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Processed 38 URLs from the user's list. 1 was already in the corpus (2072194202851549432). 9 were duplicates within the new batch (same post IDs at different anchor URLs); removed those. This commit contains the 27 unique new threads. Content spans the Jul 2026 era and is mostly Lottes's Linux development environment bringup work: - 1674757854471806977: 'Nothing Oriented Programming' (NOP) philosophy - 1990260050485797063: reversed projection matrices - 2030722033286328426: STP conspiracy theory - 2058166883044516181: 'crap-o-grammers' C++ bloat-ware critique - 2060191401883619479: relevant timestamp article - 2060730425929080874: CPU clock code (TSC) - 2061123768429211767: no-debugger-debug + mmap log pre-history - 2061416116694442141: Windows thread priority - 2061933917137932437: mmap'd page file - 2061937134756380682: Windows HIGHEST_PRIORITY_CLASS - 2062031187023982879: back running 640x480 on VGA CRTs - 2064858927829745887: on Linux now (SteamOS) - 2065804972378243476: nanorc done + iPhone font issue - 2065832746757341482: cross compilation up - 2065872197147578751: @axelgneiting bypassing libc - 2066184005632786736: Linux mlockall - 2070337342854832468: why not pselect/ppoll/epoll_pwait2 - 2070734717825986566: deterrant to ALSA = parsing through snd_pcm_open - 2071415096304193820: aplay to actually play a wave file - 2071706805114216559: 'ALSA hell month continues' - 2071937360288235902: Linux thread priority code - 2072135292115427728: Linux doesn't allow priority increase by default - 2072445315370590266: 'Wine workarounds - I could just detect' - 2072506103401754653: snd_pcm_sw_params for ALSA - 2072804183992922296: starting on WDM/KS audio for WIN32 - 2073096069529907441: re-trying WASAPI - 2073110092447203466: CPP_(obj) for mmdeviceapi Cleanup: removed 9 duplicate dirs (same posts at different anchor URLs), script temp files, and the '1857803914604618029' which was already a duplicate of '1857820858162753661' (mmap log canonical).
166 lines
6.3 KiB
JSON
166 lines
6.3 KiB
JSON
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"post_id": "2030722033286328426",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "I'll add one conspiracy theory for everyone listening: After STP's source was out there, DLSS4 'transformer' magically gained 'sharpness' but I don't believe it was from the ML model change at all, instead I think they just introduced the same error feedback mechanism in STP ...",
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"timestamp": "2026-03-08 19:07:16",
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"post_id": "2030722489538543674",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "Specifically STP samples feedback at the position of the input pixels, and uses that difference as an error term estimating the amount of blur introduced, one can subtract out some amount of that 'error' to sharpen ...",
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"timestamp": "2026-03-08 19:09:05",
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"post_id": "2030723314801357065",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "Of course that idea isn't strictly new either, fluid sims with advection use similar logic to sharpen. So I'm just another human building on the masters of the past. One can start to go beyond sharpening with this technique and get into local contrast adaption too ...",
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"timestamp": "2026-03-08 19:12:21",
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"post_id": "2030723826993213805",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "One of the marketing points of DLSS4 was 'sharpness' but it didn't actually resolve details to a higher frequency than prior, instead it just gets to a higher contrast, and then they con the consumer with that ...",
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"timestamp": "2026-03-08 19:14:24",
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"post_id": "2030725182671798689",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "Another trick one can employ, and something I used often as a pro photographer, was to deliberately thin or thicken features with sub-pixel precision during enlargement. Meaning you can take a 'feature' and thin it below source nyquist. And people go 'wow' it's detailed ...",
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"timestamp": "2026-03-08 19:19:47",
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"post_id": "2030726077312541100",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "So for instance one can take a input (lower-resolution) feature and simultaneously increase it's contrast while reducing it's thickness (when enlarged) in an energy preserving way. Can use signal bounds to estimate how much contrast and thus thin-ness one can push the feature ...",
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"timestamp": "2026-03-08 19:23:20",
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"post_id": "2030726373124317233",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "This gets easier with color signals because you can take the minimum bounds of the three channels, this is actually exactly what CAS does (just applied in a context without enlargement) ...",
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"timestamp": "2026-03-08 19:24:31",
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"post_id": "2030726690721198381",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "Anyway all this stuff is easy to artistically shape analytically in shader code, and never needed any kind of ML to be implemented (in fact ML would be slow, because it cannot do max/min style logic!!!).",
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"timestamp": "2026-03-08 19:25:46",
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"post_id": "2030734135338144038",
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"author": "Gindi4711",
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"handle": "gindi4711",
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"text": "@NOTimothyLottes Did they not just use more historical frames to get their samples and have a better model to compensate for the side effects?",
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"timestamp": "2026-03-08 19:55:21",
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"post_id": "2030737650882113897",
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"author": "NOTimothyLottes",
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"handle": "NOTimothyLottes",
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"text": "@gindi4711 Costs went up a lot so maybe. But cannot really add frames of context without getting costly. Secondary option is to store extra data in feedback sideband.",
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"timestamp": "2026-03-08 20:09:19",
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