Notivo is a private, per-person notebook for managers. Capture feedback, commitments, and 1:1 context as it happens — from web, iOS, WhatsApp, email, ChatGPT, or Claude — then get it back later, organized by person.
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Which is more popular?
Based on our record, Obsidian
seems to be more popular. It has been mentioned
1532 times
since March 2021.
social mentions
1,532 vs 0
Note Taking popularity
100% vs 0%
Base details
Website, pricing, platforms and company facts side by side.
Local-first Obsidian stores your notes locally on your device, giving you full control over your data and enhancing privacy.
Markdown Support Obsidian uses Markdown, making it easy to format text and compatible with many other text editors and tools.
Bidirectional Linking You can create bidirectional links between notes, which helps in building a knowledge graph and navigating related information more easily.
Customizability Obsidian is highly customizable with community plugins, themes, and various settings to fit different workflows.
Graph View Provides a visual graph view of your notes and their connections, aiding in understanding relationships and discovering insights.
Offline Access Since the notes are stored locally, you can access and edit them without an internet connection.
Possible disadvantages
Learning Curve Obsidian can be complex and overwhelming for beginners due to its extensive features and customizable nature.
Sync Limitations While local-first is great for privacy, it requires additional steps or third-party solutions for syncing across devices.
No True Real-time Collaboration Obsidian lacks native real-time collaboration features, making it less suitable for collaborative work compared to cloud-based solutions.
Limited Mobile Features The mobile version of Obsidian, while functional, is not as robust as the desktop application, potentially hindering productivity on the go.
Dependence on Plugins Many advanced features require the use of third-party plugins, which could lead to compatibility issues and reliance on community support.
Performance on Large Vaults Performance might degrade with a very large number of notes or complex graphs, impacting usability.
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.
ObsidianLightly
Overall verdict
Overall, Obsidian.md is an excellent tool for users looking for a versatile and powerful note-taking application. Its unique features such as backlinking, markdown support, and robust customization options make it a favorite among knowledge management enthusiasts.
Why this product is good
Obsidian.md is considered good by many users because it is a powerful knowledge management and note-taking application that leverages Markdown for document formatting. It allows for bi-directional linking between notes, which helps in creating a network of interconnected ideas, often described as a 'second brain.' Its ability to support plugins, themes, and robust graph visualization also adds to its appeal. Additionally, it stores notes locally, ensuring privacy and data ownership, while being cross-platform compatible across Windows, macOS, and Linux.
Recommended for
Knowledge workers who need a powerful tool to organize and connect their thoughts.
Students who are looking to create a structured and visually interconnected note system.
Writers and researchers who benefit from linking related ideas and maintaining detailed notes.
Tech-savvy users or developers who appreciate the local storage and open ecosystem for plugins and customization.
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
One thing to note: Notion bills itself as an Evernote competitor for personal users. It can be—but it's too much for most people, and its offline functionality isn't the best. If you love the idea of Notion, go right...
This app is the kind of thing that, if you’re into it, will have you exploring its various ins, outs, and add-ons for days and weeks on end. Obsidian uses the Markdown format for its notes (which means they can be...
However, it's important to note that Obsidian's whiteboard functionality is not as robust as dedicated collaboration tools. While it lacks real-time basic collaboration features, Obsidian compensates with its vibrant...
The reasons live somewhere else: in practice notes in an Obsidian vault, one note per convention, with the repos it has held in. A note starts as an idea, moves to trialing, and only becomes enforced once I've applied it again in a few...
- Source: dev.to
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3 days ago
It's the team version of the second brain you build with Obsidian and Claude.
- Source: dev.to
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7 days ago
Mine and yours first Obsidian plugin
The first time I downloaded Obsidian was about two years ago and I really liked the concept of storing everything locally in simple markdown files without unnecessary clutter.
- Source: dev.to
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5 days ago
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