Oh, To Walk on The Moon!

ME: hello, I would like to walk on the moon

GPT4: That's an ambitious goal! Walking on the moon would be an incredible experience. Currently, only a select few astronauts have had the opportunity to do so, as it requires rigorous training and resources. If you're serious about it, you might consider pursuing a career in space exploration or working towards becoming an astronaut. It's an exciting journey that requires dedication and perseverance.

ME:  where could I start this career

GPT4:  If you're interested in starting a career in space exploration and ultimately walking on the moon, there are several paths you can consider. Here are a few steps to get you started:

1. **Education**: Begin by focusing on your education. Pursue a degree in a field related to space exploration, such as aerospace engineering, astrophysics, or planetary science. Having a strong educational background will be crucial for your career in this field.

2. **Gain Experience**: Look for opportunities to gain hands-on experience. This could involve internships, research projects, or volunteering with organizations that are involved in space exploration initiatives. Building practical skills and experience will make you a more competitive candidate for future opportunities.

3. **Network**: Networking is key in any industry, and space exploration is no exception. Attend conferences, workshops, and events related to space exploration to connect with professionals already working in the field. Building relationships and connections can open up doors for potential opportunities in the future.

4. **Consider Joining Space Agencies**: Explore opportunities to join space agencies such as NASA, ESA (European Space Agency), or other national space agencies. These organizations often have programs and initiatives aimed at developing the next generation of astronauts and space explorers.

5. **Stay Updated**: Keep yourself informed about the latest developments and advancements in space exploration. Follow reputable sources, read books, and stay engaged with the space community to stay on top of new opportunities and trends in the field.

Remember, pursuing a career in space exploration is a challenging and competitive journey, but with dedication, passion, and hard work, you can work towards achieving your goal of walking on the moon. Good luck!

Pictures: MIDJOURNEY "A man viewed from the back, walking on the moon wearing red and black clothing" .  

Model：V6 Size：1:1

All done at https://Yeschat.ai for free.

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Best Prompts that Work Well With Bard

YouTube

Go to:  https://bard.google.com/chat.  In the prompt box at the bottom, enter (replacing the youtube link with one of your choosing): 

Example 1: "analyze this video and tell me what it's about: https://www.youtube.com/watch?v=d_8LGozG-zA"

Bard Result 6/2/2024: This video is about a parrot named BPL solving a puzzle. The parrot is presented with a puzzle with multiple shapes and is asked to sort them into the correct slots. The parrot is able to successfully solve the puzzle, even though the shapes are mixed up.

Hope you found this summary helpful!

Example 2: Analyze this video and provide a summary and breakdown: https://www.youtube.com/watch?v=d_8LGozG-zA

Bard Result 6/2/2024: This video is about a parrot named BPL solving a puzzle. The parrot is presented with a puzzle that requires it to identify and sort different shapes. The shapes are placed at the end of small branches. The parrot successfully sorts the shapes, demonstrating its intelligence and ability to solve puzzles.

Here is a breakdown of the video:

The video begins with the host explaining that parrots are intelligent birds that can solve puzzles.

The host introduces the puzzle that BPL will be solving. The puzzle consists of different shapes placed at the end of small branches.

The host asks BPL to solve the puzzle. BPL successfully sorts the shapes by color and size.

The host is impressed by BPL's intelligence and ability to solve puzzles.

Overall, this video is a short and sweet demonstration of a parrot's intelligence and problem-solving skills.

Prompts For Creative Writing:

@poem [topic or theme]: Generate a poem on a specific subject or following a certain style.

@story [opening line or prompt]: Create a story based on a starting point or theme.

@script [scene description]: Describe a scene and let Bard write the dialogue.

@song [genre or mood]: Generate lyrics for a song in a specific style.

Prompts For Content Creation:

@blog post [title and key points]: Write a blog post based on a given title and outline.

@email [recipient, subject, and key message]: Draft an email with specific tone and content.

@business plan [one-sentence summary]: Generate a basic outline for a business plan.

@marketing copy [product or service]: Create persuasive marketing copy for a specific offering.

Prompts For information and learning:

@explain [complex concept]: Break down a difficult concept into simpler terms.

@summarize [article or website]: Provide a concise overview of a lengthy text.

@compare and contrast [two things]: Highlight similarities and differences between two concepts.

@research [topic]: Gather relevant information and insights on a specific subject.

Prompts For fun and conversation:

@tell me a joke: Get a laugh with a Bard-generated joke.

@write a limerick: Enjoy a silly limerick poem created by Bard.

@what if [hypothetical scenario]: Explore the possibilities of an imaginary situation.

@have a conversation about [topic]: Engage in a casual chat with Bard on a chosen topic.

Prompts General purpose:

@inform me about: This prompt can be used to gather information on a specific topic. For example, you could ask "@inform me about the history of artificial intelligence."

@summarize: This prompt can be used to get a concise overview of a longer piece of text. For example, you could ask "@summarize the latest article on climate change."

@translate: This prompt can be used to translate text from one language to another. For example, you could ask "@translate this sentence into Spanish: Hello, how are you?"

@write: This prompt can be used to generate different creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. For example, you could ask "@write a poem about love and loss."

More specific prompts:

@write a blog post about [topic]: This prompt can be used to generate a blog post on a specific topic, complete with an engaging title and introduction.

@create a marketing campaign for [product]: This prompt can be used to generate ideas for a marketing campaign for a specific product or service.

@answer these questions about [topic]: This prompt can be used to get answers to a specific set of questions about a topic.

@compare and contrast [product 1] and [product 2]: This prompt can be used to get a comparison of two different products or services.

More Prompts

*At the time of writing, Bard can create images, but only if you have a Google account and are located in the USA. OR, use a VPN and point yourself to somewhere in the US.

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Deep Seek Ideas

1️⃣ Content Strategy Blueprint

"Create a 6-month content plan for my [niche/industry] targeting [ideal audience]. Outline themes, formats (blogs, videos, social posts), and a publishing schedule to build authority and drive [traffic/leads/sales]."

2️⃣ Audience Engagement Analyzer

"Design a system to track engagement metrics for my [blog/YouTube/Instagram] content. Monitor [click-through rates, shares, comments] and suggest data-driven tweaks to improve reach and conversions."

3️⃣ Portfolio Power-Up

"Audit my existing portfolio of [blog posts, case studies, social content]. Highlight gaps in [SEO, storytelling, audience appeal] and recommend 3 high-impact pieces to update or repurpose for [job applications/client pitching]."

4️⃣ SEO Overhaul Guide

"Optimize my top 5 underperforming articles about [topic]. Suggest keyword adjustments, meta descriptions, and internal linking strategies to boost organic traffic by 40% in 90 days."

5️⃣ Tone Chameleon Formula

"Rewrite this [blog draft/social caption] for a [formal, casual, humorous] tone to better resonate with [new audience]. Maintain the core message but adapt vocabulary and sentence structure."

6️⃣ Idea Generation Engine

"Generate 10 viral-ready content ideas for [product launch/holiday campaign] tailored to [audience pain points]. Blend trending topics with evergreen value for maximum shares."

7️⃣ Monetization Roadmap

"Create a step-by-step plan to monetize my [blog/podcast] with [X monthly visitors]. Compare affiliate partnerships, sponsored content, and digital products—include negotiation scripts and pricing tiers."

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What the Heck is Manus?

Manus is being integrated into Meta's business ecosystem—specifically WhatsApp, Messenger, and Ads Manager—to move beyond simple chat and into autonomous execution.

For businesses on Meta's platforms, Manus automates customer service and marketing through several key mechanisms:

1. Autonomous Customer Support (The "Action Engine")

Unlike traditional chatbots that only provide text answers, Manus acts as an "Action Engine" for customer service:

Task Execution: It can autonomously handle multi-step requests, such as processing a return, checking order status across different systems, or scheduling an appointment directly within a chat.

Contextual Memory: Manus maintains a deep "context window," allowing it to remember long-term customer preferences and previous interactions to provide more personalized and accurate service.

Tool Integration: It can be connected to a business's internal tools (like CRMs or inventory databases) to perform real-world actions on behalf of the customer.

2. Marketing & Audience Insights

Manus is also being integrated into Meta Ads Manager to automate the "behind-the-scenes" work of customer engagement:

Competitor Analysis: Businesses can use Manus to analyze competitor marketing strategies by leveraging its access to Meta’s extensive ad library.

Automated Reporting: It can autonomously compile and analyze performance reports, identifying which customer segments are responding best to certain messages.

Audience Research: Manus can perform deep research to help businesses understand their customers' needs and pain points, allowing for more targeted and effective communication.

3. Integration Across Platforms

Meta is rolling out Manus-powered features across its "Everything App" ecosystem:

WhatsApp Business: Integration allows for sophisticated, agent-led conversations that can handle complex sales and support workflows.

Messenger: Businesses can deploy Manus agents to handle high volumes of inquiries with human-like reasoning and execution capabilities.

Summary of Automation Capabilities

1. Feature
2. Traditional Chatbot
3. Manus-Powered Agent
4. Response Type
5. Static, pre-defined answers
6. Dynamic, reasoned execution
7. Capability
8. Answers questions
9. Completes tasks (e.g., booking, returns)
10. Data Access
11. Limited to chat history
12. Can access web, APIs, and internal tools
13. Workflow
14. Single-turn interaction
15. Multi-step, autonomous planning

While the integration is still in its early stages and some users have noted initial "hallucinations" or errors, the long-term goal is to provide businesses with a "digital workforce" that can handle the majority of customer interactions and marketing analysis autonomously.

Here are four concrete examples of multi-step requests Manus can handle:

1. Data Analysis & Business Reporting

The Request: "Analyze my coffee shop's sales data from this CSV and tell me if I should change my opening hours."

The Multi-Step Execution:

    Data Cleaning: Manus reads the raw CSV, handles missing values, and formats timestamps.

    Statistical Analysis: It writes and runs Python code to calculate total revenue, peak hours, and best-selling items.

    Simulation: It creates a dynamic model to estimate revenue loss/gain if opening hours were shifted (e.g., from 9:30 AM to 11:00 AM).

    Visualization: It generates charts and tables to present the findings in a professional report.

2. Financial Research & Market Prediction

The Request: "Analyze the impact of new global tariffs on the S&P 500 and predict a recovery timeline."

    The Multi-Step Execution:

    Real-time Data Retrieval: Manus uses APIs (like Yahoo Finance) to get the latest market data.

    Historical Research: It searches for past economic events (e.g., the 2018 trade war) to find patterns.

    Monte Carlo Simulation: It runs thousands of simulations in a Python sandbox to create confidence intervals for market bottoms.

    Web Hosting: It builds and hosts a temporary website to present the interactive charts and detailed conclusions.

3. Product Research & Comparison

The Request: "I need a dehumidifier for a 25sqm room in Portugal. Find the best options available locally and compare them."

    The Multi-Step Execution:

    Localized Search: Manus browses Portuguese retailers (like Worten or FNAC) to find current stock and prices.

    Technical Filtering: It filters products based on the specific room size (25sqm) and humidity requirements.

    Review Synthesis: It reads user reviews and expert tests to identify pros and cons for each model.

    Comparison Table: It delivers a structured table comparing price, energy efficiency, and noise levels.

4. Full-Stack App Development

The Request: "Build a simple task management app that I can use on my phone."

    The Multi-Step Execution:

    Architecture Planning: Manus decides on the tech stack (e.g., React + Tailwind).

    Coding: It writes the frontend and backend code in its sandbox environment.

    Testing & Debugging: It launches the app, tests for bugs, and fixes them autonomously.

    Deployment: It provides a live URL where the user can immediately access and use the app.

Can you show me examples of Manus helping someone to run a Facebook Group?

Here are specific examples of how Manus can help you run a Facebook Group:

1. Content Planning & Creation

Manus can take over the heavy lifting of keeping your group active with high-quality content:

Weekly Content Calendars: You can ask Manus to "Create a 30-day content calendar for my Facebook Group about sustainable gardening, including daily prompts, educational posts, and interactive polls."

Visual Asset Generation: Manus can generate relevant images or diagrams to accompany your posts, ensuring your content is visually engaging.

Trend Research: It can browse the web to find the latest news or trending topics in your niche and draft "Breaking News" posts to keep your community informed.

2. Member Engagement & Onboarding

Manus can help you build a stronger relationship with your members:

Welcome Posts: It can draft personalized welcome messages for new members, highlighting the group rules and encouraging them to introduce themselves.

Discussion Starters: If engagement is low, you can ask Manus to "Analyze the last 100 posts in my group and suggest 5 high-engagement discussion questions that haven't been asked yet."

Summarizing Discussions: For long, complex threads, Manus can provide a "TL;DR" summary to help members catch up on the key points of a conversation.

3. Community Moderation & Safety

With its integration into Meta's ecosystem, Manus is becoming a powerful tool for maintaining group health:

Rule Enforcement: Manus can help identify posts that may violate group rules and provide a reasoned explanation for why a post was flagged.

Conflict Resolution: It can analyze the tone of a heated discussion and suggest "de-escalation" replies for moderators to use.

Member Vetting: Manus can help screen membership requests by researching applicants (if they provide public links) to ensure they are a good fit for the community.

4. Growth & Analytics

Manus can help you scale your group by acting as a marketing strategist:

Competitor Benchmarking: It can research similar Facebook Groups to see what content is working for them and suggest ways to differentiate your community.

Engagement Reports: You can ask Manus to "Analyze my group's engagement data and tell me the best time of day to post for maximum reach."

Cross-Promotion: It can draft promotional posts for you to share on other platforms (like Instagram or X) to drive new members to your Facebook Group.

How many tasks can I request Manus to perform before I have to pay?

Manus uses a credit-based system to manage task execution. Each task you request consumes a certain number of credits based on the resources required (such as browser use, Python execution, or data processing).

You typically receive a set amount of complimentary credits when you sign up. These allow you to test Manus's capabilities on a variety of tasks.

Not all tasks are equal. A simple question might use very few credits, while a multi-step research project or building a full-stack app will use more.

Simple Tasks: Quick web searches or short writing tasks consume minimal credits.

Complex Tasks (like building a full app or conducting wide research): Tasks that require long-running browser sessions, heavy data analysis, or multiple iterations of "thinking" and "doing" will consume more credits.

If you run out of credits on a free plan, you will typically need to wait for a refresh or upgrade to a paid plan to continue.

The free plan includes 300 refresh credits daily and a total of 4,000 credits per month, with credits resetting each day.

These credits are designed to cover everyday tasks like quick research, drafting content, or simple automation.

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AI - The Beginning

AI’s history is longer and more uneven than is often assumed.

The field traces its intellectual roots to Alan Turing’s 1950 essay “Can Machines Think?”. In 1951, researchers ran the first working AI program on the Ferranti Mark 1 at the University of Manchester. In 1956, a team at Carnegie Mellon University in Pittsburgh produced the “Logical Theorist”, the first program built to perform automated reasoning.

Attention then shifted toward expert systems. Development began in 1964 and culminated in 1969 with DENDRAL, which assisted organic chemists in designing complex syntheses. Its performance depended on rules extracted from human laboratory experience. In 1971, MYCIN extended this approach to medical diagnosis and treatment using the LISP language. It worked best in the hands of skilled doctors who could resolve uncertainty by ordering further tests.

These successes prompted widespread adoption of expert systems throughout the 1970s and early 1980s. Most followed a common architecture based on a knowledge base and an inference engine. By the mid-1980s, this approach had reached its limits. Capturing expert knowledge in rule form proved difficult, and systems became increasingly complex. The interaction of forward chaining and backward chaining made them powerful but fragile, contributing to the decline of the first expert system era.

Expert systems were ultimately seen as slow and expensive to develop. Even so, they remain valuable in settings where transparency and review are required, such as credit scoring and job application screening, and in situations where fast execution is essential, including self-driving vehicle systems.

In parallel, two other experimental approaches were pursued, often by researchers working outside engineering. In 1957, Frank Rosenblatt, a psychologist, introduced the “Perceptron”, an early attempt to build a system capable of learning by trial and error. This connectionist model represented the first neural network. It showed limited success in image recognition, including distinguishing cats from dogs, but failed to secure continued funding. Another early initiative focused on machine translation, yet poor performance led to its termination in 1966.

These disappointments, combined with poorly structured data, hardware constraints, and limited processing power, led to a loss of confidence and funding. This period of decline, from 1974 to 1980, became known as the First AI Winter.

A cautious revival followed with explanation-based learning. This approach depended on a human expert who explained how a specific case could support a general rule. It enabled faster systems, especially when data was sparse or unreliable, but remained constrained by the quality of human input. Its strengths were focus, efficiency, and traceability, which made it suitable for use in legal reasoning systems.

In 1983, further progress was made with the development of an effective recurrent neural network. Repeated activation between connected units increased sensitivity, allowing patterns of activity to stabilise in a manner comparable to learning.

This improvement to the expert system concept resulted in earlier systems becoming obsolete and 1987 saw the final collapse of the earlier LISP-based expert systems and the start of the “Second AI Winter” (1987 – 2000).

Development did of course move along during this so-called “winter”. Of note were Support Vector Machines designed to improve data classification and regression analysis, of which the dog/cat sorting is a trivial example. In 1995 these were enhanced by the introduction of “Random Forests” – ensembles of decision trees based on random subsets of the presented data from which a corresponding number of predictions were made, which were then averaged or a majority vote taken on the classification to get the final result. Shortly after that, in 1996, the oddly named Long Short Term Memory (LSTM) units were incorporated into recurrent neural networks which, if unmodified, had a tendency to lose data that might be helpful later on in the learning process. The LSTMs took decisions on retaining or forgetting earlier assessment of data and if retained, doing so over hundreds of time intervals. So some data was indeed discarded but other was retained over a longer timescale that was represented in conventional “short term memory”. The practical effect was greater sensitivity to processing results which the LSTMs judged likely to be useful later on, at the expense of other results judged expendable. However this was expressed in terms of improved speed and accuracy.

During the next 13 years development concentrated on market testing robotic products like Roomba, a domestic robot vacuum cleaner and robot grass cutters while image processing was improved to sort and standardize machine readable images from large databases and for use in self-driving vehicles.

2009 saw the first LSTM recurrent neural network with pattern recognition software and this enabled cursive hand writing to be read and Google built an “autonomous car”. At the same time the ImageNet visual dataset containing 14 million hand annotated images was produced using a team of 49,000 individuals from 167 countries working from a base of 162 million candidate images!

The decade that followed brought fast-moving innovation. In 2013, Google significantly improved natural language processing, helping to establish the chatbot as a practical tool. Generative AI then expanded machine output beyond prediction to prompt-led content creation, producing fluent and often convincing text. Meanwhile, the now familiar AI-generated images from OpenAI’s DALL-E 3 became part of everyday digital culture.

In 2017, OpenAI released influential research on generative models. At the same time, Google DeepMind in London refined AlphaGo into AlphaGo Zero, which learned to play Go entirely through self-play. The same approach was applied to chess, with similarly strong results against both machines and human opponents.

In 2018, Google Duplex showed that an AI assistant could make telephone bookings in real-world settings. The following year saw the launch of OpenAI’s GPT-4, which was widely praised despite continued concerns about fabricated responses inherited from earlier models.

ChatGPT was released in November 2022 and quickly became a public reference point for AI. Its ongoing hallucination problems sparked political debate. In parallel, a wave of legal challenges emerged against newer AI companies, often centred on copyright infringement and the unauthorised use of private or personal data in training sets.

Late in 2024, President Joe Biden issued an executive order defining eight goals for ethical AI development in the United States. These included protecting national interests, respecting copyright, safeguarding personal data, and ensuring AI systems were accurate and non-discriminatory.

This order was rescinded by Donald Trump on his first day in office, removing regulatory obligations for US AI firms. This is likely to deepen legal conflict between major companies while discouraging public challenge.

On 10 and 11 February 2025, France hosted the Artificial Intelligence Action Summit. Sixty-one countries signed a declaration supporting inclusive and sustainable AI. The UK and the US declined. Anglo-Saxon exceptionalism?

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How We're Redesigning Audacity For The Future

Audacity is being upgraded by the folks who run Muse. This video digs into the research and design that went into gradually transforming Audacity 3 - a free and open source audio editor and production app - and how they've built on that work to develop Audacity 4. 

After some 3,500 comments, I asked Gemini to review a text file containing all those comments and spit out a review. The review is beneath the video, which, incidentally is very well made, easy to follow, and in my view an excellent no-messing about lesson on how to do project development, especially on a product which has been in the public domain for decades.

 

Overall Sentiment

The comments reflect a strong dichotomy: widespread criticism of the new logo and branding, but overwhelming praise for the planned software improvements, the video's transparency, and the development team's approach. Most commenters are excited about the future of the software itself, even if they dislike its new visual identity.

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 1. Intense Feedback on the New Logo and Rebranding

current branding

The most discussed topic by a significant margin is the rebranding, with most of the feedback being critical.

new suggestion in the video

* Loss of the Waveform: A vast number of users feel the waveform was the most iconic and essential part of the original logo, even more so than the headphones. Many believe its removal strips the logo of its identity and fails to communicate what the program does. Suggestions were frequently made to reincorporate a simplified waveform.

* Color Change: The shift from blue to red was jarring for many commenters, who strongly associate the color blue with Audacity's brand identity. They argue that the new red color feels alien and corporate.

* Aesthetic and Design: The new logo is frequently described as "lifeless," "sterile," "corporate," and "soulless". Some find the asymmetrical design to be visually unbalanced, awkward, and uncomfortable to look at. Others feel it looks like a generic stock icon that lacks personality.

* Loss of Recognisability: Many feel the new logo is so different that it's unrecognisable as Audacity, abandoning the charm and identity of the original. Several commenters compared it to other controversial corporate rebrands.

* Contextual Understanding: A smaller group of commenters noted that while the logo is strange on its own, it makes more sense and fits in well when viewed alongside the other apps in the Muse suite. Some simply expressed that they like the new modern look.

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 2. High Praise for the Video and the Presenter's Approach

There is near-universal acclaim for the video itself and the way the project is being managed and communicated.

* Transparency and Honesty: Commenters repeatedly praised the video's transparency, depth, and honesty about the development process, including the challenges of technical debt. Many wish other software projects, especially open-source ones, had this level of communication.

* Excellent Communication: The presenter is described as an inspiring and skilled leader who is excellent at communicating complex design principles and the reasoning behind decisions.

* Addressing Criticism: Viewers appreciated the direct response to poorly-researched, negative videos that criticized an early, unfinished build of the software.

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 3. Excitement and Support for Audacity 4.0's New Features

The actual changes to the software detailed in the video were met with widespread excitement and approval.

* UI/UX Improvements: Users are very enthusiastic about the modernized UI and thoughtful UX improvements. Many expressed that the video addressed long-standing frustrations they've had with the software for years.

* Specific Features: There was particular excitement for the introduction of non-destructive effects, the new "Split" tool, and the plan to eliminate confusing "modes". The solution of asking the user to choose their preferred delete behavior was lauded as a brilliant UX decision.

* Technical Decisions: The development team was commended for choosing to refactor the existing codebase rather than starting over and for migrating to Qt instead of a web-based framework like Electron.

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 4. Diverse and Creative Use Cases Mentioned by Users

The comments revealed the vast and sometimes unexpected ways people use Audacity.

* Standard Uses: Common uses mentioned include recording voice-overs, simple audio editing, cleaning up recordings, academic analysis, and as a first step into music production.

* Unconventional Uses: Several users mentioned creative and unusual applications, such as datamoshing and glitch art by importing raw image data (.bmp, .jpeg) as audio, applying effects, and exporting it back as an image.

* Niche Applications: Other uses include a lawyer using it to prepare audio evidence for court, creating maps for a rhythm game, and playing ARGs.

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Audacity: Link

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Dynamic Pattern Generator

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Using Google Drive Images in Scripts

Option 1: Direct Download Link (Simplest)

Right-click the image in Google Drive and select "Share"

Set sharing to "Anyone with the link" (if appropriate for your use case)

Get the share link

Modify the link to create a direct download URL:

Original format: https://drive.google.com/file/d/FILE_ID/view?usp=sharing

Download format: https://drive.google.com/uc?export=download&id=FILE_ID

In your script, you can then use this URL directly:

# Python example

import requests

image_url = "https://drive.google.com/uc?export=download&id=YOUR_FILE_ID"

response = requests.get(image_url)

if response.status_code == 200:

    with open('downloaded_image.jpg', 'wb') as f:

        f.write(response.content)

So,

https://drive.google.com/file/d/1Y0Y7WDpmRH0YAH6dcMG6Cul8jA5WJus2/view?usp=drive_link

becomes

https://drive.google.com/uc?export=download&id=1Y0Y7WDpmRH0YAH6dcMG6Cul8jA5WJus2

Option 2: Google Drive API (More Robust)

For more control and security, use the Google Drive API:

Set up a Google Cloud Platform project

Enable the Google Drive API

Create credentials (OAuth client ID or service account)

Install the Google Client Library for your language

# Python example using Google Drive API

from googleapiclient.discovery import build

from google.oauth2 import service_account

# Authenticate

credentials = service_account.Credentials.from_service_account_file(

    'service-account.json', scopes=['https://www.googleapis.com/auth/drive.readonly'])

drive_service = build('drive', 'v3', credentials=credentials)

# Get the file

file_id = 'YOUR_FILE_ID'

request = drive_service.files().get_media(fileId=file_id)

response = request.execute()

# Save the file

with open('downloaded_image.jpg', 'wb') as f:

    f.write(response)

Option 3: Google Drive Desktop Sync

If your script runs on a computer with Google Drive desktop sync:

Ensure the image is synced to your local Google Drive folder

Reference the file using its local path in your script

# Python example

import shutil

local_path = "/path/to/your/google/drive/image.jpg"

project_path = "/path/to/project/default_image.jpg"

shutil.copy2(local_path, project_path)

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Upload Your Picture for Kaliedoscope - Google Gemini 2.5PRO

Kaleidoscope

Create mesmerizing patterns from your images.

Upload Image

Rotation Speed

Counter-Clockwise Still Clockwise

Zoom

Zoom Out Zoom In

Created with Google Gemini 2.5PRO at https://gemini.google.com/ using the instruction: " Create a "kaleidoscope" webpage. Users will be able to upload their own picture which you will edit to be circular and placed in the center of a scrollable web page limited to a maximum of 1260px wide. If users do not have a picture to upload, they will be able to select the picture Included with these instructions. Most striking images occur when the original picture is turned slightly. The user will therefore need to be able to make subtle changes to the speed and direction of rotation of their image. The user will be able to zoom the image."

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Upload your picture for a Kaleidoscope - Z.AI GLM4.5

Interactive Kaleidoscope

Upload your own image or use our default image to create a mesmerizing kaleidoscope effect. Adjust the speed, zoom, and direction to create the perfect visual experience.

Upload Your Image

Rotation Speed

2.0

Zoom Level

1.0

Rotation Direction

Tip: Combine different zoom levels with rotation speeds for unique kaleidoscope patterns!

Created with GLMP4.5 at https://z.ai/ using the instruction: " Create a "kaleidoscope" webpage. Users will be able to upload their own picture which you will edit to be circular and placed in the center of a scrollable web page limited to a maximum of 1260px wide. If users do not have a picture to upload, they will be able to select the picture Included with these instructions. Most striking images occur when the original picture is turned slightly. The user will therefore need to be able to make subtle changes to the speed and direction of rotation of their image. The user will be able to zoom the image."

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Bubble Pop - Made Today with GLM 4.5

GLM 4.5 is at z.ai

🫧 Digital Bubble Wrap

Click the bubbles to pop them and relieve your stress!

0 Bubbles Popped

0 Current Combo

0 Best Combo

>

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