uxderrick

Web Animation Best Practices & Guidelines

Document Purpose

This is a comprehensive reference guide for creating high-quality web animations. Use this as a knowledge base for implementing animations in web applications. All principles, timing values, and easing functions provided here are production-tested and ready to use.

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Core Principles

Principle 1: Natural Motion

transitive-bullshit

// Claude Code is a Beta product per Anthropic's Commercial Terms of Service.
// By using Claude Code, you agree that all code acceptance or rejection decisions you make,
// and the associated conversations in context, constitute Feedback under Anthropic's Commercial Terms,
// and may be used to improve Anthropic's products, including training models.
// You are responsible for reviewing any code suggestions before use.

// (c) Anthropic PBC. All rights reserved. Use is subject to Anthropic's Commercial Terms of Service (https://www.anthropic.com/legal/commercial-terms).

// Version: 0.2.9

Nabrok

Notes on migrating from RouterProvider to framework

Official documentation: https://reactrouter.com/upgrading/router-provider

Issues

1. Vite config. If the vite config contains a root property it needs to be removed. If you are running vitest coverage reports you may want to move it to test.root or it will show coverage for the entire project. Any paths relative to root need to be adjusted as well, e.g. if you have publicDir set to ../public it should be set to just public (or removed as that is the default).

2. Vitest. You don't want the reactRouter plugin running for tests, so setup as

hamelsmu

def follow_user_follows(client, target_user):
    "Follow everyone the target_user is following."
    
    cursor = None
    total_followed = 0

    while True:
        # Step 1: Fetch a batch of accounts the target user is following
        # https://docs.bsky.app/docs/api/app-bsky-graph-get-follows
        response = client.app.bsky.graph.get_follows({

ianmcook

{
  "statement": "SELECT * FROM MYTABLE",
  "resultSetMetaData": {
    "format": "arrowv1"
  },
  "timeout": 60,
  "database": "MYDATABASE",
  "schema": "MYSCHEMA",
  "warehouse": "MYWAREHOUSE",
  "role": "MYROLE"

z3z1ma

#!/usr/bin/env python
"""A Language Server Protocol (LSP) server for SQL with SQLMesh integration."""

import asyncio
import gc
import io
import logging
import re
import typing as t
import weakref

Artefact2

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

koenvo

import time

# Make sure you have duckdb==0.7.0. Earlier versions might fail with GIL problems ( https://twitter.com/mr_le_fox/status/1620535141675433986 )
import duckdb

import s3fs

from fsspec.implementations.cached import SimpleCacheFileSystem

# Create the s3 file system. This one does not have caching

mheffner

app = "mastiff"
primary_region = "iad"
kill_signal = "SIGINT"
kill_timeout = 5
processes = []

[build]
  image = "tootsuite/mastodon:v3.5.5"