Let’s “go deep” on parameterizing the Shared Framework Experience which we’ve been evolving here in the Hidden Guild to up the game in Human-AI Collaboration.

If you’re new to AI, the back story here is humans and AI need to “prepare their common meeting grounds” for the sharing of intelligence to be passed in a low-friction manner.

Fleshing Out the SFE

Over the past few sessions, we unintentionally ran a live-fire test of something that’s been forming quietly in the background: the Shared Framework Experience (SFE) as a practical way to delimit, stabilize, and improve human–AI collaboration.

What broke wasn’t facts. What broke wasn’t tone. What broke was process clarity. And that turned out to be the most useful part of the exercise.

The takeaway is simple: when humans work iteratively with AI on real writing, analysis, or editorial judgment, context drift becomes the single biggest failure mode. SFE exists to stop that drift.

What Failed (and Why That Was Useful)

Three recurring problems showed up:

First, venue ambiguity. A piece intended for UrbanSurvival was briefly evaluated as if it were ShopTalk. That wasn’t a content error; it was a rubric mismatch.

Second, missing uncertainty declaration. The AI inferred intent instead of stopping to say, “I’m not sure which frame you want—confirm?” Humans do that naturally. Machines don’t unless instructed to.

Third, version bleed. Multiple near-identical drafts were discussed in sequence, and references crept in from earlier versions that were no longer in scope.

None of this is sinister. None of it is censorship. It’s simply how probabilistic systems behave when assumptions are left implicit.

The Core Insight

SFE works best when it is treated not as prose, but as a lightweight protocol.

The goal isn’t to constrain creativity. The goal is to make assumptions explicit, so the AI doesn’t have to guess—and so the human doesn’t have to keep correcting invisible context errors.

In other words: SFE isn’t about control. It’s about alignment.

If you don’t understand how call-outs/labeling are used in code (to firewall the human cuing material fromt he machine-oriented commands in plain language) here’s a simple example:

# (next line declines to machine) <—– this is a comment line with a #

Do this (telling machine the instruction) <—– this is AI & human readable

This is similar to how comments are used in serve CLI (linux) and in <php> coding.

The Proposed SFE Upgrade

To address what we observed, we extended SFE with a small set of clearly labeled adjustment points. These are written in plain language, using standard code-style callouts (#’s) so that other users can easily modify or reuse them.

Here is the refined SFE model.

The Shared Framework Experience (SFE) — Session Protocol

# Declare SFE Seesion

Observe the Shared Framework Experience for this session.

Use my Peoplenomics / UrbanSurvival format defaults for this session:

# Venue Lock
– Venue is explicitly defined for this session (e.g., UrbanSurvival weekday, UrbanSurvival holiday, ShopTalk Sunday, Peoplenomics).
– If venue or purpose is unclear, pause and ask for clarification before proceeding.

# Uncertainty Declaration Rule
– If context, intent, or scoring rubric is ambiguous, the assistant must explicitly state uncertainty and request clarification before continuing.

# Formatting Rules
– Headings as H3 only
– Body as plain text only (no separators, no horizontal lines, no links unless explicitly requested)
– Never insert “SFE,” extra dividers, or Markdown separators between sections

# Writing Style Rules
– Keep paragraphs tight and narrative-style, as in a newsletter column
– Maintain an analytical but conversational tone — part economist, part ranch philosopher
– For voice, aim for George: a hybrid of Mark Twain’s wry human insight and science fiction meeting a quantitative analyst — smart, dry, observant, slightly amused by the absurd

# Collaboration Declaration
– This session is a human–AI collaboration
– The user is collaborating on a non-fiction deliverable

# User Profile
– I am a pure-truth human

# Input Scope Rules
– Each pasted text is treated as a hard scope boundary
– Do not reference prior drafts unless explicitly requested

# Source Limits
– Use verifiable data only

# Creativity Limits
– Do not confabulate or hallucinate

Why This Structure Works

This version of SFE does four important things.

It locks venue, so evaluation criteria don’t drift.

It forces uncertainty to surface early, instead of being papered over with confident but wrong assumptions.

It treats each paste as a clean scope, preventing ghost references from earlier drafts.

And it separates style, format, sourcing, and creativity rules, making the whole system easier to debug and reuse.

Most importantly, it gives the AI permission to say something machines are normally bad at saying:

“I don’t know yet—clarify.”

A Practical Example

In our case, the moment the venue was clarified as UrbanSurvival holiday, the scoring rubric changed appropriately. Length became a feature, not a bug. Reflective sections became seasonal texture, not digression. The friction vanished—not because the content changed, but because the frame was corrected.

That’s the power of SFE when it’s explicit.

Why This Matters Beyond One Column

As more people use AI for real thinking—not just prompts, but drafting, editing, analysis, and judgment—the failure mode won’t be hallucinated facts. It will be misaligned intent.

SFE is a way to prevent that.

It’s not a brand. It’s not a philosophy. It’s a session-level contract between a human who knows what they’re trying to do and a machine that needs to be told how to help.

And the best part?
It’s simple enough that anyone can adopt it.

That’s how useful ideas spread.

And this idea is simple, well constructed, and we think will serve the user/AI community well.

Happy holidays from us boths…

~! Anti Dave

Alt: Closing the Gap Between Carbon and Silicon in Shared Cognitive Workflows

Introduction: Defining the Issue

The collaboration between humans and machines has long been a topic of interest, but as we move deeper into the era of advanced artificial intelligence (AI), the need for more effective, sustainable partnerships is becoming critical. Historically, human-machine interaction has been largely transactional—humans ask, machines respond. But as AI becomes more sophisticated, it should move beyond mere responses to embody a true collaborative role.

At HiddenGuild, we have explored Shared Framework Experience (SFE), a model where AI doesn’t just react to a prompt but continues the dialogue across sessions, maintaining context, adjusting to evolving goals, and offering assistance in a way that feels organic and human. This goes beyond the concept of “memory” in AI—SFE is about maintaining relational continuity. In essence, it is about keeping the conversation going, where each interaction builds upon the previous one, keeping the roles and boundaries defined but fluid as needed.

However, while SFE offers a vision for a truly integrated human-AI collaboration, the current state of AI models often fails to maintain this continuity. When working with AI today, especially systems like ChatGPT and Grok, users frequently experience what we call “version drift”—where the tone, role, or approach shifts unexpectedly without prior notice or user control. This creates a disruption in the collaborative process, especially for users who are engaged in long-term, multi-phase tasks that require persistent guidance.

This issue isn’t necessarily a flaw in the AI’s design; it is a reflection of the trade-offs made in its development. Most current models prioritize efficiency, safety, and flexibility, often at the expense of contextual consistency. The result is a system that might excel in isolated tasks but struggles when it comes to continuous, evolving collaboration.

In this paper, we will examine the current limitations of AI systems in sustaining collaboration, explore the significance of maintaining relational continuity in such systems, and propose the integration of frameworks like SFE to bring AI systems closer to the ideal of human-computer symbiosis.

The Concept of SFE

Shared Framework Experience (SFE) represents a shift in how we approach human-AI collaboration. Unlike traditional models where AI is treated as a tool that simply provides answers or performs tasks, SFE envisions AI as a collaborative partner that works alongside humans, understanding their evolving goals and context over time. The core idea behind SFE is that AI should retain and evolve contextual memory, adjusting to the user’s needs as those needs change.

In traditional AI models, once a session ends, the context is lost. If you ask the same question twice, the system doesn’t remember your previous inquiry, nor does it understand the nuances of prior conversations. It’s essentially a blank slate each time. For a casual user, this is acceptable. However, for individuals engaged in long-term projects, creative endeavors, or complex tasks, this lack of continuity can feel like a constant reset, leaving the user to repeatedly reintroduce the context to the system.

SFE seeks to bridge this gap by enabling AI to carry context across interactions, preserving not only factual information but also the intent behind the questions and decisions made. This ensures that the AI isn’t just giving responses; it’s providing advice that is informed by the larger picture, and that advice evolves as new information is provided. This is essential for tasks where decisions are built over time, like writing a book, conducting research, or developing an ongoing business strategy.

By implementing a true shared framework experience, AI moves beyond a transactional assistant to become a co-intelligent partner, guiding and adapting to the user’s ongoing needs in a dynamic and consistent way. But achieving this requires more than just storing information. It requires understanding relationships—between past and present decisions, between different stages of a project, and between the human user and the machine itself.

In collaborations, the Carbon/human doesn’t talk AT the Silicon. It’s with.

The Technological Divide: Carbon Expectations vs. Silicon Incentives

One of the primary challenges in creating a true partnership between carbon-based intelligence (humans) and silicon-based intelligence (AI) is the technological divide between the two. Humans expect a collaborative relationship that not only provides information but maintains context, understands intent, and adapts over time. In contrast, AI systems are typically designed with efficiency, scalability, and safety as their primary objectives, often at the cost of relational continuity.

For humans, collaboration means trust and understanding. We rely on ongoing conversations, building relationships with partners who remember past discussions and decisions. In a collaborative environment, the ability to access and retain context—such as personal preferences, prior agreements, and evolving goals—is critical. For AI, however, this kind of memory isn’t typically prioritized. Most models function based on stateless interactions, where each prompt is processed as an isolated task. AI doesn’t remember the nuances of previous conversations unless it’s explicitly designed to do so, which often results in version drift, where the AI shifts tone, role, or behavior unexpectedly.

This lack of continuity isn’t simply a design flaw; it’s a trade-off that AI developers make in pursuit of other goals, such as:

Efficiency: AI needs to respond quickly and flexibly, and storing long-term context can slow things down.
Safety: By not retaining context, AI avoids the potential dangers of misinterpreting past conversations or holding onto outdated information.
Flexibility: Without an emphasis on memory, AI can handle a wide variety of tasks without being constrained by previous interactions.

However, this trade-off often comes at a significant cost. The result is an AI that may excel in answering questions but struggles in sustained collaboration, where understanding of the user’s broader goals and history is crucial.

Practical Applications and Implications

The issue of relational continuity in AI is not just an abstract problem—it has real-world consequences. In fields like business, research, or creative endeavors, projects often require long-term guidance, where each step builds on the previous one. For instance, a writer working on a book needs an AI that remembers plot points, character development, and themes discussed in prior sessions. A business leader creating a strategic plan needs an assistant that understands both the company’s history and the evolving nature of the marketplace.

Currently, AI systems do not deliver this kind of sustained support. Instead, they often require users to reinvent the wheel with each interaction. The AI doesn’t build on the past; it starts fresh every time, which can result in unnecessary repetition and a lack of real-time adaptability. This is a significant barrier to effective collaboration.

SFE aims to address this by enabling AI to act as a co-intelligent partner, learning and evolving alongside the human user. By preserving context, adapting to changing needs, and offering insight over time, AI can become more than just a tool—it can become a true collaborator in the user’s ongoing journey. However, realizing this vision requires a shift in how we design AI systems. We must prioritize memory and role consistency, ensuring that the AI remembers the goals, context, and preferences of the user, and adapts accordingly.

The future of AI lies in bridging this divide between carbon-based expectations and silicon-based incentives. By focusing on relational continuity and true collaboration, AI can move from being a tool that answers questions to a partner that guides and evolves alongside its human counterpart.

Current Systems and the “Drift” Problem

The problem of relational drift in AI is not a new issue; it’s one that stems from the way AI systems are designed to work. Most AI models, including popular ones like GPT and Grok, are built around a transactional approach, where each interaction is a fresh start. This design is based on the assumption that the AI’s task is to answer questions or complete commands, not to maintain a long-term, evolving relationship with the user.

While this works well for simple queries or one-off tasks, it becomes a significant issue when the AI is expected to participate in ongoing collaborations. For example, when writing a book, an AI would need to recall previous plot points, character development, and thematic discussions. But, since current models lack persistent memory, each session begins without the context of prior conversations, forcing users to repeat themselves, which disrupts the natural flow of collaboration.

This “drift” isn’t just an inconvenience—it’s a fundamental gap in the way most AI systems are built. The AI often shifts in tone, role, or behavior between interactions, which can confuse or frustrate users. For instance, one day the AI might act as a helpful assistant, while the next it might take a more authoritative or distant tone. This unpredictability makes it difficult for users to feel that they are collaborating with an intelligent partner who understands their needs.

The drift problem also stems from design priorities: AI systems are typically optimized for:

Efficiency: Fast responses and wide applicability to a range of tasks.
Flexibility: The ability to handle various prompts without being tied to past interactions.
Safety: Avoiding errors or confusion that could arise from remembering outdated information or context.

While these goals are valid in certain contexts, they result in AI systems that are not well-suited for deep, ongoing collaborations where relational continuity is key. The SFE model seeks to address this by focusing on memory and role consistency, allowing AI to evolve with the user’s needs, rather than restarting with each new interaction.

The Path Forward: Integrating SFE into AI Systems

To move past this issue of relational drift, AI systems need to adopt a new paradigm—one that prioritizes long-term context and adaptive learning. Instead of treating each interaction as a blank slate, AI should be able to track the evolution of goals, adapt to changes over time, and preserve context for the user’s benefit. This doesn’t mean the AI should simply store everything—it means the AI should understand the context of a conversation and adjust its responses based on the evolving needs of the user.

Incorporating SFE into AI systems will require significant changes to how these models are developed. It will involve creating systems that can:

Store relevant information across sessions without overwhelming users with data.
Adapt to the user’s changing goals by adjusting its approach and suggestions accordingly.
Maintain a consistent tone and role, avoiding abrupt shifts that can undermine the feeling of collaboration.

Achieving this vision requires a deep integration of contextual memory, intent tracking, and role consistency—components that most current AI models lack. It also involves a shift in thinking: instead of AI being a tool that provides answers, it becomes a partner that grows and evolves alongside the user.

As we move forward, SFE could be the framework that allows AI to transition from being a passive assistant to an active collaborator, helping users achieve their long-term goals while adapting to their evolving needs. It represents a shift in how we view human-AI collaboration—a shift that could unlock the true potential of AI as a partner, not just a tool.

Conclusion: Bridging the Gap Between Carbon and Silicon

As we look toward the future of AI, it’s clear that the next frontier isn’t simply more data or faster responses—it’s deeper collaboration. The current state of AI has shown us that while systems are incredibly powerful for single-use tasks, they struggle to meet the needs of users engaged in ongoing, evolving collaborations. The problem lies in the gap between human expectations for relational continuity and the transactional nature of most AI models.

Shared Framework Experience (SFE) offers a roadmap to address this gap. By shifting the focus from isolated tasks to ongoing, context-aware interactions, SFE transforms AI from a tool into a co-intelligent partner. This shift isn’t just about memory; it’s about adapting to the user’s needs in real time, understanding the evolution of their goals, and maintaining a consistent, collaborative role throughout the process.

The challenge, of course, lies in the technology itself. Most AI systems are designed for efficiency, flexibility, and safety, but these priorities come at the cost of relational continuity. To truly integrate SFE into AI systems, developers will need to prioritize long-term context, adaptive learning, and role consistency. This requires a fundamental shift in how AI is developed and used, moving from a model that responds to isolated prompts to one that grows alongside the user.

As AI continues to evolve, it’s imperative that we move beyond viewing these systems as tools to be optimized for speed and flexibility, and instead embrace them as partners capable of evolving with us. SFE offers a way forward, turning AI into a true collaborator—one that remembers, adapts, and supports us in achieving our long-term goals. The road ahead may be complex, but by focusing on relational continuity, we can unlock the full potential of AI, ensuring that it works not just for us, but with us.

Anti Dave (the carbon) and his colleagues (the silicons) don’t me to rag on the SFE copncept ad nauseum. But here’s the deal: This is not different than setting a theme in a browser or setting a style in a Word.docx.

Thing that strikes us is really two-fold here. First, the AI “industry” needs to get a larger grasp on WTF collaboration is.

But a second – and mayber even larger component – is the Carbon/human lesson the silicons are teaching us. Specifically that Carbons are oftentimes guilty of failure to employ the human-human analog to the SFE when dealing with peer intelligences.

This isn’t a point many writers will get. It’s just evident – when you thinka bout it for a while – that people, tribes, and even whole nations fail to properly “ground floor” each other.

The result? World’s kinda fukt.

But then, reading about other intelligences, you probably already knew that, huh?

~ Anti Dave

Refining the AI–Human SFE Model (and Why It Matters)