tripleo1

#include "stdio.h"


int InBlock[81], InRow[81], InCol[81];

const int BLANK = 0;
const int ONES = 0x3fe;   // Binary 1111111110

int Entry[81];  // Records entries 1-9 in the grid, as the corresponding bit set to 1
int Block[9], Row[9], Col[9]; // Each int is a 9-bit array

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import datetime
import sys

year = int(sys.argv[1])

def calculate(year):
    dt1 = datetime.datetime(year, 1, 1)
    yield ("A", dt1)

    dt2 = datetime.timedelta(days=14)

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;;  **
;; ** Widgets config for EWW
;; ** Created by : @adi1090x
;; ** 


    

        ;;  Profile vars 
        (defvar IMAGE "images/profile.jpg")

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 ;; Close button
  (defwidget Close [] 
    (box :orientation "h" :space-evenly "true" :halign "start" :valign "center" 
      (button :class "close-app" :onclick "eww close hobby & eww close weather" :tooltip "Close" " Close")))

  ;;  Calendar 
  (defwidget Calendar [] 

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#!/usr/bin/env bash
set -e

tmpdir=$(mktemp -t -d advcp.XXXXXX)
cd ${tmpdir}

wget https://aur.archlinux.org/packages/ad/advcp/advcp.tar.gz
tar xf advcp.tar.gz

source advcp/PKGBUILD

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Install jdk and clojure

apt install openjdk-11-jdk

TBD

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Move to an incremental/query driven architecture #103

pikelet-lang/pikelet#103

Before we go too far down the path of building a traditional compiler (#9), it probably makes sense to start thinking about how we might incrementalise things. This will be super important for supporting a good editor experience (#97). If we put this off for too long we might end up having to rebuild a bunch - this is what Rust is facing, for example.

Without knowing much about it, perhaps something like the Incremental Lambda Calculus would be handy for this. We could try to find the derivative of each pass of our compiler, based on the result of a previous run. This could also be a helpful framework for formalising our incremental compiler as well (#39)!

CRDT-style data structures could also be of potential use, and perhaps projects like timely-dataflow and [differential-d

tripleo1

$ ollama run llama2 "Why doesnt ollama support text to image models?"

Ollama is a text-to-image model that uses a combination of natural 
language processing (NLP) and computer vision techniques to generate 
images from textual descriptions. While Ollama supports a wide range of 
features, it may not support all types of text-to-image models for several
reasons:

1. Model architecture: Ollama's model architecture is designed to handle 
simple text-to-image tasks, such as generating images from textual 

tripleo1

You make it so easy, archwiki

https://bugs.archlinux.org/task/78740.html#comment218649

https://bugs.archlinux.org/user/26371.html

Commit

https://git.sesse.net/?p=plocate;a=commit;h=7f39444852308224240ebfb75e0bf8f39403afa0