boxabirds

# pipenv is extremely slow but it's very easy to use.
# here are two wrapper scripts to emulate pipenv shell and pipenv install

# add to your .zshrc file

# emulate pipenv shell
function uvshell() {
    if [[ -d .venv ]]; then
        source .venv/bin/activate || { echo "Failed to activate virtual env"; return 1; }
    else

boxabirds

Deepseek r1 says this

Key Clarifications

1. APFS Global Kernel Lock (vfs-layer lock):

   • Not fully bypassed: Even with a RAM disk formatted as APFS, macOS's global I/O lock still technically exists. However, because RAM disk operations are orders of magnitude faster than disk I/O, contention for this lock is drastically reduced. This makes the lock effectively irrelevant for most workflows, as operations complete before significant contention occurs.
   • Not the same as Linux: macOS lacks a true tmpfs equivalent, so while a RAM disk helps, it’s not as seamless as Linux’s in-memory filesystem.

2. RAM Disk Format:

   • macOS RAM disks can be formatted as HFS+ or APFS. While APFS is the default, formatting as HFS+ might slightly reduce lock contention (though benchmarks vary). Either way, the speed of RAM mitigates lock-related bottlenecks.

boxabirds

At this point I want you to create a design doc that describes the following things. The things below are to be made into separate .md files in a doc/ directory.

mermaid diagrams showing the key objects and their relationships
mermaid sequence diagram showing key interactions for the main use cases
summarise the agent journey so far: review the entire chat history and highlight the things that were easy to implement (one prompt => feature) and things that were very hard to implement
following this, create a maintenance guide whose sole objective is to help the next contributors to the code base. It should explain the frameworks and services used, the components, and notes on anything about the implementation that may not be obvious to someone even if they have experience with the framework used.
finally summarise areas that need to be developed to turn this into a proper product that can be supported, maintained and expanded.E.g. logging, telemetry, security considerations etc.

boxabirds

SQL:

-- name: GetRecentPostsByTags :many
-- $1: TagNames
SELECT DISTINCT p.id, p.created_at, t.name AS tag_name
FROM posts p
JOIN post_tags pt ON p.id = pt.post_id
JOIN tags t ON pt.tag_id = t.id
WHERE t.name = ANY($1::text[]) AND p.created_at < $2
ORDER BY p.created_at DESC

boxabirds

// track every build with a unique number 
// e.g. in package.json … "build": "node scripts/increment-version-patch.js && <your normal build script>"
const fs = require('fs');
const path = require('path');

const packageJsonPath = path.join(__dirname, '..', 'package.json');
const packageJson = require(packageJsonPath);

// Split version into major, minor, and patch
const [major, minor, patch] = packageJson.version.split('.').map(Number);

boxabirds

const {GoogleGenerativeAI} = require('@google/generative-ai');
/**
 * Estimates the cost of a Gemini API invocation based on token usage.
 *
 * @param {Object} usageMetadata - The usage metadata from the API response.
 * @param {number} usageMetadata.promptTokenCount - The number of tokens in the input prompt.
 * @param {number} usageMetadata.candidatesTokenCount - The number of tokens in the output.
 * @returns {number} The estimated cost of the invocation in USD.
 *
 * Pricing information:

boxabirds

import os
import requests
from bs4 import BeautifulSoup
import argparse
import base64
import json

def download_image(url, save_path):
    response = requests.get(url)
    if response.status_code == 200:

boxabirds

package main

import (
	"fmt"
	"io/ioutil"
	"log"
	"os"

	whisper "github.com/ggerganov/whisper.cpp/bindings/go"
)

boxabirds

// Demo of how you can fix the seed with OpenAI and you'll get deterministic output.
// this is only for a given system fingerprint
package main

import (
	"context"
	"flag"
	"fmt"
	"io"
	"log"

boxabirds

// this is a demo that connects to ollama and
// Prerequisites:
// - ollama
// - open weights model "llava" 1.6 or greater
package main

import (
	"bytes"
	"encoding/base64"
	"encoding/json"