From: noreply@mixmin.net   
      
   On Mon, 25 Aug 2025 14:41:33 +0000, Anonymous    
   wrote:   
   >In article <108hiuj$j1vv$2@solani.org> ignore wrote:   
   >>Using omnimix is ??complicated compared to the simplicity of QSL   
   >   
   >Is that the reason why there are so many frustrated qsl users here who   
   >post their failing message delivery templates hoping to get them fixed?   
      
   qsl does work when templates are set correctly and stats are up to date,   
   and qsl users have for many years posted templates that work to a.p.a-s   
      
   the complainers, obfuscators, troublemakers (militarized, alphabet soup)   
   troll farm operatives live in a completely separated, compartmentalized,   
   "need to know" environment from the outside world, like star trek "borg"   
   (and a.i. has become so advanced, they're indistinguishable from people)   
   so it's the same sectarian principle, liars lie, garbage in, garbage out . . .   
      
   https://duckduckgo.com/?q=garbage+im%2C+garbage+out&ia=web&assist=true   
   >Garbage in, garbage out (GIGO) is a concept in computer science that means the   
   >quality of output is determined by the quality of input; if flawed or poor-   
   >quality data is provided, the results will also be flawed. This principle   
   >emphasizes the importance of accurate and reliable data in programming and   
   >decision-making processes. Wikipedia TechTarget   
   >Definition of Garbage In, Garbage Out   
   >Garbage in, garbage out (GIGO) is a principle in computer science and data   
   >processing. It states that the quality of output is determined by the quality   
   of   
   >input. If flawed or poor-quality data is input into a system, the resulting   
   >output will also be flawed or of low quality.   
   >Historical Context   
   >The term "garbage in, garbage out" was first recorded in 1957, but it was   
   >popularized by IBM programmer George Fuechsel in the early 1960s. He used it   
   to   
   >emphasize that computers process the data they are given; if that data is bad,   
   >the results will be bad.   
   >Applications of GIGO   
   >GIGO is relevant in various fields, including:   
   >  Computer Science: Poor input data leads to incorrect program outputs.   
   >  Machine Learning: Models trained on biased or incomplete data yield biased   
   >  results.   
   >  Decision-Making: Inaccurate data can lead to poor decisions in business and   
   >  policy-making.   
   >Types of Garbage Input   
   >Common types of poor-quality input include:   
   >  Incorrect data (errors in data collection)   
   >  Incomplete data (missing information)   
   >  Outliers (data points that differ significantly from others)   
   >  Irrelevant data (not applicable to the situation)   
   >Understanding GIGO is crucial for ensuring accurate and reliable outcomes in   
   any   
   >data-driven process.   
   [end quoted "search assist"]   
      
   --- SoupGate-Win32 v1.05   
    * Origin: you cannot sedate... all the things you hate (1:229/2)