[EuroPython 2015] Thomas Ballinger - Terminal Whispering
Bibliographic record
Abstract
Thomas Ballinger - Terminal Whispering [EuroPython 2015] [23 July 2015] [Bilbao, Euskadi, Spain] The terminal emulators we run so many of our programming tools in are more powerful than we remember to give them credit for, and the key to that power is understanding the interface. This talk will cover terminal colors and styles, writing to arbitrary portions of the screen, handling signals from the terminal, determining the terminal's dimensions and scrollback buffer behavior. Terminal programming can get hairy; along the way we'll deal with encoding issues, consider cross platform concerns, acknowledge 4 decades' worth of standards for terminal communication, and consider that humans at interactive terminals may not be the only users of our interfaces. By gaining an understanding of these issues, we'll be able choose from the abstractions over them offered by Python libraries Urwid, Blessings, and Python Prompt Toolkit. This talk requires minimal Python knowledge, but does assume familiarity with command line tools in a unix environment. An abbreviated version of this talk was presented at PyCon 2015 in Montréal: https://www.youtube.com/watch?v=WAitSilLDUA With the additional time I'd hope to present more code examples, a more in- depth tour of existing libraries and more practical advice about writing programs that use the terminal, and an additional example of a difficult terminal details: dealing with reflowing of text in modern terminal emulators like GNOME Terminal and iTerm.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.660 | 0.048 |
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; both teacher heads agree on what is shown here.
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".