John Deacon Cognitive Systems. Structured Insight. Aligned Futures.

AuthorJohn Deacon

An independent AI researcher and systems practitioner focused on semantic models of cognition and strategic logic. He developed the Core Alignment Model (CAM) and XEMATIX, a cognitive software framework designed to translate strategic reasoning into executable logic and structure. His work explores the intersection of language, design, and decision systems to support scalable alignment between human intent and digital execution. Read more at bio.johndeacon.co.za or join the email list in the menu to receive one exclusive article each week.

Predictions for AI Literacy, Interaction Skills, and Cultural Impact

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Short-Term Predictions (1–3 Years) Wide­spread AI Lit­er­a­cy Ini­tia­tives: Gov­ern­ments, edu­ca­tion­al insti­tu­tions, and com­pa­nies will roll out basic AI lit­er­a­cy pro­grams tar­get­ing under­served pop­u­la­tions. AI Tools Become Ubiq­ui­tous: LLMs will inte­grate into every­day plat­forms like search engines, work­place soft­ware, and per­son­al assis­tants. Emer­gence of Eth­i­cal...

Exploring SEC Commissioner Hester Peirce’s Proposal and How KIN Could Be the Perfect Fit for Regulatory Clarity

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In a recent move that has the cryp­tocur­ren­cy world buzzing, SEC Com­mis­sion­er Hes­ter Peirce put forth a for­ward-think­ing pro­pos­al advo­cat­ing a path­way for token projects to tran­si­tion from secu­ri­ties to non-secu­ri­ties sta­tus. This pro­pos­al is a response to the exist­ing reg­u­la­to­ry land­scape, which often views token offer­ings through a one-size-fits-all lens, result­ing...

The Unique Structure of CAM as a Model for Meta Programming

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CAM is a unique mod­el for Meta Pro­gram­ming, par­tic­u­lar­ly because it inte­grates ele­ments of metacog­ni­tion, cyber­net­ic prin­ci­ples, and a struc­tured align­ment across dif­fer­ent lay­ers of cog­ni­tion and action (Mis­sion, Vision, Strat­e­gy, Tac­tics). This dis­tinct struc­ture makes CAM more than just a high-lev­el con­cep­tu­al frame­work; it active­ly guides users through the...

Exploring Meta Concepts for High-Level Thinking and Structured Frameworks

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Here’s a brief overview of each con­cept with­in the con­text of high-lev­el think­ing and struc­tured frame­works like the Core Align­ment Mod­el (CAM): Meta Pro­gram­ming: Meta pro­gram­ming involves cre­at­ing “pro­grams” or struc­tures that define and shape oth­er pro­grams or ways of think­ing. It oper­ates at a high lev­el, set­ting the foun­da­tion­al “rules” or struc­tures that guide...

Exploring the World’s Cash Supply and the Dominance of Digital Transactions

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The world’s cash sup­ply, or mon­ey sup­ply, can be mea­sured in var­i­ous ways depend­ing on what type of mon­ey is includ­ed in the mea­sure­ment. Here’s a break­down based on recent data: M0 (Nar­row Mon­ey): This includes phys­i­cal cur­ren­cy (coins and ban­knotes) in cir­cu­la­tion. Accord­ing to recent data, the M0 sup­ply glob­al­ly was esti­mat­ed to be about $8.27 tril­lion US dol­lars...

Markdown Converter — Effortlessly Transform AI Outputs into Formatted Text

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If you’ve ever worked with AI writ­ing assis­tants like Chat­G­PT, Grok, or sim­i­lar plat­forms, you know the rou­tine: you ask a ques­tion or prompt, and the response often comes in a neat Mark­down for­mat. Mark­down is great for struc­tur­ing con­tent, but it’s not ide­al when you need ful­ly for­mat­ted text or HTML ready for direct use in pre­sen­ta­tions, web­sites, or doc­u­ments. Instead...

Markdown to HTML Converter in One Click from Clipboard to Clipboard

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Click­ing on the Mark­down box pastes text from the clip­board, auto­mat­i­cal­ly con­verts it to HTML, and copies the result­ing HTML back to the clip­board. Head Section <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Markdown to HTML Converter</title> <script src=""></script> </head>...

The ChatGPT Paradox: Impressive Yet Incomplete — YouTube

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“Prof. Thomas G. Diet­terich dis­cuss­es the cur­rent state of large lan­guage mod­els like Chat­G­PT. He explains their capa­bil­i­ties and lim­i­ta­tions, empha­siz­ing their sta­tis­ti­cal nature and ten­den­cy to hal­lu­ci­nate. Diet­terich explores the chal­lenges in uncer­tain­ty quan­tifi­ca­tion for these mod­els and pro­pos­es inte­grat­ing them with for­mal rea­son­ing sys­tems. He...

John Deacon Cognitive Systems. Structured Insight. Aligned Futures.

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