nbonamy

sudo killall kdevtmpfsi kinsing dbused
sudo rm /tmp/kdevtmpfsi /tmp/kinsing /tmp/dbused

sudo find / -iname kdevtmpfsi
sudo find / -iname kinsing
sudo find / -iname dbused

sudo crontab -u root -e
sudo crontab -u redis -e
sudo crontab -u www-data -e

nbonamy

#!/usr/bin/env npx ts-node

import * as llm from 'multi-llm-ts'
import fs from 'fs'

const DEFAULT_ENGINE = 'anthropic'
const DEFAULT_MODEL = 'claude-3-7-sonnet-20250219'

// Parse command line arguments
const args = process.argv.slice(2);

nbonamy

import * as llm from 'multi-llm-ts'
import fs from 'fs'

const system = `You are an assistant helping to add i18n to a project.
The project is an electron app written in TypesSrcipt and Vue 3.
The project uses vue-i18n for internationalization.
The project uses bootstrap-icons-vue for icons.
You will given a file to localize and your objective is to extract all the hardcoded strings and replace them with i18n keys.
If you do not detect any hard-coded string, skip the file without modifying it.

nbonamy

CI Badges

nbonamy

import os
import json
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings

os.environ['TOKENIZERS_PARALLELISM']='false'
os.environ['OPENAI_API_KEY']='YOUR_API_KEY'

input = "I would like to order 10 Garden Hoses, 5 iPhone and 10 Sump Pump."

nbonamy

import os
import json
from langchain import hub
from langchain_community.vectorstores import Chroma
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain_community.agent_toolkits import FileManagementToolkit
from langchain_community.embeddings import HuggingFaceEmbeddings

nbonamy

import os
import requests
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_community.agent_toolkits import FileManagementToolkit
from langchain_community.tools.tavily_search import TavilySearchResults

nbonamy

setInterval(() => {
  let links = document.getElementsByTagName('a')
  for (let link of links) {
    let href = link.href
    if (href.indexOf('https://t.co/') === 0) {
      link.href = `http://redirect.bonamy.fr/?url=${href}`
    }
  }
}, 500)

nbonamy

from diffusers import StableDiffusionPipeline
import torch

def progress(step, timestep, latents):
  print(step, timestep, latents[0][0][0][0])
  with torch.no_grad():
    latents = 1 / 0.18215 * latents
    image = pipe.vae.decode(latents).sample
    image = (image / 2 + 0.5).clamp(0, 1)
    image = image.cpu().permute(0, 2, 3, 1).float().numpy()

nbonamy

Option Explicit

Public Function Max(x, y As Variant) As Variant
  Max = IIf(x > y, x, y)
End Function

Public Function Min(x, y As Variant) As Variant
   Min = IIf(x < y, x, y)
End Function