2,235 results for models · 2.324s

News for “models”
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Moozonian News
vynix.app• Mar 2, 2026• 1 min read
Show HN: Vynix – Mobile app with 100 AI models for image/video/audio generationI built Vynix, a cross-platform mobile app (iOS/Android) that aggregates 100+ AI models into a single interface. Instead of switching between different AI apps, you get image generation, video creation, TTS, music generation, and LLM chat all in one place.Tech stack: - Kotlin Multiplatform + Compose Multiplatform (shared UI across iOS & Android) - Firebase (Auth, Firestore, Cloud Functions) - Backend proxies to Replicate and fal.ai APIs - Credit-based system — no subscriptions requiredThe app adds new models within days of their public availability. Currently includes models for: - Text-to-image (multiple styles: photorealistic, anime, digital art) - Text-to-video and image-to-video - Text-to-speech (50+ voice options) - AI music generation - LLM chat (multi-modal)Free to try — you get 40 credits on signup + daily free credits.Website: https://vynix.appHappy to answer questions about the KMP architecture, the API aggregation approach, or anything else.
Moozonian News
aegismind.app• Feb 25, 2026• 1 min read
Show HN: AI models debate each other on cross-domain research hypothesesWe built a research discovery pipeline that ingests papers from arXiv and Semantic Scholar, finds cross-domain connections, generates hypotheses with a multi-model ensemble, formally verifies them with Z3, then stress-tests survivors in adversarial debate.The twist: we capture and display what each model said when critiquing. No single-model black box — you see GPT-4o, Claude, Gemini, and Grok arguing for and against the same hypothesis.Example: [Distributed feedback control from microbial consortia enhances metabolic stability in Ginzburg-Landau cognition models](https://www.aegismind.app/discoveries/2af7c10d-18f8-42d5-8c9...). The hypothesis bridges synthetic biology and physics-of-cognition. The debate transcript shows Claude calling it "artificially stitched together" while Gemini finds it "a plausible theoretical synthesis." We surface both — and the evidence score (38% challenged) — instead of hiding the disagreement.Pipeline: arXiv ingestion → cross-domain matching → multi-model
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Moozonian News
github.com• Feb 18, 2026• 1 min read
Show HN: NSED is public – Mixture-of-Models to Hit SOTA using self-hosted AIHey HN, We're open-sourcing (source-available, BSL 1.1, patent pending) the orchestrator behind our paper benchmark results. NSED (N-Way Self-Evaluating Deliberation) is a Rust binary that coordinates multiple LLMs through structured rounds of proposals and cross-evaluation, using quadratic voting to prevent any single model from dominating the consensus.The result: Three open-weight models (20B, 8B, 12B) on consumer GPUs — 64GB total VRAM, ~$7K hardware — score 84% on AIME 2025. The same models individually or with naive majority voting score ~54%. That's frontier-model performance on hardware you can buy at Micro Center.How it works:Each agent independently proposes a solution Every agent evaluates every other agent's work Scores aggregate via quadratic voting (cost of influence grows quadratically → no single model can dominate) Repeat. Agents see prior results, refine, re-evaluate System converges toward the highest-quality answer through adversarial cross-checkingIt's provider-agn
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Moozonian News
news.ycombinator.com• Feb 13, 2026• 1 min read
Small Language Models (SLMs) vs. Large Language Models (LLMs)AbstractThe last five years have seen explosive progress in large language models (LLMs) — exemplified by systems such as ChatGPT and GPT-4 — which deliver broad capabilities but at heavy computational, latency, privacy, and cost budgets. In parallel, a renewed research and engineering focus on Small Language Models (SLMs) — compact, task-optimized models that run on-device or on constrained servers — has produced techniques and models that close much of the gap while enabling new applications (on-device inference, embedded robotics, low-cost production). This article/review compares SLMs and LLMs across design, training, deployment, and application dimensions; surveys core compression methods (distillation, quantization, parameter-efficient tuning); examines benchmarks and representative SLMs (e.g., TinyLlama); and proposes evaluation criteria and recommended research directions for widely deployable language intelligence. Key claims are supported by recent surveys, empirical papers,
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