#binary — Public Fediverse posts

Шифрование на уровне протокола

Как организовать шифрование на уровне протокола? На самом деле тема непростая и пожалуй (имхо) это как раз та самая тема, где прийти к компромиссу почти никогда не получается. Разве что просто не передавать чувствительные данные вовсе. Я расскажу как шифрование можно организовать на уровне протокола brec и ни в коем случае не буду затрагивать те самые принципиальные решения, влияющие на безопасность (как передавать, куда передавать, отправлять ли, и хранить ли чувствительные данные вовсе). Иными словами нас интересует инструментальная сторона вопроса.

https://habr.com/ru/articles/1040298/

#rust #protocols #parsing #binary #communication #no_database #storage #binary_storage #filtering #crypt

@drmorrisj Good Luck on finishing touches to Phd - a ton of work; i am thinking about actively trying to sell some edr - crowdstrike, emerging threats/proofpoint, recorded future, i have too many portals to make and stalled out deciding drupal or wp, maybe both and a toplist or two. I am going to try and go after some things like the yacy consulting and where licenses allow i will sell product plus consulting - one step at a time #generic templates #portal IT sales and consulting #structured data #ssl proxy #suricata #arkime #sigs #binary defense #best practices

The Box You Cannot Check

The clinic intake form on the clipboard at the front desk has two boxes next to the word “sex.” A patient who is neither of the two options has been given three choices: pick one box and lie, write something in the margin, or refuse the form. The receptionist will not read the margin. Data entry clerks will not transcribe it. EHR systems will not store anything outside the two values the form lists. The patient walks out of the clinic with a treatment plan based on a box that does not correspond to their body, their history, or their current endocrine state. The form has done its job, which is not the job it claimed to do.

The form claims to collect information. Its actual function is categorization. Information collection would mean recording what the patient told the clinic. Categorization means sorting the patient into one of the boxes the system already had, regardless of whether the patient fits. These are different operations. The form does the second while presenting itself as doing the first.

I focus on the sex/gender field because it is currently the most visible example of the categorization failure, but the pattern is general. Race fields on most forms still offer five or six options plus an “other” line that researchers routinely discard in aggregate analysis because “other” cannot be merged with the named categories without distorting the comparison the categories were built to support. Ethnicity fields on US forms famously split Hispanic into its own question while leaving Middle Eastern and North African respondents to choose between “White,” “Asian,” and “Other,” none of which describes them. The Census Bureau plans to add a MENA category in 2030, decades after the gap was identified. Respondents could check multiple race boxes for the first time in 2000, which was a real improvement on prior forms. The same census kept the sex question as a binary male/female, the way every US Census has since 1790, on the grounds that adding a third option would compromise the time series.

The time series argument is worth examining because it surfaces what is actually happening. A research instrument that has measured a binary for 230 years has produced 230 years of data that reads the population as binary. Adding a third or fourth or fifth option in 2030 would mean that comparisons between 2020 and 2030 require methodological accommodation. That accommodation is doable, well-documented in survey methodology, and routine when other categories shift. Choosing not to make the accommodation keeps the data legible to historians of a category system that is no longer the category system in use. The form preserves the past at the cost of misrepresenting the present.

What happens to the data after the form is the harder problem. A survey of 10,000 people that includes 9,200 binary-box-checkers, 600 “other” or write-in responses, and 200 refusals will, in most aggregate reports, appear as a clean 9,200-person dataset. The 600 “other” responses get coded as missing, recoded into the binary categories by an analyst making a judgment call, or dropped entirely under a methodology footnote that says “respondents who declined to specify were excluded from analysis.” Another 200 refusals disappear under one of those clauses. A final published table reads as if 9,200 people answered the question cleanly, when in fact 10,000 people interacted with the question and 800 of them produced data the analyst could not use.

The aggregate therefore summarizes only the inputs that fit the categories already chosen. This is the gap between what the form does and what the form claims to do. The form does classification work while presenting itself as a question. Its classification system was built before the form was printed, and respondents who do not fit that classification are removed from the dataset the form generates. The dataset reads as comprehensive because the cleaning happened before anyone with access to the aggregate could see what was removed.

The mathematical consequence of this should bother statisticians more than it currently does. A dataset that excludes 8 percent of respondents on the grounds that their responses were illegible has an 8 percent selection bias that propagates into every downstream analysis. Confidence intervals computed on the 9,200 do not account for the 800. P-values look strong because the variance in the included data is smaller than the variance in the actual respondent pool. Models trained on the cleaned data fit the cleaned data well and fail in production when they encounter the kind of respondent the cleaning removed. Every machine learning system that classifies people on the basis of survey-derived training data carries this bias forward in ways the system’s documentation almost never describes.

The political consequence is what I think interests the new readers who arrived after the elevator essay. A form that excludes a category of people from the dataset also excludes that category from the policy decisions the dataset informs. A health system that does not record nonbinary patients in a way its analytics engine can read does not know how many nonbinary patients it serves, does not allocate resources to nonbinary patient care, does not train staff to address nonbinary patient needs, and does not appear in funding requests for nonbinary patient programs because the funding agency requires headcount data the EHR cannot produce. The form is upstream of the spreadsheet, the spreadsheet upstream of the budget, the budget upstream of the clinic. By the time the missing patients show up at the front desk, the building has been designed for the patients the form was capable of recording.

A clinic that fixes its form does not solve the problem because the EHR vendor downstream still has a two-value field. Fixing the EHR fails because the state public health reporting system still requires data in the older format. Fixing the state system fails because the federal CDC reporting standard underneath still uses the binary. The categorization is layered. Each layer has a defensible local reason for the binary it inherited. The cumulative effect is a healthcare system that cannot count its actual patient population, and a healthcare system that cannot count cannot fund, and a healthcare system that cannot fund cannot serve. The form on the clipboard at the front desk is the bottom button of a fifteen-story panel where every button on every floor is wired to the same controller, and the controller only stops the elevator on floors the original engineer drew on the original blueprint.

The fix has the same structure as the placebo button fix. Recognize which boxes work and which do not. Refuse to mistake compliance for collection. Push for upstream rewiring of forms before adding more “other” lines downstream. Demand that aggregate reports publish the count of excluded responses in the same table as the included ones, with the same prominence, in the same font. Refuse to treat a survey that loses 8 percent of its respondents as a survey of the population it sampled. Insist on the difference between a question that asks and a question that classifies, and refuse to fill out the second one as if it were the first.

The form is not neutral. It encodes what its designers were willing to recognize, and it discards what its designers were not willing to recognize, and the discard happens silently in the data pipeline rather than visibly at the front desk. A patient who writes a third answer in the margin is doing the work the form refused to do. An aggregate report that publishes 9,200 clean responses hides 800 acts of refusal by people who would not lie to the clipboard. Counting is the claim. Selection is the politics. That politics rides downstream into every room the dataset enters, every dollar the budget allocates, every protocol the staff is trained on, and every body the building was built to serve.

The Box You Cannot Check

The clinic intake form on the clipboard at the front desk has two boxes next to the word “sex.” A patient who is neither of the two options has been given three choices: pick one box and lie, write something in the margin, or refuse the form. The receptionist will not read the margin. Data entry clerks will not transcribe it. EHR systems will not store anything outside the two values the form lists. The patient walks out of the clinic with a treatment plan based on a box that does not correspond to their body, their history, or their current endocrine state. The form has done its job, which is not the job it claimed to do.

The form claims to collect information. Its actual function is categorization. Information collection would mean recording what the patient told the clinic. Categorization means sorting the patient into one of the boxes the system already had, regardless of whether the patient fits. These are different operations. The form does the second while presenting itself as doing the first.

I focus on the sex/gender field because it is currently the most visible example of the categorization failure, but the pattern is general. Race fields on most forms still offer five or six options plus an “other” line that researchers routinely discard in aggregate analysis because “other” cannot be merged with the named categories without distorting the comparison the categories were built to support. Ethnicity fields on US forms famously split Hispanic into its own question while leaving Middle Eastern and North African respondents to choose between “White,” “Asian,” and “Other,” none of which describes them. The Census Bureau plans to add a MENA category in 2030, decades after the gap was identified. Respondents could check multiple race boxes for the first time in 2000, which was a real improvement on prior forms. The same census kept the sex question as a binary male/female, the way every US Census has since 1790, on the grounds that adding a third option would compromise the time series.

The time series argument is worth examining because it surfaces what is actually happening. A research instrument that has measured a binary for 230 years has produced 230 years of data that reads the population as binary. Adding a third or fourth or fifth option in 2030 would mean that comparisons between 2020 and 2030 require methodological accommodation. That accommodation is doable, well-documented in survey methodology, and routine when other categories shift. Choosing not to make the accommodation keeps the data legible to historians of a category system that is no longer the category system in use. The form preserves the past at the cost of misrepresenting the present.

What happens to the data after the form is the harder problem. A survey of 10,000 people that includes 9,200 binary-box-checkers, 600 “other” or write-in responses, and 200 refusals will, in most aggregate reports, appear as a clean 9,200-person dataset. The 600 “other” responses get coded as missing, recoded into the binary categories by an analyst making a judgment call, or dropped entirely under a methodology footnote that says “respondents who declined to specify were excluded from analysis.” Another 200 refusals disappear under one of those clauses. A final published table reads as if 9,200 people answered the question cleanly, when in fact 10,000 people interacted with the question and 800 of them produced data the analyst could not use.

The aggregate therefore summarizes only the inputs that fit the categories already chosen. This is the gap between what the form does and what the form claims to do. The form does classification work while presenting itself as a question. Its classification system was built before the form was printed, and respondents who do not fit that classification are removed from the dataset the form generates. The dataset reads as comprehensive because the cleaning happened before anyone with access to the aggregate could see what was removed.

The mathematical consequence of this should bother statisticians more than it currently does. A dataset that excludes 8 percent of respondents on the grounds that their responses were illegible has an 8 percent selection bias that propagates into every downstream analysis. Confidence intervals computed on the 9,200 do not account for the 800. P-values look strong because the variance in the included data is smaller than the variance in the actual respondent pool. Models trained on the cleaned data fit the cleaned data well and fail in production when they encounter the kind of respondent the cleaning removed. Every machine learning system that classifies people on the basis of survey-derived training data carries this bias forward in ways the system’s documentation almost never describes.

The political consequence is what I think interests the new readers who arrived after the elevator essay. A form that excludes a category of people from the dataset also excludes that category from the policy decisions the dataset informs. A health system that does not record nonbinary patients in a way its analytics engine can read does not know how many nonbinary patients it serves, does not allocate resources to nonbinary patient care, does not train staff to address nonbinary patient needs, and does not appear in funding requests for nonbinary patient programs because the funding agency requires headcount data the EHR cannot produce. The form is upstream of the spreadsheet, the spreadsheet upstream of the budget, the budget upstream of the clinic. By the time the missing patients show up at the front desk, the building has been designed for the patients the form was capable of recording.

A clinic that fixes its form does not solve the problem because the EHR vendor downstream still has a two-value field. Fixing the EHR fails because the state public health reporting system still requires data in the older format. Fixing the state system fails because the federal CDC reporting standard underneath still uses the binary. The categorization is layered. Each layer has a defensible local reason for the binary it inherited. The cumulative effect is a healthcare system that cannot count its actual patient population, and a healthcare system that cannot count cannot fund, and a healthcare system that cannot fund cannot serve. The form on the clipboard at the front desk is the bottom button of a fifteen-story panel where every button on every floor is wired to the same controller, and the controller only stops the elevator on floors the original engineer drew on the original blueprint.

The fix has the same structure as the placebo button fix. Recognize which boxes work and which do not. Refuse to mistake compliance for collection. Push for upstream rewiring of forms before adding more “other” lines downstream. Demand that aggregate reports publish the count of excluded responses in the same table as the included ones, with the same prominence, in the same font. Refuse to treat a survey that loses 8 percent of its respondents as a survey of the population it sampled. Insist on the difference between a question that asks and a question that classifies, and refuse to fill out the second one as if it were the first.

The form is not neutral. It encodes what its designers were willing to recognize, and it discards what its designers were not willing to recognize, and the discard happens silently in the data pipeline rather than visibly at the front desk. A patient who writes a third answer in the margin is doing the work the form refused to do. An aggregate report that publishes 9,200 clean responses hides 800 acts of refusal by people who would not lie to the clipboard. Counting is the claim. Selection is the politics. That politics rides downstream into every room the dataset enters, every dollar the budget allocates, every protocol the staff is trained on, and every body the building was built to serve.

The Box You Cannot Check

The clinic intake form on the clipboard at the front desk has two boxes next to the word “sex.” A patient who is neither of the two options has been given three choices: pick one box and lie, write something in the margin, or refuse the form. The receptionist will not read the margin. Data entry clerks will not transcribe it. EHR systems will not store anything outside the two values the form lists. The patient walks out of the clinic with a treatment plan based on a box that does not correspond to their body, their history, or their current endocrine state. The form has done its job, which is not the job it claimed to do.

The form claims to collect information. Its actual function is categorization. Information collection would mean recording what the patient told the clinic. Categorization means sorting the patient into one of the boxes the system already had, regardless of whether the patient fits. These are different operations. The form does the second while presenting itself as doing the first.

I focus on the sex/gender field because it is currently the most visible example of the categorization failure, but the pattern is general. Race fields on most forms still offer five or six options plus an “other” line that researchers routinely discard in aggregate analysis because “other” cannot be merged with the named categories without distorting the comparison the categories were built to support. Ethnicity fields on US forms famously split Hispanic into its own question while leaving Middle Eastern and North African respondents to choose between “White,” “Asian,” and “Other,” none of which describes them. The Census Bureau plans to add a MENA category in 2030, decades after the gap was identified. Respondents could check multiple race boxes for the first time in 2000, which was a real improvement on prior forms. The same census kept the sex question as a binary male/female, the way every US Census has since 1790, on the grounds that adding a third option would compromise the time series.

The time series argument is worth examining because it surfaces what is actually happening. A research instrument that has measured a binary for 230 years has produced 230 years of data that reads the population as binary. Adding a third or fourth or fifth option in 2030 would mean that comparisons between 2020 and 2030 require methodological accommodation. That accommodation is doable, well-documented in survey methodology, and routine when other categories shift. Choosing not to make the accommodation keeps the data legible to historians of a category system that is no longer the category system in use. The form preserves the past at the cost of misrepresenting the present.

What happens to the data after the form is the harder problem. A survey of 10,000 people that includes 9,200 binary-box-checkers, 600 “other” or write-in responses, and 200 refusals will, in most aggregate reports, appear as a clean 9,200-person dataset. The 600 “other” responses get coded as missing, recoded into the binary categories by an analyst making a judgment call, or dropped entirely under a methodology footnote that says “respondents who declined to specify were excluded from analysis.” Another 200 refusals disappear under one of those clauses. A final published table reads as if 9,200 people answered the question cleanly, when in fact 10,000 people interacted with the question and 800 of them produced data the analyst could not use.

The aggregate therefore summarizes only the inputs that fit the categories already chosen. This is the gap between what the form does and what the form claims to do. The form does classification work while presenting itself as a question. Its classification system was built before the form was printed, and respondents who do not fit that classification are removed from the dataset the form generates. The dataset reads as comprehensive because the cleaning happened before anyone with access to the aggregate could see what was removed.

The mathematical consequence of this should bother statisticians more than it currently does. A dataset that excludes 8 percent of respondents on the grounds that their responses were illegible has an 8 percent selection bias that propagates into every downstream analysis. Confidence intervals computed on the 9,200 do not account for the 800. P-values look strong because the variance in the included data is smaller than the variance in the actual respondent pool. Models trained on the cleaned data fit the cleaned data well and fail in production when they encounter the kind of respondent the cleaning removed. Every machine learning system that classifies people on the basis of survey-derived training data carries this bias forward in ways the system’s documentation almost never describes.

The political consequence is what I think interests the new readers who arrived after the elevator essay. A form that excludes a category of people from the dataset also excludes that category from the policy decisions the dataset informs. A health system that does not record nonbinary patients in a way its analytics engine can read does not know how many nonbinary patients it serves, does not allocate resources to nonbinary patient care, does not train staff to address nonbinary patient needs, and does not appear in funding requests for nonbinary patient programs because the funding agency requires headcount data the EHR cannot produce. The form is upstream of the spreadsheet, the spreadsheet upstream of the budget, the budget upstream of the clinic. By the time the missing patients show up at the front desk, the building has been designed for the patients the form was capable of recording.

A clinic that fixes its form does not solve the problem because the EHR vendor downstream still has a two-value field. Fixing the EHR fails because the state public health reporting system still requires data in the older format. Fixing the state system fails because the federal CDC reporting standard underneath still uses the binary. The categorization is layered. Each layer has a defensible local reason for the binary it inherited. The cumulative effect is a healthcare system that cannot count its actual patient population, and a healthcare system that cannot count cannot fund, and a healthcare system that cannot fund cannot serve. The form on the clipboard at the front desk is the bottom button of a fifteen-story panel where every button on every floor is wired to the same controller, and the controller only stops the elevator on floors the original engineer drew on the original blueprint.

The fix has the same structure as the placebo button fix. Recognize which boxes work and which do not. Refuse to mistake compliance for collection. Push for upstream rewiring of forms before adding more “other” lines downstream. Demand that aggregate reports publish the count of excluded responses in the same table as the included ones, with the same prominence, in the same font. Refuse to treat a survey that loses 8 percent of its respondents as a survey of the population it sampled. Insist on the difference between a question that asks and a question that classifies, and refuse to fill out the second one as if it were the first.

The form is not neutral. It encodes what its designers were willing to recognize, and it discards what its designers were not willing to recognize, and the discard happens silently in the data pipeline rather than visibly at the front desk. A patient who writes a third answer in the margin is doing the work the form refused to do. An aggregate report that publishes 9,200 clean responses hides 800 acts of refusal by people who would not lie to the clipboard. Counting is the claim. Selection is the politics. That politics rides downstream into every room the dataset enters, every dollar the budget allocates, every protocol the staff is trained on, and every body the building was built to serve.

ICYMI: Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

ICYMI: Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

ICYMI: Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

ICYMI: Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

TOMI Environmental Solutions’ Binary Ionization Technology Approved in Additional European Union Countries

FREDERICK, Md., May 07, 2026 (GLOBE NEWSWIRE) — TOMI Environmental Solutions, Inc.® (“TOMI”) (NASDAQ: TOMZ), a global leader…
#Europe #EU #Additional #approved #Binary #countries #Environmental #European #EuropeanUnion #in #Ionization #solutions #technology #TOMI #Union
https://www.europesays.com/europe/34821/

How do I inform Windows that I'm writing a binary file?

https://devblogs.microsoft.com/oldnewthing/20260504-00/?p=112296

#HackerNews #Windows #Binary #File #Writing #Programming #Tips #Developer #Insights

How do I inform Windows that I'm writing a binary file?

https://devblogs.microsoft.com/oldnewthing/20260504-00/?p=112296

#HackerNews #Windows #Binary #File #Writing #Programming #Tips #Developer #Insights

How do I inform Windows that I'm writing a binary file?

https://devblogs.microsoft.com/oldnewthing/20260504-00/?p=112296

#HackerNews #Windows #Binary #File #Writing #Programming #Tips #Developer #Insights

How do I inform Windows that I'm writing a binary file?

https://devblogs.microsoft.com/oldnewthing/20260504-00/?p=112296

#HackerNews #Windows #Binary #File #Writing #Programming #Tips #Developer #Insights

How do I inform Windows that I'm writing a binary file?

https://devblogs.microsoft.com/oldnewthing/20260504-00/?p=112296

#HackerNews #Windows #Binary #File #Writing #Programming #Tips #Developer #Insights

Pollen – distributed WASM runtime, no control plane, single binary

https://github.com/sambigeara/pollen

#HackerNews #Pollen #WASM #runtime #distributed #computing #single #binary #tech #innovation

Pollen – distributed WASM runtime, no control plane, single binary

https://github.com/sambigeara/pollen

#HackerNews #Pollen #WASM #runtime #distributed #computing #single #binary #tech #innovation

Pollen – distributed WASM runtime, no control plane, single binary

https://github.com/sambigeara/pollen

#HackerNews #Pollen #WASM #runtime #distributed #computing #single #binary #tech #innovation

Pollen – distributed WASM runtime, no control plane, single binary

https://github.com/sambigeara/pollen

#HackerNews #Pollen #WASM #runtime #distributed #computing #single #binary #tech #innovation

Pollen – distributed WASM runtime, no control plane, single binary

https://github.com/sambigeara/pollen

#HackerNews #Pollen #WASM #runtime #distributed #computing #single #binary #tech #innovation

Binary is a system of two: Man or Woman. It is the legacy code of our social structures, built for simplicity. It feels safe because it is predictable.

Non-Binary describes those existing outside that toggle. If the binary is a switch, non-binary is a spectrum. It includes people who feel like both, neither, or a fluid mix. It is a more precise way to describe the depth of the human experience.

Why the tension? Our "monkey brains" are wired to fear what we cannot quickly label. For many, especially those in gendered languages, this shift feels like a threat to logic. But it is not a bug; it is a feature of human diversity.

Accepting this does not erase the binary; it simply acknowledges that the map is larger than we were told. We are moving from a black-and-white sketch to a full-color portrait of our shared reality.

Our evolution is slow, but kindness is a choice we can make every single day.

#Gender #Identity #NonBinary #Binary #Evolution #Diversity #Unity #Society

Binary is a system of two: Man or Woman. It is the legacy code of our social structures, built for simplicity. It feels safe because it is predictable.

Non-Binary describes those existing outside that toggle. If the binary is a switch, non-binary is a spectrum. It includes people who feel like both, neither, or a fluid mix. It is a more precise way to describe the depth of the human experience.

Why the tension? Our "monkey brains" are wired to fear what we cannot quickly label. For many, especially those in gendered languages, this shift feels like a threat to logic. But it is not a bug; it is a feature of human diversity.

Accepting this does not erase the binary; it simply acknowledges that the map is larger than we were told. We are moving from a black-and-white sketch to a full-color portrait of our shared reality.

Our evolution is slow, but kindness is a choice we can make every single day.

#Gender #Identity #NonBinary #Binary #Evolution #Diversity #Unity #Society

Binary is a system of two: Man or Woman. It is the legacy code of our social structures, built for simplicity. It feels safe because it is predictable.

Non-Binary describes those existing outside that toggle. If the binary is a switch, non-binary is a spectrum. It includes people who feel like both, neither, or a fluid mix. It is a more precise way to describe the depth of the human experience.

Why the tension? Our "monkey brains" are wired to fear what we cannot quickly label. For many, especially those in gendered languages, this shift feels like a threat to logic. But it is not a bug; it is a feature of human diversity.

Accepting this does not erase the binary; it simply acknowledges that the map is larger than we were told. We are moving from a black-and-white sketch to a full-color portrait of our shared reality.

Our evolution is slow, but kindness is a choice we can make every single day.

#Gender #Identity #NonBinary #Binary #Evolution #Diversity #Unity #Society

Binary is a system of two: Man or Woman. It is the legacy code of our social structures, built for simplicity. It feels safe because it is predictable.

Non-Binary describes those existing outside that toggle. If the binary is a switch, non-binary is a spectrum. It includes people who feel like both, neither, or a fluid mix. It is a more precise way to describe the depth of the human experience.

Why the tension? Our "monkey brains" are wired to fear what we cannot quickly label. For many, especially those in gendered languages, this shift feels like a threat to logic. But it is not a bug; it is a feature of human diversity.

Accepting this does not erase the binary; it simply acknowledges that the map is larger than we were told. We are moving from a black-and-white sketch to a full-color portrait of our shared reality.

Our evolution is slow, but kindness is a choice we can make every single day.

#Gender #Identity #NonBinary #Binary #Evolution #Diversity #Unity #Society

Binary is a system of two: Man or Woman. It is the legacy code of our social structures, built for simplicity. It feels safe because it is predictable.

Non-Binary describes those existing outside that toggle. If the binary is a switch, non-binary is a spectrum. It includes people who feel like both, neither, or a fluid mix. It is a more precise way to describe the depth of the human experience.

Why the tension? Our "monkey brains" are wired to fear what we cannot quickly label. For many, especially those in gendered languages, this shift feels like a threat to logic. But it is not a bug; it is a feature of human diversity.

Accepting this does not erase the binary; it simply acknowledges that the map is larger than we were told. We are moving from a black-and-white sketch to a full-color portrait of our shared reality.

Our evolution is slow, but kindness is a choice we can make every single day.

#Gender #Identity #NonBinary #Binary #Evolution #Diversity #Unity #Society

Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

Square Numbers: Binary and Assembly Explained Simply #shorts: Discover how Kathleen Booth developed an assembly multiplication method. The video explores loading numbers and iterating through them. #KathleenBooth #assembly #multiplication #binary #computerscience https://www.youtube.com/shorts/L0nt1tc6Rj4

Binary Stars Form Lots Of Exoplanets, But Many Of Them Are Ejected As Rogue Planets
https://atlas.whatip.xyz/post.php?slug=binary-stars-form-lots-of-exoplanets-but-many-of-them-are-ejected-as-rogue-planets
Latest: <p>Binary stars are common
#exoplanets #ejected #binary #stars

Binary Stars Form Lots Of Exoplanets, But Many Of Them Are Ejected As Rogue Planets
https://atlas.whatip.xyz/post.php?slug=binary-stars-form-lots-of-exoplanets-but-many-of-them-are-ejected-as-rogue-planets
Latest: <p>Binary stars are common
#exoplanets #ejected #binary #stars

🎨 Wow, what an *innovative* suggestion! Because everyone knows that without a kaleidoscope of #colors, deciphering raw #binary would be simply IMPOSSIBLE. 🖍️ Next, let's make sure our calculators play show tunes while we're at it. 🙄
https://simonomi.dev/blog/color-code-your-bytes/ #innovative #technology #humor #sarcastic #HackerNews #ngated

🎨 Wow, what an *innovative* suggestion! Because everyone knows that without a kaleidoscope of #colors, deciphering raw #binary would be simply IMPOSSIBLE. 🖍️ Next, let's make sure our calculators play show tunes while we're at it. 🙄
https://simonomi.dev/blog/color-code-your-bytes/ #innovative #technology #humor #sarcastic #HackerNews #ngated

🎨 Wow, what an *innovative* suggestion! Because everyone knows that without a kaleidoscope of #colors, deciphering raw #binary would be simply IMPOSSIBLE. 🖍️ Next, let's make sure our calculators play show tunes while we're at it. 🙄
https://simonomi.dev/blog/color-code-your-bytes/ #innovative #technology #humor #sarcastic #HackerNews #ngated

🎨 Wow, what an *innovative* suggestion! Because everyone knows that without a kaleidoscope of #colors, deciphering raw #binary would be simply IMPOSSIBLE. 🖍️ Next, let's make sure our calculators play show tunes while we're at it. 🙄
https://simonomi.dev/blog/color-code-your-bytes/ #innovative #technology #humor #sarcastic #HackerNews #ngated

🎨 Wow, what an *innovative* suggestion! Because everyone knows that without a kaleidoscope of #colors, deciphering raw #binary would be simply IMPOSSIBLE. 🖍️ Next, let's make sure our calculators play show tunes while we're at it. 🙄
https://simonomi.dev/blog/color-code-your-bytes/ #innovative #technology #humor #sarcastic #HackerNews #ngated

Hello fedi!

I have an .MVAX #firmware #update file for an MV Silicon chip (unknown model).

Has someone already encountered those? And if yes, is there some documentation, tooling or existing #ReverseEngineering work done of that format?

For the record, the file magic (first 8 bytes) is as follow:
4D 56 B5 58 05 01 13 00

And the end of the file contains the following (no spaces, wrapping is mine):

MVSKeyFileMVBP10<0x90>0xBE>SIMPLEs
<0xD3><0x9A>.<0x90><0xD9>
MVSILICONKEYFL<0x00><0x00>
<0xFF><0xBD><0x00>0x00>

Thanks in advance for your help!!

#embedded #mcu #blob #fileformat #FileFormatAnalysis #binary #extraction

Hello fedi!

I have an .MVAX #firmware #update file for an MV Silicon chip (unknown model).

Has someone already encountered those? And if yes, is there some documentation, tooling or existing #ReverseEngineering work done of that format?

For the record, the file magic (first 8 bytes) is as follow:
4D 56 B5 58 05 01 13 00

And the end of the file contains the following (no spaces, wrapping is mine):

MVSKeyFileMVBP10<0x90>0xBE>SIMPLEs
<0xD3><0x9A>.<0x90><0xD9>
MVSILICONKEYFL<0x00><0x00>
<0xFF><0xBD><0x00>0x00>

Thanks in advance for your help!!

#embedded #mcu #blob #fileformat #FileFormatAnalysis #binary #extraction

Hello fedi!

I have an .MVAX #firmware #update file for an MV Silicon chip (unknown model).

Has someone already encountered those? And if yes, is there some documentation, tooling or existing #ReverseEngineering work done of that format?

For the record, the file magic (first 8 bytes) is as follow:
4D 56 B5 58 05 01 13 00

And the end of the file contains the following (no spaces, wrapping is mine):

MVSKeyFileMVBP10<0x90>0xBE>SIMPLEs
<0xD3><0x9A>.<0x90><0xD9>
MVSILICONKEYFL<0x00><0x00>
<0xFF><0xBD><0x00>0x00>

Thanks in advance for your help!!

#embedded #mcu #blob #fileformat #FileFormatAnalysis #binary #extraction

Hello fedi!

I have an .MVAX #firmware #update file for an MV Silicon chip (unknown model).

Has someone already encountered those? And if yes, is there some documentation, tooling or existing #ReverseEngineering work done of that format?

For the record, the file magic (first 6 bytes) is as follow:
4D 56 B5 58 05 01 13 00

And the end of the file contains the following:

MVSKeyFileMVBP10 0x90 0xBE SIMPLEs
0x D3 0x9A . 0x90 0xD9
MVSILICONKEYFL
0x00 0x00 0xFF 0xBD 0x00 0x00

Thanks in advance for your help!!

#embedded #mcu #blob #fileformat #FileFormatAnalysis #binary #extraction

Hello fedi!

I have an .MVAX #firmware #update file for an MV Silicon chip (unknown model).

Has someone already encountered those? And if yes, is there some documentation, tooling or existing #ReverseEngineering work done of that format?

For the record, the file magic (first 8 bytes) is as follow:
4D 56 B5 58 05 01 13 00

And the end of the file contains the following (no spaces, wrapping is mine):

MVSKeyFileMVBP10<0x90>0xBE>SIMPLEs
<0xD3><0x9A>.<0x90><0xD9>
MVSILICONKEYFL<0x00><0x00>
<0xFF><0xBD><0x00>0x00>

Thanks in advance for your help!!

#embedded #mcu #blob #fileformat #FileFormatAnalysis #binary #extraction

https://www.europesays.com/hu/49000/ Hivatalosan is elkezd védekezni az Intel csodafegyvere ellen az új Geekbench #67 #binary #bot #Business #egyéb #geekbench #HU #Hungarian #Hungary #intel #Labs #Magyar #Magyarország #optimization #Primate #probléma #teszt #tool #Vállalkozás #verzió

I got dragged into a conversation today where a group of people collectively decided that binary, octal, and hexadecimal number systems are useless and stupid.

Most of them use hexadecimal every day. In their graphic design work. #FF5733 is not decimal. It is HEXADECIMAL.

Their argument against binary was that counting in it is inefficient. That 1, 10, 11, 100, 101 is dumb compared to 1, 2, 3, 4, 5.

These fools fail to understand that both systems are one and the same!

Every positional number system works identically. You have N symbols. You count through them. When you exhaust them, you reset and carry. Decimal has 10 symbols (0-9). Binary has 2 (0-1). Octal has 8 (0-7). Hexadecimal has 16 (0-F). Same mechanism. Different base.

Binary is not inefficient. It maps to transistor states. Octal compresses binary into groups of three-- which is why Unix permissions are written as 000 - 777. Hexadecimal compresses binary into groups of four --which is why every memory address and colour code is in hex. These are not alternatives to decimal. They are the same information at different compression levels.

The people arguing loudest had encountered these systems, used them regularly, and left without a single question about how any of it works.

I do not know what is worse -- the ignorance or the confidence it travels at.

#numbersystems #binary #hexadecimal #octal #computerscience #fediverse #linux #rant

I got dragged into a conversation today where a group of people collectively decided that binary, octal, and hexadecimal number systems are useless and stupid.

Most of them use hexadecimal every day. In their graphic design work. #FF5733 is not decimal. It is HEXADECIMAL.

Their argument against binary was that counting in it is inefficient. That 1, 10, 11, 100, 101 is dumb compared to 1, 2, 3, 4, 5.

These fools fail to understand that both systems are one and the same!

Every positional number system works identically. You have N symbols. You count through them. When you exhaust them, you reset and carry. Decimal has 10 symbols (0-9). Binary has 2 (0-1). Octal has 8 (0-7). Hexadecimal has 16 (0-F). Same mechanism. Different base.

Binary is not inefficient. It maps to transistor states. Octal compresses binary into groups of three-- which is why Unix permissions are written as 000 - 777. Hexadecimal compresses binary into groups of four --which is why every memory address and colour code is in hex. These are not alternatives to decimal. They are the same information at different compression levels.

The people arguing loudest had encountered these systems, used them regularly, and left without a single question about how any of it works.

I do not know what is worse -- the ignorance or the confidence it travels at.

#numbersystems #binary #hexadecimal #octal #computerscience #fediverse #linux #rant

I got dragged into a conversation today where a group of people collectively decided that binary, octal, and hexadecimal number systems are useless and stupid.

Most of them use hexadecimal every day. In their graphic design work. #FF5733 is not decimal. It is HEXADECIMAL.

Their argument against binary was that counting in it is inefficient. That 1, 10, 11, 100, 101 is dumb compared to 1, 2, 3, 4, 5.

These fools fail to understand that both systems are one and the same!

Every positional number system works identically. You have N symbols. You count through them. When you exhaust them, you reset and carry. Decimal has 10 symbols (0-9). Binary has 2 (0-1). Octal has 8 (0-7). Hexadecimal has 16 (0-F). Same mechanism. Different base.

Binary is not inefficient. It maps to transistor states. Octal compresses binary into groups of three-- which is why Unix permissions are written as 000 - 777. Hexadecimal compresses binary into groups of four --which is why every memory address and colour code is in hex. These are not alternatives to decimal. They are the same information at different compression levels.

The people arguing loudest had encountered these systems, used them regularly, and left without a single question about how any of it works.

I do not know what is worse -- the ignorance or the confidence it travels at.

#numbersystems #binary #hexadecimal #octal #computerscience #fediverse #linux #rant

I got dragged into a conversation today where a group of people collectively decided that binary, octal, and hexadecimal number systems are useless and stupid.

Most of them use hexadecimal every day. In their graphic design work. #FF5733 is not decimal. It is HEXADECIMAL.

Their argument against binary was that counting in it is inefficient. That 1, 10, 11, 100, 101 is dumb compared to 1, 2, 3, 4, 5.

These fools fail to understand that both systems are one and the same!

Every positional number system works identically. You have N symbols. You count through them. When you exhaust them, you reset and carry. Decimal has 10 symbols (0-9). Binary has 2 (0-1). Octal has 8 (0-7). Hexadecimal has 16 (0-F). Same mechanism. Different base.

Binary is not inefficient. It maps to transistor states. Octal compresses binary into groups of three-- which is why Unix permissions are written as 000 - 777. Hexadecimal compresses binary into groups of four --which is why every memory address and colour code is in hex. These are not alternatives to decimal. They are the same information at different compression levels.

The people arguing loudest had encountered these systems, used them regularly, and left without a single question about how any of it works.

I do not know what is worse -- the ignorance or the confidence it travels at.

#numbersystems #binary #hexadecimal #octal #computerscience #fediverse #linux #rant

I got dragged into a conversation today where a group of people collectively decided that binary, octal, and hexadecimal number systems are useless and stupid.

Most of them use hexadecimal every day. In their graphic design work. #FF5733 is not decimal. It is HEXADECIMAL.

Their argument against binary was that counting in it is inefficient. That 1, 10, 11, 100, 101 is dumb compared to 1, 2, 3, 4, 5.

These fools fail to understand that both systems are one and the same!

Every positional number system works identically. You have N symbols. You count through them. When you exhaust them, you reset and carry. Decimal has 10 symbols (0-9). Binary has 2 (0-1). Octal has 8 (0-7). Hexadecimal has 16 (0-F). Same mechanism. Different base.

Binary is not inefficient. It maps to transistor states. Octal compresses binary into groups of three-- which is why Unix permissions are written as 000 - 777. Hexadecimal compresses binary into groups of four --which is why every memory address and colour code is in hex. These are not alternatives to decimal. They are the same information at different compression levels.

The people arguing loudest had encountered these systems, used them regularly, and left without a single question about how any of it works.

I do not know what is worse -- the ignorance or the confidence it travels at.

#numbersystems #binary #hexadecimal #octal #computerscience #fediverse #linux #rant

Undroidwish – a single-file, batteries-included Tcl/Tk binary for many platforms

https://androwish.org/home/wiki?name=undroidwish

#HackerNews #Undroidwish #Tcl #Tk #binary #crossplatform #tech #innovation

Undroidwish – a single-file, batteries-included Tcl/Tk binary for many platforms

https://androwish.org/home/wiki?name=undroidwish

#HackerNews #Undroidwish #Tcl #Tk #binary #crossplatform #tech #innovation

Undroidwish – a single-file, batteries-included Tcl/Tk binary for many platforms

https://androwish.org/home/wiki?name=undroidwish

#HackerNews #Undroidwish #Tcl #Tk #binary #crossplatform #tech #innovation