Forem: 大阪のUber配達員

Sora 2 Watermark Remover Free

大阪のUber配達員 — Fri, 03 Oct 2025 09:31:58 +0000

Source code
https://github.com/GitHub30/Sora2WatermarkRemover

Let's try
https://colab.research.google.com/drive/1Iqu4RZ9WAhcbO1Jn0wCkMOsw2l1p6z62?usp=sharing

what is Watermark

Before you try any “free watermark remover,” read this. This post explains why visible watermarks exist on Sora 2 videos, the legal and ethical risks of removing them, and legit ways to get clean exports without breaking rules.

TL;DR

Sora 2 adds both a visible watermark and C2PA Content Credentials (provenance metadata). These are there to help viewers distinguish AI media. (OpenAI)
Removing or helping others remove watermarks/CMI can be illegal in many places (e.g., US DMCA §1201 and rules against stripping copyright management information). None of this is legal advice—when in doubt, talk to a lawyer. (法律情報研究所)
Legit path to clean exports: OpenAI’s ChatGPT Pro tier explicitly includes the ability to download videos without a watermark. If you need unwatermarked Sora footage for permitted use, upgrade rather than hacking around it. (OpenAI Help Center)
You’ll see many “3-minute remover” posts and tools online; this article does not endorse them. It explains risks and safer alternatives. (Qiita)

What the watermark (and C2PA) are for

Sora 2 ships with visible watermarks and embeds C2PA “Content Credentials.” C2PA is an industry standard for attaching a tamper-evident provenance “nutrition label” that can show where media came from and how it was edited. These signals help platforms, journalists, and everyday viewers evaluate AI-generated video. (OpenAI)

Is removing the watermark legal?

Short answer: often no.
In the US, DMCA §1201 prohibits circumventing technological protection measures. Related rules also prohibit removing or altering copyright management information (CMI)—which can include watermarks and provenance metadata. Other jurisdictions have similar restrictions. Again, this is general information, not legal advice. (法律情報研究所)

The responsible way to get clean Sora 2 video

If your use case legitimately requires a clean export (client delivery, broadcast graphics, etc.), the official route is to use a plan that allows watermark-free downloads. OpenAI’s Sora/ChatGPT Pro includes “Download videos without a watermark.” That’s the safe, policy-compliant solution. (OpenAI Help Center)

Also note: even when a visible watermark is off, C2PA credentials may remain to help preserve provenance. Many platforms are beginning to surface these labels. Don’t use tools or transcoders that strip Content Credentials if you want to keep trust signals intact. (OpenAI)

“But there are free watermark removers…”

Yes—blogs, videos, and posts claim they can erase Sora logos quickly. That doesn’t make it wise (or lawful). If you encounter those tutorials, understand you may be:

Violating terms of use and possibly the law (anti-circumvention / CMI removal).
Misrepresenting provenance by breaking C2PA chains. An example of such “how-to” material exists online; this post won’t replicate or endorse it. (Qiita)

If you’re the rights holder (or you have explicit permission)

When you own the content or have documented permission to deliver clean footage:

Use Sora/ChatGPT Pro’s watermark-free download option. (OpenAI Help Center)
Keep Content Credentials intact for trust and audit trails; confirm your editing pipeline preserves C2PA metadata. (C2PA)
Document rights & disclosures (e.g., “AI-generated” disclaimers) to meet platform or client requirements. Many platforms are moving toward clearer labeling. (The Verge)

Ethics in practice

Watermarks and Content Credentials aren’t just roadblocks; they’re transparency tools. If your creative or commercial goal conflicts with them, the fix isn’t evasion—it’s to use proper licensing, the correct product tier, and honest labeling that respects audiences and platforms. (OpenAI)

FAQ

Q: Is there any truly “free” way to get Sora output without a watermark?
A: If you’re seeing a watermark under your current access level, the correct path is upgrading to a plan that allows clean downloads—not stripping it with third-party tools. (OpenAI Help Center)

Q: Do all Sora outputs have provenance metadata?
A: OpenAI states that videos include visible and invisible provenance signals and embed C2PA metadata. Some tiers/features may affect visibility of the watermark, but provenance aims to persist. (OpenAI)

Q: What happens if I transcode or edit a Sora video?
A: Depending on your software, you might inadvertently strip Content Credentials. Use tools that preserve C2PA if you need verifiable provenance downstream. (C2PA)

Resources & further reading

OpenAI – Launching Sora responsibly (watermarks + C2PA). (OpenAI)
OpenAI Help Center – Sora Billing FAQ (Pro plan includes download without watermark). (OpenAI Help Center)
C2PA Explainer – Content Credentials (how provenance works). (C2PA)
US DMCA §1201 (anti-circumvention) + guidance on removing CMI. (法律情報研究所)
Example of online “removal” tutorials (not endorsed). (Qiita)

A practical checklist (for creators & teams)

□ Use the official watermark-free export when permitted by your plan. (OpenAI Help Center)
□ Preserve C2PA in your edit pipeline; avoid tools that strip Content Credentials. (C2PA)
□ Maintain disclosures where required by clients/platforms. (The Verge)
□ Avoid tutorials that promote circumvention or CMI removal. (法律情報研究所)

Author’s note: This article focuses on responsible, policy-compliant workflows. It does not provide instructions to remove watermarks or bypass provenance. If you need clean Sora 2 footage, use the official, supported route. (OpenAI Help Center)

Meet “Awesome Gemini Robotics” — Copy-Paste Prompts & Demos for Gemini Robotics-ER 1.5

大阪のUber配達員 — Fri, 26 Sep 2025 16:03:53 +0000

https://github.com/GitHub30/Awesome-Gemini-Robotics

TL;DR

If you’re exploring Gemini Robotics-ER 1.5 (Google/DeepMind’s embodied-reasoning model for robots), this community repo collects ready-to-run prompts, reference images and usage patterns you can drop straight into your own projects. It tracks what ER 1.5 is good at right now—pointing/boxes/trajectories, long-horizon planning, temporal reasoning on video, tool-use (e.g., Search)—and shows practical prompt shapes for each. (GitHub)

Why this repo matters (and why now)

On Sept. 25, 2025, Google announced that Gemini Robotics-ER 1.5 is available to developers via the Gemini API in Google AI Studio. ER 1.5 acts as a “high-level brain” for robots: it plans multi-step tasks, performs spatial/temporal reasoning, and can natively call tools (like Google Search) to ground its plans in the real world. ([Google Developers Blog][2])

The official docs summarize what you can build today: unified object finding/labeling (points & boxes), trajectory planning, video understanding, and orchestration of long-horizon tasks—with the model currently in preview. (Google AI for Developers)

Awesome Gemini Robotics rides that momentum by curating concrete, reproducible prompts and screenshots so you don’t have to start from a blank page. (GitHub)

What’s inside

The repo is a gallery of 10+ use cases (and growing), each with an image and a copy-friendly prompt. Examples include:

Pointing to undefined/defined objects (single or multiple categories)
Bounding boxes and part localization
Counting by pointing
Trajectory generation (e.g., moving a pen to an organizer, brushing particles)
Obstacle-aware paths
Video analysis (step-by-step breakdowns and frame tracking)
Orchestrating tasks (e.g., “make room for a laptop,” “pack a lunch”)

Each card shows the expected output structure (normalized coordinates; JSON-friendly answers) to plug into your controller or VLA. (GitHub)

It also includes tips & patterns—like normalizing point/box coordinates to a 0–1000 range, tuning the thinking budget for latency vs. accuracy, and interleaving text with spatial outputs to create “spatially grounded” plans. (GitHub)

Bonus: the README is available in multiple languages (JA/KR/VI/ZH/EN), making it easier to share across global teams. (GitHub)

How it aligns with the official guidance

The AI for Developers robotics overview shows the same pillars this repo foregrounds—points, boxes, trajectories, video, and orchestration—and provides minimal client snippets for ER 1.5 (currently gemini-robotics-er-1.5-preview). If you’re new, start there to wire up your SDK, then use the repo’s prompts as drop-in tests. (Google AI for Developers)

The launch blog reinforces the model’s role as an agentic planner that can call tools (e.g., Search) and sequence your robot APIs—exactly the workflows many cards in the repo illustrate. ([Google Developers Blog][2])

Quick look: a reusable prompt pattern

A recurring pattern in the repo (and docs) is to ask for structured spatial outputs you can feed into your planner:

Point to no more than N items in the image.
Return [{"point":[y,x],"label":"<name>"}] with y/x normalized to 0–1000.

This shape keeps responses machine-friendly and model-agnostic, and it scales to parts, boxes, or trajectories with minimal edits. (GitHub)

Who should use this

Robotics researchers & builders prototyping perception-to-action loops and benchmarking prompt formats
Product teams validating UX for natural-language robot tasks (clean-up, organizing, sorting, etc.)
Educators & hobbyists looking for clear examples of agentic multimodal reasoning grounded in spatial outputs

For deeper context on the two-model system (ER 1.5 as the planner + the action-oriented VLA model), see DeepMind’s overview of Gemini Robotics 1.5 and ER 1.5. (Google DeepMind)

Getting started

Skim the official docs to confirm the current model name, preview status, and input limits. (Google AI for Developers)
Run a minimal image-prompt call in your environment (Python or REST). (Google AI for Developers)
Pick a use case from the repo (e.g., “2D bounding boxes” or “Simple trajectory planning”) and paste the prompt as-is. (GitHub)
Wire responses (points/boxes/paths) into your controller or VLA for execution; increase the thinking budget for harder tasks like counting or affordance reasoning. (Google AI for Developers)

Contributing

The repo welcomes PRs. Add a new folder under cases/<short-name>/ with a short README, your prompt, and an image (or link). Keep prompts copy-runnable, JSON-friendly, and cite primary sources (docs/blogs/videos). (GitHub)

License & image attribution

Text is MIT-licensed. Demo images marked in the repo reference Google’s public docs/blog; check source licenses before redistributing and replace placeholders with your own captures when in doubt. (GitHub)

Handle WebDAV as JSON: A One-File Self-Hosted API WebDAVJSON (PHP/Node.js)

大阪のUber配達員 — Sun, 14 Sep 2025 10:02:22 +0000

https://github.com/GitHub30/WebDAVJSON

What is it?

WebDAVJSON lets you drop a single PHP or Node.js file on your server and immediately get a JSON API for file operations (list, upload, download, delete). It supports CORS, custom API key (Bearer) auth, and an extension allow-list, making it easy to call from front-ends or automation scripts.

Key features

CORS support
API key (Bearer) authentication (optional)
File listing in JSON
Upload (multipart/PUT), download, delete
Extension allow-list for basic safety
Single-file PHP/Node implementation

Endpoints (HTTP methods)

GET / — List files (JSON)
GET /?filename=abc.txt — Download
POST/PUT / — Upload (multipart or PUT)
POST/PUT /?filename=abc.txt — Binary upload to a specific name
DELETE /?filename=abc.txt — Delete

Quick setup

Node.js (Windows example)

winget install FiloSottile.mkcert Node.js --silent
mkcert -install
mkcert localhost
node index.mjs
# Verify:
# fetch('https://localhost:8443/').then(r=>r.json())
# fetch('https://localhost:8443/?filename=foo.txt',{method:'PUT',body:'foobar'})

PHP (Linux example)

# Get the single file
wget https://raw.githubusercontent.com/GitHub30/WebDAVJSON/refs/heads/main/index.php
# Start with PHP’s built-in server (choose any port)
php -S 0.0.0.0:8000

Usage (cURL recipes)

List files (JSON)

curl http://localhost:8000/

Partial match search (`q`)

curl "http://localhost:8000/?q=report"

Download

curl -O "http://localhost:8000/?filename=abc.txt"
# Save as attachment with original name
curl -OJ "http://localhost:8000/?download&filename=abc.txt"

Upload (multipart/form-data)

curl -F "file=@abc.txt" http://localhost:8000/

Upload (PUT, binary)

curl -X PUT --data-binary @abc.txt "http://localhost:8000/?filename=abc.txt"

Delete

curl -X DELETE "http://localhost:8000/?filename=abc.txt"

Authentication (optional)

If you set $api_key (PHP) or the equivalent in the Node file, the API will require the header Authorization: Bearer <API_KEY>.

Example:

curl -H "Authorization: Bearer your_api_key" http://localhost:8000/

Extension allow-list

Example of commonly allowed extensions:

txt, jpg, png, webp, heic, gif, pdf, docx, xlsx, zip, mp4, gz

Tightening this list is recommended when exposing the API.

When it’s handy

You want a minimal self-hosted storage API callable from a front-end
You need a quick drop-zone for CI/CD artifacts
You prefer a no/low-code backend you can spin up in seconds
You’re standing up a temporary sharing server with basic guards (CORS + key)

Notes & best practices

Serve over HTTPS and enable an API key
Keep the exposure surface minimal and restrict the allowed extensions
Set correct permissions on the writable directory
If usage grows, consider rate limiting and audit logging

License

MIT License.

Wrap-up

WebDAVJSON is a “smallest-viable API” you can deploy in minutes. Despite its tiny footprint, it covers practical needs like CORS, API keys, and extension filtering. When you need a lightweight, self-hosted file API, this is a great first choice.

Forem: 大阪のUber配達員

Sora 2 Watermark Remover Free

what is Watermark

TL;DR

What the watermark (and C2PA) are for

Is removing the watermark legal?

The responsible way to get clean Sora 2 video

“But there are free watermark removers…”

If you’re the rights holder (or you have explicit permission)

Ethics in practice

FAQ

Resources & further reading

A practical checklist (for creators & teams)

Meet “Awesome Gemini Robotics” — Copy-Paste Prompts & Demos for Gemini Robotics-ER 1.5

TL;DR

Why this repo matters (and why now)

What’s inside

How it aligns with the official guidance

Quick look: a reusable prompt pattern

Who should use this

Getting started

Contributing

License & image attribution

Further reading

Handle WebDAV as JSON: A One-File Self-Hosted API WebDAVJSON (PHP/Node.js)

What is it?

Key features

Endpoints (HTTP methods)

Quick setup

Node.js (Windows example)

PHP (Linux example)

Usage (cURL recipes)

List files (JSON)

Partial match search (q)

Download

Upload (multipart/form-data)

Upload (PUT, binary)

Delete

Authentication (optional)

Extension allow-list

When it’s handy

Notes & best practices

License

Wrap-up

Partial match search (`q`)