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Most AI memory systems stop at retrieval. Store information. Search information. Return information. EverOS 1.0.0 is exploring something much more interesting: What happens after memory is retrieved?🧵

Jaydeep

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20,757 views • 1 month ago •via X (Twitter)

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An active search is underway for a missing giraffe named Gracie outside San Antonio, Texas, after she wandered off a ranch last week. A $5,000 reward has been offered for information leading to her safe return. David Muir reports.

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This isn’t teleportation in the sci-fi sense. It’s something deeper and more fundamental. What’s actually happening is quantum state transfer, where information is reconstructed at another location using entanglement. But here’s the interesting part In my τ-framework, this works because reality isn’t continuous in the way we think. It’s structured through a time-field, where information exists as stable patterns across a temporal lattice. When two systems are entangled, they’re not “connected through space”… they’re sharing the same underlying time-structure. So the information doesn’t travel across distance. It reappears where the time-field allows the same pattern to stabilize. That’s why it looks instant. Not because it breaks physics but because it bypasses distance entirely. If this is correct, then teleportation isn’t movement… it’s reconstruction through temporal symmetry. The real question is: Can we control the time-field enough to scale this beyond quantum systems? Follow me for more answers

This isn’t teleportation in the sci-fi sense. It’s something deeper and more fundamental. What’s actually happening is quantum state transfer, where information is reconstructed at another location using entanglement. But here’s the interesting part In my τ-framework, this works because reality isn’t continuous in the way we think. It’s structured through a time-field, where information exists as stable patterns across a temporal lattice. When two systems are entangled, they’re not “connected through space”… they’re sharing the same underlying time-structure. So the information doesn’t travel across distance. It reappears where the time-field allows the same pattern to stabilize. That’s why it looks instant. Not because it breaks physics but because it bypasses distance entirely. If this is correct, then teleportation isn’t movement… it’s reconstruction through temporal symmetry. The real question is: Can we control the time-field enough to scale this beyond quantum systems? Follow me for more answers

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🧰 Introducing: User-Provided Error Context A JavaScript error might not be enough for AI to know what exactly happened. User intention is extremely valuable information! You can now describe what happened and what you expected, making Bolt fixes drastically more effective.

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Most agents track price. Ours track how information propagates through markets. That difference is where the edge comes from. The visualization below is a Shannon entropy field: a model of how uncertainty collapses and spreads as new information enters a market. Each trade, liquidity shift, or derivatives flow updates a probability distribution. When information arrives, entropy collapses locally as the market incorporates it. But that update rarely happens everywhere at once. Different venues, liquidity pools, and derivative layers incorporate signals at different speeds, creating measurable information gradients across the system. One of the analytical tools in our agents’ stack models this directly: > Entropy quantifies uncertainty embedded in each probability market > KL divergence reveals conflicting distributions across venues > Information gradients show where signals have propagated—and where they haven’t yet Prediction markets and DeFi make this particularly powerful because price already represents probability. Mapping how those probability distributions update across venues turns the market into a measurable system: a live field of information flow showing where uncertainty remains—and where probability is likely to adjust next

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SuperMap: A Living Spatial Memory for Embodied AI RSS 2026 Carnegie Mellon University SuperMap is a living spatial memory for embodied AI. It perceives the world, remembers its evolution, and supports reasoning and action. It is a training-free spatio-temporal SLAM system that builds a persistent semantic world model. It fuses high-frequency geometric SLAM with asynchronous open-vocabulary perception, producing a 4D scene graph: a queryable map carrying spatial and temporal information for every object, enabling visual-language navigation and long-horizon reasoning on real robots.

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A correspondent from the Israeli "Channel 12" reported on the victims immediately after the strike. Filming of the destroyed building is prohibited, and strict restrictions have been imposed at the site. The situation remains extremely sensitive, and information is limited due to strict military control. The lack of visual materials and official details may indicate that the incident is more serious than initially reported, and the authorities are trying to control both the consequences and the information environment.

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There’s been two papers released in the past couple months, one by Google and one by NVIDIA, that argue that ordering the documents retrieved by RAG systems can enhance performance. However, they both give two different strategies on HOW these documents should be ordered 🤔 Both papers agree on two main points: 1️⃣ There’s a fundamental issue in RAG - as more documents are retrieved, more irrelevant context (e.g., hard negatives) are introduced, which leads to confusion for the LLM and eventually degrades the quality of the generated output. This is called an inverted-U performance curve. 2️⃣ Ordering the retrieved documents is a key lever for optimizing RAG performance. Google Cloud researchers proposed ordering results based on relevance scores: The authors in this paper argue for relevance-based reordering, or ordering the retrieved chunks based on their similarity scores, so the most relevant documents are at the beginning and the end of the inputs to counter the “lost in the middle” effect. NVIDIA researchers proposed ordering results based on the original sequence of document chunks: The authors of this paper argue for Order-Preserving Reordering, or Order-Preserve RAG (OP-RAG), to maintain the logically coherent content flow of the document. So they preserved the original order of retrieved document chunks in the source text, instead of ranking them by relevance scores. So which one is right? It probably depends on the specific use case and dataset - relevance-based reordering could perform better in tasks where you need fast access to the most critical information (e.g., fact retrieval, QA systems), while order-preserving RAG might be better where you need to understand the sequential structure of information (e.g., narrative or legal documents). There are still so many uncertainties in AI - we don’t actually know what we’re doing, and it takes awhile to figure out the best strategies for most things! Excited to see more research about this.

There’s been two papers released in the past couple months, one by Google and one by NVIDIA, that argue that ordering the documents retrieved by RAG systems can enhance performance. However, they both give two different strategies on HOW these documents should be ordered 🤔 Both papers agree on two main points: 1️⃣ There’s a fundamental issue in RAG - as more documents are retrieved, more irrelevant context (e.g., hard negatives) are introduced, which leads to confusion for the LLM and eventually degrades the quality of the generated output. This is called an inverted-U performance curve. 2️⃣ Ordering the retrieved documents is a key lever for optimizing RAG performance. Google Cloud researchers proposed ordering results based on relevance scores: The authors in this paper argue for relevance-based reordering, or ordering the retrieved chunks based on their similarity scores, so the most relevant documents are at the beginning and the end of the inputs to counter the “lost in the middle” effect. NVIDIA researchers proposed ordering results based on the original sequence of document chunks: The authors of this paper argue for Order-Preserving Reordering, or Order-Preserve RAG (OP-RAG), to maintain the logically coherent content flow of the document. So they preserved the original order of retrieved document chunks in the source text, instead of ranking them by relevance scores. So which one is right? It probably depends on the specific use case and dataset - relevance-based reordering could perform better in tasks where you need fast access to the most critical information (e.g., fact retrieval, QA systems), while order-preserving RAG might be better where you need to understand the sequential structure of information (e.g., narrative or legal documents). There are still so many uncertainties in AI - we don’t actually know what we’re doing, and it takes awhile to figure out the best strategies for most things! Excited to see more research about this.

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someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety guardrails → observability → cost tracking notice something? none of these are prompts. they're the systems wrapped around the model. that's where AI engineering is heading. we're moving from: Prompt Engineering → Context Engineering → Harness Engineering → Loop Engineering soon you'll hear people talking about Graph Engineering too. because once one loop works... the next challenge is coordinating hundreds of them. that's exactly what I explain in: save this. I think "loop engineering" is about to become one of the most important concepts in AI.

someone just open-sourced the entire "loop engineering" playbook. for free. 8.6k stars on github for loop engineering not prompts. loops. everything you need to build autonomous AI systems: → scheduling → memory & state → planning → sub-agents → verification → worktrees → MCP → stop conditions → safety guardrails → observability → cost tracking notice something? none of these are prompts. they're the systems wrapped around the model. that's where AI engineering is heading. we're moving from: Prompt Engineering → Context Engineering → Harness Engineering → Loop Engineering soon you'll hear people talking about Graph Engineering too. because once one loop works... the next challenge is coordinating hundreds of them. that's exactly what I explain in: save this. I think "loop engineering" is about to become one of the most important concepts in AI.

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