Data Analysis on Producthunt daily

Product Hunt Monthly Top | 2025-09

Mon, 13 Oct 2025 13:21:34 +0000

1. VidAU - AI Video

Tagline: Turn Product Images into Scroll-Stopping Video Ads
Description: Transform product photos into high-converting videos in minutes with VidAU’s AI. Upload images and instantly get TikTok and Instagram ready ads no studio, no stress, just scroll-stopping content that sells.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI Video, Video Ads, Product Videos, TikTok Ads, Instagram Ads, AI Marketing, Video Creation, Easy Video, Convert Images, High Converting, Scroll-Stopping Content
VotesCount: 🔺1541
Featured: Yes
CreatedAt: 2025-09-08 07:01 AM (UTC)

2. Ada

Tagline: Your own AI data analyst
Description: Ada is the world’s first AI data analyst creating professional reports from any dataset. Automate data tasks, speed up analysis, and skip manual work in one click — no code or expertise needed. Effortless reporting for analysts and business pros.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI data analyst, data analysis, automated reporting, AI, no code, data tasks, business intelligence, effortless reporting, data analysis tool
VotesCount: 🔺1456
Featured: Yes
CreatedAt: 2025-09-03 07:01 AM (UTC)

3. Capalyze

Tagline: ChatGPT for datavores: scrape → ask → visualize
Description: Scrape real data from websites in real-time into Univers (our spreadsheet engine with 27.5k stars on GitHub!). Ask questions, visualize with interactive tables & charts, and then slice, dice, and export — all in one place.
Website: open
Product Hunt: View on Product Hunt

Keyword: Capalyze, ChatGPT, data scraping, data visualization, interactive tables, charts, Univers, spreadsheet engine, data analysis, data exploration, real-time data
VotesCount: 🔺1228
Featured: Yes
CreatedAt: 2025-09-17 07:01 AM (UTC)

4. Person search API

Tagline: Find anyone with just one API call
Description: The ultimate search for people data on the web for AI agents and platforms - comprehensive, targeted, and programmatic With 60+ filters (& unlimited ways to combine them), you can find exactly who you need to sell to, hire, or invest in with one API call
Website: open
Product Hunt: View on Product Hunt

Keyword: person search, API, people data, AI, filters, programmatic, targeted, lead generation, data enrichment, hiring, investment
VotesCount: 🔺1125
Featured: Yes
CreatedAt: 2025-09-17 07:01 AM (UTC)

5. CREAO

Tagline: Infinitely personalized software for every team member
Description: Teams get stuck with one-size-fits-all SaaS that doesn’t fit anyone perfectly. CREAO lets anyone build custom AI apps with natural language, each one with a built-in copilot. Build the right tools for any job.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI software, personalized software, custom AI apps, no-code AI, team productivity, copilot, SaaS alternative, natural language, build your own tools, CREAO
VotesCount: 🔺1123
Featured: Yes
CreatedAt: 2025-09-09 07:01 AM (UTC)

6. YouMind

Tagline: The first AI creation studio where learning meets writing
Description: YouMind helps anyone start creating, from capturing ideas to drafting and polishing articles, podcasts, videos, and more. With YouMind, everyone can turn curiosity into creation.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI creation, writing, learning, articles, podcasts, videos, idea capture, content creation, studio, YouMind
VotesCount: 🔺1099
Featured: Yes
CreatedAt: 2025-09-09 07:01 AM (UTC)

7. Snapdeck

Tagline: Lovable for Slides. No more wrestling with PowerPoint.
Description: Build a winning deck in a snap. Create professional presentations effortlessly with AI-powered slide generation, intuitive editing, and beautiful, customizable designs.
Website: open
Product Hunt: View on Product Hunt

Keyword: presentation software, AI, slides, PowerPoint alternative, deck creation, design, effortless, customizable, professional, intuitive editing
VotesCount: 🔺1055
Featured: Yes
CreatedAt: 2025-09-22 07:01 AM (UTC)

8. C1 by Thesys

Tagline: Make AI apps respond with interactive UI in real-time
Description: C1 is a Generative UI API that augments LLMs to respond with interactive UI like charts, forms, cards, and more, in real time. It integrates seamlessly with any LLM, framework, or MCP in just 2 lines of code. Ship AI apps 10X faster with 80% less UI overhead.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI UI, Generative UI, Real-time UI, LLM, API, Interactive UI, Charts, Forms, Cards, AI Apps, Rapid Development, UI Overhead
VotesCount: 🔺992
Featured: Yes
CreatedAt: 2025-09-30 07:01 AM (UTC)

9. Aikido Security

Tagline: Secure everything you build, host, and run.
Description: Your central code, cloud, and runtime security platform. Fix vulnerabilities automatically with AI AutoFix and AutoTriage. Cut false positives by 85%. Security is an everyone problem. So get security done, and get devs back to building.
Website: open
Product Hunt: View on Product Hunt

Keyword: Aikido Security, code security, cloud security, runtime security, AI AutoFix, AutoTriage, vulnerability management, false positive reduction, developer security, automated security
VotesCount: 🔺975
Featured: Yes
CreatedAt: 2025-09-11 07:01 AM (UTC)

10. Magiclight

Tagline: Intelligent story creation agent that creates long videos
Description: MagicLight gives you the power to turn any script into a cinematic story video—within minutes. Whether you’re creating YouTube content, children’s stories, ads, or brand films, MagicLight is the AI Story Video Agent that makes storytelling effortless.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI video creation, story video, video maker, YouTube videos, children’s stories, AI storytelling, script to video, cinematic video, brand films, content creation, effortless storytelling, MagicLight
VotesCount: 🔺961
Featured: Yes
CreatedAt: 2025-09-19 07:01 AM (UTC)

aipyapp

Sun, 21 Sep 2025 15:25:16 +0800

knownsec/aipyapp

Python use

AIPy is an implementation of the Python-use concept, demonstrating its practical value and potential.

Mission: unleash the full potential of large language models.
Vision: a future where LLMs can think independently and proactively leverage AIPy to solve complex problems.

What

Python use provides the entire Python execution environment to LLM. Imagine LLM sitting in front of a computer, typing various commands into the Python command-line interpreter, pressing Enter to execute, observing the results, and then typing and executing more code.

Unlike Agents, Python use does not define any tools interface. LLM can freely use all the features provided by the Python runtime environment.

Why

If you are a data engineer, you are likely familiar with the following scenarios:

Handling various data file formats: csv/excel, json, html, sqlite, parquet, etc.
Performing operations like data cleaning, transformation, computation, aggregation, sorting, grouping, filtering, analysis, and visualization.

This process often requires:

Starting Python, importing pandas as pd, and typing a bunch of commands to process data.
Generating a bunch of intermediate temporary files.
Describing your needs to ChatGPT/Claude, copying the generated data processing code, and running it manually.

So, why not start the Python command-line interpreter, directly describe your data processing needs, and let it be done automatically? The benefits are:

No need to manually input a bunch of Python commands temporarily.
No need to describe your needs to GPT, copy the program, and run it manually.

This is the problem Python use aims to solve!

How

Python use (aipython) is a Python command-line interpreter integrated with LLM. You can:

Enter and execute Python commands as usual.
Describe your needs in natural language, and aipython will automatically generate Python commands and execute them.

Moreover, the two modes can access data interchangeably. For example, after aipython processes your natural language commands, you can use standard Python commands to view various data.

Usage

AIPython has two running modes:

Task mode: Very simple and easy to use, just input your task, suitable for users unfamiliar with Python.
Python mode: Suitable for users familiar with Python, allowing both task input and Python commands, ideal for advanced users.

The default running mode is task mode, which can be switched to Python mode using the --python parameter.

Basic Config

~/.aipyapp/aipyapp.toml:

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[llm.deepseek]
type = "deepseek"
api_key = "Your DeepSeek API Key"

Task Mode

uv run aipy

>>> Get the latest posts from Reddit r/LocalLLaMA
......
......
>>> /done

pip install aipyapp and run with aipy

-> % aipy
🚀 Python use - AIPython (0.1.22) [https://aipy.app]
>> Get the latest posts from Reddit r/LocalLLaMA
......
>>

Python Mode

Basic Usage

Automatic task processing:

`1`	`>>> ai("Get the title of Google's homepage")`

Automatically Request to Install Third-Party Libraries

Python use - AIPython (Quit with 'exit()')
>>> ai("Use psutil to list all processes on MacOS")

📦 LLM requests to install third-party packages: ['psutil']
If you agree and have installed, please enter 'y [y/n] (n): y

Thanks

Hei Ge: Product manager/senior user/chief tester
Sonnet 3.7: Generated the first version of the code, which was almost ready to use without modification.
ChatGPT: Provided many suggestions and code snippets, especially for the command-line interface.
Codeium: Intelligent code completion
Copilot: Code improvement suggestions and README translation

pandas-ai

Sun, 10 Aug 2025 15:28:25 +0800

sinaptik-ai/pandas-ai

PandasAI is a Python platform that makes it easy to ask questions to your data in natural language. It helps non-technical users to interact with their data in a more natural way, and it helps technical users to save time, and effort when working with data.

🔧 Getting started

You can find the full documentation for PandasAI here.

You can either decide to use PandasAI in your Jupyter notebooks, Streamlit apps, or use the client and server architecture from the repo.

📚 Using the library

Python Requirements

Python version 3.8+ <3.12

📦 Installation

You can install the PandasAI library using pip or poetry.

With pip:

`1`	`pip install "pandasai>=3.0.0b2"`

With poetry:

`1`	`poetry add "pandasai>=3.0.0b2"`

💻 Usage

Ask questions

import pandasai as pai
from pandasai_openai.openai import OpenAI

llm = OpenAI("OPEN_AI_API_KEY")

pai.config.set({
    "llm": llm
})

# Sample DataFrame
df = pai.DataFrame({
    "country": ["United States", "United Kingdom", "France", "Germany", "Italy", "Spain", "Canada", "Australia", "Japan", "China"],
    "revenue": [5000, 3200, 2900, 4100, 2300, 2100, 2500, 2600, 4500, 7000]
})

df.chat('Which are the top 5 countries by sales?')

`1`	`China, United States, Japan, Germany, Australia`

Or you can ask more complex questions:

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df.chat(
    "What is the total sales for the top 3 countries by sales?"
)

`1`	`The total sales for the top 3 countries by sales is 16500.`

Visualize charts

You can also ask PandasAI to generate charts for you:

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df.chat(
    "Plot the histogram of countries showing for each one the gd. Use different colors for each bar",
)

Multiple DataFrames

You can also pass in multiple dataframes to PandasAI and ask questions relating them.

import pandasai as pai
from pandasai_openai.openai import OpenAI

employees_data = {
    'EmployeeID': [1, 2, 3, 4, 5],
    'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'],
    'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance']
}

salaries_data = {
    'EmployeeID': [1, 2, 3, 4, 5],
    'Salary': [5000, 6000, 4500, 7000, 5500]
}

llm = OpenAI("OPEN_AI_API_KEY")

pai.config.set({
    "llm": llm
})

employees_df = pai.DataFrame(employees_data)
salaries_df = pai.DataFrame(salaries_data)


pai.chat("Who gets paid the most?", employees_df, salaries_df)

`1`	`Olivia gets paid the most.`

Docker Sandbox

You can run PandasAI in a Docker sandbox, providing a secure, isolated environment to execute code safely and mitigate the risk of malicious attacks.

Python Requirements

`1`	`pip install "pandasai-docker"`

Usage

import pandasai as pai
from pandasai_docker import DockerSandbox
from pandasai_openai.openai import OpenAI

# Initialize the sandbox
sandbox = DockerSandbox()
sandbox.start()

employees_data = {
    'EmployeeID': [1, 2, 3, 4, 5],
    'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'],
    'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance']
}

salaries_data = {
    'EmployeeID': [1, 2, 3, 4, 5],
    'Salary': [5000, 6000, 4500, 7000, 5500]
}

llm = OpenAI("OPEN_AI_API_KEY")

pai.config.set({
    "llm": llm
})

employees_df = pai.DataFrame(employees_data)
salaries_df = pai.DataFrame(salaries_data)

pai.chat("Who gets paid the most?", employees_df, salaries_df, sandbox=sandbox)

# Don't forget to stop the sandbox when done
sandbox.stop()

`1`	`Olivia gets paid the most.`

You can find more examples in the examples directory.

📜 License

PandasAI is available under the MIT expat license, except for the pandasai/ee directory of this repository, which has its license here.

If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, contact us.

Resources

Beta Notice
Release v3 is currently in beta. The following documentation and examples reflect the features and functionality in progress and may change before the final release.

Docs for comprehensive documentation
Examples for example notebooks
Discord for discussion with the community and PandasAI team

🤝 Contributing

Contributions are welcome! Please check the outstanding issues and feel free to open a pull request. For more information, please check out the contributing guidelines.

Thank you!

dataease

Sun, 27 Jul 2025 15:29:46 +0800

dataease/dataease

人人可用的开源 BI 工具

什么是 DataEase？

DataEase 是开源的 BI 工具，帮助用户快速分析数据并洞察业务趋势，从而实现业务的改进与优化。DataEase 支持丰富的数据源连接，能够通过拖拉拽方式快速制作图表，并可以方便的与他人分享。

DataEase 的优势：

开源开放：零门槛，线上快速获取和安装，按月迭代；
简单易用：极易上手，通过鼠标点击和拖拽即可完成分析；
全场景支持：多平台安装和多样化嵌入支持；
安全分享：支持多种数据分享方式，确保数据安全。

DataEase 支持的数据源：

OLTP 数据库： MySQL、Oracle、SQL Server、PostgreSQL、MariaDB、Db2、TiDB、MongoDB-BI 等；
OLAP 数据库： ClickHouse、Apache Doris、Apache Impala、StarRocks 等；
数据仓库/数据湖： Amazon RedShift 等；
数据文件： Excel、CSV 等；
API 数据源。

如果您需要向团队介绍 DataEase，可以使用这个官方 PPT 材料。

快速开始

桌面版：

你可以在 PC 上安装 DataEasae 桌面版，下载地址为：https://dataease.cn/desktop/index.html

服务器版：

# 准备一台 2 核 4G 以上的 Linux 服务器，并以 root 用户运行以下一键安装脚本：

curl -sSL https://dataease.oss-cn-hangzhou.aliyuncs.com/quick_start_v2.sh | bash

# 用户名: admin
# 密码: DataEase@123456

你也可以通过 1Panel 应用商店快速部署 DataEase。如果是用于生产环境，推荐使用离线安装包方式进行安装部署。

如你有更多问题，可以查看在线文档，或者通过论坛与我们交流。

UI 展示

技术栈

前端：Vue.js、Element
图库：AntV
后端：Spring Boot
数据库：MySQL
数据处理：Apache Calcite、Apache SeaTunnel
基础设施：Docker

飞致云的其他明星项目

1Panel - 现代化、开源的 Linux 服务器运维管理面板
MaxKB - 基于 LLM 大语言模型的开源知识库问答系统
JumpServer - 广受欢迎的开源堡垒机
Halo - 强大易用的开源建站工具
MeterSphere - 新一代的开源持续测试工具

License

Licensed under The GNU General Public License version 3 (GPLv3) (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

https://www.gnu.org/licenses/gpl-3.0.html

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

suna

Tue, 24 Jun 2025 15:30:37 +0800

kortix-ai/suna

Suna - Open Source Generalist AI Agent

(that acts on your behalf)

Suna is a fully open source AI assistant that helps you accomplish real-world tasks with ease. Through natural conversation, Suna becomes your digital companion for research, data analysis, and everyday challenges—combining powerful capabilities with an intuitive interface that understands what you need and delivers results.

Suna’s powerful toolkit includes seamless browser automation to navigate the web and extract data, file management for document creation and editing, web crawling and extended search capabilities, command-line execution for system tasks, website deployment, and integration with various APIs and services. These capabilities work together harmoniously, allowing Suna to solve your complex problems and automate workflows through simple conversations!

[

Project Architecture

Suna consists of four main components:

Backend API

Python/FastAPI service that handles REST endpoints, thread management, and LLM integration with Anthropic, and others via LiteLLM.

Frontend

Next.js/React application providing a responsive UI with chat interface, dashboard, etc.

Agent Docker

Isolated execution environment for every agent - with browser automation, code interpreter, file system access, tool integration, and security features.

Supabase Database

Handles data persistence with authentication, user management, conversation history, file storage, agent state, analytics, and real-time subscriptions.

Use Cases

Competitor Analysis (Watch) - “Analyze the market for my next company in the healthcare industry, located in the UK. Give me the major players, their market size, strengths, and weaknesses, and add their website URLs. Once done, generate a PDF report.”
VC List (Watch) - “Give me the list of the most important VC Funds in the United States based on Assets Under Management. Give me website URLs, and if possible an email to reach them out.”
Looking for Candidates (Watch) - “Go on LinkedIn, and find me 10 profiles available - they are not working right now - for a junior software engineer position, who are located in Munich, Germany. They should have at least one bachelor’s degree in Computer Science or anything related to it, and 1-year of experience in any field/role.”
Planning Company Trip (Watch) - “Generate me a route plan for my company. We should go to California. We’ll be in 8 people. Compose the trip from the departure (Paris, France) to the activities we can do considering that the trip will be 7 days long - departure on the 21st of Apr 2025. Check the weather forecast and temperature for the upcoming days, and based on that, you can plan our activities (outdoor vs indoor).”
Working on Excel (Watch) - “My company asked me to set up an Excel spreadsheet with all the information about Italian lottery games (Lotto, 10eLotto, and Million Day). Based on that, generate and send me a spreadsheet with all the basic information (public ones).”
Automate Event Speaker Prospecting (Watch) - “Find 20 AI ethics speakers from Europe who’ve spoken at conferences in the past year. Scrapes conference sites, cross-references LinkedIn and YouTube, and outputs contact info + talk summaries.”
Summarize and Cross-Reference Scientific Papers (Watch) - “Research and compare scientific papers talking about Alcohol effects on our bodies during the last 5 years. Generate a report about the most important scientific papers talking about the topic I wrote before.”
Research + First Contact Draft (Watch) - “Research my potential customers (B2B) on LinkedIn. They should be in the clean tech industry. Find their websites and their email addresses. After that, based on the company profile, generate a personalized first contact email where I present my company which is offering consulting services to cleantech companies to maximize their profits and reduce their costs.”
SEO Analysis (Watch) - “Based on my website suna.so, generate an SEO report analysis, find top-ranking pages by keyword clusters, and identify topics I’m missing.”
Generate a Personal Trip (Watch) - “Generate a personal trip to London, with departure from Bangkok on the 1st of May. The trip will last 10 days. Find an accommodation in the center of London, with a rating on Google reviews of at least 4.5. Find me interesting outdoor activities to do during the journey. Generate a detailed itinerary plan.”
Recently Funded Startups (Watch) - “Go on Crunchbase, Dealroom, and TechCrunch, filter by Series A funding rounds in the SaaS Finance Space, and build a report with company data, founders, and contact info for outbound sales.”
Scrape Forum Discussions (Watch) - “I need to find the best beauty centers in Rome, but I want to find them by using open forums that speak about this topic. Go on Google, and scrape the forums by looking for beauty center discussions located in Rome. Then generate a list of 5 beauty centers with the best comments about them.”

Self-Hosting

Suna can be self-hosted on your own infrastructure using our comprehensive setup wizard. For a complete guide to self-hosting Suna, please refer to our Self-Hosting Guide.

The setup process includes:

Setting up a Supabase project for database and authentication
Configuring Redis for caching and session management
Setting up Daytona for secure agent execution
Integrating with LLM providers (Anthropic, OpenAI, OpenRouter, etc.)
Configuring web search and scraping capabilities (Tavily, Firecrawl)
Setting up QStash for background job processing and workflows
Configuring webhook handling for automated tasks
Optional integrations (RapidAPI, Smithery for custom agents)

Quick Start

Clone the repository:

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git clone https://github.com/kortix-ai/suna.git
cd suna

Run the setup wizard:

`1`	`python setup.py`

The wizard will guide you through 14 steps with progress saving, so you can resume if interrupted.

Start or stop the containers:

`1`	`python start.py`

Manual Setup

See the Self-Hosting Guide for detailed manual setup instructions.

The wizard will guide you through all necessary steps to get your Suna instance up and running. For detailed instructions, troubleshooting tips, and advanced configuration options, see the Self-Hosting Guide.

Contributing

We welcome contributions from the community! Please see our Contributing Guide for more details.

Acknowledgements

Main Contributors

Technologies

Daytona - Secure agent execution environment
Supabase - Database and authentication
Playwright - Browser automation
OpenAI - LLM provider
Anthropic - LLM provider
Tavily - Search capabilities
Firecrawl - Web scraping capabilities
QStash - Background job processing and workflows
RapidAPI - API services
Smithery - Custom agent development

License

Kortix Suna is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.

Product Hunt Daily | 2025-05-17

Sat, 17 May 2025 07:25:35 +0000

1. Inkr 2.0

Tagline: Transcribe, quick notes, and search smarter in real time
Description: Inkr turns audio into accurate, structured content in seconds. With real-time transcription, AI-enhanced notes, smart templates, and searchable transcripts, it’s more than a transcriber—it’s your productivity co-pilot. Try it, no account needed.
Website: open
Product Hunt: View on Product Hunt

Keyword: Transcription, AI, Notes, Real-time, Search, Productivity, Inkr, Inkr 2.0, Transcriber, Audio to Text, Smart Templates
VotesCount: 🔺432
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

2. Quick Mock 2.0

Tagline: Practice AI interviews from LinkedIn with insider questions
Description: QuickMock turns LinkedIn jobs into AI-powered mock interviews—instantly. Now featuring Hot Interviews and a real Question Bank from top companies. Practice trending roles, get insider questions, and receive instant feedback to level up fast.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI interview practice, LinkedIn mock interview, mock interview, AI interview prep, job interview practice, interview skills, interview questions, Quick Mock 2.0, Hot Interviews, Question Bank
VotesCount: 🔺356
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

3. Surrealist

Tagline: Build smarter knowledge graphs for data-rich applications
Description: Surrealist lets you visualise SurrealDB queries as interactive knowledge graphs. Instantly spot patterns, explore relationships, and bring your data to life with real-time insights powered by graphs, vectors, and documents—all in one place.
Website: open
Product Hunt: View on Product Hunt

Keyword: knowledge graph, SurrealDB, data visualization, graph database, knowledge graph visualization, data exploration, real-time insights, vector database, document database, interactive graph, data analysis
VotesCount: 🔺258
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

4. Tersa

Tagline: Visualize your AI workflows
Description: Tersa is an open source canvas for building AI workflows. Drag, drop connect and run nodes to build your own workflows powered by various industry-leading AI models.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI workflow, workflow builder, AI canvas, open source AI, drag and drop AI, AI nodes, visual AI, AI modeling, Tersa
VotesCount: 🔺248
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

5. Tensorlake

Tagline: Parse documents like a human & build Python-based workflows
Description: Tensorlake Cloud is a platform for document ingestion and data orchestration. Parse real-world documents with human-like layout understanding and build Python-based workflows at scale and ready for production.
Website: open
Product Hunt: View on Product Hunt

Keyword: Tensorlake, document parsing, data orchestration, Python workflow, document ingestion, layout understanding, AI, machine learning, cloud platform, data processing
VotesCount: 🔺196
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

6. Note API Connector

Tagline: Import and sync data from any API into Notion
Description: Connect any API to Notion without coding. Automate data updates, schedule syncs, and turn live data into powerful Notion dashboards—analytics, content, CRM, finance, and more. Official Notion integration, secure by design.
Website: open
Product Hunt: View on Product Hunt

Keyword: Notion API, API Connector, Notion Integration, Data Integration, API to Notion, Automate Notion, Sync API Data, No-code API, Notion Dashboard, Live Data Notion
VotesCount: 🔺188
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

7. SurfChat

Tagline: Chat with anyone on any website
Description: SurfChat is a Chrome extension that enables live, real-time chatting across any website you visit. Whether you’re browsing blogs, shopping, or reading articles, you can easily start live chats and meet people from all over the world.
Website: open
Product Hunt: View on Product Hunt

Keyword: Chrome extension, live chat, real-time chat, website chat, online chat, SurfChat, meet people, chat extension
VotesCount: 🔺165
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

8. queryinside

Tagline: Powerful AI-based data searching and system monitoring tool
Description: query Inside revolutionizes how any web developer or analyst handles data. Whether you’re searching through thousands of data points, monitoring user activity in real-time, or analyzing detailed event logs, Query Inside is your ultimate tool.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI, data search, system monitoring, data analysis, web developer, real-time monitoring, event logs, queryinside
VotesCount: 🔺164
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

9. EarlyFinder

Tagline: Track startup growth from first tweet to first million
Description: We track early-stage companies building in public, analyze their growth, and update you monthly with fresh data—traffic, revenue estimates, and more. No more messy bookmarks. Just clean insights and cool startups.
Website: open
Product Hunt: View on Product Hunt

Keyword: startup, growth, tracking, early-stage companies, funding, revenue, traffic, analytics, insights, market research, company data
VotesCount: 🔺155
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

10. ClipZap Workflow V2.0

Tagline: Your Automate money printer by ClipZap workflow
Description: ClipZap is an AI workflow tool for content creators, marketers, influencers, etc. ClipZap Workflow boosts your creativity 5x and expands your impact 10x. Integrates 50+ top models and 20+ templates for effortless task completion and business growth.
Website: open
Product Hunt: View on Product Hunt

Keyword: ClipZap, Workflow, AI, Automation, Content Creation, Marketing, Influencer, Productivity, AI Tools, Workflow Automation, Business Growth
VotesCount: 🔺153
Featured: Yes
CreatedAt: 2025-05-16 07:01 AM (UTC)

Python-100-Days

Sun, 04 May 2025 15:26:27 +0800

jackfrued/Python-100-Days

Python - 100天从新手到大师

作者：骆昊

说明：如果访问 GitHub 比较慢的话，可以关注我的知乎号（Python-Jack），上面的“从零开始学Python”专栏（对应本项目前 20 天的内容）比较适合初学者，其他的专栏如“数据思维和统计思维”、“基于Python的数据分析”、“说走就走的AI之旅”等也在持续创作和更新中，欢迎大家关注、点赞和评论。如果希望免费学习打卡或者参与问题讨论，可以加入下面的 QQ 交流群（三个群加一个即可），请不要重复加群，也不要在群里发布广告和其他色情、低俗或敏感内容。如果有付费学习或付费咨询的需求，可以添加我的私人微信（微信号：jackfrued），备注好自己的称呼和需求，我会为大家提供力所能及的帮助。

本项目对应的部分视频已经同步到 Bilibili，有兴趣的小伙伴可以点赞、投币、关注，一键三连支持一下！

Python应用领域和职业发展分析

简单的说，Python是一个“优雅”、“明确”、“简单”的编程语言。

学习曲线低，非专业人士也能上手
开源系统，拥有强大的生态圈
解释型语言，完美的平台可移植性
动态类型语言，支持面向对象和函数式编程
代码规范程度高，可读性强

Python在以下领域都有用武之地。

后端开发 - Python / Java / Go / PHP
DevOps - Python / Shell / Ruby
数据采集 - Python / C++ / Java
量化交易 - Python / C++ / R
数据科学 - Python / R / Julia / Matlab
机器学习 - Python / R / C++ / Julia
自动化测试 - Python / Shell

作为一名Python开发者，根据个人的喜好和职业规划，可以选择的就业领域也非常多。

Python后端开发工程师（服务器、云平台、数据接口）
Python运维工程师（自动化运维、SRE、DevOps）
Python数据分析师（数据分析、商业智能、数字化运营）
Python数据科学家（机器学习、深度学习、算法专家）
Python爬虫工程师（不推荐此赛道！！！）
Python测试工程师（自动化测试、测试开发）

说明：目前，数据科学赛道是非常热门的方向，因为不管是互联网行业还是传统行业都已经积累了大量的数据，各行各业都需要数据科学家从已有的数据中发现更多的商业价值，从而为企业的决策提供数据的支撑，这就是所谓的数据驱动决策。

给初学者的几个建议：

Make English as your working language. （让英语成为你的工作语言）
Practice makes perfect. （熟能生巧）
All experience comes from the mistakes you’ve made. （所有的经验都源于你犯过的错误）
Don’t be a freeloader. （不要当伸手党）
Either outstanding or out. （要么出众，要么出局）

Day01~20 - Python语言基础

Day01 - 初识Python

Python简介
- Python编年史
- Python优缺点
- Python应用领域
安装Python环境
- Windows环境
- macOS环境

Day02 - 第一个Python程序

编写代码的工具
你好世界
注释你的代码

Day03 - Python语言中的变量

一些常识
变量和类型
变量命名
变量的使用

Day04 - Python语言中的运算符

算术运算符
赋值运算符
比较运算符和逻辑运算符
运算符和表达式应用
- 华氏和摄氏温度转换
- 计算圆的周长和面积
- 判断闰年

Day05 - 分支结构

使用if和else构造分支结构
使用match和case构造分支结构
分支结构的应用
- 分段函数求值
- 百分制成绩转换成等级
- 计算三角形的周长和面积

Day06 - 循环结构

for-in循环
while循环
break和continue
嵌套的循环结构
循环结构的应用
- 判断素数
- 最大公约数
- 猜数字游戏

Day07 - 分支和循环结构实战

例子1：100以内的素数
例子2：斐波那契数列
例子3：寻找水仙花数
例子4：百钱百鸡问题
例子5：CRAPS赌博游戏

Day08 - 常用数据结构之列表-1

创建列表
列表的运算
元素的遍历

Day09 - 常用数据结构之列表-2

列表的方法
- 添加和删除元素
- 元素位置和频次
- 元素排序和反转
列表生成式
嵌套列表
列表的应用

Day10 - 常用数据结构之元组

元组的定义和运算
打包和解包操作
交换变量的值
元组和列表的比较

Day11 - 常用数据结构之字符串

字符串的定义
- 转义字符
- 原始字符串
- 字符的特殊表示
字符串的运算
- 拼接和重复
- 比较运算
- 成员运算
- 获取字符串长度
- 索引和切片
字符的遍历
字符串的方法
- 大小写相关操作
- 查找操作
- 性质判断
- 格式化
- 修剪操作
- 替换操作
- 拆分与合并
- 编码与解码
- 其他方法

Day12 - 常用数据结构之集合

创建集合
元素的变量
集合的运算
- 成员运算
- 二元运算
- 比较运算
集合的方法
不可变集合

Day13 - 常用数据结构之字典

创建和使用字典
字典的运算
字典的方法
字典的应用

Day14 - 函数和模块

定义函数
函数的参数
- 位置参数和关键字参数
- 参数的默认值
- 可变参数
用模块管理函数
标准库中的模块和函数

Day15 - 函数应用实战

例子1：随机验证码
例子2：判断素数
例子3：最大公约数和最小公倍数
例子4：数据统计
例子5：双色球随机选号

Day16 - 函数使用进阶

高阶函数
Lambda函数
偏函数

Day17 - 函数高级应用

装饰器
递归调用

Day18 - 面向对象编程入门

类和对象
定义类
创建和使用对象
初始化方法
面向对象的支柱
面向对象案例
- 例子1：数字时钟
- 例子2：平面上的点

Day19 - 面向对象编程进阶

可见性和属性装饰器
动态属性
静态方法和类方法
继承和多态

Day20 - 面向对象编程应用

扑克游戏
工资结算系统

Day21~30 - Python语言应用

Day21 - 文件读写和异常处理

打开和关闭文件
读写文本文件
异常处理机制
上下文管理器语法
读写二进制文件

Day22 - 对象的序列化和反序列化

JSON概述
读写JSON格式的数据
包管理工具pip
使用网络API获取数据

Day23 - Python读写CSV文件

CSV文件介绍
将数据写入CSV文件
从CSV文件读取数据

Day24 - Python读写Excel文件-1

Excel简介
读Excel文件
写Excel文件
调整样式
公式计算

Day25 - Python读写Excel文件-2

Excel简介
读Excel文件
写Excel文件
调整样式
生成统计图表

Day26 - Python操作Word和PowerPoint文件

操作Word文档
生成PowerPoint

Day27 - Python操作PDF文件

从PDF中提取文本
旋转和叠加页面
加密PDF文件
批量添加水印
创建PDF文件

Day28 - Python处理图像

入门知识
用Pillow处理图像
使用Pillow绘图

Day29 - Python发送邮件和短信

发送电子邮件
发送短信

Day30 - 正则表达式的应用

正则表达式相关知识
Python对正则表达式的支持
- 例子1：输入验证
- 例子2：内容提取
- 例子3：内容替换
- 例子4：长句拆分

Day31~35 - 其他相关内容

Python语言进阶

重要知识点
数据结构和算法
函数的使用方式
面向对象相关知识
迭代器和生成器
并发编程

Web前端入门

用HTML标签承载页面内容
用CSS渲染页面
用JavaScript处理交互式行为
Vue.js入门
Element的使用
Bootstrap的使用

玩转Linux操作系统

操作系统发展史和Linux概述
Linux基础命令
Linux中的实用程序
Linux的文件系统
Vim编辑器的应用
环境变量和Shell编程
软件的安装和服务的配置
网络访问和管理
其他相关内容

Day36~45 - 数据库基础和进阶

Day36 - 关系型数据库和MySQL概述

关系型数据库概述
MySQL简介
安装MySQL
MySQL基本命令

Day37 - SQL详解之DDL

建库建表
删除表和修改表

Day38 - SQL详解之DML

insert操作
delete操作
update操作

Day39 - SQL详解之DQL

投影和别名
筛选数据
空值处理
去重
排序
聚合函数
嵌套查询
分组操作
表连接
- 笛卡尔积
- 内连接
- 自然连接
- 外连接
窗口函数
- 定义窗口
- 排名函数
- 取数函数

Day40 - SQL详解之DCL

创建用户
授予权限
召回权限

Day41 - MySQL新特性

JSON类型
窗口函数
公共表表达式

Day42 - 视图、函数和过程

视图
- 使用场景
- 创建视图
- 使用限制
函数
- 内置函数
- 用户自定义函数（UDF）
过程
- 创建过程
- 调用过程

Day43 - 索引

执行计划
索引的原理
创建索引
- 普通索引
- 唯一索引
- 前缀索引
- 复合索引
注意事项

Day44 - Python接入MySQL数据库

安装三方库
创建连接
获取游标
执行SQL语句
通过游标抓取数据
事务提交和回滚
释放连接
编写ETL脚本

Day45 - 大数据平台和HiveSQL

Hadoop生态圈
Hive概述
准备工作
数据类型
DDL操作
DML操作
数据查询

Day46~60 - 实战Django

Day46 - Django快速上手

Web应用工作机制
HTTP请求和响应
Django框架概述
5分钟快速上手

Day47 - 深入模型

关系型数据库配置
使用ORM完成对模型的CRUD操作
管理后台的使用
Django模型最佳实践
模型定义参考

Day48 - 静态资源和Ajax请求

加载静态资源
Ajax概述
用Ajax实现投票功能

Day49 - Cookie和Session

实现用户跟踪
cookie和session的关系
Django框架对session的支持
视图函数中的cookie读写操作

Day50 - 报表和日志

通过HttpResponse修改响应头
使用StreamingHttpResponse处理大文件
使用xlwt生成Excel报表
使用reportlab生成PDF报表
使用ECharts生成前端图表

Day51 - 日志和调试工具栏

配置日志
配置Django-Debug-Toolbar
优化ORM代码

Day52 - 中间件的应用

什么是中间件
Django框架内置的中间件
自定义中间件及其应用场景

Day53 - 前后端分离开发入门

返回JSON格式的数据
用Vue.js渲染页面

Day54 - RESTful架构和DRF入门

REST概述
DRF库使用入门
前后端分离开发
JWT的应用

Day55 - RESTful架构和DRF进阶

使用CBV
数据分页
数据筛选

Day56 - 使用缓存

网站优化第一定律
在Django项目中使用Redis提供缓存服务
在视图函数中读写缓存
使用装饰器实现页面缓存
为数据接口提供缓存服务

Day57 - 接入三方平台

文件上传表单控件和图片文件预览
服务器端如何处理上传的文件

Day58 - 异步任务和定时任务

网站优化第二定律
配置消息队列服务
在项目中使用Celery实现任务异步化
在项目中使用Celery实现定时任务

Day59 - 单元测试

Day60 - 项目上线

Python中的单元测试
Django框架对单元测试的支持
使用版本控制系统
配置和使用uWSGI
动静分离和Nginx配置
配置HTTPS
配置域名解析

Day61~65 - 网络数据采集

Day61 - 网络数据采集概述

网络爬虫的概念及其应用领域
网络爬虫的合法性探讨
开发网络爬虫的相关工具
一个爬虫程序的构成

Day62 - 数据抓取和解析

使用requests三方库实现数据抓取
页面解析的三种方式
- 正则表达式解析
- XPath解析
- CSS选择器解析

Day63 - Python中的并发编程

Day64 - 使用Selenium抓取网页动态内容

安装Selenium
加载页面
查找元素和模拟用户行为
隐式等待和显示等待
执行JavaScript代码
Selenium反爬破解
设置无头浏览器

Day65 - 爬虫框架Scrapy简介

Scrapy核心组件
Scrapy工作流程
安装Scrapy和创建项目
编写蜘蛛程序
编写中间件和管道程序
Scrapy配置文件

Day66~80 - Python数据分析

Day66 - 数据分析概述

数据分析师的职责
数据分析师的技能栈
数据分析相关库

Day67 - 环境准备

安装和使用anaconda
- conda相关命令
安装和使用jupyter-lab
- 安装和启动
- 使用小技巧

Day68 - NumPy的应用-1

创建数组对象
数组对象的属性
数组对象的索引运算
- 普通索引
- 花式索引
- 布尔索引
- 切片索引
案例：使用数组处理图像

Day69 - NumPy的应用-2

数组对象的相关方法
- 获取描述性统计信息
- 其他相关方法

Day70 - NumPy的应用-3

数组的运算
- 数组跟标量的运算
- 数组跟数组的运算
通用一元函数
通用二元函数
广播机制
Numpy常用函数

Day71 - NumPy的应用-4

向量
行列式
矩阵
多项式

Day72 - 深入浅出pandas-1

创建Series对象
Series对象的运算
Series对象的属性和方法

Day73 - 深入浅出pandas-2

创建DataFrame对象
DataFrame对象的属性和方法
读写DataFrame中的数据

Day74 - 深入浅出pandas-3

数据重塑
- 数据拼接
- 数据合并
数据清洗
- 缺失值
- 重复值
- 异常值
- 预处理

Day75 - 深入浅出pandas-4

数据透视
- 获取描述性统计信息
- 排序和头部值
- 分组聚合
- 透视表和交叉表
数据呈现

Day76 - 深入浅出pandas-5

计算同比环比
窗口计算
相关性判定

Day77 - 深入浅出pandas-6

索引的使用
- 范围索引
- 分类索引
- 多级索引
- 间隔索引
- 日期时间索引

Day78 - 数据可视化-1

安装和导入matplotlib
创建画布
创建坐标系
绘制图表
- 折线图
- 散点图
- 柱状图
- 饼状图
- 直方图
- 箱线图
显示和保存图表

Day79 - 数据可视化-2

高阶图表
- 气泡图
- 面积图
- 雷达图
- 玫瑰图
- 3D图表

Day80 - 数据可视化-3

Seaborn
Pyecharts

Day81~90 - 机器学习

Day81 - 浅谈机器学习

人工智能发展史
什么是机器学习
机器学习应用领域
机器学习的分类
机器学习的步骤
第一次机器学习

Day82 - k最近邻算法

距离的度量
数据集介绍
kNN分类的实现
模型评估
参数调优
kNN回归的实现

Day83 - 决策树和随机森林

决策树的构建
- 特征选择
- 数据分裂
- 树的剪枝
实现决策树模型
随机森林概述

Day84 - 朴素贝叶斯算法

贝叶斯定理
朴素贝叶斯
算法原理
- 训练阶段
- 预测阶段
- 代码实现
算法优缺点

Day85 - 回归模型

回归模型的分类
回归系数的计算
新数据集介绍
线性回归代码实现
回归模型的评估
引入正则化项
线性回归另一种实现
多项式回归
逻辑回归

Day86 - K-Means聚类算法

算法原理
数学描述
代码实现

Day87 - 集成学习算法

算法分类
AdaBoost
GBDT
XGBoost
LightGBM

Day88 - 神经网络模型

基本构成
工作原理
代码实现
模型优缺点

Day89 - 自然语言处理入门

词袋模型
词向量
NPLM和RNN
Seq2Seq
Transformer

Day90 - 机器学习实战

数据探索
特征工程
模型训练
模型评估
模型部署

Day91~99 - 团队项目开发

第91天：团队项目开发的问题和解决方案

软件过程模型
- 经典过程模型（瀑布模型）
  - 可行性分析（研究做还是不做），输出《可行性分析报告》。
  - 需求分析（研究做什么），输出《需求规格说明书》和产品界面原型图。
  - 概要设计和详细设计，输出概念模型图（ER图）、物理模型图、类图、时序图等。
  - 编码 / 测试。
  - 上线 / 维护。
  瀑布模型最大的缺点是无法拥抱需求变化，整套流程结束后才能看到产品，团队士气低落。
- 敏捷开发（Scrum）- 产品所有者、Scrum Master、研发人员 - Sprint
  - 产品的Backlog（用户故事、产品原型）。
  - 计划会议（评估和预算）。
  - 日常开发（站立会议、番茄工作法、结对编程、测试先行、代码重构……）。
  - 修复bug（问题描述、重现步骤、测试人员、被指派人）。
  - 发布版本。
  - 评审会议（Showcase，用户需要参与）。
  - 回顾会议（对当前迭代周期做一个总结）。
  补充：敏捷软件开发宣言
  - 个体和互动 高于流程和工具
  - 工作的软件 高于详尽的文档
  - 客户合作 高于合同谈判
  - 响应变化 高于遵循计划
  角色：产品所有者（决定做什么，能对需求拍板的人）、团队负责人（解决各种问题，专注如何更好的工作，屏蔽外部对开发团队的影响）、开发团队（项目执行人员，具体指开发人员和测试人员）。
  
  准备工作：商业案例和资金、合同、憧憬、初始产品需求、初始发布计划、入股、组建团队。
  
  敏捷团队通常人数为8-10人。
  
  工作量估算：将开发任务量化，包括原型、Logo设计、UI设计、前端开发等，尽量把每个工作分解到最小任务量，最小任务量标准为工作时间不能超过两天，然后估算总体项目时间。把每个任务都贴在看板上面，看板上分三部分：to do（待完成）、in progress（进行中）和done（已完成）。
项目团队组建
- 团队的构成和角色
- 编程规范和代码审查（flake8、pylint）
- Python中的一些“惯例”（请参考《Python惯例-如何编写Pythonic的代码》）
- 影响代码可读性的原因：
  - 代码注释太少或者没有注释
  - 代码破坏了语言的最佳实践
  - 反模式编程（意大利面代码、复制-黏贴编程、自负编程、……）
团队开发工具介绍
- 版本控制：Git、Mercury
- 缺陷管理：Gitlab、Redmine
- 敏捷闭环工具：禅道、JIRA
- 持续集成：Jenkins、Travis-CI
请参考《团队项目开发的问题和解决方案》。

项目选题和理解业务

选题范围设定
- CMS（用户端）：新闻聚合网站、问答/分享社区、影评/书评网站等。
- MIS（用户端+管理端）：KMS、KPI考核系统、HRS、CRM系统、供应链系统、仓储管理系统等。
- App后台（管理端+数据接口）：二手交易类、报刊杂志类、小众电商类、新闻资讯类、旅游类、社交类、阅读类等。
- 其他类型：自身行业背景和工作经验、业务容易理解和把控。
需求理解、模块划分和任务分配
- 需求理解：头脑风暴和竞品分析。
- 模块划分：画思维导图（XMind），每个模块是一个枝节点，每个具体的功能是一个叶节点（用动词表述），需要确保每个叶节点无法再生出新节点，确定每个叶子节点的重要性、优先级和工作量。
- 任务分配：由项目负责人根据上面的指标为每个团队成员分配任务。

制定项目进度表（每日更新）

模块	功能	人员	状态	完成	工时	计划开始	计划结束	备注
评论	添加评论	王大锤	正在进行	50%	4	2018/8/7	2018/8/7
	删除评论	王大锤	等待	0%	2	2018/8/7	2018/8/7
	查看评论	白元芳	正在进行	20%	4	2018/8/7	2018/8/7	需要进行代码审查
	评论投票	白元芳	等待	0%	4	2018/8/8	2018/8/8

OOAD和数据库设计

UML（统一建模语言）的类图
通过模型创建表（正向工程），例如在Django项目中可以通过下面的命令创建二维表。
1 2

python manage.py makemigrations app python manage.py migrate
使用PowerDesigner绘制物理模型图。
通过数据表创建模型（反向工程），例如在Django项目中可以通过下面的命令生成模型。
1

python manage.py inspectdb > app/models.py

第92天：Docker容器技术详解

Docker简介
安装Docker
使用Docker创建容器（Nginx、MySQL、Redis、Gitlab、Jenkins）
构建Docker镜像（Dockerfile的编写和相关指令）
容器编排（Docker-compose）
集群管理（Kubernetes）

第93天：MySQL性能优化

基本原则
InnoDB引擎
索引的使用和注意事项
数据分区
SQL优化
配置优化
架构优化

第94天：网络API接口设计

设计原则
- 关键问题
- 其他问题
文档撰写

第95天：[使用Django开发商业项目](./Day91-100/95.使用Django开发商业项目.md)

项目开发中的公共问题

数据库的配置（多数据库、主从复制、数据库路由）
缓存的配置（分区缓存、键设置、超时设置、主从复制、故障恢复（哨兵））
日志的配置
分析和调试（Django-Debug-ToolBar）
好用的Python模块（日期计算、图像处理、数据加密、三方API）

REST API设计

项目中的重点难点剖析

使用缓存缓解数据库压力 - Redis
使用消息队列做解耦合和削峰 - Celery + RabbitMQ

第96天：软件测试和自动化测试

单元测试

测试的种类
编写单元测试（unittest、pytest、nose2、tox、ddt、……）
测试覆盖率（coverage）

Django项目部署

部署前的准备工作
- 关键设置（SECRET_KEY / DEBUG / ALLOWED_HOSTS / 缓存 / 数据库）
- HTTPS / CSRF_COOKIE_SECUR / SESSION_COOKIE_SECURE
- 日志相关配置
Linux常用命令回顾
Linux常用服务的安装和配置
uWSGI/Gunicorn和Nginx的使用
- Gunicorn和uWSGI的比较
  - 对于不需要大量定制化的简单应用程序，Gunicorn是一个不错的选择，uWSGI的学习曲线比Gunicorn要陡峭得多，Gunicorn的默认参数就已经能够适应大多数应用程序。
  - uWSGI支持异构部署。
  - 由于Nginx本身支持uWSGI，在线上一般都将Nginx和uWSGI捆绑在一起部署，而且uWSGI属于功能齐全且高度定制的WSGI中间件。
  - 在性能上，Gunicorn和uWSGI其实表现相当。
使用虚拟化技术（Docker）部署测试环境和生产环境

性能测试

AB的使用
SQLslap的使用
sysbench的使用

自动化测试

使用Shell和Python进行自动化测试
使用Selenium实现自动化测试
- Selenium IDE
- Selenium WebDriver
- Selenium Remote Control
测试工具Robot Framework介绍

第97天：电商网站技术要点剖析

商业模式和需求要点
物理模型设计
第三方登录
缓存预热和查询缓存
购物车的实现
支付功能集成
秒杀和超卖问题
静态资源管理
全文检索方案

第98天：项目部署上线和性能调优

MySQL数据库调优
Web服务器性能优化
- Nginx负载均衡配置
- Keepalived实现高可用
代码性能调优
- 多线程
- 异步化
静态资源访问优化
- 云存储
- CDN

第99天：面试中的公共问题

计算机基础
Python基础
Web框架相关
爬虫相关问题
数据分析
项目相关

第100天 - 补充内容

面试宝典
- Python 面试宝典
- SQL 面试宝典（数据分析师）
- 商业分析面试宝典
- 机器学习面试宝典
机器学习数学基础
深度学习
- 计算机视觉
- 大语言模型

ImHex

Wed, 23 Apr 2025 15:33:14 +0800

WerWolv/ImHex

A Hex Editor for Reverse Engineers, Programmers and people who value their retinas when working at 3 AM.
/ˈɪmhɛks/

Supporting

If you like my work, please consider supporting me on GitHub Sponsors, Patreon or PayPal. Thanks a lot!

Screenshots

More Screenshots

Features

Featureful hex view

Byte patching
Patch management
Infinite Undo/Redo
“Copy bytes as…”
- Bytes
- Hex string
- C, C++, C#, Rust, Python, Java & JavaScript array
- ASCII-Art hex view
- HTML self-contained div
Simple string and hex search
Goto from start, end and current cursor position
Colorful highlighting
- Configurable foreground highlighting rules
- Background highlighting using patterns, find results and bookmarks
Displaying data as a list of many different types
- Hexadecimal integers (8, 16, 32, 64 bit)
- Signed and unsigned decimal integers (8, 16, 32, 64 bit)
- Floats (16, 32, 64 bit)
- RGBA8 Colors
- HexII
- Binary
Decoding data as ASCII and custom encodings
- Built-in support for UTF-8, UTF-16, ShiftJIS, most Windows encodings and many more
Paged data view

Custom C++-like pattern language for parsing highlighting a file's content

Automatic loading based on MIME types and magic values
Arrays, pointers, structs, unions, enums, bitfields, namespaces, little and big endian support, conditionals and much more!
Useful error messages, syntax highlighting and error marking
Support for visualizing many different types of data
- Images
- Audio
- 3D Models
- Coordinates
- Time stamps

Theming support

Doesn’t burn out your retinas when used in late-night sessions
- Dark mode by default, but a light mode is available as well
Customizable colors and styles for all UI elements through shareable theme files
Support for custom fonts

Importing and Exporting data

Base64 files
IPS and IPS32 patches
Markdown reports

Data Inspector

Interpreting data as many different types with endianness, decimal, hexadecimal and octal support and bit inversion
- Unsigned and signed integers (8, 16, 24, 32, 48, 64 bit)
- Floats (16, 32, 64 bit)
- Signed and Unsigned LEB128
- ASCII, Wide and UTF-8 characters and strings
- time32_t, time64_t, DOS date and time
- GUIDs
- RGBA8 and RGB65 Colors
Copying and modifying bytes through the inspector
Adding new data types through the pattern language
Support for hiding rows that aren’t used

Node-based data pre-processor

Modify, decrypt and decode data before it’s being displayed in the hex editor
Modify data without touching the underlying source
Support for adding custom nodes

Loading data from many different data sources

Local Files
- Support for huge files with fast and efficient loading
Raw Disks
- Loading data from raw disks and partitions
GDB Server
- Access the RAM of a running process or embedded devices through GDB
Intel Hex and Motorola SREC data
Process Memory
- Inspect the entire address space of a running process

Data searching

Support for searching the entire file or only a selection
String extraction
- Option to specify minimum length and character set (lower case, upper case, digits, symbols)
- Option to specify encoding (ASCII, UTF-8, UTF-16 big and little endian)
Sequence search
- Search for a sequence of bytes or characters
- Option to ignore character case
Regex search
- Search for strings using regular expressions
Binary Pattern
- Search for sequences of bytes with optional wildcards
Numeric Value search
- Search for signed/unsigned integers and floats
- Search for ranges of values
- Option to specify size and endianness
- Option to ignore unaligned values

Data hashing support

Many different algorithms available
- CRC8, CRC16 and CRC32 with custom initial values and polynomials
  - Many default polynomials available
- MD5
- SHA-1, SHA-224, SHA-256, SHA-384, SHA-512
- Adler32
- AP
- BKDR
- Bernstein, Bernstein1
- DEK, DJB, ELF, FNV1, FNV1a, JS, PJW, RS, SDBM
- OneAtTime, Rotating, ShiftAndXor, SuperFast
- Murmur2_32, MurmurHash3_x86_32, MurmurHash3_x86_128, MurmurHash3_x64_128
- SipHash64, SipHash128
- XXHash32, XXHash64
- Tiger, Tiger2
- Blake2B, Blake2S
Hashing of specific regions of the loaded data
Hashing of arbitrary strings

Diffing support

Compare data of different data sources
Difference highlighting
Table view of differences

Integrated disassembler

Support for all architectures supported by Capstone
- ARM32 (ARM, Thumb, Cortex-M, AArch32)
- ARM64
- MIPS (MIPS32, MIPS64, MIPS32R6, Micro)
- x86 (16-bit, 32-bit, 64-bit)
- PowerPC (32-bit, 64-bit)
- SPARC
- IBM SystemZ
- xCORE
- M68K
- TMS320C64X
- M680X
- Ethereum
- RISC-V
- WebAssembly
- MOS65XX
- Berkeley Packet Filter

Bookmarks

Support for bookmarks with custom names and colors
Highlighting of bookmarked region in the hex editor
Jump to bookmarks
Open content of bookmark in a new tab
Add comments to bookmarks

Featureful data analyzer and visualizer

File magic-based file parser and MIME type database
Byte type distribution graph
Entropy graph
Highest and average entropy
Encrypted / Compressed file detection
Digram and Layered distribution graphs

YARA Rule support

Scan a file for vulnerabilities with official yara rules
Highlight matches in the hex editor
Jump to matches
Apply multiple rules at once

Helpful tools

Itanium, MSVC, Rust and D-Lang demangler based on LLVM
ASCII table
Regex replacer
Mathematical expression evaluator (Calculator)
Graphing calculator
Hexadecimal Color picker with support for many different formats
Base converter
Byte swapper
UNIX Permissions calculator
Wikipedia term definition finder
File utilities
- File splitter
- File combiner
- File shredder
IEEE754 Float visualizer
Division by invariant multiplication calculator
TCP Client/Server
Euclidean algorithm calculator

Built-in Content updater

Download all files found in the database directly from within ImHex
- Pattern files for decoding various file formats
- Libraries for the pattern language
- Magic files for file type detection
- Custom data processor nodes
- Custom encodings
- Custom themes
- Yara rules

Modern Interface

Support for multiple workspaces
Support for custom layouts
Detachable windows

Easy to get started

Support for many different languages
Simplified mode for beginners
Extensive documentation
Many example files available on the Database
Achievements guiding you through the features of ImHex
Interactive tutorials

Pattern Language

The Pattern Language is the completely custom programming language developed for ImHex. It allows you to define structures and data types in a C-like syntax and then use them to parse and highlight a file’s content.

Source Code: Link
Documentation: Link

Database

For format patterns, libraries, magic and constant files, check out the ImHex-Patterns repository.

Feel free to PR your own files there as well!

Requirements

To use ImHex, the following minimal system requirements need to be met.

[!IMPORTANT] ImHex requires a GPU with OpenGL 3.0 support in general. There are releases available (with the -NoGPU suffix) that are software rendered and don’t require a GPU, however these can be a lot slower than the GPU accelerated versions.

If possible at all, make ImHex use the dedicated GPU on your system instead of the integrated one. ImHex will usually run fine with integrated GPUs as well but certain Intel HD GPU drivers on Windows are known to cause graphical artifacts.

OS:
- Windows: Windows 7 or higher (Windows 10/11 recommended)
- macOS: macOS 13 (Ventura) or higher,
  - Lower versions should still work too, but you’ll need to compile ImHex yourself. The release binaries will NOT work.
  - The macOS build is not signed and will require you to manually allow them in the Security & Privacy settings.
- Linux: “Modern” Linux. The following distributions have official releases available. Other distros are supported through the AppImage and Flatpak releases.
  - Ubuntu and Debian
  - Fedora
  - RHEL/AlmaLinux
  - Arch Linux
  - Basically any other distro will work as well when compiling ImHex from sources.
CPU: Officially supported are x86_64 and ARM64, though any Little Endian 64 bit CPU should work.
GPU: OpenGL 3.0 or higher
- Integrated Intel HD iGPUs are supported, however certain drivers are known to cause various graphical artifacts, especially on Windows. Use at your own risk.
- In case you don’t have a GPU available, there are software rendered releases available for Windows and macOS
RAM: ~150MiB, more is required for more complex analysis
Storage: 150MiB

Installing

Information on how to install ImHex can be found in the Install guide

Compiling

To compile ImHex on any platform, GCC (or Clang) is required with a version that supports C++23 or higher. Windows and Linux releases are being built using latest available GCC. MacOS releases are being built using latest available LLVM Clang.

Important to note is, the MSVC and AppleClang compilers are both NOT supported since they’re both generally severely outdated and lack features GCC and LLVM Clang have.

[!NOTE] Many dependencies are bundled into the repository using submodules so make sure to clone it using the --recurse-submodules option. All dependencies that aren’t bundled, can be installed using the dependency installer scripts found in the /dist folder.

For more information, check out the Compiling guide.

Contributing

See Contributing

Plugin development

To develop plugins for ImHex, use the following template project to get started. You then have access to the entirety of libimhex as well as the ImHex API and the Content Registry to interact with ImHex or to add new content.

ImHex Plugin Template

Credits

Contributors

iTrooz for getting ImHex onto the Web as well as hundreds of contributions in every part of the project
jumanji144 for huge contributions to the Pattern Language and ImHex’s infrastructure
Mary for her immense help porting ImHex to MacOS and help during development
Roblabla for adding MSI Installer support to ImHex
Mailaender for getting ImHex onto Flathub
Everybody else who has reported issues on Discord or GitHub that I had great conversations with :)

Dependencies

Thanks a lot to ocornut for their amazing Dear ImGui which is used for building the entire interface
- Thanks to epezent for ImPlot used to plot data in various places
- Thanks to Nelarius for ImNodes used as base for the data processor
- Thanks to BalazsJako for ImGuiColorTextEdit used for the pattern language syntax highlighting
Thanks to nlohmann for their json library used for configuration files
Thanks to vitaut for their libfmt library which makes formatting and logging so much better
Thanks to btzy for nativefiledialog-extended and their great support, used for handling file dialogs on all platforms
Thanks to danyspin97 for xdgpp used to handle folder paths on Linux
Thanks to aquynh for capstone which is the base of the disassembly window
Thanks to rxi for microtar used for extracting downloaded store assets
Thanks to VirusTotal for Yara used by the Yara plugin
Thanks to Martinsos for edlib used for sequence searching in the diffing view
Thanks to ron4fun for HashLibPlus which implements every hashing algorithm under the sun
Thanks to mackron for miniaudio used to play audio files
Thanks to all other groups and organizations whose libraries are used in ImHex

License

The biggest part of ImHex is under the GPLv2-only license. Notable exceptions to this are the following parts which are under the LGPLv2.1 license:

/lib/libimhex: The library that allows Plugins to interact with ImHex.
/plugins/ui: The UI plugin library that contains some common UI elements that can be used by other plugins

The reason for this is to allow for proprietary plugins to be developed for ImHex.

Data Analysis on Producthunt daily

Product Hunt Monthly Top | 2025-09

1. VidAU - AI Video

2. Ada

3. Capalyze

4. Person search API

5. CREAO

6. YouMind

7. Snapdeck

8. C1 by Thesys

9. Aikido Security

10. Magiclight

aipyapp

Python use

What

Why

How

Usage

Basic Config

Task Mode

Python Mode

Basic Usage

Automatically Request to Install Third-Party Libraries

Thanks

pandas-ai

🔧 Getting started

📚 Using the library

Python Requirements

📦 Installation

💻 Usage

Ask questions

Visualize charts

Multiple DataFrames

Docker Sandbox

Python Requirements

Usage

📜 License

Resources

🤝 Contributing

Thank you!

dataease

人人可用的开源 BI 工具

什么是 DataEase？

快速开始

UI 展示

技术栈

飞致云的其他明星项目

License

suna

Suna - Open Source Generalist AI Agent

Table of Contents

Project Architecture

Backend API

Frontend

Agent Docker

Supabase Database

Use Cases

Self-Hosting

Quick Start

Manual Setup

Contributing

Acknowledgements

Main Contributors

Technologies

License

Product Hunt Daily | 2025-05-17

1. Inkr 2.0

2. Quick Mock 2.0

3. Surrealist

4. Tersa

5. Tensorlake

6. Note API Connector

7. SurfChat

8. queryinside

9. EarlyFinder

10. ClipZap Workflow V2.0

Python-100-Days

Python - 100天从新手到大师

Python应用领域和职业发展分析

Day01~20 - Python语言基础