IgorWarzocha

GPT‑5.6 Sol, Terra, and Luna: a practical model-selection guide

Data snapshot: 11 July 2026
Primary source: Artificial Analysis GPT‑5.6 launch analysis and its live LLM leaderboard
Scope: all 15 combinations of Sol, Terra, and Luna at low, medium, high, xhigh, and max reasoning effort.

Short answer: Default to Luna for volume work and Sol when quality matters. Treat Terra as a dominated middle tier, not a default. Raise reasoning effort before moving to a larger model when the next effort remains on the better price/intelligence frontier. Use Sol max only where the last few quality points can change the outcome.

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karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

emschwartz

This is an OPML version of the HN Popularity Contest results for 2025, for importing into RSS feed readers.

Plug: if you want to find content related to your interests from thousands of obscure blogs and noisy sources like HN Newest, check out Scour. It's a free, personalized content feed I work on where you define your interests in your own words and it ranks content based on how closely related it is to those topics.

urakozz

Intel Arc B70: High-Performance vLLM (oneAPI 2026.1 + Triton XPU)

This repository contains the necessary setup instructions, docker configuration, and patch modifications to run highly optimized LLM inference on Intel XPU architectures using vLLM.

Everyone is buzzing that only NVIDIA RTX or AMD hardware can achieve elite inference speeds is outdated and Intel's software is not good enough (only if you use in 2026 software from 2025)

By leveraging the oneAPI 2026.1 toolchain, Triton XPU, and direct Intel XMX hardware acceleration, Intel GPUs deliver exceptional token-per-second throughput numbers on modern hybrid architectures.

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forkyau

ryanofsky

$ git rev-parse HEAD
2a583406c00761c0ac1d162531256b401d5b16c3
$ brew list --versions
autoconf 2.69
automake 1.16.1_1
berkeley-db@4 4.8.30
boost 1.67.0_1
gdbm 1.17
libevent 2.1.8
libpng 1.6.35

k16shikano

name	japanese-tech-writing
description	日本語の技術文書・書籍原稿の文章規範。整形（一文一行、引用ブロック、脚注、コラム記法）、段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

整形

mariotaku

Usage

1. Have your TV rooted
2. Open Terminal with SSH client or dev-manager-desktop
3. Run curl -fsSL https://gist.githubusercontent.com/mariotaku/f7228c5459fc7ad2172a2b69dd51a4eb/raw/webosbrew-install-tailscale.sh | sh and wait for it to finish