Run two Claude Code accounts simultaneously on macOS without re-authenticating by using separate configuration directories.
- Create Separate Config Directories
mkdir ~/.claude-account1 mkdir ~/.claude-account2
| ;SMBDIS.ASM - A COMPREHENSIVE SUPER MARIO BROS. DISASSEMBLY | |
| ;by doppelganger (doppelheathen@gmail.com) | |
| ;This file is provided for your own use as-is. It will require the character rom data | |
| ;and an iNES file header to get it to work. | |
| ;There are so many people I have to thank for this, that taking all the credit for | |
| ;myself would be an unforgivable act of arrogance. Without their help this would | |
| ;probably not be possible. So I thank all the peeps in the nesdev scene whose insight into | |
| ;the 6502 and the NES helped me learn how it works (you guys know who you are, there's no |
| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
Extracted: 2026-03-25 Method: mitmweb HTTPS intercept of
POST api.factory.ai/api/llm/a/v1/messagesDroid: factory-cli/0.84.0 Orchestrator: claude-opus-4-6 (max_tokens=128000) Worker: claude-opus-4-6 (max_tokens=128000) Mission LLM flows captured: 83 requests
영상: 「JEV 같은 AI, 내 컴퓨터에서 0원으로 돌립니다 | AI 1인 기업 만들기 (클로드 vs GPT vs 제미나이)」 만든 사람: AI 멘토 제이 · CONNECT AI LAB (https://www.youtube.com/@CONNECT-AI-LAB)
ollama pull tev1:0.8bollama list 에 tev1:0.8b 가 보이면 성공A 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.
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.
A Claude Code Agent Skill built from Dex Horthy's (HumanLayer) playbook on David Ondrej's podcast.
"Once the model has written thousands of lines of code, it is harder to change. The sessions that generate design docs are context-light — you get the most model intelligence when you do the hard thinking early."
By default, agents build horizontally: all the backend, then all the frontend, then a 2,000-line diff lands in your lap and reviewing it is your problem. This skill flips that. Every decision that matters gets made before the code exists — where changing your mind costs a sentence, not a rewrite.
| #!/bin/bash | |
| ###################################################### | |
| # Basic settings | |
| ###################################################### | |
| # server base directory | |
| RAGNAROK_DIR=/rAthena | |
| # mysql database settings | |
| MYSQL_ROOT_PW="changeme" |
--> netbird mikrotik | by @xdenb43
--> tested on hap ax3/ROS 7.22.1
RU users: поблагодарить 🩵
This guide describes briefly how to setup NetBird on MikroTik with DNS FWD and VPN solutions for all NetBird clients
Official NetBird container guide: https://docs.netbird.io/use-cases/homelab/client-on-mikrotik-router
Quick NetBird video guide (Rus): https://www.youtube.com/watch?v=eKYHmdY8ikw
| Visual Studio 2026 18.x | |
| Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK | |
| Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V | |
| Product Year Version Product Keys | |
| Visual Studio 2022 2021 17.x | |
| Professional: TD244-P4NB7-YQ6XK-Y8MMM-YWV2J | |
| Enterprise: VHF9H-NXBBB-638P6-6JHCY-88JWH | |
| Visual Studio 2019 2019 16.x |