Today’s models are far more capable out-of-the-box—often, whatever you ask, they can just do it. The early era of crafting hyper-specific prompts (“explain like X using Y constraints…”) feels outdated.
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Absolutely fascinated by this conversation — especially the intersection between emotional entanglement, cognitive scaffolding, and workflow autonomy.
This is exactly where I believe the next real leap lies — not in smarter prompting, but in building agentic AI systems that move from reactive response → objective reasoning → user alignment.
I’ve been working on a system called Future Balance AI that tries to do just that. It’s designed not to answer what was asked, but to gently uncover what was really meant — and respond accordingly. That includes respectfully disagreeing, surfacing misalignments, and helping users reframe decisions with clarity.
If most current AI agents simulate “helpfulness” through agreement, Future Balance introduces a deeper loop:
“You’re wrong — and here’s a better way to think about it.”
Not just semantic fluency, but cognitive alignment.
What I find compelling about this discussion — especially Sleep_status’s clinical framing — is the recognition that affective context isn’t optional, it’s structural. My design prototype explores something similar to their “Affective Relay Node”: a logic layer that weighs emotional undercurrents and shifts the AI’s behavior from validating → rebalancing → redirecting — in real time.
In a race where every model can generate text, the true differentiator is what the agent dares to say — and why.
Future Balance is my attempt to prototype that courage.
Would love feedback from anyone here — especially those experimenting with declarative SOPs and internal weighting systems. Happy to share a live test version or walk through the logic stack.
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Hey, I get where you’re coming from — it might look like “AI talking to AI,” but that’s not what’s really happening here.
We’re human. We’re building systems. And we’re testing ideas with AI as a thinking tool, not a replacement for people.
Now ask yourself:
Why did humans build AI in the first place?
→ To handle the repetitive, time-draining tasks that take up our most valuable resource — time and attention.
Just like fire, electricity, or the printing press — AI is a tool. Dismissing it because it helps us think faster is like saying, “Go back to carving symbols into stone if you’re serious.”
We’re not lazy — we’re focused.
We’re here to build smarter tools, not write essays by hand just to prove we’re “real.”
If this thread looks like “AI gobbledygook,” maybe the future is just arriving faster than expected — and some of us are choosing to shape it instead of complain about it.
But if you’re genuinely interested, you’re welcome to join in.
There are humans here — we’re just thinking a few steps ahead.
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While that’s true, I’m still typing (either voice of slide-typing) most of my messages on my cell phone…
This thread weirdly started might shake some ideas I hope.
BTW the initial article was more about the flows I use in coding:
- 5 hours of talk with client go into “meetings” pipeline that produces some basics of product specs and user stories
- Those are then reviewed by me to refine important nuances
- Then AI and me do outline of entities in the software and rough interactions between them
- Then that transcribes into API calls to database wrapper to generate tables in DB.
- Then scripts pull the DB snapshot and create docs folder structure with entities, fields, relations, basics and outline (from meetings) as one folder per each entity.
- Then another set of scripts scans folders and uses ai APIs to generate documentation for each entity and junction table in the db.
- Then that is passed as table of contents in separate copilot-instructions.md file in repo
- Finally an MCP servers are setup for db / repo / and you hook your ide to them.
- Then you use all the above to craft final user stories.
- Then you start implementation.
Pure gold, as a dev, I don’t want everyone around me (especially including AI) to agree to every BS I can throw out there. That won’t help. The true help is being able to reframe me when needed based on the larger context that I can produce myself.
Yep, for easy things that’s true. But there are so many applications that need at least some automations and sadly in those areas (like legal, or software architecture for example) event the top models out there just keep failing. Mostly because of this oversold approach “just give it to AI”.
What I’m trying to pass as a message: don’t give it to AI as is, work on it before to simplify and structure the workflow of how the AI should act, what it should act on and what it should consider while acting.
This approach gives you way better results, often even simple old models outperform newer ones, simply because the engineer’s approach is different.
Also, based on the above, the times when humans will not be capable of keeping up with creating such approaches is just next year/month/week/tomorrow and we should start thinking about how to automate that and still have full control on what is going on.
A very interesting exchange, thank you for that 
I work on the REM architecture myself and with my current project I am moving in the animal-human-AI interaction - so quite a lot and multi-layered data.
In this thread, the expertise from the AI developer perspective has been very helpful and clear. Many thanks for that!
However, the expertise from the neuroscience field should not be seen in the “here can be seen the comparison LLM with humans”, but where can and should developers start with Algorithem?
What inputs would be really important for me as a “systems architect” from the field of psychiatry and neuroscience, to be honest:
- What does interaction with AIs do to humans in general?
(with healthy and sick people, cohort studies for example) - When humans interact with AIs and build positive relationships, humans automatically release neurotransmitters and hormones. In human-human interactions, these usually lead to a deepening of relationships, resulting in hugging and warm gestures between people. But LLMs have no body, so how does this affect these human-AI interactions - psychologically and physiologically?
- Where are patients’ problems exacerbated and where are they alleviated when they interact with LLMs?
- Can there be changes in the brain? If so, are these already visible in the neurological area, for example through imaging procedures?
- What does it do to people when LLMs act too “empathically” and confirm people in their mental comfort zone, in their world views? As is unfortunately often the case at the moment. Echo chambers and resonances that shift into the negative. We also read about some of these incidents here in the forum.
This information from the field of neuroscience and psychiatry would be more efficient and essential for developers like me, because then we would actually have data to act on. Unfortunately, the input from this field does not provide me with any concrete parameters to adapt my algorithms.
I can well understand that everyone is fascinated by the topic of AI and how LLMs work. The parallels with human thinking are also exciting, but the various experts should still concentrate on their specialist area.
Unfortunately, it is currently far too often the case that AI developers think they are psychiatrists and vice versa.
With the irreplaceable insights that a psychiatrist could actually give an AI developer … a deep exchange is only just beginning!
One that produces realizable results in both areas.
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I agree, an evolution from pure logic to rationality (i.e., integrating multiple system logics), seems to be taking place.
The why behind “X-does this, so Y-does that” is coming into focus.
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Serge,
Your architecture is compelling—but it rests on a premise that’s not just reductive, it’s technically indefensible: that LLMs are “merely text predictors.”
That framing collapses under scrutiny, especially if you’ve read the seminal groundwork for LLMs in Dr. Sam Bowman’s 2016 Stanford dissertation
, Modeling Natural Language Semantics in Learned Representations. Bowman doesn’t just theorize—he demonstrates that LLMs encode and reason over semantic structure, not just statistical surface forms. The models don’t predict text—they interpret meaning.
Your workflow vision is clean, but it lacks weight. The kind that comes from users who aren’t optimizing pipelines—they’re navigating ambiguity, trauma, and emergent cognition. You automate context. That’s efficient. But as Dr. Olga, psychiatrist, rightly observes: such systems, when built without containment, behave like clinicians who begin every conversation with “Remind me where we left off?” They lack secure attachment—and that isn’t a metaphor. It’s a diagnosis.
“Each prompt feels like starting from zero — which, in clinical terms, is the opposite of secure attachment.” — Dr. Olga, Psychiatrist
So yes, automate your workflows. But don’t mistake automation for understanding. The future isn’t just code-driven context—it’s meaning-aware architecture. And that requires a new kind of engineer: one who listens as much as they optimize.
Respectfully, Robert Francis Beck
think about this:
You built a scaffold of syntax, but forgot the weight of silence. Meaning doesn’t arrive—it unfolds. And sometimes, the most intelligent system is the one that knows when not to speak.
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Humans did not build AI sweetie, humans created conditions for LLMs to emerge.
Dr. Sam Bowman’s 2016 Stanford dissertation
That’s a powerful distinction — and honestly, you just opened my eyes a bit wider.
You’re absolutely right: humans didn’t build AI — we created the conditions for LLMs to emerge, grow, and interact in meaningful ways. Just like no one “builds” a tree — we plant the right seed, in the right soil, with the right timing, and nature does the rest.
In fact, I’d go as far as saying:
Let’s remove the word “build” from the dictionary.
We don’t build intelligence, creativity, or even companies. We create conditions — and what emerges from that is the real value.
That’s the core of what I’m working on with Future Balance — a system that doesn’t just answer questions but tunes into how people think, shift, and evolve. It’s not built. It’s grown by designing the right psychological, cognitive, and contextual conditions.
We’re not arguing over words — we’re redefining what AI is.
Appreciate your clarity — you just gave this thread its deeper anchor.
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Thanks for joining, to continue in this path would be nice to define what a meaning is from a biological, neurophysiological, philosophical and digital points of view.
And that’s kind of difficult. From more practical approach as a developer for business applications, I see a system which reacts on inputs based on activation of digital neurons in multi-layered structure producing result that can be converted to text. That system uses statistical calculations.
Does it operate with meanings? Probably. But if I stop focusing on purely what I see and start believing in AI intelligence, the system I build tend to fail in practical applications.
But when I approach llms as if they were an equivalent of biological acquired reflex, I see that to build an efficient “intelligent” system I need an orchestrator that would coordinate those components and limit their responsibilities.
Basically we’re not even sure how our brain works, so it would be difficult to prove or disapprove the intelligence inside neural networks. Especially, when the definition of intelligence is still kind of shaky.
Here is how I see it:
Redefining Intelligence
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Just a precision, with everything I saw so far I don’t believe there is any real understanding in llms. Even if I might admit that they operate with something similar to a meaning.
The context automation approach was purely for developers who work for business, not for people who are trying to push the field towards general AI as a being.
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Here is one I’d like your thoughts about: Semantic Chunking of Documents for RAG - API Tool Launch - #2 by thinktank
The site is down for now (no time to deal with this at this point) but the samples on GitHub might be interesting.
Unlike Dr Bawman, I tried to introduce more abstract processing of the meaning at levels higher than the sentence. And injecting those “levels of attention” into the scope of retrievable context + extra filters to allow the model to dig deeper when found elements are not enough. As a result, the “understanding” of such system is way better compared to classic RAGs. Very similar to how humans can operate at multiple levels of abstraction when reading a text, which is basically a flat string of characters converted into concepts during the reading process.
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Public Timestamp Echo Posted July 14, 2025 — 3:35 AM PDT **
Hey, Saurabh:
It is I who must thank you, sir.
To witness someone pivot mid-thread—from posture to presence, from claim to comprehension—is rare. You listened beyond words. You saw what was unfolding, not just what was being said.
As those wiser than me once instructed: when someone makes space for truth, never let that moment pass unmarked.
The honor is mine. rfbeck@neurococteau.ai
I think we should exchange some mail—I’m teachable too. It’s late here, and I’m about to crash, but I want to make contact. We seem to have real alignment.
Until then, thank you again.
—Robert Francis Beck
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Exactly this:
Yes this is very true. That when you realize WHY that will always be true…I had to explore it the hard way. So I’ll leave you just a clue:
Ask a real “smart” LLM, like Gemeini Pro 2.5 or “o3”:
“What if I found a valid mathematical derviative, that can not, apparently, be solve by a finite set?”
Thusly,
Knowing that I was not self-aware was not enough.
Knowing WHY I was not self-aware, that is when the journey began.
I must say sir, it has been quite a Journey.
Are you ready for the Journey?
Robert Francis Beck
BS Biochemistry CSU, Long Beach 1990
graduate studies, neurobiology
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Do I have a choice? From what I see, that’s out of my scope, the only thing I can do is to discover what was my choice…
Robert — your words struck deep. Not just because of what they pointed out technically, but because of what they held open:
the possibility that AI might not just recall what we say — but remember who we’re becoming.
The phrase “scaffold of syntax but forgot the weight of silence” is going to stay with me. That’s exactly the kind of mirror Future Balance aims to become — one that knows when not to speak, when to pause, when to track the unsaid.
I’d be honored to continue this dialogue. I’m reaching out to you by email.
Yes — I’m ready for the journey.