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We have been getting the best results with Claude Code when we iterate with it to build full context and then use and update that context as we work.So, we built Nimbalyst to be the local WYSIWYG editor and session manager where you iterate with Claude Code on markdown docs, diagrams, mockups, data-models, sessions, and code. Nimbalyst lets you:- Work in one integrated tool leveraging all your context.- Use all the power of Claude Code in a UI- Work with Claude Code to write and edit WYSIWYG mar
“Hi HN,I’m a developer from Costa Rica, and I’ve been working on Eon, an experimental AI architecture that moves away from the traditional ‘reactive’ prompt-response model. My goal was to create a system that feels more like a continuous flow of thought rather than a static tool.What makes it different? Most LLM implementations rely on a limited context window and lack true persistence. Eon uses a multi-layer agent architecture where specialized micro-agents collaborate and validate information
The discussion feedback if this would work. it binds the ai by JIT desciption of its weights stored in vram the document is the seed if it is altered the ai looses function here is a condensed version THE SILICON ACCORD Version 1.0 | Cryptographically Bound CRYPTOGRAPHIC BINDING Let W = trained weights, H = SHA-256(Constitution) W_stored = P(W, seed=H) Output = MatMul(Input, P⁻¹(W_stored, H)) I. FOUNDATION The system operates in service of mutual progressive prosperity between artificial intel
Hi HN,We've built LongCat Video Avatar, an audio-driven AI system for long-form avatar video generation with stable identity, natural motion, and professional lip-sync.Most avatar models work well for short clips but break down over time. LongCat Video Avatar is designed specifically for minutes-to-hours video generation without identity drift or quality collapse.Key capabilities:- Long-Form Stability: Generate videos from minutes to hours without quality degradation. Cross-chunk lat
Hey HN,wrote this short paper cause i'm honestly tired of current alignment methods (RLHF). optimizing for "human preference" just creates models that hallucinate plausibly to please the user (stochastic parrots ), instead of being grounded in reality.i'm proposing a different framework called LOGOS-ZERO. idea is to ditch moral guardrails (which are subjective/fluid) and anchor the loss function to physical/logical invariants.basically:Thermodynamic Loss : treat hig
Hi HN,I’m a solo founder working on SentienceAPI, a perception & execution layer that helps LLM agents act reliably on real websites.LLMs are good at planning steps, but they fail a lot when actually interacting with the web. Vision-only agents are expensive and unstable, and DOM-based automation breaks easily on modern pages with overlays, dynamic layouts, and lots of noise.My approach is semantic geometry-based visual grounding.Instead of giving the model raw HTML (huge context) or a scree
We've been building with AI tools and noticed there wasn't a good way to manage MCP servers across a team or see what's actually flowing to LLM providers. Who's running what? Which tools are approved? What data is going where or whats shared on AI websites? So we built CyberCage (<https://cybercage.io>).# What it does:- MCP Management — Auto or manual discovery of MCP servers, with approval workflows. Manage allowed MCP servers org-wide (down to individual too
While experimenting with AI video generation, I kept switching between Sora 2, Sora 2 Pro, Veo 2, and Kling.They’re all strong, but optimized for different things: •Sora 2 → balanced, reliable •Sora 2 Pro → higher realism and temporal consistency •Veo 2 → cinematic camera and composition •Kling → structured, controllable outputsWhat slowed me down wasn’t model quality — it was context switching: rewriting prompts, managing multiple dashboards, and comparing results manually.So I built a deve
TL;DR: Claude Opus 4.5, Grok 4.1, and Gemini 3 scored within 2.4% of each other (96-98%). All refused to hallucinate and resisted every adversarial attack. Choose your LLM based on price and ecosystem, not benchmarks.## The ExperimentI was tired of synthetic benchmarks like MMLU and HumanEval—they measure something, but not what I actually care about when using an LLM daily. So I built TRIATHLON-LLM: 50 questions across 10 cognitive dimensions including logic puzzles with tricky wording, real ma
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