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Provides a complete, university-level computer science curriculum assembled from free online courses and books. Curates degree-aligned course sequences (Intro / Core / Advanced) with community support, project guidance, and checklists to track progress for self-directed learners.
Aggregates Summer 2026 internship postings across software engineering, data science/AI, quant, product and hardware; curated and updated daily by the Pitt Computer Science Club and Simplify. Lists 250+ active roles with categories, direct application links, and age-since-posting to help applicants find timely openings.
Benchmark dataset for evaluating long-horizon coding agents and software-engineering tasks, containing English code and tabular metadata in Parquet format; small scale (<1K examples) for fast prototyping and evaluation.
Provides agentic instruction‑tuning trajectories for software‑engineering tasks, formatted for supervised fine‑tuning and agent training. Contains multi‑file edits, tests, docs and structured agent traces (≈5,115 records, 1.9 GiB). Intended for commercial use; licensed CC‑BY 4.0 with additional permissive licenses.
Provides 2,170 reference-grounded evaluation samples across seven agent domains (MCP, Search, Terminal, SWE, Android, Web, OS) to score language world models on Format, Factuality, Consistency, Realism and Quality. Includes per-domain JSONL files, judge prompts and an evaluation script for reproducible scoring.
Provides 100,891 JSON-formatted agent conversation examples where each assistant turn includes a short <think> internal reasoning trace before tool/function calls. Human-facing text and tool calls are preserved; intended to fine-tune models to produce concise, cost-efficient chain-of-thought for tool use.
Acquires repository knowledge via a targeted QA loop before generating patches, decoupling knowledge acquisition from repair. A Questioner and Answerer produce evidence-grounded QA pairs that a Resolver uses to generate fixes; improves Pass@1 on SWE-bench Verified with modest overhead.
Prunes tool-output lines inside a coding LLM agent by turning the agent's own internal representations into per-line keep-or-prune labels. Implements a small classification head plus a length-aware embedding, saving up to 39% of tokens across benchmarks while preserving task quality.
Provides a curated benchmark of 170 real-world, multilingual code-refactoring instances to evaluate AI coding agents on large-scale, behavior-preserving, cross-file refactors. Each task includes rewritten issue descriptions and manually reviewed test suites to avoid over- and under-constraining evaluations.
Measures how individual harness components—planning, action space, and context management—affect coding agents' success, cost, and behavior. Uses a modular harness across 176 matched settings on SWE-Bench Verified and Terminal-Bench 2.1 to isolate component effects and surface model- and budget-dependent trade-offs.