Almost no one can reliably identify AI‑made songs 

In a blind test of three tracks, 97% of the roughly 9,000 participants failed. Even a looser metric (correct guess per track) showed only about 43% accuracy.

Strong demand for labeling AI music 

80% of survey participants said they want AI‑generated music to be clearly labeled. Many felt “uncomfortable” that they couldn’t tell the difference. 

Massive influx of AI content on streaming platforms 

Deezer reportedly receives tens of thousands of fully AI‑generated tracks daily; uploads now make up a substantial portion of new music additions.

Streaming services reacting differently, transparency vs crediting

Deezer has implemented a detection system and tags AI‑generated tracks, removing them from recommendation algorithms. By contrast, Spotify leans toward a credit‑oriented approach rather than explicit labeling. 

Concerns over artists’ livelihoods and music quality

Many respondents believe AI‑music threatens musician income and reduces creativity, though only a minority (about 40%) said they would proactively skip AI songs when knowingly encountered.  

Free‑user caps introduced for Sora and Nano Banana Pro

OpenAI now limits free users to six video generations per day on Sora. Meanwhile, Google reduced free image generation on Nano Banana Pro from three per day to just two. 

Reason: surging demand and infrastructure strain 

The companies cite overwhelming popularity and high GPU load as the reason for rate‑limiting: OpenAI’s Sora lead even joked that “our GPUs are melting.” 

Paid users remain mostly unaffected; the monetization push is clear

Users with paying subscriptions to ChatGPT (Plus/Pro) or Google AI plans reportedly maintain higher or unchanged generation limits, indicating a push toward paid tiers. 

Limits may fluctuate; free usage is now unpredictable 

For free users, the new caps come with a caution: generation limits may change frequently or without notice, especially on Google’s Nano Banana Pro / Gemini 3 Pro. 

Sign of broader shift; free‑at‑scale AI may be ending

The simultaneous throttling by two major AI providers suggests we’re entering a new phase where unlimited free access to compute‑intensive AI tools becomes unsustainable, making “freemium” or paid‑first models more likely.

Set Up Your Development Environment & AI Tool

Start by choosing a code editor that supports AI integrations, such as VS Code, JetBrains IDEs, or GitHub Copilot. Install the AI extension or plugin, and make sure your project is properly configured with the correct programming language and dependencies.

Feed AI Your Project Context & Coding Requirements

Provide AI with context about your project. Include relevant files, functions, variable names, and comments. The more specific the information, the more accurate and context-aware the AI’s suggestions and completions will be.

Use AI for Code Completion

As you type, let the AI suggest code snippets, function implementations, or even entire loops. You can accept, modify, or reject suggestions. AI can also help auto-generate repetitive code patterns, saving time and reducing manual errors.

Leverage AI for Debugging & Error Fixing

When your code throws errors or warnings, paste the error messages or problematic code into the AI tool. Ask it to identify bugs, suggest fixes, or explain why a function or method isn’t working as expected. AI can also suggest optimizations for performance or readability.

Review, Test, and Refine AI-Generated Code

Always review AI-suggested code carefully, run tests, and check for edge cases. AI can accelerate coding, but human oversight ensures correctness, security, and maintainability. Iterate with AI feedback until your code works reliably.