k1000

{
  "name": "Fallow Scan → Parallel Simplify Loop",
  "startAt": "DiscoverStructure",
  "states": {
    "DiscoverStructure": {
      "type": "Task",
      "comment": "Discover all domain packages. Outputs object with 'domains' array.",
      "agent": "pi",
      "agentConfig": { "model": "deepseek/deepseek-v4-flash" },
      "prompt": "List all domain packages in packages/domains/ with their paths and sizes.\n\nRun this: for d in packages/domains/*/; do name=$(basename \"$d\"); size=$(du -sh \"$d\" | cut -f1); echo \"{\\\"name\\\":\\\"$name\\\",\\\"path\\\":\\\"${d%/}\\\",\\\"size\\\":\\\"$size\\\"}\"; done\n\nWrap those lines into: {\"domains\": [{\"name\":\"...\",\"path\":\"...\",\"size\":\"...\"}, ...]}\n\nOutput ONLY that JSON object, no markdown.",

k1000

{
  "name": "Fallow Scan → Parallel Simplify Loop",
  "startAt": "DiscoverStructure",
  "states": {
    "DiscoverStructure": {
      "type": "Task",
      "comment": "Discover all domain packages. Outputs object with 'domains' array.",
      "agent": "pi",
      "agentConfig": { "model": "deepseek/deepseek-v4-flash" },
      "prompt": "List all domain packages in packages/domains/ with their paths and sizes.\n\nRun this: for d in packages/domains/*/; do name=$(basename \"$d\"); size=$(du -sh \"$d\" | cut -f1); echo \"{\\\"name\\\":\\\"$name\\\",\\\"path\\\":\\\"${d%/}\\\",\\\"size\\\":\\\"$size\\\"}\"; done\n\nWrap those lines into: {\"domains\": [{\"name\":\"...\",\"path\":\"...\",\"size\":\"...\"}, ...]}\n\nOutput ONLY that JSON object, no markdown.",

k1000

{
  "name": "Example: Analyze and Fix Type Issues",
  "defaults": {
    "agent": "claude"
  },
  "actions": [
    {
      "id": "01-analyze-types",
      "description": "Scan for type issues in broker domain",
      "agent": "pi",

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{
  "dependencies": {
    "@llama-node/llama-cpp": "^0.0.32",
    "llama-node": "^0.0.32"
  },
  "devDependencies": {
    "@types/node": "^18.16.3"
  }
}

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CURRICULUM VITAE

Kamil Selwa
Email: selwak@gmail.com
Phone: +39 345 813 0128
GitHub: https://github.com/k1000/
LinkedIn: https://www.linkedin.com/in/kamilselwa/
Nationality: Polish
Residence: Italy

k1000

isVirgin

The isVirgin property is used to indicate whether the credential subject is a virgin or not. 1 means that the credential subject is a virgin, 0 means that the credential subject is not a virgin.

Demo

https://issuer-demo.polygonid.me/main https://self-hosted-platform.polygonid.me/#tag--Claim https://self-hosted-platform.polygonid.me/#post-/v1/identities

DID

k1000

const crypto = require("crypto");
const fetch = require('node-fetch');
const express = require('express');
const lusca = require('lusca');
const bodyParser = require('body-parser');
const mongoose = require('mongoose');
const session = require('express-session');
const passport = require('passport');
const GithubStrategy = require('passport-github').Strategy;
require('dotenv').config();

k1000

const express = require('express')
const crypto = require("crypto");
const fetch = require('node-fetch');
const mongoose = require('mongoose');
const { auth } = require('express-openid-connect');

require('dotenv').config();
const PORT = process.env.PORT || 5000

const mongo_uri = `mongodb+srv://${process.env.MONGO_USER}:${process.env.MONGO_PASS}@cluster0.u4tzh.mongodb.net/myFirstDatabase?retryWrites=true&w=majority`;

k1000

import {AsyncStorage } from 'react-native';
import {Actions} from 'react-native-router-flux';
import Calendar from '../app/calendar';

const moment = require('moment');
const callib = require('./callib.js');
const aspects = require('../data/favorable_days.json');
const constellations = require('../data/constellations.json');
const signsByNum = require('../data/signs_num.json');
const signsByName = require('../data/tib_signs.json');

k1000

# FUNK from https://colab.research.google.com/drive/12rWn1enSZP6OTgYgRT1OYFhFnt7QFfIu#scrollTo=dwe_prwCn2HH

def moving_average(df, n):
  MA = pd.Series(df['Close'].rolling(n, min_periods=n).mean(), name='MA_' + str(n))
  df = df.join(MA)
  return df

def exponential_moving_average(df, n):
  EMA = pd.Series(df['Close'].ewm(span=n, min_periods=n).mean(), name="EMA_" + str(n))
  df = df.join(EMA)