GOD.Queen.47m
Collection
The works of the small but self mutating "God Queen" recursive seed ai implementation from "WithIn Us Ai". at core ... • 13 items • Updated
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Task: data_pipeline
Topic: SWE-bench style real-repo evaluation
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 t... |
Task: agent_loop
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Go
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops w... |
Task: agent_loop
Topic: Self-improving agents and feedback loops
Difficulty: expert
Target language: Bash
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test ga... |
Task: failure_analysis
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- ... |
Task: eval
Topic: Model merging, distillation, and continued pretraining
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 te... |
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: JavaScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test g... |
Task: patch_diff
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: Java
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gat... |
Task: data_pipeline
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.
... |
Task: failure_analysis
Topic: Code-specialized model families and sizing tradeoffs
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.
- Agen... |
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 ... |
Task: design
Topic: Agentic coding systems (plan→edit→test→reflect)
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 ou... |
Task: design
Topic: Secure code generation and policy gates
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 ... |
Task: failure_analysis
Topic: SWE-bench style real-repo evaluation
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 ... |
Task: review
Topic: Tool calling, sandboxes, and CI integration
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 te... |
Task: design
Topic: Reasoning-first coding models and tunable deliberation
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 wi... |
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
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... |
Task: explain
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... |
Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: TypeScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops wi... |
Task: explain
Topic: Reasoning-first coding models and tunable deliberation
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 ... |
Task: review
Topic: Dataset curation pipelines (filter, dedupe, quality)
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 t... |
Task: review
Topic: Secure code generation and policy gates
Difficulty: intermediate
Target language: Rust
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates ou... |
Task: patch_diff
Topic: Multimodal dev workflows (docs, diagrams, traces)
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... |
Task: agent_loop
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 ... |
Task: agent_loop
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.
- Age... |
Task: compare
Topic: SWE-bench style real-repo evaluation
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 ou... |
Task: data_pipeline
Topic: Governance, provenance, and licensing for code data
Difficulty: advanced
Target language: Java
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loo... |
Task: failure_analysis
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: intermediate
Target language: JavaScript
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.... |
Task: patch_diff
Topic: Code-specialized model families and sizing tradeoffs
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 loop... |
Task: failure_analysis
Topic: Secure code generation and policy gates
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 ... |
Task: agent_loop
Topic: Mixture-of-Experts (MoE) for code
Difficulty: advanced
Target language: Go
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates out... |
Task: review
Topic: Dataset curation pipelines (filter, dedupe, quality)
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 loop... |
Task: review
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
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 loop... |
Task: data_pipeline
Topic: Secure code generation and policy gates
Difficulty: advanced
Target language: C#
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates o... |
Task: failure_analysis
Topic: Latency, cost, and reliability optimization
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 t... |
Task: eval
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 g... |
Task: patch_diff
Topic: Tool calling, sandboxes, and CI integration
Difficulty: intermediate
Target language: TypeScript
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loo... |
Task: agent_loop
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: JavaScript
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic ... |
Task: design
Topic: SWE-bench style real-repo evaluation
Difficulty: intermediate
Target language: Rust
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates outpe... |
Task: data_pipeline
Topic: Multimodal dev workflows (docs, diagrams, traces)
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 ... |
Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: JavaScript
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agen... |
Task: patch_diff
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: Rust
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic... |
Task: eval
Topic: Code-specialized model families and sizing tradeoffs
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... |
Task: failure_analysis
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: Rust
Context: Research team validating claims against real repos.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- A... |
Task: patch_diff
Topic: SWE-bench style real-repo evaluation
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 g... |
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
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 ga... |
Task: compare
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: Java
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops w... |
Task: explain
Topic: Reasoning-first coding models and tunable deliberation
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 w... |
Task: code
Topic: Governance, provenance, and licensing for code data
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 ... |
Task: patch_diff
Topic: Latency, cost, and reliability optimization
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 ... |
Task: data_pipeline
Topic: Governance, provenance, and licensing for code data
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 l... |
Task: eval
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 g... |
Task: compare
Topic: Model merging, distillation, and continued pretraining
Difficulty: expert
Target language: JavaScript
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic l... |
Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
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... |
Task: code
Topic: Secure code generation and policy gates
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 outpe... |
Task: agent_loop
Topic: Dataset curation pipelines (filter, dedupe, quality)
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... |
Task: data_pipeline
Topic: Extended context and repo-scale understanding
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... |
Task: patch_diff
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agen... |
Task: eval
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: expert
Target language: TypeScript
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops ... |
Task: failure_analysis
Topic: Governance, provenance, and licensing for code data
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 ... |
Task: failure_analysis
Topic: Tool calling, sandboxes, and CI integration
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 loop... |
Task: eval
Topic: Code-specialized model families and sizing tradeoffs
Difficulty: advanced
Target language: Go
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with t... |
Task: failure_analysis
Topic: Secure code generation and policy gates
Difficulty: expert
Target language: JavaScript
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with tes... |
Task: agent_loop
Topic: Multimodal dev workflows (docs, diagrams, traces)
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.
- Agen... |
Task: explain
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: expert
Target language: Python
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test g... |
Task: compare
Topic: Agentic coding systems (plan→edit→test→reflect)
Difficulty: expert
Target language: Java
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates... |
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
Difficulty: intermediate
Target language: Python
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops wit... |
Task: explain
Topic: Extended context and repo-scale understanding
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 ... |
Task: code
Topic: Code-specialized model families and sizing tradeoffs
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 w... |
Task: agent_loop
Topic: Tool calling, sandboxes, and CI integration
Difficulty: advanced
Target language: Go
Context: High-traffic service with latency SLOs.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates ... |
Task: design
Topic: Dataset curation pipelines (filter, dedupe, quality)
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 w... |
Task: explain
Topic: Secure code generation and policy gates
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 g... |
Task: compare
Topic: Secure code generation and policy gates
Difficulty: intermediate
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 ga... |
Task: code
Topic: Latency, cost, and reliability optimization
Difficulty: intermediate
Target language: Python
Context: Large monorepo with flaky tests and strict CI.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with te... |
Task: explain
Topic: Latency, cost, and reliability optimization
Difficulty: intermediate
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 tes... |
Task: data_pipeline
Topic: Model merging, distillation, and continued pretraining
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 ... |
Task: explain
Topic: Extended context and repo-scale understanding
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 gat... |
Task: review
Topic: Model merging, distillation, and continued pretraining
Difficulty: intermediate
Target language: JavaScript
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agent... |
Task: data_pipeline
Topic: Mixture-of-Experts (MoE) for code
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 o... |
Task: review
Topic: Extended context and repo-scale understanding
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 tes... |
Task: compare
Topic: Reasoning-first coding models and tunable deliberation
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 wi... |
Task: eval
Topic: Tool calling, sandboxes, and CI integration
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... |
Task: compare
Topic: Code-specialized model families and sizing tradeoffs
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 t... |
Task: code
Topic: Model merging, distillation, and continued pretraining
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 w... |
Task: data_pipeline
Topic: Extended context and repo-scale understanding
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... |
Task: data_pipeline
Topic: Extended context and repo-scale understanding
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... |
Task: data_pipeline
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.
- ... |
Task: eval
Topic: Self-improving agents and feedback loops
Difficulty: expert
Target language: Rust
Context: Offline/local deployment with limited compute.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops with test gates ou... |
Task: compare
Topic: Multimodal dev workflows (docs, diagrams, traces)
Difficulty: intermediate
Target language: Rust
Context: Regulated environment requiring audit trails.
Produce expert-level, production-ready artifacts.
Facts:
- Modern AI coding prioritizes correctness, evaluation, and governance.
- Agentic loops w... |
Task: failure_analysis
Topic: Reasoning-first coding models and tunable deliberation
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.
- Agent... |
Task: patch_diff
Topic: Multimodal dev workflows (docs, diagrams, traces)
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 w... |
Task: design
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... |
Task: eval
Topic: Model merging, distillation, and continued pretraining
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 w... |
Task: explain
Topic: Governance, provenance, and licensing for code data
Difficulty: intermediate
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 ... |
Task: agent_loop
Topic: SWE-bench style real-repo evaluation
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 ... |
Task: agent_loop
Topic: Model merging, distillation, and continued pretraining
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... |
Task: review
Topic: Mixture-of-Experts (MoE) for code
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 outpe... |
Task: explain
Topic: Dataset curation pipelines (filter, dedupe, quality)
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 wi... |
Task: agent_loop
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 gat... |
Task: code
Topic: SWE-bench style real-repo evaluation
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 gat... |
Task: eval
Topic: Reasoning-first coding models and tunable deliberation
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 wit... |
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