The Art of Debugging Open Book
1.7k
Stars
102
Forks
0
Open issues
3
Contributors
AI Analysis
An open-source educational book teaching practical debugging methodologies and tools across Unix, compiled programs, Python, and PyTorch environments. It serves software developers, DevOps engineers, and machine learning practitioners who need systematic approaches to troubleshooting complex software issues. Not intended for beginners with no programming experience, nor for those seeking general software engineering education beyond debugging.
Inferred from signals mentioned in the README (tests, CI, type safety) — not a review of the actual code.
AI's overall editorial judgment — not an average of the bars above, can weigh other factors too.
Educational guide on debugging methodologies and tools, maintained by ML engineer with 1.4k GitHub stars
The Art of Debugging is an evolving open-source book documenting debugging methodologies, tools, and recipes for both simple and complex software problems. Created and maintained by Stas Bekman (author of the ML Engineering book), it targets software developers across domains—particularly those working with compiled programs, Python, PyTorch, and machine learning systems. The project serves as a reference guide rather than executable tooling; adoption metrics are indirect (GitHub stars, issue engagement) rather than installation counts or production telemetry.
Launched October 2023 by Stas Bekman, a software engineer with 30+ years of development experience. The project emerged from repeated requests to formalize informal debugging practices he had developed. It exists within Bekman's portfolio of educational repositories, including the popular ML Engineering book (18k stars). The CC-BY-SA-4.0 license reflects its role as shareable, reusable knowledge rather than executable code.
The repository gained 1,409 stars over approximately 2.75 years (October 2023 to June 2026), averaging ~510 stars per year. Recent weekly velocity is 14 stars per week (~28 per month annualized). Growth appears steady but moderate, consistent with niche educational content. The project benefits from association with Bekman's higher-traffic ML Engineering repository and targeted promotion within debugging/ML practitioner communities. No evidence of viral adoption or major institutional backing.
Adoption not verified through standard metrics. No package manager downloads, library dependencies, or production deployment data available. Star count (1,409) and fork count (71) suggest visibility within developer communities, but these metrics cannot confirm whether the guide is actually read, applied, or influences debugging practices in production environments. Indirect adoption signals: inclusion in Bekman's portfolio suggests internal use within ML/AI teams he works with; no published case studies or testimonials found in README.
Based on README, this is a structured educational repository (not executable code) organized into sections: Methodology, Compiled Programs (gdb, ldd, nm, LD_LIBRARY_PATH, LD_PRELOAD), Python debugging (py-spy, paths), PyTorch debugging (memory, performance), Unix tools (bash, strace, make), and cross-references to external ML debugging content. Likely uses Markdown or similar format for content delivery, possibly with example scripts. README does not specify build process, dependencies, or code structure.
Not documented in README. This is an educational book project, not a software library with traditional test suites. Quality assurance likely relies on community issue reports and editorial review.
Last commit on 2026-06-30 06:57:26 (same day as analysis date), indicating active maintenance. Created 2023-10-01; nearly 3 years of sustained activity. Issue-based contribution workflow described. No evidence of abandonment; regular small commits and responsive issue handling appear consistent with an educational project maintained by one primary author with occasional community contributions.
ADOPT IF: you are a software developer seeking structured, experiential debugging methodologies applicable across Python, compiled, and ML contexts, and prefer learning from worked examples over formal documentation. AVOID IF: you need an interactive debugging tool, automated issue detection, or real-time debugging support—this is a reference guide, not a tool. MONITOR IF: you are evaluating whether to recommend this as organizational learning material; verify coverage for your specific tech stack before endorsing, as sections are described as WIP and unevenly complete.
Independent dimensions
Mainstream potential
3/10
Technical importance
6/10
Adoption evidence
2/10
- Work-in-progress status means some sections are incomplete or unstarted; users expecting comprehensive coverage may encounter gaps
- Single primary author (Bekman) creates maintenance dependency; long-term viability tied to one maintainer's continued engagement
- Adoption metrics are opaque; no way to verify whether the guide is actually used or influences real debugging outcomes
- Content may become outdated in rapidly evolving areas (e.g., PyTorch, GPU debugging, new tools); no stated versioning or deprecation policy
- Niche applicability limits mainstream relevance; highly specialized for ML/compiled program debugging rather than general web or mobile development
The project will likely remain a modest but stable reference resource within ML engineering and systems debugging communities. Growth will probably plateau in the 2–5k star range unless major institutional adoption (e.g., university curricula, corporate training) drives visibility. Content completeness and tooling updates (e.g., coverage of newer debuggers, LLM-assisted debugging) will determine whether it remains relevant beyond 2030.
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Languages
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Information
- Language
- Python
- License
- CC-BY-SA-4.0
- Last updated
- 1d ago
- Created
- 34mo ago
- Analyzed with
- anthropic/claude-haiku-4-5
Stars over time
No commit data available.
Contributors over time
Top 100 contributors only — repos with more will plateau at 100.
Open issues
No open issues — clean slate.
Open pull requests
No open pull requests.
Top contributors
Contributor data not available yet.
Recent releases
No releases published yet.
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