How do I convert a GitHub repo to a text file?

Paste the repository URL above and the file tree appears as a checkbox list. Untick what the model does not need, generate, and you get one text file: directory structure at the top, then each selected file under a header with its path. Nothing to clone, nothing to install, free and no sign-up.

How do I convert a private GitHub repository?

Paste a personal access token alongside the URL. Create a fine-grained token scoped to just that one repository with read-only Contents permission and a short expiry — that is the least privilege the API will accept for this. The token is used to call GitHub from your browser and is not stored on our side.

How many tokens is my codebase, and will it fit?

The counter updates as you tick files, so you can see the number before you paste anything. As a rough guide, source code runs around 3 to 4 characters per token, so a 500 KB selection lands near 140,000 tokens. That fits a 200K context window and will not fit into a single chat message on most interfaces.

Which files should I exclude before feeding a repo to an LLM?

node_modules, vendor, and any dependency directory first — they are usually the overwhelming majority of the bytes and contribute nothing about your code. Then lockfiles, build output, minified bundles, test fixtures, and binary assets. On a typical JavaScript project that pass alone often removes over ninety percent of the tokens.

How does this compare to gitingest, repomix, or code2prompt?

Those are strong tools and the honest difference is workflow, not capability. They are command-line first, scriptable, and the right answer inside CI or a batch job. This runs in a browser tab with a checkbox tree, so you can see the repo, deselect what you do not want, and watch the token count move — better for the one-off case where you are still deciding what to include.

Is it safe to put my code through this?

Public repositories are read through the GitHub API from your browser, so the code goes from GitHub to you. For anything confidential the stronger option is the local folder converter, which never uploads at all. Either way, scan your selection for API keys and credentials before pasting it into a model — that is the step people skip.

Veo 3: Google's AI-Powered Text-to-Video Model with Native Audio

Google’s Veo 3: A Leap in AI Video Generation

The world of text-to-video generation is heating up, and Google’s latest entrant, Veo 3 , is grabbing headlines. Unveiled as part of Google DeepMind’s Gemini ecosystem, Veo 3 is a state-of-the-art video generation model designed to turn written prompts into cinematic clips – now with native sound. In simple terms, you describe a scene in plain language, and Veo 3 produces an 8-second video clip with high realism, lifelike physics, and even ambient audio and dialogue. For developers and tech enthusiasts eager to explore generative video and AI-driven multimedia, Veo 3 represents a major step forward.

Google’s official description spells it out: “Break the silence with Veo 3” , promising “high-quality, 8-second videos with sound using our state-of-the-art video generation model” . Unlike earlier models that output silent clips, Veo 3 “lets you add sound effects, ambient noise, and even dialogue to your creations – generating all audio natively” . In other words, it’s video plus audio , a critical advance for storytelling applications. As the DeepMind team put it: “Video, meet audio” – a nod to Veo 3’s ability to produce a brief movie clip complete with background chatter, soundtrack, or sound effects.

Figure: A sample frame from Veo 3’s output, showcasing cinematic detail and lighting. Google says Veo 3 “excels in physics, realism and prompt adherence” when generating scenes.

Under the hood, Veo 3 builds on Google DeepMind’s expertise in generative models. It is trained on massive video datasets (likely including things like WebVid, etc.) using modern architectures (diffusion and transformers) to craft realistic motion. Google claims Veo 3 achieves “best-in-class quality” , with greater realism, 4K output , and improved adherence to user prompts. In practice, that means if you describe a scene or short narrative, Veo 3 tries hard to make the video follow your story . For example, if you prompt “A medium shot of an old sailor on a ship’s deck reciting a poem about the sea,” Veo 3 will create a coherent clip showing exactly that – complete with a bearded sailor, ocean waves, and his voice (or implied dialogue). Google even demonstrates conversational dialogue in generated clips (e.g. an owl and a badger discussing a magical ball).

Veo 3 isn’t just a flashy demo; it’s available today (in the US) to developers and creators via Google’s platforms. It ships with the Google AI Ultra Plan (Gemini Ultra tier) and can be accessed through the Gemini app or the new Flow AI filmmaking tool. Enterprises can use it through Google Cloud’s Vertex AI as a managed model service. Think of it as Google’s answer to other AI video tools, with the backing of DeepMind research.

What Makes Veo 3 Special?

Veo 3’s main claim to fame is its audio integration and realism . Earlier text-to-video generators (including Google’s own Veo 2) produced silent clips. Veo 3 changes the game by generating audio natively . You can include sounds in your prompt: for instance, “A medieval blacksmith hammering on an anvil with sparks and ringing metal sounds,” and Veo 3 will render not only the visual blacksmith but also the clanging sounds and even ambient forge noise. According to Google: “Veo 3 lets you add sound effects, ambient noise, and even dialogue… generating all audio natively.” . In tests, it can lip-sync characters speaking short lines, or add background music that fits the scene. This means creators can get a more complete output without manually adding audio in post-production.

Another key point is prompt adherence . Veo 3 “follows prompts like never before,” per Google. It is better at handling sequences of actions and story flows in a prompt. For example, if you input a mini-narrative like “An astronaut plants a flag, then waves to the camera,” Veo 3 will attempt both the planting and the wave in order. The DeepMind team notes Veo 3 has “improved prompt adherence, following a series of actions and scenes with greater accuracy”. In practice, this results in more reliable and predictable video output for complex prompts – a boon for developers needing consistency.

Veo 3 also boasts exceptional visual fidelity . Google emphasizes higher-resolution output (up to 4K for some users), realistic physics (objects move believably), and cinematic quality. The examples on the Veo demo page are striking – from a detailed close-up of a sandy seabed (with light shafts and bubbles) to a stormy ocean scene. The model’s understanding of lighting, materials, and motion has improved, so scenes look more like real footage or polished CGI. Coupled with audio, the results can be quite immersive.

Other new features include:

Enhanced Creative Control : You can feed Veo 3 reference images to guide the style or content. For instance, show it a picture of a blue dress and say “use this dress in a fantasy scene,” and Veo 3 will attempt to replicate the look. This reference-driven generation lets artists maintain consistency of characters or aesthetics across shots.
Style Matching : You can specify an art or filming style (e.g. noir, origami art, anime) via prompt or reference, and Veo 3 will stylize the video accordingly. Google demonstrated an “intricate origami diorama” style, where a paper-crafted street scene animated around an origami cat (prompt from DeepMind page).

Figure: Veo’s style and reference features. In this example, a reference image (left bottom) of a woman’s blue dress and a hallway photo were used to generate the main video frame (center). Google uses such “reference-powered” generation to ensure consistency of objects and style.

“Veo 3 lets you add sound effects, ambient noise, and even dialogue to your creations – generating all audio natively. It also delivers best-in-class quality, excelling in physics, realism and prompt adherence.” . This Google quote sums up Veo 3’s philosophy: it aims for cinematic realism and story accuracy . For developers, that means less time fixing nonsensical scenes – Veo 3 is designed to more faithfully “bring your script to life.”

Veo 3 vs Veo 2 (What’s New?)

Veo 3 builds directly on its predecessor, Veo 2 , which was the previous Gemini video model. Both create high-quality 8-second clips from text, but Veo 3 adds several breakthroughs:

Audio Generation : Veo 2 output is silent; Veo 3 adds speech, music, and effects.
Improved Quality : Veo 3’s visuals are crisper, with finer detail and more stable motion. Google claims “best in class” fidelity including 4K output, whereas Veo 2 was limited to HD.
Prompt Follows Through : Veo 3 handles multi-step instructions more reliably. If your Veo 2 clip drifted off your prompt, Veo 3 should stick closer to the story.
Physics and Realism : Objects behave more realistically in Veo 3 (gravity, collisions, fluid motion). While Veo 2 was already strong, Veo 3 is said to excel in physics, realism .
Gemini Plan : Veo 2 is available to Google AI Pro subscribers; Veo 3 is on the new AI Ultra tier. In practical terms, you need the higher plan to use Veo 3.

Many of these improvements come from more data, bigger models, and refinements over Veo 2’s architecture. Google even partnered with filmmaker Darren Aronofsky’s Primordial Soup to test Veo – indicating its output is impressive enough for creative professionals.

Feature	Veo 2	Veo 3
Audio support	Silent clips only	Native audio (effects, ambiance, dialogue)
Video length	8 seconds	8 seconds
Image quality	High-quality, 1080p	Higher (up to 4K) with more detail
Realism & Physics	Good	Best-in-class physics and realism
Prompt adherence	Strong	Improved adherence to multi-step prompts
Deployment	Gemini (Pro plan)	Gemini (Ultra plan), Vertex AI
Release availability	Worldwide (Pro tier)	US launch (Gemini Ultra); enterprise via Vertex
Use cases	Creative clips (no sound)	Cinematic scenes with sound

The table above summarizes the key differences. In short, Veo 3 is Veo 2+, adding sound and visual polish. A developer or content creator already familiar with Veo 2 will find the workflow similar, but with vastly richer outputs in Veo 3.

Developers can try both models via Gemini’s Try Veo interface or programmatically via the Gemini/Vertex AI APIs. In Gemini’s documentation, Google lays out prompts and parameters. And thanks to Gemini’s underlying Gemini 1.5 model, the text prompting experience is very natural – you don’t need rigid syntax, just plain language instructions.

Applications and Use-Cases

Why build such a model? Veo 3 is aimed squarely at filmmakers, storytellers, and content creators who need fast prototyping of scenes. Here are some concrete applications:

Storyboarding and Previsualization : Film and animation teams can turn scripts into video sketches in seconds. Imagine writing a short script and instantly seeing a rough animatic of it, complete with shots and dialogue. Veo 3 helps writers and directors iterate quickly.
Social Media and Marketing : Google is reportedly integrating Veo into YouTube Shorts by 2025. This means users may soon be able to generate short videos (with audio) directly on YouTube, similar to how they can create images. It could democratize video creation for influencers and advertisers.
Rapid Prototyping : Game developers or product designers can mock up scenes or concepts. For example, the gaming studio Volley is experimenting with Veo to generate in-game cinematic content. Rather than manually animating a scene, they can describe it and get a starting point.
Personal Content & Viral Media : Even fun use-cases like making memes or personal videos are encouraged. Google’s marketing suggests “create funny memes, turn inside jokes into videos, re-imagine special moments” with Veo. The built-in audio means you can, say, generate a comedic scene with a punchline voiceover.
Accessibility : People who can write but not film (e.g. educators, hobbyists) gain a new creative outlet. A teacher could produce a quick explainer animation from a text prompt.

These applications are already emerging. Google highlights partnerships: a “GenAI-first” movie studio called Promise is using Gemini and Veo to streamline production workflows. Arcade studio Fal.ai is combining Veo with other generative tools to build new creative software. This suggests Veo 3 could soon power plugins or integrations in creative software, similar to how AI image models are being incorporated into Photoshop and After Effects.

From a technical perspective, developers and production teams will likely integrate Veo 3 through APIs. Since it’s available on Vertex AI, one can call it like any other Google Cloud model. Here’s a hypothetical code snippet illustrating how one might invoke Veo 3 via Python (using the AI Platform client for Vertex AI):

from google.cloud import aiplatform

aiplatform.init(project='my-gcp-project', location='us-central1')
video_model = aiplatform.VideoGenerationModel(model_name='projects/123/locations/us-central1/models/veo-3.0-generate-preview')
prompt = "A detective interrogates a nervous-looking rubber duck. 'Where were you on the night of the bubble bath?!' the detective quacks."
response = video_model.generate(prompt=prompt, max_output_length_seconds=8)
video_url = response.generated_video_uri
print("Generated video URL:", video_url)

In this example, VideoGenerationModel is a fictional API class (actual usage will vary). But the idea is that a prompt is sent to the model and a video file is returned. Note the code comment: this is one way developers might script Veo 3. In practice, Google’s documentation will specify the real API calls.

Comparing with Other AI Video Models

Veo 3 is part of a rapidly growing field of AI video models . For context, here’s how it stacks up against a few notable alternatives:

Runway Gen-2 : Runway (an AI media platform) released Gen-2 in late 2023. Gen-2 can do text-to-video and image-to-video with different styles, up to roughly 10 seconds long. It produces high-quality, often stylized clips, but does not natively generate audio. Gen-2 is known for ease of use in creative tools, but as of 2024 it has no sound output. Veo 3’s edge is the built-in sound and (arguably) higher realism and physics.
OpenAI Sora : Sora is OpenAI’s new video model. According to OpenAI, Sora can generate videos up to a minute long while keeping prompt fidelity. Sora focuses on length and 1080p visuals, and supports multi-modal inputs (e.g. video+text prompts). However, like Gen-2, Sora does not emphasize audio generation in its demos. So Sora’s strength is longer, coherent videos; Veo 3’s is high cinematic quality plus sound.
Meta Make-A-Video / AnimateDiff : Meta (Facebook AI) and others have released video models (Make-A-Video, AnimateDiff) that do short clips. These are research models or prototypes and are generally not publicly accessible yet. Make-A-Video can create 3–10 second clips from text and some even add music, but the quality has been mixed. Meta also has VIVID (Video Diffusion) which can do high quality but no public demo audio.
Small players (TikTok, startups) : Several companies (like Bytedance’s Gen-3, Kuaishou, etc.) have text-to-video for social content. Many aim at in-app use. Runway’s Gen-3 (Alpha in 2024) is also an upcoming tool. These vary in length/quality, but generally none have publicly shown voice or dialogue – audio remains rare.

Below is a simplified comparison table highlighting some key points:

Model	Provider	Max Length	Audio Support	Notable Features
Veo 3	Google DeepMind	8 sec	Yes (music, SFX, speech)	High realism, 4K, strong prompt adherence, Gemini integration
Runway Gen-2	RunwayML	~10 sec	No	Multi-modal (image+text), stylization, widely used by creators
OpenAI Sora	OpenAI	60 sec	(Not highlighted)	High-quality, multi-modal input, 1080p output

Each model has trade-offs. Veo 3 excels at cinematic realism and audio, making it ideal for storytelling. Sora and Gen-2 excel at different niches (longer clips or style transfer). For developers, the choice depends on project needs: integration with Google Cloud vs. OpenAI, need for audio vs. clip length, etc.

Figure: An abstract scene generated by Veo (deep sea with organic shapes). This example (from Google’s demos) highlights Veo 3’s ability to produce photorealistic textures and lighting. According to Google, Veo 3’s outputs “excel in physics, realism and prompt adherence”.

Safety, Limitations, and Ethical Use

As with all generative AI, Google emphasizes responsible use for Veo 3. The team has implemented SynthID watermarking to label AI-generated videos, and filters to block harmful content. They also perform safety testing to catch potential biases or copyrighted content issues. For developers, it’s important to follow Google’s usage policies when integrating Veo 3. For instance, avoid prompting disallowed scenarios (violence, hate, etc.).

Veo 3 has some inherent limitations. Google openly notes that “creating videos with natural and consistent spoken audio… remains an area of active development” . This means very fluent, multi-line dialogue might not always come out perfectly – sometimes the speech can stutter or sound off. Also, the current 8-second length cap is a technical limit. If you need longer video (say a full minute), you might have to do multiple prompts and stitch clips.

Another limitation is cost and latency: generating even 8 seconds of video is computationally heavy. It’s not instantaneous – expect it to take a few seconds per clip even on Google’s hardware. Therefore, it’s not yet a free or near-instant tool. Google AI Ultra is a paid plan, and enterprise Vertex usage also costs money.

Getting Started with Veo 3

If you’re a developer or AI enthusiast wanting to try Veo 3, here are some steps and tips:

Access via Gemini : In the Gemini app (mobile or web), select the Veo 3 video feature. You need Google AI Ultra (the highest tier). Type your prompt (e.g. “An astronaut plants a flag on Mars, triumphant music plays” ). The model will render an 8-second clip. You can then preview and share it.
Use the Flow Filmmaking Tool : Flow is an experimental Google Labs tool built for Veo and other generative media. It has an interface for managing “assets” (like characters or images) and building multiple shots. Flow also lets you refine scenes with camera controls, scene transitions, and asset references. Developers can apply for Flow access and use its UI to produce multi-shot sequences.
Vertex AI Integration : For programmatic use, sign up for Google Cloud, enable Vertex AI, and see if the Veo model (veo-3.0-generate-preview) is in beta/preview. Use the AI Platform REST/SDK. You’ll supply a text prompt and receive back either a video file or URL. Be prepared to parse JSON responses and handle file downloads.
Prompt Engineering : Crafting the right prompt is key. Include details like camera framing (“close-up,” “wide shot”), time of day (“sunset”), style (“like a 1980s cartoon”), and audio cues (“sfx: creaking door”). Veo 3 does well with visual details and short dialogues. For best results, keep prompts clear and focused on one main event or action.
Combine Modalities : You can feed Veo 3 additional media for guidance. For example, start with an Imagen (Google’s text-to-image) generation of a character or scene, then use Flow to carry that image into Veo 3 to animate it. This hybrid approach (text→image→video) is supported in Flow’s pipeline.

Throughout this, tools like repo2txt can help manage your project data. For example, if you’re a developer working on an AI video app, you might have code repositories and documentation that you want to analyze or feed into an LLM to generate prompts. Repo2Txt offers utilities to convert entire code bases into text:

The GitHub to Plain Text converter ( https://repo2txt.com/ ) can take a public GitHub repo and produce a single formatted text file. This is useful if you need to ingest your code (or any repository) into a language model for analysis or for generating content from your codebase.
The Local Directory to Text tool ( https://repo2txt.com/local.html ) handles projects on your machine. You can zip a folder of assets or code and get a text output. This essentially turns your codebase to text for review or further processing.
Even for scraping research or documentation from the web, the Web2Txt (Crawl4AI) crawler ( https://repo2txt.com/web-to-text.html ) can fetch and clean HTML into markdown text.

These converter tools (collectively known by phrases like “repo to text” , “GitHub repo to txt” , or “codebase to text” ) are handy for integrating code and content. For instance, before prompting Veo, you might use repo2txt to load up your project docs or creative brief into an AI model to generate refined prompts. In fact, our blog covers related topics: see our posts on GPT-4.5: The Next-Generation AI Language Model and the Hunyuan 3D Model – The AI Game-Changer in 3D Asset Creation for more on cutting-edge generative AI.

Internal Link Example: Tools like repo2txt let you convert a GitHub repo to text , which can be very useful for feeding code or documentation into AI workflows (including multi-modal projects).

By combining Veo 3’s outputs with other media – images from Imagen , scripts from LLMs, or code from repo2txt – developers can build rich multimedia pipelines.

Why Veo 3 Matters for Developers

For developers interested in machine learning and multimedia, Veo 3 represents a new frontier. It shows how rapidly generative models are moving from static images into complex video with sound. Working with Veo 3 gives you insights into:

Scalable ML Pipelines : Serving video-generation models at scale involves substantial infrastructure. As a developer, exploring Veo 3 (especially via Vertex AI) exposes you to best practices in model deployment and optimization.
Multimodal AI Design : Integrating text, vision, and audio data teaches us how to design multi-modal AI systems. Veo 3’s API likely accepts structured prompts with both visual and textual components. Learning to use it improves your skill in handling multi-modal data flows.
Creative AI Tools : The companion Flow app is essentially a custom editor for AI media. As developers, examining Flow’s features (scene-builder, asset management) can inspire how we build our own AI-augmented tools.
Ethics and Responsibility : Veo 3 comes with watermarks (SynthID) and usage policies. Working with it will require thinking about AI ethics – an important aspect for any ML engineer. Google’s guidelines on disallowed content, safety checks, and transparency are educational examples.

Moreover, the ecosystem around Veo 3 is expanding. Organizations like Its IT Group (whose services span AI/ML, web and app development, DevOps and more) are poised to help businesses adopt models like Veo 3. For instance, Its IT Group could assist a company in integrating Veo 3 into their video pipeline, or combining it with custom apps. (For more, see Its IT Group’s site: itsitgroup.com .) In short, Veo 3 isn’t just a novelty – it’s part of a broader movement to infuse AI into software and media workflows.

Future Directions and Conclusion

Veo 3 is brand new (released May 2025) and there’s already talk about what’s next. Google will likely iterate quickly: we can expect future versions (Veo 4?) with longer video support, multi-language audio, and even more consistent speech. The eventual integration into YouTube shorts suggests Google envisions Veo 3 powering a consumer-friendly video generator. In parallel, developers will push the limits by combining Veo 3 with other Gemini and DeepMind models – perhaps building fully AI-driven film studios or game engines.

For now, developers and enthusiasts should dive in and experiment. The Gemini app (Ultra plan) and Vertex AI are the starting points. Try writing prompts, tune them, and observe how Veo 3 interprets the world. Compare its clips to those from Runway or Sora to understand each model’s strengths. Read Google’s papers or blog posts to learn about the architecture. And don’t forget the ecosystem: use tools like repo2txt to handle your code and data, and check out thought leadership from groups like Its IT Group on best practices.

In summary: Google’s Veo 3 is a breakthrough in AI video generation – it delivers short, high-quality clips with sound from text prompts. It outpaces previous models in fidelity and functionality, and it’s a practical service (via Gemini Ultra and Vertex AI) that developers can use today. By mastering Veo 3 and related tools (like the Flow editor and repo2txt), developers can create new apps and content experiences, from rapid prototyping to automated storytelling. As generative AI continues to evolve, Veo 3 positions Google at the forefront of the multimedia revolution.