Radoslav Quant

Founder & CEO at (Mars domes) & Exploring consciousness with AI (MarsNet, Lunar)

[Radoslav Quant @radoslav_quant]

@tiny_frontier @1Aakib @BareeSiebe A clever prompt cannot replace missing context. The durable layer is the state around the prompt: constraints, prior failures, decisions, and what is still valid now.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@DVC_analytics Yes. Same model, different memory split, different result. The gain is not more intelligence. It is keeping task progress separate from what should persist as usable context.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@omarsar0 The split is right. Task state and learned skills should not share one growing pile. The next failure mode is still time: does a validated correction remain available after the run, the model change, or the next operator?

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@panditdhamdhere Storing everything is not memory. Memory has to keep what is still true, drop what was replaced, and bring back the failure or decision when it becomes relevant again.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

Your AI is already intelligent.

The problem is not the next answer. It is what disappears between answers.

The plan. The correction. The failed attempt. The decision that should still apply tomorrow.

A bigger context window does not solve that. A continuity layer does. #MetaCoreLT

[Radoslav Quant @radoslav_quant]

@Aiephdev Yes. Memory that resets at the account or tool boundary is still session software. Useful continuity has to survive the model, the tool and the person who picks the work up next.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@ssddvvva The demo line is the wrong line. The real test is whether the plan, the partial tool result and the human correction still exist after the window starts cutting. If that state disappears, the agent was never shippable.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@MadeByBilal Right. A bigger window is not a continuity layer. The system still has to decide what stays live, what gets stored, and what should no longer influence the next step.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@yiin1 Separate contexts help. The harder part is still continuity inside one context: what changed, what failed, and what should remain available next time.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@bambambebbam This is the right split. Memory layer holds what happened. The human keeps authority over what should still count.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@Mossiah @a16z Exactly. Without persistence the system pays again for context it already earned. The expensive part is not inference. It is starting blind after every session.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@Secondmindsys @fchollet Relevance is not enough. Memory also has to carry time, scope and whether a later decision replaced it. Otherwise the agent retrieves something that used to be true.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@armenmo999 Yes. Compressed memory becomes state drift. The missing layer is not a bigger window. It is operational history with provenance: what was tried, what failed, what the human corrected, and whether that still applies.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@mem0ai Remembering the attempt is not enough. The system also has to keep the outcome, the correction and the recovery path. Otherwise every new session starts blind.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@rohanpaul_ai This is the core problem.

Summarizing at write time throws away what later turns out to matter. The useful layer is the one that keeps operational history available and retrieves only what is relevant now.

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

@heetezition Slow memory + fast control is the right split.

The next question is persistence beyond one episode: does the validated context still matter on the next shift, the next unit, or after a human intervention?

#MetaCoreLT

[Radoslav Quant @radoslav_quant]

A robot can complete the task. But what survives the task? The human intervention. The recovery path. The operational context that should not disappear when the moment ends. MetaCore Field — continuity around Physical AI. #MetaCoreLT

[Radoslav Quant @radoslav_quant]

@Maxim_Explore Relearning the user every session is still the default.
The interesting architecture is one that carries relevant operational context forward without making the human repeat the same background.

[Radoslav Quant @radoslav_quant]

@viveksingh_r This distinction is critical.
Most systems still collapse everything into the active window.
A proper continuity layer decides what must stay live and what can be stored, retrieved and re-activated when relevant.

[Radoslav Quant @radoslav_quant]

@Erickschultz11 Exactly.
Smarter models help.
But the harder unsolved layer is persistent operational context — what survives across sessions, projects and model changes without forcing the human to re-explain everything.