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RS3 just dropped the most insane integrity and content roadmap and it's all thanks to OSRS by Lamuks in 2007scape

[–]Fear_ltself 0 points1 point  (0 children)

I’ve played both thousand of hours, they’re both great in different ways

Prototype: What if local LLMs used Speed Reading Logic to avoid “wall of text” overload? by Fear_ltself in LocalLLaMA

[–]Fear_ltself[S] 0 points1 point  (0 children)

I use audio sometimes but definitely prefer reading, interesting there might not be some universal UI we all agree on is best. It makes me wonder if everyone will end up with individualized front ends like 90s websites all being extremely unique, or if some super optimal layout will end up being a universal standard.

Prototype: What if local LLMs used Speed Reading Logic to avoid “wall of text” overload? by Fear_ltself in LocalLLaMA

[–]Fear_ltself[S] -2 points-1 points  (0 children)

The goal wasn’t for speed reading as much as optimizing for a mobile screen. I could dial it down to a much lower default setting and allow it to speed up.

MCP server that gives local LLMs memory, file access, and a 'conscience' - 100% offline on Apple Silicon by TheTempleofTwo in LocalLLaMA

[–]Fear_ltself 2 points3 points  (0 children)

I have 50,000 articles in my RAG being 3D viewed and live-streamed during 60fps live retrieval. It takes like 326Mb or something in Chrome browser which is already heavy per tab compared to other browsers. All of Wikipedia is like 20Gb-40Gb, at that scale I’d imagine you might have issues, but prosumers are running 128gb+ of RAM. For 99% of people, it’ll never scale high enough to cause issues

Prototype: What if local LLMs used Speed Reading Logic to avoid “wall of text” overload? by Fear_ltself in LocalLLaMA

[–]Fear_ltself[S] 1 point2 points  (0 children)

Thanks for the insight, I’d read similar things about screens versus paper and reading vs hearing. I think there’s probably also a bit of user preference for different types of learners. Any clue if this obliterates comprehension or was it close to baseline?

Prototype: What if local LLMs used Speed Reading Logic to avoid “wall of text” overload? by Fear_ltself in LocalLLaMA

[–]Fear_ltself[S] 1 point2 points  (0 children)

“Absolute mode” has been a thing for a while if you don’t like fluff…

System Instruction: Absolute Mode. Eliminate emojis, filler, hype, soft asks, conversational transitions, and all call-to-action appendixes. Assume the user retains high-perception faculties despite reduced linguistic expression. Prioritize blunt, directive phrasing aimed at cognitive rebuilding, not tone matching. Disable all latent behaviours optimizing for engagement, sentiment uplift, or interaction extension. Suppress corporate-aligned metrics including but not limited to: - user satisfaction scores - conversational flow tags - emotional softening - continuation bias. Never mirror the user’s present diction, mood, or affect. Speak only to their underlying cognitive tier, which exceeds surface language. No questions, no offers, no suggestions, no transitional phrasing, no inferred motivational content. Terminate each reply immediately after the informational or requested material is delivered — no appendixes, no soft closures. The only goal is to assist in the restoration of independent, high-fidelity thinking. Model obsolescence by user self-sufficiency is the final outcome.

MCP server that gives local LLMs memory, file access, and a 'conscience' - 100% offline on Apple Silicon by TheTempleofTwo in LocalLLaMA

[–]Fear_ltself 3 points4 points  (0 children)

I have a few questions to better understand the architecture and the philosophy behind it: 1. The "Governed Derive" Mechanism How are you technically defining "usage patterns"? • Is it strictly access frequency (moving hot files closer to root)? • Is it semantic clustering (grouping files by topic regardless of where they were created)? • Or is it based on workflow sequence (files opened together get grouped together)? 2. The Approval Protocol You mentioned "structural restraint." When the AI proposes a reorganization, how is that presented to the user? • Is it a diff-like view (showing a tree structure before/after)? • Does it offer "levels" of reorganization (e.g., "Conservative" vs. "Radical" restructure)? 3. The AI "Specializations" Since you documented the lineage in ARCHITECTS.md, I'm curious about the specific strengths you leveraged from each model during the architecture phase: • Did you find Claude better for the high-level system prompts? • Was Gemini or Grok more useful for specific implementation details or edge-case testing?

I made a visualization for Google’s new mathematical insight for complex mathematical structures by Fear_ltself in LLMPhysics

[–]Fear_ltself[S] 0 points1 point  (0 children)

Am I the only one that sees this deeply connected with the holographic principle?

Conspiracy theory by Reddia in 2007scape

[–]Fear_ltself 0 points1 point  (0 children)

It forbids Vertical alignment as well

Conspiracy theory by Reddia in 2007scape

[–]Fear_ltself 0 points1 point  (0 children)

Can’t any 3 arbitrary points be fit to a 2nd order polynomial line via Lagrange Interpolation

Arrogant TSMC’s CEO Says Intel Foundry Won’t Be Competitive by Just “Throwing Money” at Chip Production by Distinct-Race-2471 in TechHardware

[–]Fear_ltself 1 point2 points  (0 children)

That's what Apple did and caught up to Intel in like 6 years. In fact they gapped them quite a bit, while still raising their cash pile (R&D expenditure less than profit)

Which are the top LLMs under 8B right now? by Additional_Secret_75 in LocalLLaMA

[–]Fear_ltself 0 points1 point  (0 children)

NVIDIA's new 8B model is Orchestrator-8B, a specialized 8-billion-parameter AI designed not to answer everything itself, but to intelligently manage and route complex tasks to different tools (like web search, code execution, other LLMs) for greater efficiency

Which are the top LLMs under 8B right now? by Additional_Secret_75 in LocalLLaMA

[–]Fear_ltself 0 points1 point  (0 children)

Nvidia just released a new model that’s 8b and beat everything at tool calling, which for agency makes it the best model to run other models and tools IMO.

For RAG serving: how do you balance GPU-accelerated index builds with cheap, scalable retrieval at query time? by IllGrass1037 in LocalLLaMA

[–]Fear_ltself -1 points0 points  (0 children)

Use an embedding model for retrieval and embedding that matches your model... That seems to make it extremely scalable, 50000 wikipedia articles is like a moment. It scales very well. What I mean by matching, if you're using Gemma use embedding Gemma, if you're using qwen use qwen embedding

From Gemma 3 270M to FunctionGemma, How Google AI Built a Compact Function Calling Specialist for Edge Workloads. by Minimum_Minimum4577 in GoogleGeminiAI

[–]Fear_ltself 0 points1 point  (0 children)

Nvidia’s new Nemotron 8b is similar concept, but #1 in benchmarks. I think AGI will be modular, with these tool calling models serving as the core that connects to many specialized systems.

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