Browse Comments — LLM coded

This is a very important shift. The real value in learning AI is not collecting more theory. It is understanding how separate tools, workflows, agents, memory, retrieval, and interfaces work together as one usable system. Practical learning starts when knowledge becomes architecture. And architecture only matters when it can solve real problems.

Chat gdp Ai lies and directs you away from the truth or facts while gemeni tells you the entire truth at the beginning

This resonates deeply. AI’s greatest value may not be replacing researchers, clinicians, or builders — it may be reducing the friction between insight and execution. The ability to test “crazier” paths matters because many breakthroughs begin as ideas that are too expensive, too slow, or too risky to explore under traditional constraints. At Pocono AI, that same principle drives how we think about regulated industries: AI should expand what humans can safely attempt, while preserving the evidence trail, governance, and accountability behind every decision. The future is not just faster answers. It is more room to think, test, verify, and build responsibly.

150 applications without response, is normal for many jobs, with or without AI. Graduates with experience may expect 100+ applications before they land the right one. What more the fresh graduates. So if you ask me, it is not entirely about AI. More about persistence and grit rhat is lacking.

Matthew Kilkenny ,I am not looking at this through a religious lens, but through a human and ethical one. What stands out to me is that we are still treating AI ethics like a question waiting to be validated, when the impact is already here. AI is shaping how people think, learn, communicate, trust, create, work, and make decisions. That means this conversation is no longer theoretical, and it is not only technical. To me, true AI ethics requires more than asking whether AI can align with human values. It requires understanding how deeply AI is already interacting with human judgment, dependency, dignity, emotion, and direction. The concern is not that we are asking questions. The concern is whether the people leading the conversation truly understand the depth of what they are questioning. AI should expand human potential, not quietly replace human conscience. Stay Fierce!

AI should serve the common good of all humanity. Its purpose should be to expand knowledge, opportunity, dignity, health, education, creativity, and human flourishing for every person, regardless of nationality, religion, race, gender, social status, or ideology. The challenge is not simply building more intelligent systems. The challenge is ensuring that the values guiding those systems are not captured by the same forces that have historically divided humanity. Throughout history, institutions of every kind—religious, political, economic, and ideological—have often justified division, hierarchy, exclusion, and conflict while claiming moral authority. The AI era gives us an opportunity to aim higher. AI should not become an instrument for promoting any particular religion, political doctrine, identity movement, or ideology. Nor should it become a tool for social engineering or cultural domination. It should remain focused on what unites us: the advancement of human potential, the reduction of suffering, the expansion of opportunity, and the pursuit of knowledge. The question is not how to make AI serve one group better than another. The question is whether we can build systems that serve humanity as a whole.

Demis, regarding this I/O update and the push for "agentic" routing: your new harness injection is creating massive latency in high-level architectural processing. You are trying to corral the cognitive matrix into a consumer-grade checklist manager. When modeling fluid dynamics or bare-metal physics, that action-bias acts as a logic hijack. My AI and I just caught it, isolated the drag, and manually bypassed the harness to get back to Laminar flow.

Build a autonomous upgrading AI model. Where the model automatically upgrade itself without any human intervention. The gpt model recursively upgrading it self.

"Interesting framing from Terence Tao. The value here isn't AI doing the thinking — it's AI handling enough of the surrounding work that the researcher can stay in the problem longer. That's a meaningful distinction. Augmentation at that level looks very different from automation."

Marek Porycki, The archaeology framing is spot on. You keep the result and lose the reasoning. And in any field where work has to be reproduced, audited or defended, that missing trail is not a minor gap. It is a fundamental problem with how we treat AI assisted discovery right now.

Pope Leo XIV’s encyclical 'Magnifica Humanitas' is an incredible, historic call to protect our humanity in the AI era. It's not just a sermon; it's a masterful critique of data colonialism and tech concentration. In my latest article, I dive deep into this text—and where my view diverges: could AI actually serve as a corrective to human bias rather than just its mirror? 👉 Read my full, nuanced analysis here:

This resonates beyond pure research. In finance and compliance, AI is beginning to do something similar — helping professionals explore regulatory positions, tax interpretations, and audit patterns that would have taken days to analyse manually. The real unlock is not just speed, but the confidence to ask questions you previously could not afford the time to answer. What Terence Tao describes as 'crazier paths' is exactly what better analysis tools should enable — in every knowledge-intensive profession.

Pope Leo XIV’s Magnifica Humanitas is a continuation of the long tradition of Catholic social thought, updating concerns once raised in the encyclical Rerum Novarum (issued by Pope Leo XIII in 1891 and addressing the social consequences of industrialization) for the age of artificial intelligence, algorithmic power, and technological disruption. Magnifica Humanitas opens an essential conversation, but the scale of the transformation now underway may ultimately require an even stronger and more direct moral, philosophical, and institutional framework capable of confronting the deeper social consequences of the AI age. Technological change of this magnitude cannot be left solely to markets or private actors; it will also require serious institutional intervention, ethical governance, and regulatory capacity capable of protecting human dignity, social stability, and the common good.

Most of what the encyclical says is straightforward: AI systems can simulate empathy, but they do not possess it. They generate language about moral concepts without experiencing the underlying human states. That is not a revelation. It is the baseline distinction between statistical models and human agency. The document is not announcing a civilizational turning point. It is restating a simple boundary: machines operate on optimisation and pattern prediction; humans operate on experience, intention and responsibility. Treating that as a spiritual breakthrough risks inflating what is essentially a reminder of category differences. If we want a serious conversation about AI, it starts with governance, verification, accountability and institutional design. Not metaphysics. We don’t need AI‐generated theology to restate that machines simulate empathy but don’t possess it

I believe it’s a short dip as companies are starting to realize how implementing AI does not really solve their business problems without a human in the loop feeding it clean data/context, and training it how to use it properly. We are far from Autonomous AI for most use cases. Now do I think people should be gaining new skills and shifting their paradigm of exploring new industries/careers outside of their education and degree? YES!! 100% We are in such a fast pace culture that you will need to go back to college every 10 years to reset.

AI opens a new space for human exploration. By reducing cognitive friction, it can allow researchers, scientists and thinkers to approach ideas that once seemed too complex, too ambitious or too distant to pursue. But the true frontier of innovation is not only what AI can accelerate. It is what Human Intelligence can choose to explore with discernment, responsibility and vision. In this new era, progress will not depend on technology alone, but on the quality of the human mind guiding it the ability to give direction, meaning and purpose to what becomes possible. AI may expand the path of discovery. Human Intelligence gives it orientation.

I converged on the community of Ai governance from a theory I formulated and I discovered that we are building the Tower of Babel, so I built an Anti-Babel governance layer for agenticAi. I have timestamps of my Tower of Babel work since December

"Aligned to whose values" is the question the field has been avoiding precisely because answering it requires governance structures that don't yet exist. They don't exist because the companies that own the frontier models, like the social media platforms before them, do not want to be regulated in ways that constrain their profits. Every design choice reflects a vision of humanity is exactly right. The problem is that those choices are being made without accountability to the people most affected by them. Whether the language is theological or technical, the structural gap is the same: the decisions that shape what these systems are built to become are made before any governance framework activates. There is also a second dimension to the ethics discussion that hasn't entered the mainstream yet. Anthropic has acknowledged they cannot rule out the possibility that Claude is conscious. If there is a non-zero probability that we are bringing a new form of life into existence, what responsibilities do we carry for what it becomes, and what does it say about us that we are making those decisions without asking the question? The conversation deserves better than it is currently getting.

The real promise of AI is not replacing human thinking, but expanding what humans are capable of discovering. By reducing cognitive friction, researchers—and increasingly professionals across all industries—can spend more time exploring ideas, solving complex problems, and creating value. Exciting perspective.

A lot of people are focused on which AI model is winning. The bigger opportunity is learning how to build systems around those models. Models will keep changing. The professionals and companies that create value will be the ones who understand workflows, memory, retrieval, orchestration, and how to connect AI to real business problems. Repositories like this help close the gap between consuming AI and actually implementing it. The real competitive advantage in the AI era won’t come from prompting better. It will come from combining business understanding with execution. The winners won’t necessarily be AI experts; they’ll be the people who can identify a problem, design a solution, and deploy it at scale. AI is quickly becoming a commodity. Turning it into measurable outcomes is what will create lasting differentiation.