So you wanna be a Linus Torvalds? The Do's and Don't of running a small open source project
Bibliographic record
Abstract
In this paper, I present a series of lessons learned on how to effectively run a small Open Source experience. These lessons are based on my own experience as the leader of VoiceCode [2] [6], a project that aims at developing an integrated programming-by-voice toolbox. By this we mean tools that allow programmers with Repetitive Strain Injury (RSI) to write computer code by talking to their machine instead of typing. The VoiceCode project started in 1999 by the National Research Council of Canada, and was first officially released in 2003. The system is now at a point where it can be used by programmers to do real work, and there have been over 7200 downloads so far. The project has also attracted the attention of the media [4]. In the process of leading this project, I have learned many important lessons; too many to discuss exhaustively here. I will however share three that seem particularly important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".