Working with APIs

Python basics

Python
Basics
Author

Kunal Khurana

Published

October 13, 2023

Learning outcomes

  • with web applicaiton programming interphase request specific information from a website to generate a visualization
  • write programs that gather data they need and create a visualiztion
  • use Github’s API to explore the most starred projects on GitHub
  • Use Requests package to issue and process results.
  • Use plotly to generate and customize the appearance of charts

Resources

import requests

# make an API and store the response

url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
headers = {'Accept': 'application/vnd.github.v3+json'}  #uses specific version
r = requests.get(url, headers = headers)  #using requests to make a call to API
print(f"Status code: {r.status_code}")

#store API response in a variable

response_dict = r.json()  #using json method to convert it into a pyton dictionary

# process results

print(response_dict.keys())
Status code: 200
dict_keys(['total_count', 'incomplete_results', 'items'])
response_dict = r.json()  

print (f"Total repositories: {response_dict['total_count']}")  #prints total count

# explore information about repos
repo_dicts = response_dict ['items']  #storing the list in repo_dicts
print(f"Repositories returned: {len(repo_dicts)}")

# examine the first repository
repo_dict = repo_dicts[0]
print(f"\nKeys: {len(repo_dict)}")
for key in sorted(repo_dict.keys()):
    print(key)
Total repositories: 9344484
Repositories returned: 30

Keys: 80
allow_forking
archive_url
archived
assignees_url
blobs_url
branches_url
clone_url
collaborators_url
comments_url
commits_url
compare_url
contents_url
contributors_url
created_at
default_branch
deployments_url
description
disabled
downloads_url
events_url
fork
forks
forks_count
forks_url
full_name
git_commits_url
git_refs_url
git_tags_url
git_url
has_discussions
has_downloads
has_issues
has_pages
has_projects
has_wiki
homepage
hooks_url
html_url
id
is_template
issue_comment_url
issue_events_url
issues_url
keys_url
labels_url
language
languages_url
license
merges_url
milestones_url
mirror_url
name
node_id
notifications_url
open_issues
open_issues_count
owner
private
pulls_url
pushed_at
releases_url
score
size
ssh_url
stargazers_count
stargazers_url
statuses_url
subscribers_url
subscription_url
svn_url
tags_url
teams_url
topics
trees_url
updated_at
url
visibility
watchers
watchers_count
web_commit_signoff_required

Pull out values from keys in repo_dict

# explore information about repos
repo_dicts = response_dict ['items']  

# examine first
repo_dict = repo_dicts[0]

print ("\n Selected information about first repository:")
print(f"Name: {repo_dict['name']}")
print(f"Owner: {repo_dict['owner']['login']}")
print(f"Stars: {repo_dict['stargazers_count']}")
print (f"Repository: {repo_dict['html_url']}")
print(f"Created: {repo_dict['created_at']}")
print(f"Updated: {repo_dict['updated_at']}")
print(f"Description: {repo_dict['description']}")

 Selected information about first repository:
Name: Python-100-Days
Owner: jackfrued
Stars: 142124
Repository: https://github.com/jackfrued/Python-100-Days
Created: 2018-03-01T16:05:52Z
Updated: 2023-11-13T16:14:16Z
Description: Python - 100天从新手到大师
# examine second
repo_dict = repo_dicts[1]

print ("\n Selected information about second repository:")
print(f"Name: {repo_dict['name']}")
print(f"Owner: {repo_dict['owner']['login']}")
print(f"Stars: {repo_dict['stargazers_count']}")
print (f"Repository: {repo_dict['html_url']}")
print(f"Created: {repo_dict['created_at']}")
print(f"Updated: {repo_dict['updated_at']}")
print(f"Description: {repo_dict['description']}")

 Selected information about second repository:
Name: ColossalAI
Owner: hpcaitech
Stars: 35236
Repository: https://github.com/hpcaitech/ColossalAI
Created: 2021-10-28T16:19:44Z
Updated: 2023-11-13T20:52:01Z
Description: Making large AI models cheaper, faster and more accessible

Summarizing top repositories

#prints total count first
response_dict = r.json()  

print (f"Total repositories: {response_dict['total_count']}")  

# explore information first
repo_dicts = response_dict['items']
print(f"\nRepositories returned: {len(repo_dicts)}")

print("\nSelected information about each repository:")
for repo_dict in repo_dicts:
    print(f"Name: {repo_dict['name']}")
    print(f"Owner: {repo_dict['owner']['login']}")
    print(f"Stars: {repo_dict['stargazers_count']}")
    print (f"Repository: {repo_dict['html_url']}")
    print(f"Description: {repo_dict['description']}")
Total repositories: 9344484

Repositories returned: 30

Selected information about each repository:
Name: Python-100-Days
Owner: jackfrued
Stars: 142124
Repository: https://github.com/jackfrued/Python-100-Days
Description: Python - 100天从新手到大师
Name: ColossalAI
Owner: hpcaitech
Stars: 35236
Repository: https://github.com/hpcaitech/ColossalAI
Description: Making large AI models cheaper, faster and more accessible
Name: DragGAN
Owner: XingangPan
Stars: 33710
Repository: https://github.com/XingangPan/DragGAN
Description: Official Code for DragGAN (SIGGRAPH 2023)
Name: open-interpreter
Owner: KillianLucas
Stars: 33035
Repository: https://github.com/KillianLucas/open-interpreter
Description: OpenAI's Code Interpreter in your terminal, running locally
Name: XX-Net
Owner: XX-net
Stars: 32300
Repository: https://github.com/XX-net/XX-Net
Description: A proxy tool to bypass GFW.
Name: MockingBird
Owner: babysor
Stars: 31783
Repository: https://github.com/babysor/MockingBird
Description: 🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time
Name: HanLP
Owner: hankcs
Stars: 30751
Repository: https://github.com/hankcs/HanLP
Description: Natural Language Processing for the next decade. Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Syntactic & Semantic Dependency Parsing, Document Classification
Name: ray
Owner: ray-project
Stars: 28605
Repository: https://github.com/ray-project/ray
Description: Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Name: ItChat
Owner: littlecodersh
Stars: 24369
Repository: https://github.com/littlecodersh/ItChat
Description: A complete and graceful API for Wechat. 微信个人号接口、微信机器人及命令行微信，三十行即可自定义个人号机器人。
Name: hosts
Owner: StevenBlack
Stars: 24161
Repository: https://github.com/StevenBlack/hosts
Description: 🔒 Consolidating and extending hosts files from several well-curated sources. Optionally pick extensions for porn, social media, and other categories.
Name: dash
Owner: plotly
Stars: 19624
Repository: https://github.com/plotly/dash
Description: Data Apps & Dashboards for Python. No JavaScript Required.
Name: chatgpt-on-wechat
Owner: zhayujie
Stars: 17214
Repository: https://github.com/zhayujie/chatgpt-on-wechat
Description: Wechat robot based on ChatGPT,  which using OpenAI api and itchat library. 使用大模型搭建微信聊天机器人，基于 GPT3.5/GPT4.0/Claude/文心一言/讯飞星火/LinkAI，支持个人微信、公众号、企业微信部署，能处理文本、语音和图片，访问操作系统和互联网，支持基于知识库定制专属机器人。
Name: Hitomi-Downloader
Owner: KurtBestor
Stars: 17151
Repository: https://github.com/KurtBestor/Hitomi-Downloader
Description: :cake: Desktop utility to download images/videos/music/text from various websites, and more.
Name: recommenders
Owner: recommenders-team
Stars: 16663
Repository: https://github.com/recommenders-team/recommenders
Description: Best Practices on Recommendation Systems
Name: loguru
Owner: Delgan
Stars: 16585
Repository: https://github.com/Delgan/loguru
Description: Python logging made (stupidly) simple
Name: awesome-oss-alternatives
Owner: RunaCapital
Stars: 14225
Repository: https://github.com/RunaCapital/awesome-oss-alternatives
Description: Awesome list of open-source startup alternatives to well-known SaaS products 🚀
Name: learn_python3_spider
Owner: wistbean
Stars: 13936
Repository: https://github.com/wistbean/learn_python3_spider
Description: python爬虫教程系列、从0到1学习python爬虫，包括浏览器抓包，手机APP抓包，如 fiddler、mitmproxy，各种爬虫涉及的模块的使用，如：requests、beautifulSoup、selenium、appium、scrapy等，以及IP代理，验证码识别，Mysql，MongoDB数据库的python使用，多线程多进程爬虫的使用，css 爬虫加密逆向破解，JS爬虫逆向，分布式爬虫，爬虫项目实战实例等
Name: mlc-llm
Owner: mlc-ai
Stars: 13821
Repository: https://github.com/mlc-ai/mlc-llm
Description: Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
Name: mackup
Owner: lra
Stars: 13780
Repository: https://github.com/lra/mackup
Description: Keep your application settings in sync (OS X/Linux)
Name: ChuanhuChatGPT
Owner: GaiZhenbiao
Stars: 13206
Repository: https://github.com/GaiZhenbiao/ChuanhuChatGPT
Description: GUI for ChatGPT API and many LLMs. Supports agents, file-based QA, GPT finetuning and query with web search. All with a neat UI.
Name: searx
Owner: searx
Stars: 13193
Repository: https://github.com/searx/searx
Description: Privacy-respecting metasearch engine
Name: PySimpleGUI
Owner: PySimpleGUI
Stars: 12208
Repository: https://github.com/PySimpleGUI/PySimpleGUI
Description: Launched in 2018. It's 2023 and PySimpleGUI is actively developed & supported. Create complex windows simply. Supports tkinter, Qt, WxPython, Remi (in browser). Create GUI applications trivially with a full set of widgets. Multi-Window applications are also simple. 3.4 to 3.11 supported. 325+ Demo programs & Cookbook for rapid start. Extensive docs
Name: redis-py
Owner: redis
Stars: 11878
Repository: https://github.com/redis/redis-py
Description: Redis Python Client
Name: pelican
Owner: getpelican
Stars: 11872
Repository: https://github.com/getpelican/pelican
Description: Static site generator that supports Markdown and reST syntax. Powered by Python.
Name: awesome-aws
Owner: donnemartin
Stars: 11799
Repository: https://github.com/donnemartin/awesome-aws
Description: A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources.  Featuring the Fiery Meter of AWSome.
Name: numba
Owner: numba
Stars: 9026
Repository: https://github.com/numba/numba
Description: NumPy aware dynamic Python compiler using LLVM
Name: kedro
Owner: kedro-org
Stars: 8981
Repository: https://github.com/kedro-org/kedro
Description: Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Name: OpenChatKit
Owner: togethercomputer
Stars: 8928
Repository: https://github.com/togethercomputer/OpenChatKit
Description: None
Name: Python
Owner: injetlee
Stars: 8825
Repository: https://github.com/injetlee/Python
Description: Python脚本。模拟登录知乎， 爬虫，操作excel，微信公众号，远程开机
Name: Reinforcement-learning-with-tensorflow
Owner: MorvanZhou
Stars: 8362
Repository: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
Description: Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Visualizing repositories using Plotly

import requests

from plotly.graph_objs import Bar
from plotly import offline

# make an API call and store the response
url = 'https://api.github.com/search/repositories?q=language:python&sort=stars'
headers = {'Accept': 'application/vnd.github.v3+json'}  #uses specific version
r = requests.get(url, headers = headers)  #using requests to make a call to API
print(f"Status code: {r.status_code}")

# process results
response_dict = r.json()
repo_dicts = response_dict['items']
repo_names, stars = [], []  #empty lists
for repo_dict in repo_dicts:
    repo_names.append(repo_dict['name'])
    stars.append(repo_dict['stargazers_count'])

# make visualization
data = [{
    'type': 'bar',
    'x': repo_names,
    'y': stars, 
}]
my_layout = {
    'title': 'Most-starred python projects in Github',
    'xaxis': {'title': 'Repository'},
    'yaxis': {'title' : 'Stars'},
}

fig = {'data': data, 'layout': my_layout}
offline.plot(fig, filename= 'python_repos.html')
Status code: 200
'python_repos.html'

Refining plotly charts

# make visualization
## modifying data
data_1 = [{
    'type': 'bar',
    'x': repo_names,
    'y': stars,
    'marker': {
        'color' : 'rgb(255,0,0)',
        'line' : {'width' : 1.5, 'color' : 'rgb(255,0,1)'}
    },
    'opacity' : 0.6,
}]
my_layout = {
    'title': 'Most-starred python projects in Github',
    'xaxis': {'title': 'Repository'},
    'yaxis': {'title' : 'Stars'},
}

fig = {'data': data_1, 'layout': my_layout}
offline.plot(fig, filename= 'python_repos_1.html')
'python_repos_1.html'
# make visualization
data_1 = [{
    'type': 'bar',
    'x': repo_names,
    'y': stars,
    'marker': {
        'color' : 'rgb(255,0,0)',
        'line' : {'width' : 1.5, 'color' : 'rgb(255,0,1)'}
    },
    'opacity' : 0.6,
}]
## modifying layout
my_layout_1 = {
    'title': 'Most-starred python projects in Github',
    'titlefont': {'size': 28},
    'xaxis': {
        'title': 'Repository',
        'titlefont' : {'size': 24},
        'tickfont' : {'size': 14},
    },
    'yaxis': {
        'title' : 'Stars',
        'titlefont' : {'size' : 24},
        'tickfont' : {'size' : 14},
     },
}

fig = {'data': data_1, 'layout': my_layout_1}
offline.plot(fig, filename= 'python_repos_2.html')
'python_repos_2.html'

Adding Custom Tooltips

# process results
response_dict = r.json()
repo_dicts = response_dict['items']
repo_names, stars, labels = [], [], []  #empty lists
for repo_dict in repo_dicts:
    repo_names.append(repo_dict['name'])
    stars.append(repo_dict['stargazers_count'])

    owner = repo_dict['owner']['login']
    description = repo_dict['description']
    label = f"{owner}<br />{description}"
    labels.append(label)
    
# make visualization
data = [{
    'type': 'bar',
    'x': repo_names,
    'y': stars, 
    'hovertext': labels,
    'marker': {
        'color' : 'rgb(250,0,0)',
        'line' : {'width' : 1.5, 'color' : 'rgb(255,0,1)'}
    },
    'opacity' : 0.6,
}]
my_layout = {
    'title': 'Most-starred python projects in Github',
    'xaxis': {'title': 'Repository'},
    'yaxis': {'title' : 'Stars'},
}

fig = {'data': data, 'layout': my_layout}
offline.plot(fig, filename= 'python_repos.html')
'python_repos.html'

The Hacker News API

  • contains articles about programming and technology (http://news.ycombinator.com/)
import requests 
import json 

# make an API call, and store the response
 
url = 'https://hacker-news.firebaseio.com/v0/item/19155826.json'
r = requests.get(url)
print (f"Status code: {r.status_code}")

# Explore data structure
#filename = "E:\\machine learning projects\\readable_hn_data.json"
#with open(filename, encoding = 'utf-8') as f:
#    all_eq_data = json.load(f)

# opening the readable file that we just created

readable_file = 'E:\\machine learning projects\\readable_hn_data.json'
with open(readable_file, 'r', encoding = 'utf-8') as f:
    content = f.read()
    print(content)
Status code: 200
{
    "by": "jimktrains2",
    "descendants": 221,
    "id": 19155826,
    "kids": [
        19156572,
        19158857,
        19156773,
        19157251,
        19156415,
        19159820,
        19157154,
        19156385,
        19156489,
        19158522,
        19156755,
        19156974,
        19158319,
        19157034,
        19156935,
        19158935,
        19157531,
        19158638,
        19156466,
        19156758,
        19156565,
        19156498,
        19156335,
        19156041,
        19156704,
        19159047,
        19159127,
        19156217,
        19156375,
        19157945
    ],
    "score": 728,
    "time": 1550085414,
    "title": "Nasa\u2019s Mars Rover Opportunity Concludes a 15-Year Mission",
    "type": "story",
    "url": "https://www.nytimes.com/2019/02/13/science/mars-opportunity-rover-dead.html"
}

Analysing all the top articles from Hackernews

from operator import itemgetter

import requests

# make an API and store the response
url = 'https://hacker-news.firebaseio.com/v0/topstories.json'
r = requests.get(url)
print(f"Status code: {r.status_code}")

#process information about each submission
submission_ids = r.json()
submission_dicts = []

for submission_id in submission_ids[:30]:
    #make a seperate API call for each submission
    url = f"https://hacker-news.firebaseio.com/v0/item/{submission_id}.json"
    r = requests.get(url)
    print(f"id: {submission_id}/tstatus: {r.status_code}")
    response_dict = r.json()
    
    #build a dictionary for each article
    submission_dict = {
        'title' : response_dict['title'],
        'hn_link' : f"http://news.ycombinator.com/item?id={submission_id}",
        'comments': response_dict['descendants'],
    }
    submission_dicts.append(submission_dict)
    
    submission_dicts = sorted(submission_dicts, key=itemgetter('comments'), reverse= True)
    
    for sumbission_dict in submission_dicts:
        print(f"\nTitle: {submission_dict['title']}")
        print(f"Discussion link: {submission_dict['hn_link']}")
        print(f"Comments: {submission_dict['comments']}")
    
    
Status code: 200
id: 38290145/tstatus: 200

Title: The real realtime preemption end game
Discussion link: http://news.ycombinator.com/item?id=38290145
Comments: 163
id: 38292553/tstatus: 200

Title: Migrating to OpenTelemetry
Discussion link: http://news.ycombinator.com/item?id=38292553
Comments: 27

Title: Migrating to OpenTelemetry
Discussion link: http://news.ycombinator.com/item?id=38292553
Comments: 27
id: 38295179/tstatus: 200

Title: Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks
Discussion link: http://news.ycombinator.com/item?id=38295179
Comments: 0

Title: Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks
Discussion link: http://news.ycombinator.com/item?id=38295179
Comments: 0

Title: Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks
Discussion link: http://news.ycombinator.com/item?id=38295179
Comments: 0
id: 38290613/tstatus: 200

Title: From email to phone number, a new OSINT approach (2019)
Discussion link: http://news.ycombinator.com/item?id=38290613
Comments: 76

Title: From email to phone number, a new OSINT approach (2019)
Discussion link: http://news.ycombinator.com/item?id=38290613
Comments: 76

Title: From email to phone number, a new OSINT approach (2019)
Discussion link: http://news.ycombinator.com/item?id=38290613
Comments: 76

Title: From email to phone number, a new OSINT approach (2019)
Discussion link: http://news.ycombinator.com/item?id=38290613
Comments: 76
id: 38291139/tstatus: 200

Title: Emu Video and Emu Edit, our latest generative AI research milestones
Discussion link: http://news.ycombinator.com/item?id=38291139
Comments: 15

Title: Emu Video and Emu Edit, our latest generative AI research milestones
Discussion link: http://news.ycombinator.com/item?id=38291139
Comments: 15

Title: Emu Video and Emu Edit, our latest generative AI research milestones
Discussion link: http://news.ycombinator.com/item?id=38291139
Comments: 15

Title: Emu Video and Emu Edit, our latest generative AI research milestones
Discussion link: http://news.ycombinator.com/item?id=38291139
Comments: 15

Title: Emu Video and Emu Edit, our latest generative AI research milestones
Discussion link: http://news.ycombinator.com/item?id=38291139
Comments: 15
id: 38291199/tstatus: 200

Title: Zimbra 0-day used to steal email data from government organizations
Discussion link: http://news.ycombinator.com/item?id=38291199
Comments: 13

Title: Zimbra 0-day used to steal email data from government organizations
Discussion link: http://news.ycombinator.com/item?id=38291199
Comments: 13

Title: Zimbra 0-day used to steal email data from government organizations
Discussion link: http://news.ycombinator.com/item?id=38291199
Comments: 13

Title: Zimbra 0-day used to steal email data from government organizations
Discussion link: http://news.ycombinator.com/item?id=38291199
Comments: 13

Title: Zimbra 0-day used to steal email data from government organizations
Discussion link: http://news.ycombinator.com/item?id=38291199
Comments: 13

Title: Zimbra 0-day used to steal email data from government organizations
Discussion link: http://news.ycombinator.com/item?id=38291199
Comments: 13
id: 38295638/tstatus: 200
KeyError: 'descendants'
## improved code

from operator import itemgetter
import requests

# Make an API call to get the top story IDs
url_top_stories = 'https://hacker-news.firebaseio.com/v0/topstories.json'
response_top_stories = requests.get(url_top_stories)
print(f"Status code: {response_top_stories.status_code}")

# Process information about each submission
submission_ids = response_top_stories.json()
submission_dicts = []

# Make a separate API call for each submission and store relevant information
for submission_id in submission_ids[:30]:
    url_submission = f"https://hacker-news.firebaseio.com/v0/item/{submission_id}.json"
    response_submission = requests.get(url_submission)
    print(f"id: {submission_id}\tstatus: {response_submission.status_code}")

    # Check if the API call was successful
    if response_submission.status_code == 200:
        submission_dict = {
            'title': response_submission.json().get('title', 'N/A'),
            'hn_link': f"http://news.ycombinator.com/item?id={submission_id}",
            'comments': response_submission.json().get('descendants', 0),
        }
        submission_dicts.append(submission_dict)

# Sort the submission dictionaries based on the number of comments in descending order
submission_dicts = sorted(submission_dicts, key=itemgetter('comments'), reverse=True)

# Print information about each submission
for submission_dict in submission_dicts:
    print(f"\nTitle: {submission_dict['title']}")
    print(f"Discussion link: {submission_dict['hn_link']}")
    print(f"Comments: {submission_dict['comments']}")
Status code: 200
id: 38290145    status: 200
id: 38295179    status: 200
id: 38292553    status: 200
id: 38290613    status: 200
id: 38291139    status: 200
id: 38291199    status: 200
id: 38295638    status: 200
id: 38291015    status: 200
id: 38276418    status: 200
id: 38294723    status: 200
id: 38295524    status: 200
id: 38294569    status: 200
id: 38292102    status: 200
id: 38294203    status: 200
id: 38289327    status: 200
id: 38291735    status: 200
id: 38291880    status: 200
id: 38291427    status: 200
id: 38294623    status: 200
id: 38293817    status: 200
id: 38276727    status: 200
id: 38287257    status: 200
id: 38275698    status: 200
id: 38269866    status: 200
id: 38295819    status: 200
id: 38288980    status: 200
id: 38288130    status: 200
id: 38287299    status: 200
id: 38291399    status: 200
id: 38288743    status: 200

Title: Privacy is priceless, but Signal is expensive
Discussion link: http://news.ycombinator.com/item?id=38291427
Comments: 557

Title: I think I need to go lie down
Discussion link: http://news.ycombinator.com/item?id=38288130
Comments: 391

Title: A failed AI girlfriend product, and my lessons
Discussion link: http://news.ycombinator.com/item?id=38287299
Comments: 198

Title: Smart drugs reduce quality of effort, and slow decision-making
Discussion link: http://news.ycombinator.com/item?id=38287257
Comments: 173

Title: The real realtime preemption end game
Discussion link: http://news.ycombinator.com/item?id=38290145
Comments: 163

Title: Operating on a minimal two-core Postgres instance: Query optimization insights
Discussion link: http://news.ycombinator.com/item?id=38276727
Comments: 128

Title: Sweden Gov Announces 'Massive Expansion' of Nuclear Energy
Discussion link: http://news.ycombinator.com/item?id=38291015
Comments: 122

Title: Moving from AWS to Bare-Metal saved us 230k$ /yr
Discussion link: http://news.ycombinator.com/item?id=38294569
Comments: 80

Title: From email to phone number, a new OSINT approach (2019)
Discussion link: http://news.ycombinator.com/item?id=38290613
Comments: 76

Title: Why thinking hard makes us feel tired
Discussion link: http://news.ycombinator.com/item?id=38294723
Comments: 59

Title: Ransomware Group Files SEC Complaint over Victim's Failure Disclose Data Breach
Discussion link: http://news.ycombinator.com/item?id=38291399
Comments: 45

Title: Hackers know everything is securities fraud
Discussion link: http://news.ycombinator.com/item?id=38293817
Comments: 40

Title: Frutiger Aero
Discussion link: http://news.ycombinator.com/item?id=38276418
Comments: 38

Title: Printed robots with bones, ligaments, and tendons
Discussion link: http://news.ycombinator.com/item?id=38288980
Comments: 38

Title: You don't need a CRDT to build a collaborative experience
Discussion link: http://news.ycombinator.com/item?id=38289327
Comments: 35

Title: std::source_location Is Broken
Discussion link: http://news.ycombinator.com/item?id=38292102
Comments: 28

Title: Migrating to OpenTelemetry
Discussion link: http://news.ycombinator.com/item?id=38292553
Comments: 27

Title: Hello World on the GPU (2019)
Discussion link: http://news.ycombinator.com/item?id=38275698
Comments: 22

Title: Emu Video and Emu Edit, our latest generative AI research milestones
Discussion link: http://news.ycombinator.com/item?id=38291139
Comments: 15

Title: Federated finetuning of Whisper on Raspberry Pi 5
Discussion link: http://news.ycombinator.com/item?id=38294203
Comments: 15

Title: Zimbra 0-day used to steal email data from government organizations
Discussion link: http://news.ycombinator.com/item?id=38291199
Comments: 13

Title: AI-Exploits: Repo of multiple unauthenticated RCEs in AI tools
Discussion link: http://news.ycombinator.com/item?id=38291880
Comments: 10

Title: Serverless development experience for embedded computer vision
Discussion link: http://news.ycombinator.com/item?id=38288743
Comments: 8

Title: The CWEB System of Structured Documentation
Discussion link: http://news.ycombinator.com/item?id=38291735
Comments: 2

Title: OCapN, Interoperable Capabilities over the Network
Discussion link: http://news.ycombinator.com/item?id=38295524
Comments: 1

Title: A floating solar-powered device produces clean water and clean fuel
Discussion link: http://news.ycombinator.com/item?id=38269866
Comments: 1

Title: Microsoft support 'cracks' Windows for customer after activation fails
Discussion link: http://news.ycombinator.com/item?id=38295819
Comments: 1

Title: Show HN: Tiny LLMs – Browser-based private AI models for a wide array of tasks
Discussion link: http://news.ycombinator.com/item?id=38295179
Comments: 0

Title: In-person YC Startup Tech Talk and hiring mixer on 12/4 in SF
Discussion link: http://news.ycombinator.com/item?id=38295638
Comments: 0

Title: Planning for Unplanned Work in Linear
Discussion link: http://news.ycombinator.com/item?id=38294623
Comments: 0