UK virtual numbers that forward to any phone, with recording, voicemail-to-email and prepaid billing. Local, 07 mobile, 0800 and 03 lines from £4.99/mo.
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Which is more popular?
Based on our record, Pastebin.com
seems to be more popular. It has been mentioned
2058 times
since March 2021.
social mentions
2,058 vs 0
Design Playground popularity
100% vs 0%
Base details
Website, pricing, platforms and company facts side by side.
Ease of Use Pastebin.com offers a straightforward and user-friendly interface, making it simple to paste and share text quickly without the need for an account.
Syntax Highlighting The platform supports syntax highlighting for various programming languages, making it easier for developers to share code snippets with proper formatting.
Privacy Options Users can set their pastes to be public, unlisted, or private, offering different levels of accessibility based on their needs.
Expiration Settings Pastebin.com allows users to set an expiration date for pastes, providing options for automatic deletion after a specific period.
API Access The platform offers an API that allows developers to programmatically create and manage pastes, adding convenience for automated workflows.
Possible disadvantages
Ads and Pop-ups The free version of Pastebin.com contains ads and pop-ups, which can be distracting and may degrade the user experience.
Limited Free Features Some advanced features, such as password protection and enhanced privacy options, are only available to Pro users.
Security Concerns Public pastes can be indexed by search engines, which may lead to unintentional exposure of sensitive information if not properly managed.
Content Control The platform hosts a significant amount of publicly shared content, which could include inappropriate or illegal material. Monitoring and moderating such content can be challenging.
No Collaboration Tools Pastebin.com lacks real-time collaboration features, which limits its utility for users looking to work on shared documents or code simultaneously.
Efficient Data Curation Lightly uses active learning and self-supervised learning techniques to help teams select the most valuable and diverse subset of data for labeling, reducing the amount of data needed for training while maintaining model performance.
Reduces Labeling Costs By intelligently selecting which images or videos to label, Lightly can significantly cut down on the manual annotation costs and time required for building computer vision datasets.
Self-Supervised Learning Integration The platform leverages self-supervised learning methods to extract meaningful embeddings from unlabeled data, enabling better understanding of dataset structure and redundancy before labeling.
Scalable for Large Datasets Lightly is designed to handle large-scale image and video datasets, making it suitable for enterprises dealing with massive amounts of visual data in computer vision projects.
Integration with ML Workflows Lightly offers APIs and integrations that allow it to fit into existing machine learning pipelines, working alongside popular annotation tools and cloud storage solutions.
Possible disadvantages
Learning Curve Teams new to active learning and self-supervised learning concepts may face a learning curve in understanding how to best utilize Lightly's data curation capabilities.
Narrow Use Case Focus Lightly is primarily focused on computer vision data curation, which may limit its usefulness for teams working with other data types such as text, audio, or tabular data.
Dependency on Data Quality The effectiveness of Lightly's curation algorithms depends heavily on the quality and representativeness of the initial dataset, which can limit results if the source data is poor.
Pricing for Smaller Teams Depending on the pricing tier, smaller teams or individual developers might find the cost of enterprise-level data curation features less accessible compared to open-source alternatives.
Requires Technical Expertise Effectively leveraging Lightly's advanced features like self-supervised learning models may require a certain level of machine learning expertise, which could be a barrier for less technical teams.
Analysis
An editorial look at what each product does well and who it suits.
Pastebin.comLightly
Overall verdict
Pastebin.com is a useful tool for sharing text files, particularly beneficial for developers and those in need of sharing snippets of code or logs. However, it is important to be cautious about sharing sensitive information as the site is public by default.
Why this product is good
Pastebin.com is a popular service for storing and sharing plain text, especially code snippets, configuration files, error logs, and other data that can be accessed easily without clutter.
It offers both public and private pastes, allowing users to control who can view their content.
The platform is simple to use and does not usually require creating an account for quick paste sharing.
There is a syntax highlighting feature for a variety of programming languages, making it useful for developers.
It has a wide user base and has been in service for a considerable amount of time, increasing its reliability and trustworthiness.
Recommended for
Software developers and programmers looking for a quick way to share code.
IT professionals and system administrators who wish to share configuration files and server logs.
Educators and students who need to share programming examples or text snippets during collaboration.
Anyone needing to share plain text content quickly without the need for complex formatting.
Overall verdict
Lightly is a solid, well-regarded platform for data curation and active learning in computer vision, offering strong tooling for selecting the most valuable images/frames to label, which helps teams reduce annotation costs and improve model performance efficiently.
Why this product is good
Focuses on smart data selection and curation, helping avoid wasted labeling budget on redundant data
Offers both open-source (Lightly SSL) and commercial (Lightly Worker/Platform) options for flexibility
Uses self-supervised learning techniques to find diverse, representative, or edge-case samples
Integrates with common MLOps and labeling tools, fitting into existing computer vision pipelines
Scales to large unlabeled datasets, making it useful for teams with massive raw data but limited labeling resources
Provides visualization and embedding tools to explore dataset distribution and detect biases or gaps
Recommended for
Computer vision teams looking to reduce labeling costs through smarter data selection
ML engineers building active learning pipelines
Companies with large volumes of unlabeled image or video data
Teams wanting to improve model performance by curating more diverse or representative training sets
Organizations needing to detect dataset bias, redundancy, or edge cases before annotation
It's because Claude Code inserts a "hidden" (e.g. Not visible to the user and very unexpected) info block to the system prompt that shows your email: >The user's email address is xxx@example.com. Use it only to identify the user, such as...
- Source: Hacker News
/
4 days ago
Runme Gist: A Pastebin for Terminals Inside Your Docs
Pastebins make me nostalgic. I’m told they existed well before the web in the IRC days. The first notable one I remember, Pastebin.com, was created in 2002 by Paul Dixon, introducing features like syntax highlighting and private pastes....
- Source: dev.to
/
over 2 years ago
Gradient Trail Effect
When you get something started feel free to put your code on pastebin.com or gist.github.com and share a link for feedback/help.
Source:
almost 3 years ago
Originally founded as a project to simplify sharing code, GitHub has grown into an application used by over a million people to store over two million code repositories, making GitHub the largest code host in the world.
GitHub
GitHub Gist
PrivateBin
hastebin
CodePen
JustPaste.it
Rentry.co
