#calibration — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #calibration, aggregated by home.social.
-
Calibrite Colorimeter Is the First That Hardware Calibrates Apple Displays https://petapixel.com/2026/05/26/calibrite-colorimeter-is-the-first-that-hardware-calibrates-apple-displays/ #applestudiodisplay #Post-Processing #postprocessing #photoediting #calibration #Technology #calibrite #display #apple #News
-
The superglue-on-alu held fine.
Calibrating against a laser level. Its easier than a transparent hose with water. Glued down the bubble on one end, shaved away some plastic so the other can move a little, aligned roughly, add a drop of glue, align, let glue dry and re-check.
The horizontal bubble is correct now and nothing wobbles or moves anymore.
Just needs repeating for the other two.
What fun can be had by buying crap tools! 🤪
-
#CEOs starting to find #AI productivity gains ‘underwhelming,’ -
https://kensbookinfo.blogspot.com/p/etc.html#Canada5 Common #Wi-Fi Mistakes Far Too Many People Make -
https://kensbookinfo.blogspot.com/p/infotech.html#8513 Best Monitor #Calibration Tools for 2026 -
https://kensbookinfo.blogspot.com/p/infotech.html#55MP warns of #Israeli “takeover” of island through land -
https://kensbookinfo.blogspot.com/p/uk.html#bbcwales#World's Fastest Date -
https://kensbookinfo.blogspot.com/p/etc.html#JordanView all news from Capitals in the US https://kensbookinfo.blogspot.com/2026/03/latest-news-from-capitals-in-united.html
-
#CEOs starting to find #AI productivity gains ‘underwhelming,’ -
https://kensbookinfo.blogspot.com/p/etc.html#Canada5 Common #Wi-Fi Mistakes Far Too Many People Make -
https://kensbookinfo.blogspot.com/p/infotech.html#8513 Best Monitor #Calibration Tools for 2026 -
https://kensbookinfo.blogspot.com/p/infotech.html#55MP warns of #Israeli “takeover” of island through land -
https://kensbookinfo.blogspot.com/p/uk.html#bbcwales#World's Fastest Date -
https://kensbookinfo.blogspot.com/p/etc.html#JordanView all news from Capitals in the US https://kensbookinfo.blogspot.com/2026/03/latest-news-from-capitals-in-united.html
-
#CEOs starting to find #AI productivity gains ‘underwhelming,’ -
https://kensbookinfo.blogspot.com/p/etc.html#Canada5 Common #Wi-Fi Mistakes Far Too Many People Make -
https://kensbookinfo.blogspot.com/p/infotech.html#8513 Best Monitor #Calibration Tools for 2026 -
https://kensbookinfo.blogspot.com/p/infotech.html#55MP warns of #Israeli “takeover” of island through land -
https://kensbookinfo.blogspot.com/p/uk.html#bbcwales#World's Fastest Date -
https://kensbookinfo.blogspot.com/p/etc.html#JordanView all news from Capitals in the US https://kensbookinfo.blogspot.com/2026/03/latest-news-from-capitals-in-united.html
-
#CEOs starting to find #AI productivity gains ‘underwhelming,’ -
https://kensbookinfo.blogspot.com/p/etc.html#Canada5 Common #Wi-Fi Mistakes Far Too Many People Make -
https://kensbookinfo.blogspot.com/p/infotech.html#8513 Best Monitor #Calibration Tools for 2026 -
https://kensbookinfo.blogspot.com/p/infotech.html#55MP warns of #Israeli “takeover” of island through land -
https://kensbookinfo.blogspot.com/p/uk.html#bbcwales#World's Fastest Date -
https://kensbookinfo.blogspot.com/p/etc.html#JordanView all news from Capitals in the US https://kensbookinfo.blogspot.com/2026/03/latest-news-from-capitals-in-united.html
-
#CEOs starting to find #AI productivity gains ‘underwhelming,’ -
https://kensbookinfo.blogspot.com/p/etc.html#Canada5 Common #Wi-Fi Mistakes Far Too Many People Make -
https://kensbookinfo.blogspot.com/p/infotech.html#8513 Best Monitor #Calibration Tools for 2026 -
https://kensbookinfo.blogspot.com/p/infotech.html#55MP warns of #Israeli “takeover” of island through land -
https://kensbookinfo.blogspot.com/p/uk.html#bbcwales#World's Fastest Date -
https://kensbookinfo.blogspot.com/p/etc.html#JordanView all news from Capitals in the US https://kensbookinfo.blogspot.com/2026/03/latest-news-from-capitals-in-united.html
-
jwst 2.0.1
https://atlas.whatip.xyz/post.php?slug=jwst-201
<p>Library for calibration of science observations from the James Webb Space Telescope</p>
#observations #calibration #library #science -
jwst 2.0.1
https://atlas.whatip.xyz/post.php?slug=jwst-201
<p>Library for calibration of science observations from the James Webb Space Telescope</p>
#observations #calibration #library #science -
jwst 2.0.1
https://atlas.whatip.xyz/post.php?slug=jwst-201
<p>Library for calibration of science observations from the James Webb Space Telescope</p>
#observations #calibration #library #science -
jwst 2.0.1
https://atlas.whatip.xyz/post.php?slug=jwst-201
<p>Library for calibration of science observations from the James Webb Space Telescope</p>
#observations #calibration #library #science -
jwst 2.0.1
https://atlas.whatip.xyz/post.php?slug=jwst-201
<p>Library for calibration of science observations from the James Webb Space Telescope</p>
#observations #calibration #library #science -
How do we make LLM output more trustworthy? A short survey note on three lines of recent work covering five papers: conformal-prediction coverage guarantees, behavioral calibration of the model's prose, and sample-disagreement detection. All three pay the same multi-sample inference tax; the choice is about what you want back.
https://benjaminhan.net/posts/20260505-llm-uncertainty-survey/?utm_source=mastodon&utm_medium=social
-
How do we make LLM output more trustworthy? A short survey note on three lines of recent work covering five papers: conformal-prediction coverage guarantees, behavioral calibration of the model's prose, and sample-disagreement detection. All three pay the same multi-sample inference tax; the choice is about what you want back.
https://benjaminhan.net/posts/20260505-llm-uncertainty-survey/?utm_source=mastodon&utm_medium=social
-
How do we make LLM output more trustworthy? A short survey note on three lines of recent work covering five papers: conformal-prediction coverage guarantees, behavioral calibration of the model's prose, and sample-disagreement detection. All three pay the same multi-sample inference tax; the choice is about what you want back.
https://benjaminhan.net/posts/20260505-llm-uncertainty-survey/?utm_source=mastodon&utm_medium=social
-
How do we make LLM output more trustworthy? A short survey note on three lines of recent work covering five papers: conformal-prediction coverage guarantees, behavioral calibration of the model's prose, and sample-disagreement detection. All three pay the same multi-sample inference tax; the choice is about what you want back.
https://benjaminhan.net/posts/20260505-llm-uncertainty-survey/?utm_source=mastodon&utm_medium=social
-
How do we make LLM output more trustworthy? A short survey note on three lines of recent work covering five papers: conformal-prediction coverage guarantees, behavioral calibration of the model's prose, and sample-disagreement detection. All three pay the same multi-sample inference tax; the choice is about what you want back.
https://benjaminhan.net/posts/20260505-llm-uncertainty-survey/?utm_source=mastodon&utm_medium=social
-
Semantic Entropy (Nature 2024) detects LLM confabulations by clustering sampled answers by meaning and computing entropy over the cluster distribution. "Paris" and "It's Paris" cluster together, so paraphrase noise doesn't inflate the signal. Cost: it only catches hallucinations that vary across samples. If the model is consistently wrong, all samples cluster and the detector says "confident".
https://benjaminhan.net/posts/20260505-semantic-entropy/?utm_source=mastodon&utm_medium=social
-
Semantic Entropy (Nature 2024) detects LLM confabulations by clustering sampled answers by meaning and computing entropy over the cluster distribution. "Paris" and "It's Paris" cluster together, so paraphrase noise doesn't inflate the signal. Cost: it only catches hallucinations that vary across samples. If the model is consistently wrong, all samples cluster and the detector says "confident".
https://benjaminhan.net/posts/20260505-semantic-entropy/?utm_source=mastodon&utm_medium=social
-
Semantic Entropy (Nature 2024) detects LLM confabulations by clustering sampled answers by meaning and computing entropy over the cluster distribution. "Paris" and "It's Paris" cluster together, so paraphrase noise doesn't inflate the signal. Cost: it only catches hallucinations that vary across samples. If the model is consistently wrong, all samples cluster and the detector says "confident".
https://benjaminhan.net/posts/20260505-semantic-entropy/?utm_source=mastodon&utm_medium=social
-
Semantic Entropy (Nature 2024) detects LLM confabulations by clustering sampled answers by meaning and computing entropy over the cluster distribution. "Paris" and "It's Paris" cluster together, so paraphrase noise doesn't inflate the signal. Cost: it only catches hallucinations that vary across samples. If the model is consistently wrong, all samples cluster and the detector says "confident".
https://benjaminhan.net/posts/20260505-semantic-entropy/?utm_source=mastodon&utm_medium=social
-
Semantic Entropy (Nature 2024) detects LLM confabulations by clustering sampled answers by meaning and computing entropy over the cluster distribution. "Paris" and "It's Paris" cluster together, so paraphrase noise doesn't inflate the signal. Cost: it only catches hallucinations that vary across samples. If the model is consistently wrong, all samples cluster and the detector says "confident".
https://benjaminhan.net/posts/20260505-semantic-entropy/?utm_source=mastodon&utm_medium=social
-
Linguistic Calibration trains Llama 2 to emit confidence phrases that let a downstream reader make calibrated forecasts on related questions. The key move is defining calibration through reader utility instead of self-reported probability. Hedged text that doesn't help the reader makes no forecasting progress, so generic hedging can't game the objective.
https://benjaminhan.net/posts/20260505-linguistic-calibration/?utm_source=mastodon&utm_medium=social
-
Linguistic Calibration trains Llama 2 to emit confidence phrases that let a downstream reader make calibrated forecasts on related questions. The key move is defining calibration through reader utility instead of self-reported probability. Hedged text that doesn't help the reader makes no forecasting progress, so generic hedging can't game the objective.
https://benjaminhan.net/posts/20260505-linguistic-calibration/?utm_source=mastodon&utm_medium=social
-
Linguistic Calibration trains Llama 2 to emit confidence phrases that let a downstream reader make calibrated forecasts on related questions. The key move is defining calibration through reader utility instead of self-reported probability. Hedged text that doesn't help the reader makes no forecasting progress, so generic hedging can't game the objective.
https://benjaminhan.net/posts/20260505-linguistic-calibration/?utm_source=mastodon&utm_medium=social
-
Linguistic Calibration trains Llama 2 to emit confidence phrases that let a downstream reader make calibrated forecasts on related questions. The key move is defining calibration through reader utility instead of self-reported probability. Hedged text that doesn't help the reader makes no forecasting progress, so generic hedging can't game the objective.
https://benjaminhan.net/posts/20260505-linguistic-calibration/?utm_source=mastodon&utm_medium=social
-
Linguistic Calibration trains Llama 2 to emit confidence phrases that let a downstream reader make calibrated forecasts on related questions. The key move is defining calibration through reader utility instead of self-reported probability. Hedged text that doesn't help the reader makes no forecasting progress, so generic hedging can't game the objective.
https://benjaminhan.net/posts/20260505-linguistic-calibration/?utm_source=mastodon&utm_medium=social
-
Conformal Factuality casts LM correctness as uncertainty quantification. Decompose the answer into sub-claims, score each, drop the low-confidence ones until the retained set is ~1-α factual. The sub-claim decomposition is doing most of the work, and the conformal machinery rides on top. Atomic-claim splitters have known failure modes, and the guarantee inherits them.
https://benjaminhan.net/posts/20260505-conformal-factuality/?utm_source=mastodon&utm_medium=social
#ConformalPrediction #Calibration #Hallucination #LLMs #ICML #AI
-
Conformal Factuality casts LM correctness as uncertainty quantification. Decompose the answer into sub-claims, score each, drop the low-confidence ones until the retained set is ~1-α factual. The sub-claim decomposition is doing most of the work, and the conformal machinery rides on top. Atomic-claim splitters have known failure modes, and the guarantee inherits them.
https://benjaminhan.net/posts/20260505-conformal-factuality/?utm_source=mastodon&utm_medium=social
#ConformalPrediction #Calibration #Hallucination #LLMs #ICML #AI
-
Conformal Factuality casts LM correctness as uncertainty quantification. Decompose the answer into sub-claims, score each, drop the low-confidence ones until the retained set is ~1-α factual. The sub-claim decomposition is doing most of the work, and the conformal machinery rides on top. Atomic-claim splitters have known failure modes, and the guarantee inherits them.
https://benjaminhan.net/posts/20260505-conformal-factuality/?utm_source=mastodon&utm_medium=social
#ConformalPrediction #Calibration #Hallucination #LLMs #ICML #AI
-
Conformal Factuality casts LM correctness as uncertainty quantification. Decompose the answer into sub-claims, score each, drop the low-confidence ones until the retained set is ~1-α factual. The sub-claim decomposition is doing most of the work, and the conformal machinery rides on top. Atomic-claim splitters have known failure modes, and the guarantee inherits them.
https://benjaminhan.net/posts/20260505-conformal-factuality/?utm_source=mastodon&utm_medium=social
#ConformalPrediction #Calibration #Hallucination #LLMs #ICML #AI
-
Conformal Factuality casts LM correctness as uncertainty quantification. Decompose the answer into sub-claims, score each, drop the low-confidence ones until the retained set is ~1-α factual. The sub-claim decomposition is doing most of the work, and the conformal machinery rides on top. Atomic-claim splitters have known failure modes, and the guarantee inherits them.
https://benjaminhan.net/posts/20260505-conformal-factuality/?utm_source=mastodon&utm_medium=social
#ConformalPrediction #Calibration #Hallucination #LLMs #ICML #AI
-
A primer on conformal prediction: the recipe for distribution-free coverage guarantees that doesn't require your model to be calibrated. Rank-based non-conformity scores plus a calibration quantile give you valid prediction sets. Easy inputs get one-class sets; hard ones get many alternatives. Set size is where the uncertainty shows up.
#ConformalPrediction #Calibration #UncertaintyQuantification #AI
-
A primer on conformal prediction: the recipe for distribution-free coverage guarantees that doesn't require your model to be calibrated. Rank-based non-conformity scores plus a calibration quantile give you valid prediction sets. Easy inputs get one-class sets; hard ones get many alternatives. Set size is where the uncertainty shows up.
#ConformalPrediction #Calibration #UncertaintyQuantification #AI
-
A primer on conformal prediction: the recipe for distribution-free coverage guarantees that doesn't require your model to be calibrated. Rank-based non-conformity scores plus a calibration quantile give you valid prediction sets. Easy inputs get one-class sets; hard ones get many alternatives. Set size is where the uncertainty shows up.
#ConformalPrediction #Calibration #UncertaintyQuantification #AI
-
A primer on conformal prediction: the recipe for distribution-free coverage guarantees that doesn't require your model to be calibrated. Rank-based non-conformity scores plus a calibration quantile give you valid prediction sets. Easy inputs get one-class sets; hard ones get many alternatives. Set size is where the uncertainty shows up.
#ConformalPrediction #Calibration #UncertaintyQuantification #AI
-
A primer on conformal prediction: the recipe for distribution-free coverage guarantees that doesn't require your model to be calibrated. Rank-based non-conformity scores plus a calibration quantile give you valid prediction sets. Easy inputs get one-class sets; hard ones get many alternatives. Set size is where the uncertainty shows up.
#ConformalPrediction #Calibration #UncertaintyQuantification #AI
-
Scale buys calibration in exactly the format you tested in. Lettered MC works at 52B; swap one option for "none of the above" and it collapses; True/False rephrasing restores it. RLHF policies need a temperature fix. "Do I know this?" heads miscalibrate out of distribution. Three years on, SelfReflect lands the same conclusion: only sampling your own answers and summarizing gets an LLM to describe its own distribution.
https://benjaminhan.net/posts/20260504-kadavath-mostly-know/?utm_source=mastodon&utm_medium=social
-
Scale buys calibration in exactly the format you tested in. Lettered MC works at 52B; swap one option for "none of the above" and it collapses; True/False rephrasing restores it. RLHF policies need a temperature fix. "Do I know this?" heads miscalibrate out of distribution. Three years on, SelfReflect lands the same conclusion: only sampling your own answers and summarizing gets an LLM to describe its own distribution.
https://benjaminhan.net/posts/20260504-kadavath-mostly-know/?utm_source=mastodon&utm_medium=social
-
Scale buys calibration in exactly the format you tested in. Lettered MC works at 52B; swap one option for "none of the above" and it collapses; True/False rephrasing restores it. RLHF policies need a temperature fix. "Do I know this?" heads miscalibrate out of distribution. Three years on, SelfReflect lands the same conclusion: only sampling your own answers and summarizing gets an LLM to describe its own distribution.
https://benjaminhan.net/posts/20260504-kadavath-mostly-know/?utm_source=mastodon&utm_medium=social
-
Scale buys calibration in exactly the format you tested in. Lettered MC works at 52B; swap one option for "none of the above" and it collapses; True/False rephrasing restores it. RLHF policies need a temperature fix. "Do I know this?" heads miscalibrate out of distribution. Three years on, SelfReflect lands the same conclusion: only sampling your own answers and summarizing gets an LLM to describe its own distribution.
https://benjaminhan.net/posts/20260504-kadavath-mostly-know/?utm_source=mastodon&utm_medium=social
-
Scale buys calibration in exactly the format you tested in. Lettered MC works at 52B; swap one option for "none of the above" and it collapses; True/False rephrasing restores it. RLHF policies need a temperature fix. "Do I know this?" heads miscalibrate out of distribution. Three years on, SelfReflect lands the same conclusion: only sampling your own answers and summarizing gets an LLM to describe its own distribution.
https://benjaminhan.net/posts/20260504-kadavath-mostly-know/?utm_source=mastodon&utm_medium=social
-
TestTone by Jun Murakami 🎛️
Sine waves + pink noise gen, freq/lvl ctrl, gear setup/calibration tool, MIT open-source💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://jun-murakami.web.app/apps/testtoneMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #utilityplugin #opensource #calibration #sinewave #pinknoise #audiotools
-
TestTone by Jun Murakami 🎛️
Sine waves + pink noise gen, freq/lvl ctrl, gear setup/calibration tool, MIT open-source💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://jun-murakami.web.app/apps/testtoneMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #utilityplugin #opensource #calibration #sinewave #pinknoise #audiotools
-
TestTone by Jun Murakami 🎛️
Sine waves + pink noise gen, freq/lvl ctrl, gear setup/calibration tool, MIT open-source💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://jun-murakami.web.app/apps/testtoneMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #utilityplugin #opensource #calibration #sinewave #pinknoise #audiotools
-
TestTone by Jun Murakami 🎛️
Sine waves + pink noise gen, freq/lvl ctrl, gear setup/calibration tool, MIT open-source💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://jun-murakami.web.app/apps/testtoneMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #utilityplugin #opensource #calibration #sinewave #pinknoise #audiotools
-
TestTone by Jun Murakami 🎛️
Sine waves + pink noise gen, freq/lvl ctrl, gear setup/calibration tool, MIT open-source💻 macOS/Win/Linux (VST3/AU/AAX/LV2/CLAP)
🎁 FREE https://jun-murakami.web.app/apps/testtoneMore freeware 👉 https://linktr.ee/legalvst
#freeplugin #utilityplugin #opensource #calibration #sinewave #pinknoise #audiotools
-
After testing 7 #LilyGO #TBeamSupreme units, one thing is clear:
👉 Magnetometer calibration is not transferable between devices.
Even in similar environments, raw readings varied dramatically between units. Proper calibration isn’t optional—it’s per-device.
I also looked at how #Meshtastic handles the onboard #QMC6310… and it essentially doesn’t (no heading, no calibration).
https://salemdata.net/johnpress/?p=661
#Magnetometer #EmbeddedSystems #LoRa #Sensors #Electronics #Calibration #IoT
-
After testing 7 #LilyGO #TBeamSupreme units, one thing is clear:
👉 Magnetometer calibration is not transferable between devices.
Even in similar environments, raw readings varied dramatically between units. Proper calibration isn’t optional—it’s per-device.
I also looked at how #Meshtastic handles the onboard #QMC6310… and it essentially doesn’t (no heading, no calibration).
https://salemdata.net/johnpress/?p=661
#Magnetometer #EmbeddedSystems #LoRa #Sensors #Electronics #Calibration #IoT
-
After testing 7 #LilyGO #TBeamSupreme units, one thing is clear:
👉 Magnetometer calibration is not transferable between devices.
Even in similar environments, raw readings varied dramatically between units. Proper calibration isn’t optional—it’s per-device.
I also looked at how #Meshtastic handles the onboard #QMC6310… and it essentially doesn’t (no heading, no calibration).
https://salemdata.net/johnpress/?p=661
#Magnetometer #EmbeddedSystems #LoRa #Sensors #Electronics #Calibration #IoT