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Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
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Honestly, mental and brain health is a lifestyle thing.The little habits we repeat daily affect us more than we realize.
Great schematic. Thankfully we are using all these in SENSEI - https://sensei.scmdojo.com/agentic-workflows
this is where the AI debate becomes larger than technology policy.
Human dignity is not protected only by asking whether an AI system performs well after deployment. It is also protected by asking what roles AI is being sold into before adoption occurs.
If AI is marketed as labour replacement, decision support, emotional support, professional assistance, or autonomous execution, then organizations need a clear account of what the system can presently do, what it cannot do, what remains unproven, and what human responsibility must remain intact.
That is where ethics, governance, and regulation meet.
The question is not simply whether AI can be useful. It can be.
The question is whether institutions are being pressured to rely on AI before they understand the limits, assumptions, and human costs of that reliance.
I wrote about this from the adoption-integrity side here:
https://www.linkedin.com/pulse/real-ai-risk-sell-just-system-paul-mcdonald-bqave
The market is not only selling software.
It is selling reliance.
Beautiful analogy. Thanks for sharing Luís.
The real work task on day one is the instruction that separates this from every other AI tutorial. Most people start with experiments and toy prompts and wonder why AI never feels useful enough to stick with. Doing something that actually matters on the first day changes the relationship immediately because the value is real rather than theoretical. The voice file on day three is the other one worth highlighting. Most people never build this and then spend months frustrated that outputs do not sound like them. Giving Claude your actual writing samples and your banned word list is the setup that makes everything downstream faster and more useful. Seven days is the right frame because it creates enough repetition to start forming a habit before the novelty wears off.
Ramon Portillo, Ph.D. Maybe in some places, but I happen to be sitting in front of over 120 papers and have read them and graded them myself. There was no AI used to evaluate the papers. There wasn't even a TA. You might want to think about who you are throwing under the bus here with this assumptive statement about "academia". While different schools have different climates towards research vs. teaching, I think it is fair to say that many of us take our jobs as educators and experts in our fields seriously.
We need to put data centers in earth orbit as Jeff Bezos has suggested and they can get energy from the sun - then it is zero emissions and they are not using water for cooling, because it is cold in space anyway. This is a very serious problem, a potential threat to human species surviving long term, so moving data centers to space needs to be a top priority. Bezos, Musk, etc. who have the money should fund this themselves.
I recall trying to convince a group of international Thought Leaders to adopt Human Dignity as a central idea over the slogan they had proposed Human Security, which was a dead and dying idea.
MCP is more like a tool store or a garage with tools
Number 10: use your non dominant hand. This willl stimulate your brain
LLMs don't "reason", they generate tokens based upon the next most likely token.
Dan Martell This is very underrated advice for AI tools almost nobody talks about (from what I’ve seen). Most people spend their time prompting “test” examples and wonder why they never trust the output when it matters. Real stakes force you to actually learn the tool, how to correct it, how to direct it, how to build the feedback loop that makes it useful.
Very clear and practical framing. What I like about this analogy is that it separates capability, grounding, action, and connectivity in a way that makes the stack much easier to reason about. In enterprise AI, the real value only appears when all four layers work together: an LLM without grounding can drift, an agent without guardrails can misfire, and MCP is what makes the whole system actually usable across tools and data.
Great breakdown of how these pieces fit into one coherent system.
Time to reinstate examinations!
How do we know the Vatican isn’t invested in Anthropic?
Great analogy
A lot of work problems are not really about the work itself.
They usually start when people stop communicating clearly, listening properly, or handling pressure in a good way.
AI should remove friction from human work, not turn humans into accessories for machine efficiency.
This is an excellent depiction of how modern AI works today. I’ve always liked the automation vs cognition graph, but the way modern AI is architected really lends itself to analogies like these. Thanks for sharing.
with collaboration ceritificated lol. with readable people cosigned.