "moe_experts": [ "coding_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ebdf2da374b96f062706d6b58f048b0468ec5a95
train_02213
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: SQL Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Loop: Plan → Edit → Test → Reflect → Human gate
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "SQL", "developer_needs": [ "security_gates", "cost_latency_tradeoffs", "auditability", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a62bc769854c3df5fde7f0c9abed1e8df341508a
train_02214
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
design
intermediate
Task: design Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: 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": [ "governance", "ci_integration", "reproducibility", "cost_latency_tradeoffs" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
08d517b3b53f278623901f963f3c5ba7a65e17e8
train_02215
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
code
expert
Task: code Topic: Agentic coding systems (plan→edit→test→reflect) 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. 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": [ "repo_scale_reasoning", "governance", "reproducibility", "cost_latency_tradeoffs" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
4e57c4265c8c7844e06cd0577c05cde1e5518983
train_02216
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: 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. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "TypeScript", "developer_needs": [ "tooling", "tests_are_truth", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f22a10b48336a687494e825b606ccd7e768020d2
train_02217
2026-01-01T00:00:00
Self-improving agents and feedback loops
compare
expert
Task: compare Topic: Self-improving agents and feedback loops Difficulty: expert Target language: Bash Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Bash", "developer_needs": [ "documentation", "evaluation_metrics", "tooling", "cost_latency_tradeoffs" ], "moe_experts": [ "performance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
861372f45563d49d95a880e7cfcb750d7031792f
train_02218
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
data_pipeline
intermediate
Task: data_pipeline Topic: Tool calling, sandboxes, and CI integration Difficulty: intermediate Target language: Java Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Java", "developer_needs": [ "governance", "ci_integration", "security_gates", "tests_are_truth" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5136f4f035e7c487a4433fb55c0a8f969fa82ae1
train_02219
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: TypeScript Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "auditability", "evaluation_metrics", "documentation", "cost_latency_tradeoffs" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b0fa2aba56899e7b3fca9b08fb527e9fdd75594d
train_02220
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
compare
advanced
Task: compare Topic: Multimodal dev workflows (docs, diagrams, traces) 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. 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": [ "tests_are_truth", "reproducibility", "security_gates", "tooling" ], "moe_experts": [ "coding_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
098d23dd976c463521dd60ab14847b5dd8d38853
train_02221
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
patch_diff
intermediate
Task: patch_diff Topic: Governance, provenance, and licensing for code data Difficulty: intermediate Target language: Java Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Patch (diff-style): ```diff - if x == 0: - return 1/x + if x == 0: + raise ValueError('division by zero') ``` Acceptance: - Tests pass - No new regressions
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Java", "developer_needs": [ "cost_latency_tradeoffs", "tests_are_truth", "reproducibility", "repo_scale_reasoning" ], "moe_experts": [ "agentic_systems_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5dccc9285dbfc53981f4acaa9ba114ed056579f9
train_02222
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: 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
[]
{ "target_language": "Python", "developer_needs": [ "cost_latency_tradeoffs", "documentation", "evaluation_metrics", "reproducibility" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a0b4ec6a57829bb98cca743860c53b4e3152fc9b
train_02223
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: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Go", "developer_needs": [ "evaluation_metrics", "ci_integration", "security_gates", "repo_scale_reasoning" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bad4aac8e328ef35b756680fadd16858311e9e4e
train_02224
2026-01-01T00:00:00
Latency, cost, and reliability optimization
code
advanced
Task: code Topic: Latency, cost, and reliability optimization 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Bash", "developer_needs": [ "documentation", "tooling", "repo_scale_reasoning", "ci_integration" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
0497ae48ebc4d4f488c9d18ca74e135f3fc755fd
train_02225
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: Java Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Java", "developer_needs": [ "auditability", "ci_integration", "evaluation_metrics", "repo_scale_reasoning" ], "moe_experts": [ "agentic_systems_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ad1bc191f2ad801cff55ddc09db44e0e44c40a8d
train_02226
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
review
advanced
Task: review Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: 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. Review: correctness, security, performance, governance
[]
{ "target_language": "Go", "developer_needs": [ "reproducibility", "tooling", "security_gates", "documentation" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
042e7a645ab11133703c8d67a7b73f3ba38835e0
train_02227
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
explain
expert
Task: explain Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: expert Target language: 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": [ "governance", "repo_scale_reasoning", "auditability", "evaluation_metrics" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5f1a82a4a56f25b25ec1aad06ca17987995101a0
train_02228
2026-01-01T00:00:00
Latency, cost, and reliability optimization
compare
advanced
Task: compare Topic: Latency, cost, and reliability optimization 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. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Rust", "developer_needs": [ "auditability", "governance", "cost_latency_tradeoffs", "reproducibility" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
49215e6c7fb5a554bd1c072c802424182587d8d8
train_02229
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: 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": "TypeScript", "developer_needs": [ "ci_integration", "tests_are_truth", "governance", "auditability" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
34d726b2648eb29dea639058c967fbc4295d76a2
train_02230
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
design
intermediate
Task: design Topic: SWE-bench style real-repo evaluation Difficulty: intermediate Target language: JavaScript Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "JavaScript", "developer_needs": [ "tests_are_truth", "governance", "repo_scale_reasoning", "documentation" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
838847b678bb9a097cb4ad91188e85d54e4f730b
train_02231
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: Go Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "Go", "developer_needs": [ "ci_integration", "repo_scale_reasoning", "tooling", "governance" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
da277eafc9ae803f9ddc84e0a9646f198c2d4512
train_02232
2026-01-01T00:00:00
Mixture-of-Experts (MoE) for code
code
intermediate
Task: code Topic: Mixture-of-Experts (MoE) for code 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "JavaScript", "developer_needs": [ "documentation", "repo_scale_reasoning", "evaluation_metrics", "ci_integration" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
9dc5317574ed5cee21e721ebab6064b7c6a91036
train_02233
2026-01-01T00:00:00
Self-improving agents and feedback loops
eval
intermediate
Task: eval Topic: Self-improving agents and feedback loops 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. 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": [ "cost_latency_tradeoffs", "documentation", "tooling", "auditability" ], "moe_experts": [ "security_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fcb4f3d795176c651478b644755fb48e33d4d7d1
train_02234
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: Java Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Java", "developer_needs": [ "auditability", "tests_are_truth", "reproducibility", "cost_latency_tradeoffs" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a51c9dd11d5ef6f9ccafc46ff751169affb39dd2
train_02235
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: 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": [ "documentation", "auditability", "ci_integration", "security_gates" ], "moe_experts": [ "coding_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
31d75345c63c5eef815550af1af0de712b0c1ed4
train_02236
2026-01-01T00:00:00
Secure code generation and policy gates
eval
expert
Task: eval Topic: Secure code generation and policy gates Difficulty: expert Target language: 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "SQL", "developer_needs": [ "tooling", "governance", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
d36d17606321f4b601195ffe150df8001d382da0
train_02237
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: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Python", "developer_needs": [ "ci_integration", "security_gates", "reproducibility", "auditability" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
c9671b66ab56c51f7fceb59c4f93f9b09ce4bc4e
train_02238
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
eval
advanced
Task: eval Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: advanced Target language: 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "Bash", "developer_needs": [ "tooling", "cost_latency_tradeoffs", "evaluation_metrics", "governance" ], "moe_experts": [ "agentic_systems_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8e40e82949b17b905d58b01519bc31757b7c031c
train_02239
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: 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. Review: correctness, security, performance, governance
[]
{ "target_language": "Python", "developer_needs": [ "cost_latency_tradeoffs", "governance", "documentation", "auditability" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
8bf5e786a5c18d4b47b282b8960a4da1312c9929
train_02240
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: 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": [ "auditability", "tests_are_truth", "documentation", "ci_integration" ], "moe_experts": [ "coding_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
4465e0e68ab0c1d3e49b9509684f03e638890a38
train_02241
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
agent_loop
intermediate
Task: agent_loop Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: intermediate Target language: Python Context: 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": [ "repo_scale_reasoning", "evaluation_metrics", "cost_latency_tradeoffs", "tooling" ], "moe_experts": [ "evaluation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
339ecdbc2ca38fdc9c300b8ce8dbb8fe7feac0c9
train_02242
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: TypeScript Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Review: correctness, security, performance, governance
[]
{ "target_language": "TypeScript", "developer_needs": [ "tests_are_truth", "tooling", "ci_integration", "evaluation_metrics" ], "moe_experts": [ "performance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fd3353fd2a27f5b60ef8a6074b00007c906c6530
train_02243
2026-01-01T00:00:00
Latency, cost, and reliability optimization
failure_analysis
advanced
Task: failure_analysis Topic: Latency, cost, and reliability optimization 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
[]
{ "target_language": "Bash", "developer_needs": [ "tests_are_truth", "reproducibility", "auditability", "tooling" ], "moe_experts": [ "performance_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
00e6a6c0eede6038c05876621b8b0daecc710d5f
train_02244
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
data_pipeline
intermediate
Task: data_pipeline 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Python", "developer_needs": [ "governance", "security_gates", "tooling", "ci_integration" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
672c74641191544321e4cc08f0f39f4150a9ad03
train_02245
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
explain
intermediate
Task: explain Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: intermediate Target language: TypeScript Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "documentation", "security_gates", "ci_integration", "cost_latency_tradeoffs" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
14b0b5f3da3d0f388ef8efe7078c7209fc06d1ee
train_02246
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
eval
advanced
Task: eval Topic: Code-specialized model families and sizing tradeoffs Difficulty: advanced Target language: Bash Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Eval: pass@k, time-to-green, regressions, diff size
[ "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", "auditability", "security_gates" ], "moe_experts": [ "performance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e832c9f95ab85484a80cb3852f946debfee2f631
train_02247
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: 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": [ "repo_scale_reasoning", "reproducibility", "auditability", "cost_latency_tradeoffs" ], "moe_experts": [ "data_curation_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
66b1ecb6492c7da4cc1d7362ebcdf8f69b0c1062
train_02248
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: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "TypeScript", "developer_needs": [ "cost_latency_tradeoffs", "tests_are_truth", "documentation", "ci_integration" ], "moe_experts": [ "agentic_systems_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
0a2b0e5deb1645b93165858a30f0a426cc4bef21
train_02249
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
explain
expert
Task: explain Topic: Reasoning-first coding models and tunable deliberation 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": [ "auditability", "cost_latency_tradeoffs", "ci_integration", "governance" ], "moe_experts": [ "performance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
3166df9a678c95bfbe651947d3e9ec74865d1516
train_02250
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
design
advanced
Task: design Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: advanced Target language: C# Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "C#", "developer_needs": [ "reproducibility", "tooling", "security_gates", "auditability" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1cfd3a085c6747710841efe0b4d62809c8f260b6
train_02251
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: 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. 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": "Go", "developer_needs": [ "tooling", "evaluation_metrics", "security_gates", "governance" ], "moe_experts": [ "evaluation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e1b3716a1357a90fbb88f24a811bb16ecd76d07c
train_02252
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
code
expert
Task: code Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: expert Target language: Bash Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. 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": [ "auditability", "cost_latency_tradeoffs", "documentation", "evaluation_metrics" ], "moe_experts": [ "agentic_systems_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
16f33fb57db0d4f2809d7d3a9b214c13856d405a
train_02253
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
failure_analysis
advanced
Task: failure_analysis Topic: Governance, provenance, and licensing for code data Difficulty: advanced Target language: C# Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "C#", "developer_needs": [ "ci_integration", "tests_are_truth", "documentation", "security_gates" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bf947b5b23140f80e80522f861a5af8ca15755e3
train_02254
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: Python Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Patch (diff-style): ```diff - if x == 0: - return 1/x + if x == 0: + raise ValueError('division by zero') ``` Acceptance: - Tests pass - No new regressions
[]
{ "target_language": "Python", "developer_needs": [ "auditability", "security_gates", "tooling", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
75b1b9d4ce67d3e2ac4d5d19c0fd5f4a8e8545ba
train_02255
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
data_pipeline
advanced
Task: data_pipeline Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Go", "developer_needs": [ "security_gates", "governance", "evaluation_metrics", "tests_are_truth" ], "moe_experts": [ "agentic_systems_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e690abdfb8e8e7506207eb3f6abdbc8018fb00d0
train_02256
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
failure_analysis
advanced
Task: failure_analysis Topic: Reasoning-first coding models and tunable deliberation Difficulty: advanced Target language: Python Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "Python", "developer_needs": [ "documentation", "evaluation_metrics", "auditability", "governance" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
e8842b35f24ef34e4728b46eff664c8fa4994e95
train_02257
2026-01-01T00:00:00
Latency, cost, and reliability optimization
review
intermediate
Task: review Topic: Latency, cost, and reliability optimization Difficulty: intermediate Target language: C# Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Review: correctness, security, performance, governance
[]
{ "target_language": "C#", "developer_needs": [ "ci_integration", "tests_are_truth", "documentation", "evaluation_metrics" ], "moe_experts": [ "agentic_systems_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a551c75926c5c03ab7f269ac45b4d6bca59c9f46
train_02258
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
failure_analysis
advanced
Task: failure_analysis Topic: Model merging, distillation, and continued pretraining Difficulty: advanced Target language: Rust Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "Rust", "developer_needs": [ "reproducibility", "auditability", "security_gates", "repo_scale_reasoning" ], "moe_experts": [ "evaluation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a0d49a1a8a2372251fa41ebd856ea8ff7c276fce
train_02259
2026-01-01T00:00:00
Extended context and repo-scale understanding
explain
intermediate
Task: explain Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: C# Context: Regulated environment requiring audit trails. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. 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": [ "governance", "reproducibility", "documentation", "tooling" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
54942d9c9bc0ecbbba042d28bc61b74dae0ac974
train_02260
2026-01-01T00:00:00
Self-improving agents and feedback loops
design
advanced
Task: design Topic: Self-improving agents and feedback loops Difficulty: advanced Target language: SQL Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "SQL", "developer_needs": [ "auditability", "cost_latency_tradeoffs", "ci_integration", "tooling" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
5f8cb4bca748ef1ef5e1102f6ae90f41bb48b764
train_02261
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
patch_diff
expert
Task: patch_diff Topic: Agentic coding systems (plan→edit→test→reflect) 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
[]
{ "target_language": "TypeScript", "developer_needs": [ "documentation", "repo_scale_reasoning", "tooling", "governance" ], "moe_experts": [ "coding_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
c20f74b7c85e7d7e5817cb789e4ea896a920bf53
train_02262
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
eval
advanced
Task: eval Topic: SWE-bench style real-repo evaluation Difficulty: advanced Target language: Python Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Eval: pass@k, time-to-green, regressions, diff size
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Python", "developer_needs": [ "evaluation_metrics", "tooling", "security_gates", "auditability" ], "moe_experts": [ "governance_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
41fe188746bd8ea14673f36c3abd48f8eaf1bd10
train_02263
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: Python Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Python", "developer_needs": [ "auditability", "reproducibility", "evaluation_metrics", "ci_integration" ], "moe_experts": [ "coding_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bfd5446ad75b418567355c28e5ba5cbf52dc7006
train_02264
2026-01-01T00:00:00
Extended context and repo-scale understanding
compare
expert
Task: compare Topic: Extended context and repo-scale understanding Difficulty: expert Target language: JavaScript Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[]
{ "target_language": "JavaScript", "developer_needs": [ "ci_integration", "tooling", "tests_are_truth", "repo_scale_reasoning" ], "moe_experts": [ "data_curation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1087713866939887d947f06be605f1b69be33bbb
train_02265
2026-01-01T00:00:00
Self-improving agents and feedback loops
agent_loop
intermediate
Task: agent_loop Topic: Self-improving agents and feedback loops Difficulty: intermediate Target language: Python Context: 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": "Python", "developer_needs": [ "governance", "tooling", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "performance_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a25badcf5883972223371731e3aca4e0b2fef08b
train_02266
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: 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": [ "evaluation_metrics", "auditability", "reproducibility", "security_gates" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6668298056bb64c5197d13fdaa1af40d92419ea6
train_02267
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
code
expert
Task: code Topic: Multimodal dev workflows (docs, diagrams, traces) 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "JavaScript", "developer_needs": [ "governance", "tests_are_truth", "ci_integration", "tooling" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
4522bed8aff81b3f3cd80f2222f96ea09cf6a510
train_02268
2026-01-01T00:00:00
Model merging, distillation, and continued pretraining
data_pipeline
advanced
Task: data_pipeline Topic: Model merging, distillation, and continued pretraining Difficulty: advanced Target language: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "C#", "developer_needs": [ "ci_integration", "security_gates", "cost_latency_tradeoffs", "reproducibility" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
38312eeb1577296c5e31d3d04fbcce71f4568edc
train_02269
2026-01-01T00:00:00
Code-specialized model families and sizing tradeoffs
failure_analysis
expert
Task: failure_analysis Topic: Code-specialized model families and sizing tradeoffs Difficulty: expert Target language: 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Go", "developer_needs": [ "tests_are_truth", "security_gates", "auditability", "tooling" ], "moe_experts": [ "evaluation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
67af70d06b292b3d211f158d1825a8dafe137bf8
train_02270
2026-01-01T00:00:00
SWE-bench style real-repo evaluation
patch_diff
expert
Task: patch_diff Topic: SWE-bench style real-repo evaluation 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. 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": [ "auditability", "security_gates", "documentation", "governance" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
077a24815cf57ac380c4646a7762fb10a6318869
train_02271
2026-01-01T00:00:00
Self-improving agents and feedback loops
review
expert
Task: review Topic: Self-improving agents and feedback loops Difficulty: expert Target language: Bash Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Review: correctness, security, performance, governance
[]
{ "target_language": "Bash", "developer_needs": [ "security_gates", "documentation", "reproducibility", "tests_are_truth" ], "moe_experts": [ "security_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
68a694b82aab70114fefe3fd2bbf1cebed6ea09a
train_02272
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
data_pipeline
advanced
Task: data_pipeline Topic: Reasoning-first coding models and tunable deliberation 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Go", "developer_needs": [ "security_gates", "cost_latency_tradeoffs", "tooling", "evaluation_metrics" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
82f24ee5a4d8b78d5d5323cede4c68d897caa674
train_02273
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
compare
expert
Task: compare Topic: Governance, provenance, and licensing for code data Difficulty: expert Target language: SQL Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[]
{ "target_language": "SQL", "developer_needs": [ "governance", "evaluation_metrics", "tooling", "auditability" ], "moe_experts": [ "coding_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
01c6a988f94d03b97450b4059c38940ecd5872a3
train_02274
2026-01-01T00:00:00
Secure code generation and policy gates
compare
expert
Task: compare Topic: Secure code generation and policy gates Difficulty: expert Target language: Bash Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Compare: capability, cost, latency, reliability
[]
{ "target_language": "Bash", "developer_needs": [ "reproducibility", "tests_are_truth", "documentation", "repo_scale_reasoning" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
ba35ea621a2d76c72cbd5936dd46daac1f469b2a
train_02275
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
failure_analysis
intermediate
Task: failure_analysis Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: intermediate Target language: Go Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "Go", "developer_needs": [ "cost_latency_tradeoffs", "evaluation_metrics", "auditability", "documentation" ], "moe_experts": [ "governance_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
4abd6d6a3ded36956166150ae96e74834474d502
train_02276
2026-01-01T00:00:00
Self-improving agents and feedback loops
agent_loop
intermediate
Task: agent_loop Topic: Self-improving agents and feedback loops Difficulty: intermediate Target language: 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. Loop: Plan → Edit → Test → Reflect → Human gate
[]
{ "target_language": "Rust", "developer_needs": [ "reproducibility", "tests_are_truth", "auditability", "governance" ], "moe_experts": [ "coding_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
85d38e0f51a7dbfc07e874f4553e03dc65e8bb44
train_02277
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
design
intermediate
Task: design Topic: Reasoning-first coding models and tunable deliberation 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
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Java", "developer_needs": [ "ci_integration", "documentation", "tooling", "cost_latency_tradeoffs" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6873812fa69b1cbc6e603c16830743fd15ab21cf
train_02278
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: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "SQL", "developer_needs": [ "documentation", "repo_scale_reasoning", "tooling", "governance" ], "moe_experts": [ "evaluation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
bf9af4b22c77dfde85d3ef57664083a4b72d66a4
train_02279
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: 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. Patch (diff-style): ```diff - if x == 0: - return 1/x + if x == 0: + raise ValueError('division by zero') ``` Acceptance: - Tests pass - No new regressions
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Rust", "developer_needs": [ "security_gates", "tooling", "cost_latency_tradeoffs", "evaluation_metrics" ], "moe_experts": [ "governance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
1558f4e2afa32fd0ef5c767a02ea7f20a5115159
train_02280
2026-01-01T00:00:00
Agentic coding systems (plan→edit→test→reflect)
eval
advanced
Task: eval Topic: Agentic coding systems (plan→edit→test→reflect) Difficulty: advanced Target language: 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "SQL", "developer_needs": [ "repo_scale_reasoning", "ci_integration", "cost_latency_tradeoffs", "tooling" ], "moe_experts": [ "data_curation_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
309e738d632b52c97b7016396b4f6288aaeea831
train_02281
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: 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. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "SQL", "developer_needs": [ "repo_scale_reasoning", "security_gates", "auditability", "evaluation_metrics" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fac310d7d385e1c4832e3d89be4f36d8e89194c2
train_02282
2026-01-01T00:00:00
Latency, cost, and reliability optimization
failure_analysis
intermediate
Task: failure_analysis Topic: Latency, cost, and reliability optimization Difficulty: intermediate Target language: Go Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Failure: - Initial patch broke edge case Reflection: - Missing zero-input guard Correction: - Add explicit validation + test
[]
{ "target_language": "Go", "developer_needs": [ "governance", "reproducibility", "repo_scale_reasoning", "ci_integration" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
c8556c2808dbed540f43f545ba3751cc772d18c1
train_02283
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
design
expert
Task: design Topic: Multimodal dev workflows (docs, diagrams, traces) 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
[]
{ "target_language": "Bash", "developer_needs": [ "security_gates", "evaluation_metrics", "cost_latency_tradeoffs", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f9600a66c85af994b364f010ae2809a3fce3e8a9
train_02284
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: 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. 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": [ "tests_are_truth", "reproducibility", "tooling", "security_gates" ], "moe_experts": [ "evaluation_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
407b9fb72debba701439306af977e0718726e2b2
train_02285
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: 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": [ "governance", "auditability", "cost_latency_tradeoffs", "ci_integration" ], "moe_experts": [ "governance_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
39f85ead6ff16bb3772f87d3c3d66c2a3f7b5fe9
train_02286
2026-01-01T00:00:00
Reasoning-first coding models and tunable deliberation
patch_diff
expert
Task: patch_diff Topic: Reasoning-first coding models and tunable deliberation Difficulty: expert Target language: Java Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. 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": [ "evaluation_metrics", "tooling", "cost_latency_tradeoffs", "repo_scale_reasoning" ], "moe_experts": [ "security_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
47919346305490f4fa9f26359ded7aa4ab98e462
train_02287
2026-01-01T00:00:00
Latency, cost, and reliability optimization
eval
advanced
Task: eval Topic: Latency, cost, and reliability optimization 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. Eval: pass@k, time-to-green, regressions, diff size
[]
{ "target_language": "Rust", "developer_needs": [ "repo_scale_reasoning", "tests_are_truth", "auditability", "security_gates" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
71e29496cdba2867c2fe9ca0481ce2b9b06306b1
train_02288
2026-01-01T00:00:00
Multimodal dev workflows (docs, diagrams, traces)
eval
advanced
Task: eval Topic: Multimodal dev workflows (docs, diagrams, traces) Difficulty: advanced Target language: C# Context: 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": [ "auditability", "reproducibility", "governance", "cost_latency_tradeoffs" ], "moe_experts": [ "data_curation_expert", "security_expert" ], "governance": { "audit_required": true, "tests_required": true } }
13ba0ed0d6ba2b53cb33b1e54b295dc0218e9d36
train_02289
2026-01-01T00:00:00
Governance, provenance, and licensing for code data
agent_loop
expert
Task: agent_loop Topic: Governance, provenance, and licensing for code data Difficulty: expert Target language: Python Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Loop: Plan → Edit → Test → Reflect → Human gate
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "Python", "developer_needs": [ "cost_latency_tradeoffs", "repo_scale_reasoning", "reproducibility", "auditability" ], "moe_experts": [ "performance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
fab301c6d58bcc2446b890448abce64ea2dcb3ef
train_02290
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
agent_loop
expert
Task: agent_loop Topic: Dataset curation pipelines (filter, dedupe, quality) 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. Loop: Plan → Edit → Test → Reflect → Human gate
[ "Identify failing behavior via tests", "Minimize change surface", "Apply patch", "Run targeted + full suite", "Verify no regressions" ]
{ "target_language": "C#", "developer_needs": [ "governance", "tests_are_truth", "documentation", "auditability" ], "moe_experts": [ "security_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
10e6bc6d048e41a011fa69c2b8d167c614e62b46
train_02291
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: SQL Context: Offline/local deployment with limited compute. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "SQL", "developer_needs": [ "ci_integration", "tests_are_truth", "evaluation_metrics", "governance" ], "moe_experts": [ "governance_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
f8b6d9bcd59d8a010c8c3dad4b82d01c491fd39e
train_02292
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: 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": [ "tests_are_truth", "evaluation_metrics", "ci_integration", "security_gates" ], "moe_experts": [ "coding_expert", "data_curation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
19dc817ce69bb058b5d473a7e162d04d1eba1c0c
train_02293
2026-01-01T00:00:00
Dataset curation pipelines (filter, dedupe, quality)
data_pipeline
advanced
Task: data_pipeline Topic: Dataset curation pipelines (filter, dedupe, quality) Difficulty: advanced Target language: 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. Pipeline: Ingest → Normalize → Filter → Dedupe → Quality → Mix → Audit
[]
{ "target_language": "Go", "developer_needs": [ "tests_are_truth", "evaluation_metrics", "repo_scale_reasoning", "ci_integration" ], "moe_experts": [ "security_expert", "governance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
6f239373ce1f5c7ece1bbe8535d0c408954f9265
train_02294
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: SQL Context: Research team validating claims against real repos. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "SQL", "developer_needs": [ "tests_are_truth", "tooling", "auditability", "security_gates" ], "moe_experts": [ "governance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
36748c22f2c8c34fe1affd7d9b5a7e23d0982dac
train_02295
2026-01-01T00:00:00
Extended context and repo-scale understanding
explain
intermediate
Task: explain Topic: Extended context and repo-scale understanding Difficulty: intermediate Target language: JavaScript Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Design with risks, metrics, acceptance criteria
[]
{ "target_language": "JavaScript", "developer_needs": [ "reproducibility", "governance", "tests_are_truth", "evaluation_metrics" ], "moe_experts": [ "data_curation_expert", "agentic_systems_expert" ], "governance": { "audit_required": true, "tests_required": true } }
888344fc78028f4d6d979f6b6ae16ef690ab8c84
train_02296
2026-01-01T00:00:00
Tool calling, sandboxes, and CI integration
patch_diff
intermediate
Task: patch_diff Topic: Tool calling, sandboxes, and CI integration Difficulty: intermediate Target language: 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. Patch (diff-style): ```diff - if x == 0: - return 1/x + if x == 0: + raise ValueError('division by zero') ``` Acceptance: - Tests pass - No new regressions
[]
{ "target_language": "Rust", "developer_needs": [ "evaluation_metrics", "tests_are_truth", "repo_scale_reasoning", "tooling" ], "moe_experts": [ "evaluation_expert", "performance_expert" ], "governance": { "audit_required": true, "tests_required": true } }
133ee636bb85b1697cca80272c3a367a66fb0d3e
train_02297
2026-01-01T00:00:00
Extended context and repo-scale understanding
review
expert
Task: review Topic: Extended context and repo-scale understanding Difficulty: expert Target language: C# Context: High-traffic service with latency SLOs. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Review: correctness, security, performance, governance
[]
{ "target_language": "C#", "developer_needs": [ "cost_latency_tradeoffs", "tests_are_truth", "governance", "repo_scale_reasoning" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
9056d08fd25b89b3e17fd6183d1cd2a2fee20003
train_02298
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: 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": [ "tests_are_truth", "repo_scale_reasoning", "security_gates", "cost_latency_tradeoffs" ], "moe_experts": [ "governance_expert", "evaluation_expert" ], "governance": { "audit_required": true, "tests_required": true } }
b060a80ff3b9f114b7ed02daec80233d33517eb0
train_02299
2026-01-01T00:00:00
Latency, cost, and reliability optimization
review
intermediate
Task: review Topic: Latency, cost, and reliability optimization Difficulty: intermediate Target language: C# Context: Large monorepo with flaky tests and strict CI. Produce expert-level, production-ready artifacts.
Facts: - Modern AI coding prioritizes correctness, evaluation, and governance. - Agentic loops with test gates outperform single-pass generation. Review: correctness, security, performance, governance
[]
{ "target_language": "C#", "developer_needs": [ "repo_scale_reasoning", "governance", "reproducibility", "auditability" ], "moe_experts": [ "data_curation_expert", "coding_expert" ], "governance": { "audit_required": true, "tests_required": true } }
a551c75926c5c03ab7f269ac45b4d6bca59c9f46