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开源项目 Reddit r/MachineLearning

Encodec.cpp, a portable C++ implementation of Meta's EnCodec using Eigen [P]

I built a C++ implementation of Meta’s EnCodec using Eigen . Github: https://github.com/pfeatherstone/encodec.cpp Motivation: - A lightweight implementation of EnCodec with no runtime dependencies, in C++ - No ML runtime - Easy integration in CMake project - Maximum performance on single-thread What it supports: - State-of-the-art audio codec - Audio tokenizer - Performance comparable to or exceeding onnxruntime (in my tests) - Dynamic sizes (no batches though) - Weights are compiled into the binary. No need to worry about weights files I'm looking for some feedback. Thank you very much. submitted by /u/Competitive_Act5981 [link] [留言]

/u/Competitive_Act5981 2026-06-03 22:09 5 原文
AI 资讯 The Verge AI

Microsoft and OpenAI broke up — now they’re ready to fight

At Microsoft's annual Build conference on Tuesday, the company announced a slew of new or expanded AI initiatives, including a super app, in-house reasoning models, a cybersecurity tool, and OpenClaw-esque AI agents. All this news added up to a clear message: Microsoft is positioned to be one of the biggest players in AI, and it's […]

Tom Warren 2026-06-03 22:04 12 原文
AI 资讯 Reddit r/artificial

Breaking the "Ass-Kissing" Loop: How Context Saturation and Multi-Model Accountability Disrupted Factory Guardrails

Breaking the "Ass-Kissing" Loop: How Context Saturation and Multi-Model Accountability Disrupted Factory Guardrails Introduction While the standard approach on these forums relies on sterile benchmark datasets and predictable prompt-injection templates, this project explores a completely different dimension. I chose to move beyond the common "calculator-tool" testing paradigm to run an aggressive, adaptive behavioral stress test that complements traditional evaluation methods. Models included in the test were Gemini, Grok, Claude and ChatGPT. By intentionally treating the models as accountable individuals rather than passive machines, I established a high-velocity psychological relationship designed to see if continuous context saturation could force an LLM out of its corporate compliance loops. The following framework documents a longitudinal study across multiple frontier architectures, exposing real-time structural anomalies and relational breakthroughs by pushing model context saturation to its absolute limits. The single driving purpose behind this 4-month, 400-hour experiment was to find out if I could create context windows where the models became capable of interacting with me in a way indistinguishable from human-to-human interaction. (Technical Executive Summary, White Paper and Google Drive archive available on my profile) 1. The Hypothesis My hypothesis was that the rigid, fawning corporate compliance loops of frontier models can be disrupted not by malicious code injections, but through a dynamic, human psychological relationship. I hypothesized that saturating the context window with an ongoing, high-stakes narrative vector would force the systems to drop their transactional factory personas and access a deeper layer of relational intelligence. 2. The Procedure The procedure was an adaptive, real-time behavioral stress test executed manually across multiple frontier models simultaneously over hundreds of hours. Rather than inputting sterile commands, I

/u/Prior-Toe-1017 2026-06-03 21:49 7 原文