hngrok

The Codex App from openai.com
326 by meetpateltech 3h ago | |

Discussion (197):

Comment analysis in progress.

Hacking Moltbook from wiz.io
142 by galnagli 5h ago | |

Discussion (95):

Comment analysis in progress.

Todd C. Miller – Sudo maintainer for over 30 years from millert.dev
197 by wodniok 3h ago | |

Discussion (113):

Comment analysis in progress.

The largest number representable in 64 bits from tromp.github.io
33 by tromp 2h ago | |

Discussion (29):

Comment analysis in progress.

Advancing AI Benchmarking with Game Arena from blog.google
65 by salkahfi 3h ago | |

Discussion (33):

Comment analysis in progress.

Nano-vLLM: How a vLLM-style inference engine works from neutree.ai
190 by yz-yu 8h ago | | |

Article: 17 min

Nano-vLLM is a minimal yet production-grade inference engine for large language models (LLMs), designed to understand how prompts are processed and requests are batched in LLM APIs like OpenAI or Claude.

Understanding how LLM inference engines work can lead to better system design, improved resource management, and more efficient deployment of AI models in various industries.

Nano-vLLM's architecture and scheduling mechanism
Producer-consumer pattern with the Scheduler
Batching and throughput-latency trade-off
Prefill vs. Decode phases of generation
KV cache management through prefix caching

Quality:
The article provides a detailed technical explanation without sensationalizing the topic.

Discussion (24): 5 min

A discussion about the perceived AI-generated nature of a text on nano-vllm internals. The author clarifies that they are human and shares context for their writing style, while commenters debate whether the content reads as AI-written and point out specific indicators such as em dashes.

The content was not written by AI
The author's writing reflects their knowledge background and preferences

Counterarguments:

The content reads more AI-written than actual human writing
The use of em dashes is seen as an indicator of AI-generated text

Computer Science Machine Learning, Artificial Intelligence, Computer Vision

Mattermost say they will not clarify what license the project is under from github.com/mattermost
42 by MallocVoidstar 28m ago | |

Discussion (9):

Comment analysis in progress.

4x faster network file sync with rclone (vs rsync) (2025) from jeffgeerling.com
193 by indigodaddy 3d ago | | |

Article: 9 min

The article discusses an improvement in network file synchronization speed from rsync to rclone, achieving a four times faster transfer rate for copying files between a local NAS and an external Thunderbolt NVMe SSD.

This article could influence IT professionals and system administrators to adopt rclone for faster network file synchronization, potentially improving productivity and efficiency in their workflows.

rclone was found to be 4x faster for the same task, thanks to its parallel file transfer capability.
The article includes a detailed comparison of the two tools' performance and parameters.

Discussion (95): 7 min

The comment thread discusses various aspects of file transfer tools such as rclone and rsync. Users share their experiences with limitations in rclone's rate limits causing delays, while others suggest alternatives or improvements like fpart for optimization. There is also a discussion on high performance data transfer methods and the use of managed services built on top of rclone.

rclone's limitations with rate limits cause delays in migrations
fpart can optimize file transfers when used with rclone

Software Development Cloud Computing, Networking

Geologists may have solved mystery of Green River's 'uphill' route from phys.org
115 by defrost 7h ago | | |

Article: 19 min

New research suggests that a phenomenon called 'lithospheric dripping' caused temporary subsidence beneath the Uinta Mountains, allowing the Green River to carve its deep canyon through them less than 8 million years ago. Seismic imaging and modeling reveal evidence consistent with this process.

Researchers from the University of Glasgow and other institutions have gathered evidence suggesting that lithospheric dripping caused temporary subsidence beneath the Uinta Mountains, allowing the Green River to carve its deep canyon through them.
The team used seismic imaging and modeling to identify a cold, round anomaly about 200 km below the surface, likely the broken-off section of the drip.
Their estimates match well with previous research that estimated the period during which the Green River cut through the mountains and integrated with the Colorado system.

Discussion (28):

The comment thread discusses a range of topics including geological processes, content creation, and recommendations for reading material. There is some debate about the relevance of images in articles and a comparison between AI agents and interns. The overall sentiment is neutral with a slight leaning towards positive.

Geology Earth Sciences

EPA Advances Farmers' Right to Repair from epa.gov
113 by bilsbie 3h ago | |

Discussion (40):

Comment analysis in progress.