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Windows Robocopy – efficiently copying files and directories
Read more: Windows Robocopy – efficiently copying files and directoriesThe Windows “Robust File Copy” utility for efficiently copying files and directories, with built-in retry, logging, and mirroring capabilities.
https://learn.microsoft.com/en-us/windows-server/administration/windows-commands/robocopy
https://www.pdq.com/blog/hitchhikers-guide-to-robocopy/
https://activedirectorypro.com/robocopy-examples/
The Windows “Robust File Copy” utility for efficiently copying files and directories, with built-in retry, logging, and mirroring capabilities.
By default, Robocopy skips copying existing files if the specific metadata of the files match.
/mir : Mirror the source directory tree to the destination (equivalent to /e + /purge):
/e : copies all subdirectories, including empty ones
/purge : deletes files/folders at the destination that no longer exist at the source
/sj : Copy NTFS junction points (“soft-links”) themselves rather than what they point to. This preserves the junction at the destination.
/sl : Copy symbolic links as links (don’t follow them and copy the target file/directory).
/mt:24 :Run the copy operation using 24 concurrent threads (range 1–128; default is 8), for higher throughput on multi-core systems. This switch cannot be combined with /ipg or /efsraw.
/J : unbuffered I/O (good for large files)
/R:<N> : to limit retries (default is 1 000 000)
/W:<S> : to set the wait time in seconds (default is 30)
/NJS : No Job Summary.
/NC : No Class – don’t log file classes.
/NFL : (No File List) Suppresses the per-file lines (e.g. “New File …”, “Copied …”). Benefit: Cuts down console I/O, often shaving 10–20 % off total runtime on large trees.
/NDL : (No Directory List) Suppresses the per-directory header lines. Benefit: Further reduces console chatter.
/NP : (No Progress) Suppresses the percentage-complete on each file. Drawback: You lose the “XX %” indicator
/NJH : (No Job Header) and /NJS (No Job Summary) Only affect the very first and very last block; negligible on runtime and you probably want at least the summary.
/NS (No Size) and /NC (No Class) Remove size and class columns from each line. If you’re suppressing file/directory lists anyway, these don’t help much.
/XD : ignore given types
/ETA : Show Estimated Time of Arrival of copied files.Note:
Robocopy’s support for NTFS reparse-points (symbolic links and junctions) is a bit quirky:- /SL only affects symbolic links (it tells RoboCopy to replicate the link itself rather than follow it).
- /SJ only affects junction points (ditto for directory-junction reparse points).
- If you use both /SL and /SJ, RoboCopy can still attempt to enumerate a link as a real folder—leading to that “ERROR 267: The directory name is invalid.”
Because of this, this errors can be ignored:
2025/05/15 12:09:11 ERROR 267 (0x0000010B) Copying Directory C:\redux-remember\ The directory name is invalid.Bat file example:
@echo off setlocal :: Define the source and destination directories set "src=%cd%" set "dst=C:\TEMP" echo Copying %src% to %dst% :: Use robocopy to copy the contents robocopy "%src%" "%dst%" /MIR /SJ /SL /MT:24 /J /R:0 /W:2 /NFL /NDL /ETA /NP /NC /XD "__pycache__" ".pnpm" pause endlocal -
Daniel Wee – Free Automate Render Output Setup for Blender
Read more: Daniel Wee – Free Automate Render Output Setup for Blenderhttps://danielicious615.gumroad.com/l/wtbtrx
Link all your passes and aovs into the output node automatically. It can works with EEVEE and CYCLES. The default output for Blender is really bad, but after use this script can really save your life and make your life easier!
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AI and the Law – The AI-Copyright Trap document by Carys Craig
Read more: AI and the Law – The AI-Copyright Trap document by Carys Craighttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=4905118
“There are many good reasons to be concerned about the rise of generative AI(…). Unfortunately, there are also many good reasons to be concerned about copyright’s growing prevalence in the policy discourse around AI’s regulation. Insisting that copyright protects an exclusive right to use materials for text and data mining practices (whether for informational analysis or machine learning to train generative AI models) is likely to do more harm than good. As many others have explained, imposing copyright constraints will certainly limit competition in the AI industry, creating cost-prohibitive barriers to quality data and ensuring that only the most powerful players have the means to build the best AI tools (provoking all of the usual monopoly concerns that accompany this kind of market reality but arguably on a greater scale than ever before). It will not, however, prevent the continued development and widespread use of generative AI.”
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“(…) As Michal Shur-Ofry has explained, the technical traits of generative AI already mean that its outputs will tend towards the dominant, likely reflecting ‘a relatively narrow, mainstream view, prioritizing the popular and conventional over diverse contents and narratives.’ Perhaps, then, if the political goal is to push for equality, participation, and representation in the AI age, critics’ demands should focus not on exclusivity but inclusivity. If we want to encourage the development of ethical and responsible AI, maybe we should be asking what kind of material and training data must be included in the inputs and outputs of AI to advance that goal. Certainly, relying on copyright and the market to dictate what is in and what is out is unlikely to advance a public interest or equality-oriented agenda.”
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“If copyright is not the solution, however, it might reasonably be asked: what is? The first step to answering that question—to producing a purposively sound prescription and evidence-based prognosis, is to correctly diagnose the problem. If, as I have argued, the problem is not that AI models are being trained on copyright works without their owners’ consent, then requiring copyright owners’ consent and/or compensation for the use of their work in AI-training datasets is not the appropriate solution. (…)If the only real copyright problem is that the outputs of generative AI may be substantially similar to specific human-authored and copyright-protected works, then copyright law as we know it already provides the solution.” -
Björn Ottosson – OKlch color space
Read more: Björn Ottosson – OKlch color spaceBjörn Ottosson proposed OKlch in 2020 to create a color space that can closely mimic how color is perceived by the human eye, predicting perceived lightness, chroma, and hue.
The OK in OKLCH stands for Optimal Color.
- L: Lightness (the perceived brightness of the color)
- C: Chroma (the intensity or saturation of the color)
- H: Hue (the actual color, such as red, blue, green, etc.)
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