omergorur

Set-SmbClientConfiguration -RequireSecuritySignature $false
Set-SmbClientConfiguration -EnableInsecureGuestLogons $true

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.

BoWang816

ubuntu中文支持，及中文乱码问题

该篇博文，是本人踩了一下午的坑的成果，亲测有效。对服务器同样有效。

状况：所用的ubuntu系统不支持中文，遇见中文就????。ORZ…

目标：使系统/服务器支持中文，能够正常显示。

首先，安装中文支持包language-pack-zh-hans：

grantwinney

var _ac = ["aj_indx_tact", "bind", "driver", "doe_cnt", "mousedown", "Batang", "clientHeight", "emit", "indexOf", "enReadDocUrl", "Palatino", "fpValstr", "onpointerup", "\"", "dm_en", "/get_params", "rVal", "return/*@cc_on!@*/!1", "-1,2,-94,-70,", "http://", "storage", "", "onkeypress", "navigator", "mn_update_challenge_details", "keydown", "spawn", "bm-telemetry", "y1", "-1,2,-94,-121,", "mediaDevices", "fillText", "mn_ct", "requestWakeLock", "Noto", "=", "//", "rotationRate", "call", "mn_mc_lmt", "toLowerCase", "uar", "Palatino-Bold", "arc", "-1,2,-94,-120,", "td", "unk", "z", "devicemotion", "gf", "push", ":", "TI-Nspire", "Edge PDF Viewer", "dme_cnt_lmt", "prototype", "hpu", "hku", "hkd", "mn_ts", "mn_tout", "Version/4.0", "non:", "sensor_data", "display", "getFloatVal", "sort", "ambient-light-sensor", "number", "~", "WebKit-integrierte PDF", "RTCPeerConnection", "0a46G5m17Vrp4o4c", "now", "value", "iPad;", "map", "GET", "mme_cnt", "msHidden", "vc", "||", "-1,2,-94,-124,", "-1", "x12:", "hf", "pstate", "c

kazzohikaru

<script type="importmap">
  {
    "imports": {
      "three": "https://unpkg.com/three@0.184.0/build/three.module.js",
      "three/addons/": "https://unpkg.com/three@0.184.0/examples/jsm/"
    }
  }
</script>
<script>
  const fbm = `

bbrowning

{%- macro format_parameters(properties, required) -%}
    {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
    {%- set ns = namespace(found_first=false) -%}
    {%- for key, value in properties | dictsort -%}
        {%- set add_comma = false -%}
        {%- if key not in standard_keys -%}
            {%- if ns.found_first %},{% endif -%}
            {%- set ns.found_first = true -%}
            {{ key }}:{
            {%- if value['description'] -%}

grantwinney

var _cf = _cf || [],
    bmak = bmak || {
        ver: 1.54,
        ke_cnt_lmt: 150,
        mme_cnt_lmt: 100,
        mduce_cnt_lmt: 75,
        pme_cnt_lmt: 25,
        pduce_cnt_lmt: 25,
        tme_cnt_lmt: 25,
        tduce_cnt_lmt: 25,

kanmadigital

LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 10K Stars ⭐️, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

Currently, Working on AKBP: Agent Knowledge Base Protocol based on my findings, a protocol for creating, updating, retrieving, and sharing durable knowledge across AI agents.

What the original gets right