Forem: charudatta

I Found a 160-Line AI Framework That Makes Lang Chain Look Embarrassing

charudatta — Wed, 13 May 2026 15:17:00 +0000

I want to show you a number: 166 MB.

That's how much disk space LangChain takes up. For a library whose entire job is to connect a few LLM API calls together.

Now here's another number: 10 KB.

That's Orbit. A Lua framework that does much of what LangChain does — agents, multi-agent systems, RAG, map-reduce, structured workflows — in roughly 160 lines of code.

Let that sink in.

The Framework Size Wall of Shame

Framework	Lines of Code	Install Size
LangChain	405K	166 MB
CrewAI	18K	173 MB
LangGraph	37K	51 MB
AutoGen	7K	26 MB
Orbit	~160	~10 KB

I'm not sharing this to dunk on those projects. I'm sharing it because it forces a real question:

What are we actually paying for?

The Core Idea: LLM Apps Are Just Graphs

Orbit makes one bet: every LLM application is a directed graph. Nodes execute, return a string, and that string routes to the next node. Shared state flows through like a lightweight message bus.

Here's what that looks like in practice:

local fetch = pf.node(function(shared)
    shared.value = 42
    return 'ok'
end)

local process = pf.node(function(shared)
    if shared.value > 40 then
        return 'large'
    end
    return 'small'
end)

fetch:to('ok', process)

No decorators. No YAML. No plugin registries. No lifecycle hooks you have to Google.

Just flow.

It's less "framework" and more "state machine with a good API." And once you see it that way, you start wondering why AI tooling ever needed to be more complicated.

Why Lua? (Seriously, Hear This Out)

I know. Lua in 2025 raises eyebrows. But the choice isn't nostalgic — it's strategic.

Lua has:

A tiny embeddable runtime
Native coroutine support
Minimal syntax surface
Extreme portability

But here's the angle nobody's talking about: Lua is great for AI-generated code.

Big frameworks are hard for LLMs to code against. The API surface is enormous, abstractions are layered 5 levels deep, and hidden behavior lurks everywhere. The model hallucinates. You debug. Everyone suffers.

Orbit's entire runtime fits in a context window. An LLM can hold the whole framework in its head while generating node implementations. That means:

Fewer hallucinated API calls
More deterministic generation
Easier error repair
Actually usable autonomous coding workflows

This reframes what Orbit is. It's not just a small framework. It's a framework deliberately designed for AI-generated code. That's a different product category entirely.

Composability > Abstraction

Modern AI frameworks love to hardcode concepts: agents, tools, memories, chains, evaluators. Those abstractions feel helpful until they become constraints — and they always become constraints eventually.

Orbit takes the opposite approach. It defines exactly three primitives:

Nodes (execution units)
Transitions (routing logic)
Shared state (communication)

Agents? That's a graph topology. RAG? Graph topology. Multi-agent coordination? Still just a graph topology. Nothing is hardcoded because nothing needs to be.

It's the difference between a platform and a primitive. Platforms make assumptions about your use case. Primitives let you build the platform you actually need.

The Psychology of Small

There's something real that happens when a framework is 160 lines long.

You read it. You understand the whole thing. You stop being afraid to fork it or modify it or throw it away. You stop treating the framework as infrastructure and start treating it as readable code.

Compare that to the experience of debugging a LangChain agent at 2am. You're not debugging your code anymore. You're archaeologizing someone else's abstraction tower.

Orbit is closer to Unix philosophy than to enterprise software: do one thing, do it well, compose freely.

The SQLite Parallel

SQLite is the most deployed database in the world. It won not by being the most powerful, but by refusing to become enterprise middleware. Single file. No server. Copy-paste deployment. Zero operational complexity.

Orbit is aiming for the same lane in AI orchestration. And that niche is genuinely underserved:

Local-first AI apps
Edge deployments
Embedded agents
Offline workflows
Experimental architectures

Every heavy framework optimizes for the SaaS cloud ecosystem. Nobody's optimizing for portability. Orbit is.

Where This Is Headed

Orbit ends its pitch with: "Enable LLMs to Program Themselves."

That sounds like conference slide hyperbole. But given the architecture, there's something concrete underneath it.

If AI systems are going to generate and modify software autonomously, the runtimes they target need to be smaller, more predictable, and easier to reason about. Massive frameworks with sprawling APIs are not that. A 160-line graph executor with shared state absolutely could be.

We're in the post-hype phase of AI tooling. The first wave gave us abstraction stacks. The second wave is quietly dismantling them — direct API calls, explicit control flow, minimal runtimes, graph execution.

Orbit fits that second wave perfectly.

The Takeaway

You probably won't swap LangChain for Orbit tomorrow. But Orbit makes you ask the right question: what is all that code actually doing for you?

If the answer is "handling complexity I didn't need in the first place," that's worth sitting with.

The industry needs more 160-line ideas.

Have you tried building with minimal AI frameworks? Dropped heavyweight orchestration for something leaner? Would love to hear what the ecosystem looks like from your side — drop it in the comments.

AI-Optimized FPGA Deployments: Challenges & Solutions

charudatta — Tue, 25 Feb 2025 07:06:12 +0000

As the realm of artificial intelligence (AI) continues to expand, so does the demand for robust and efficient hardware accelerators. Field Programmable Gate Arrays (FPGAs) have emerged as a promising solution, offering flexibility and high performance. However, deploying AI-optimized FPGAs is not without its challenges. This article explores the hurdles and provides practical solutions for successful AI-optimized FPGA deployments.

The Rise of AI-Optimized FPGAs

FPGAs are integrated circuits that can be configured by the user after manufacturing, allowing for tailored optimization to specific applications. In the context of AI, FPGAs provide several advantages:

Parallelism: FPGAs can handle multiple tasks simultaneously, making them suitable for AI workloads that require parallel processing.
Low Latency: FPGAs offer real-time processing capabilities, which are crucial for applications like autonomous driving and high-frequency trading.
Energy Efficiency: Compared to general-purpose CPUs and GPUs, FPGAs consume less power, making them ideal for edge AI deployments.

Challenges in AI-Optimized FPGA Deployments

1. Complexity of Design

Designing FPGA-based AI systems is a complex task that requires expertise in both hardware and software. The lack of standardized tools and frameworks adds to this complexity. Traditional software engineers may find it challenging to transition to FPGA development due to the steep learning curve.

Solution: To address this challenge, organizations should invest in training programs and collaborate with FPGA vendors to leverage their expertise. Additionally, using high-level synthesis (HLS) tools can simplify the design process by allowing developers to write code in high-level languages like C++ or Python.

2. Integration with Existing Infrastructure

Integrating AI-optimized FPGAs into existing infrastructure can be daunting. Compatibility issues, data transfer bottlenecks, and synchronization problems are common hurdles. Moreover, FPGAs often require custom drivers and middleware, which can complicate integration efforts.

Solution: A well-planned integration strategy is essential. Organizations should conduct thorough compatibility assessments and collaborate with FPGA vendors to develop custom solutions. Utilizing FPGA development platforms and frameworks can also streamline integration by providing pre-built components and modules.

3. Scalability and Maintenance

Scalability is a significant concern when deploying AI-optimized FPGAs. As AI models grow in complexity, the need for more FPGA resources increases. Managing and maintaining a large fleet of FPGAs can be challenging, especially in dynamic environments where workloads fluctuate.

Solution: Implementing an FPGA management platform can simplify scalability and maintenance. These platforms offer features like dynamic resource allocation, automated updates, and real-time monitoring, ensuring efficient use of FPGA resources. Cloud-based FPGA services, such as those offered by major cloud providers, can also provide scalable and manageable FPGA deployments.

4. Cost Considerations

The initial cost of FPGA hardware and development tools can be high, potentially deterring organizations from adopting this technology. Additionally, the ongoing costs of maintaining and upgrading FPGA-based systems can add up over time.

Solution: To mitigate costs, organizations should conduct a thorough cost-benefit analysis before deploying FPGAs. Exploring rental or pay-per-use models offered by cloud providers can reduce upfront costs. Additionally, investing in reusable IP cores and design automation tools can lower development costs in the long run.

Future Outlook

Despite the challenges, AI-optimized FPGAs hold immense potential for transforming various industries. As advancements in FPGA technology continue, we can expect to see improved tools, frameworks, and support systems that simplify deployment and integration. The key to successful FPGA deployment lies in continuous learning, collaboration with vendors, and adopting a strategic approach to design and integration.

By addressing the challenges and leveraging the right solutions, organizations can unlock the full potential of AI-optimized FPGAs, driving innovation and achieving new levels of performance and efficiency.

Gemini vs. Copilot: A Tale of Two AIs – Where Gemini Falls Short

charudatta — Fri, 20 Dec 2024 07:31:18 +0000

The AI landscape is rapidly evolving, with large language models (LLMs) taking center stage. Two prominent contenders are Google's Gemini and Microsoft's Copilot. While both aim to augment human capabilities, their approaches and current functionalities paint a starkly different picture. This article argues that, in its current state, Gemini lags significantly behind Copilot, particularly in practical application and seamless integration.

Gemini's Unfulfilled Promise:

Gemini, touted as a multimodal AI capable of understanding and generating various data types, holds immense theoretical potential. However, this potential remains largely untapped. While Google showcases impressive demos, the actual user experience and practical applications of Gemini are limited. Its multimodal capabilities, while intriguing, haven't translated into tangible benefits for everyday users. This lack of concrete implementation leaves Gemini feeling more like a research project than a practical tool.

A key criticism of Gemini lies in its lack of deep integration within existing ecosystems. Unlike Copilot, which is seamlessly woven into the Microsoft suite, Gemini lacks a similar level of integration with Google's products. This absence of cohesive integration hinders its usability and limits its practical value. The promise of multimodal interaction becomes less impressive when there's no clear, convenient way to utilize it within familiar workflows.

Furthermore, Gemini's performance in specific tasks, such as code generation, doesn't yet match the proficiency of Copilot. While Gemini can generate code, it often lacks the precision and contextual awareness that Copilot demonstrates. This makes Copilot a far more reliable and efficient tool for developers.

Copilot's Practical Prowess:

In contrast to Gemini's theoretical promise, Copilot delivers tangible benefits through its deep integration with the Microsoft ecosystem. Its tight integration with Visual Studio, GitHub, Microsoft 365, and Windows 11 provides a seamless and intuitive user experience. This integration allows Copilot to anticipate user needs, provide contextually relevant suggestions, and streamline workflows in a way that Gemini simply cannot match.

Copilot's strength lies in its practical application. It excels at tasks like code completion, bug fixing, and generating documentation, significantly boosting developer productivity. Within Microsoft 365, it assists with tasks like summarizing documents, generating emails, and creating presentations, enhancing productivity for a broader range of users.

Moreover, Copilot's foundation on OpenAI's GPT-4 architecture gives it a significant edge in natural language understanding and generation. This translates to more accurate, contextually relevant, and human-like interactions. Copilot understands the nuances of human language and can generate responses that are both informative and engaging.

A Clear Winner in Practicality:

While Gemini's multimodal capabilities are theoretically exciting, its current lack of practical application and ecosystem integration makes it fall short compared to Copilot. Copilot's deep integration, focus on practical tasks, and robust performance make it a clear winner in terms of usability and real-world value.

In conclusion, while Gemini holds promise for the future, Copilot currently provides a more compelling and practical AI experience. Copilot's seamless integration, focus on tangible benefits, and strong performance make it an invaluable tool for developers and everyday users alike. Until Gemini can bridge the gap between theoretical potential and practical application, Copilot remains the superior choice.

Why Obsidian Falls Short as a Note-Taking Tool

charudatta — Mon, 29 Jul 2024 16:26:45 +0000

Obsidian has gained a significant following among productivity enthusiasts and knowledge workers due to its unique features and flexibility. However, despite its strengths, there are several reasons why some users find Obsidian lacking as a note-taking tool. This article explores the key drawbacks of Obsidian and why it might not be the best choice for everyone.

1. Steep Learning Curve

One of the most common complaints about Obsidian is its steep learning curve. Unlike more straightforward note-taking apps, Obsidian requires users to understand and use Markdown, a lightweight markup language³. For those unfamiliar with Markdown or hierarchical note-taking, this can be a significant barrier to entry⁴. The initial setup and customization can also be time-consuming, which may deter new users.

2. Lack of Real-Time Collaboration

In an era where collaboration is key, Obsidian falls short. The app does not support real-time collaboration, making it difficult for teams to work together on shared notes³⁵. This limitation can be a deal-breaker for users who need to collaborate frequently, as they might prefer tools like Google Docs or Notion, which offer robust collaboration features.

3. No Built-In Cloud Storage

Obsidian stores all notes locally on the user's device, which can be both a strength and a weakness¹. While this ensures privacy and control over data, it also means that users need to manage their own backups and syncing across devices. The lack of built-in cloud storage can be inconvenient, especially for users who are accustomed to seamless cloud integration provided by other note-taking apps¹.

4. Limited Mobile Support

Although Obsidian has made strides in mobile support, it still lags behind other note-taking apps in terms of functionality and ease of use on mobile devices⁴. The mobile app can feel clunky and less intuitive, which can be frustrating for users who need to take notes on the go.

5. Absence of Advanced Features

Obsidian lacks some advanced features that are available in other note-taking apps. For instance, it does not have a web clipper, which is a handy tool for saving content directly from the web³. Additionally, it does not support advanced databases or OCR (Optical Character Recognition), limiting its utility for users who need these functionalities¹.

6. Customization Overload

While Obsidian's high degree of customization is a selling point for many, it can also be overwhelming. The extensive options for plugins, themes, and settings can lead to decision fatigue and make the app feel cluttered¹. Users who prefer a more streamlined and out-of-the-box experience might find Obsidian's customization options excessive and distracting.

Conclusion

Obsidian is a powerful tool with a dedicated user base, but it is not without its flaws. The steep learning curve, lack of real-time collaboration, absence of built-in cloud storage, limited mobile support, and missing advanced features are significant drawbacks that can hinder its effectiveness as a note-taking app. For users who prioritize ease of use, collaboration, and advanced functionalities, other note-taking tools might be a better fit.

What are your thoughts on these points? Have you experienced any of these issues with Obsidian?

Exploring the "legendary-dollop" Repository: An SVG Generator

charudatta — Mon, 24 Jun 2024 15:43:42 +0000

The legendary-dollop repository, created by charudatta10, is an intriguing project that generates SVGs. Whether you're designing banners, badges, or other visual elements, this tool can be a valuable addition to your toolkit.

Key Features

SVG Generation:
- The primary purpose of this repository is to create SVGs programmatically.
- SVGs (Scalable Vector Graphics) are versatile, resolution-independent images that can be easily customized and scaled without loss of quality.
Built with Python:
- The project is implemented in Python.
- It leverages the Flask web framework for serving the SVGs and uses Waitress as the production server.

Getting Started

Installation:
- You have two options:
  - Binary Installation:
  - Download the binary file from the releases section.
  - Double-click the binary to run the application.
  - Source Installation:
  - Clone the repository using:
```
 gh repo clone charudatta10/legendary-dollop
```

   - Explore the code and set up your development environment.

Usage:
- The main entry point is api/app.py.
- Customize the SVG generation logic according to your needs.
- Refer to the README for detailed instructions.

Conclusion

The legendary-dollop repository provides a straightforward way to create SVGs dynamically. Whether you're a designer, developer, or hobbyist, give it a try and unleash your creativity!

Explore the repository: charudatta10/legendary-dollop

Happy SVG crafting! 🎨🚀

Organization schemes for note taking

charudatta — Mon, 10 Jun 2024 18:22:57 +0000

Let's delve into various organization schemes for effective note-taking, along with practical examples:

File and Folder Organization:
- Files: Think of each note as a separate file (e.g., "MeetingNotes.md" or "ProjectIdeas.txt").
- Folders (or Notebooks): Create folders to group related notes. For example:
  - Inbox: A default folder for quick notes or temporary storage.
  - Archive: Store completed or less frequently accessed notes.
  - Projects: Organize notes by specific projects or topics (e.g., "ProjectA," "Personal," "Work").
  - Reference: For long-term reference material (e.g., research papers, manuals).
Labels and Tags:
- Labels: Assign descriptive labels to notes (e.g., "Urgent," "Important," "Review").
- Tags: Use tags for cross-referencing (e.g., "Meeting," "Ideas," "Code Snippets").
Metadata:
- Creation Date: Include timestamps to track when notes were created.
- Author: Useful for collaborative notes (e.g., "Created by John").
- Location: If relevant (e.g., "Conference Notes - Seattle").
Links:
- Internal Links: Create hyperlinks within notes to connect related content.
- External Links: Reference web pages, articles, or other resources.

Example Folder Structure:

Inbox:
- Unprocessed notes go here initially.
- Regularly review and move them to appropriate folders.
Archive:
- Completed or historical notes.
- Rarely accessed but still valuable.
Projects:
- Subfolders for each project or topic.
- E.g., "ProjectA," "ProjectB," "Personal," "Work."
Reference:
- Subfolders for specific types (e.g., "Research," "Tutorials").
- Store long-term reference material.

Remember, adapt these structures to your needs and preferences. Consistency is key! 📂📝✨

VS Code for Note-Taking

charudatta — Thu, 23 May 2024 18:23:13 +0000

Using VS Code for Note-Taking

1. Markdown Support

VS Code has excellent support for Markdown, making it a great choice for note-taking. You can create Markdown files (.md) to organize your notes, ideas, and thoughts. Markdown allows you to format text, create lists, add headings, and include code snippets.

2. Workspace Setup

Open VS Code and create a new folder for your notes.
Save the folder as a workspace (File > Open Folder). This step ensures that you can manage multiple notes within the same workspace³.

3. File and Folder Organization

Create subfolders within your workspace to categorize your notes. For example:
- Personal
- Work
- Projects
- Ideas
Use descriptive folder names to make it easy to find specific notes later.

4. Task Management

VS Code allows you to automate tasks using the built-in Tasks feature. You can configure tasks to run scripts, start processes, and perform various actions. Here's how:

Create a tasks.json file in the .vscode folder within your workspace.
Define tasks using the appropriate task runner (e.g., TypeScript Compiler, linters, build systems).
Execute tasks directly from VS Code without entering the command line¹.

5. Repetitive Tasks and Automation

Use tasks to automate repetitive actions. For example:
- Compile TypeScript files automatically when you save them.
- Run linters to check code quality.
- Trigger build processes for your projects.

6. Project Management

Create separate folders for each project within your workspace.
Organize project-related notes, code snippets, and documentation in these folders.
Use Markdown files to maintain project-specific to-do lists, milestones, and progress updates.

7. Getting Things Done (GTD) Methodology

Apply GTD principles to your note-taking process:
- Capture: Quickly jot down ideas, tasks, and notes.
- Organize: Sort your notes into relevant folders.
- Review: Regularly review and update your notes.
- Prioritize: Identify high-priority tasks and focus on them.

Remember that VS Code is highly customizable, so explore extensions and settings that enhance your note-taking experience. Happy note-taking! 📝✨⁴

Improved-Barnacle repository: (The Cleanup Python Script)

charudatta — Tue, 07 May 2024 17:53:46 +0000

Streamlining Your Download Folder with Improved Barnacle: The Cleanup Python Script

In the world of desktop management, maintaining clean and efficient downloads is crucial. It's not just about organizing images and PDFs; it's about nurturing an ecosystem that is easy to manage and evolve. That's where tools like Improved Barnacle come into play.

What is an Improved Barnacle?

Improved Barnacle is a Python script designed to help developers clean up their download folders. Hosted on GitHub, this script is the brainchild of a developer in me as I saw the need for a straightforward, no-nonsense tool for download folder maintenance.

The Genesis of Improved Barnacle

The project began as a personal endeavor by me, as I was frustrated with the clutter in their download folder. The goal was simple: create a script that could automate the tedious task of cleaning up redundant files and organizing the download folder.

How Does Improved Barnacle Work?

The script operates by scanning the files in the folder by extension, such as .jpg, .png, .pdf, etc. It then proceeds to move or delete these files based on predefined rules, ensuring that only the miscellaneous files remain.

The Benefits of Using Improved Barnacle- Simplicity:

The script is easy to use, with a clear and concise README that guides you through the process.

Efficiency: It saves time by automating what would otherwise be a manual and error-prone task.
Customizability: Users can modify the script to suit their specific needs, making it a versatile tool for any project.

Getting Started with Improved Barnacle

To get started, simply clone the repository from GitHub, review the README for instructions, and run the script in your project directory. It's that easy!

Open Source and Community-Driven Improved-Barnacle is available under the GPL-3.0 license, inviting contributions and modifications from the community. It's a testament to the collaborative spirit of open-source software.

In conclusion, Improved-Barnacle is more than just a cleanup script; it's a statement about the importance of code hygiene. By incorporating such tools into your workflow, you can ensure that your download folder remains clean, organized, and ready for future development.

Note: The repository improved-barnacle by Charudatta Gurudas Korde is a cleanup Python script. It is available under the GPL-3.0 license.

Choosing Your Digital Brain: A Comparative Analysis of PKM Tools

charudatta — Mon, 15 Apr 2024 12:31:24 +0000

Let's delve into a comprehensive comparison of the popular personal knowledge management (PKM) tools: Obsidian, Logseq, Notion, and Visual Studio Code (VSCode).

1. Obsidian

Pros:

Graph-Based: Obsidian's bi-directional linking creates a network of interconnected notes, fostering a second brain.
Customization: Extensive plugin ecosystem for personalized workflows.
Markdown Support: Uses Markdown for easy formatting.
Local Storage: Stores data as plain text Markdown files on your local machine.
Community: Active community and support.

Cons:

Learning Curve: The graph-based approach can be overwhelming initially.
No Mobile App: Lacks a dedicated mobile app (though you can use third-party apps).

Best Suited For:

Visual Thinkers: Obsidian's graph view appeals to those who like visualizing connections.
Customization Enthusiasts: Users who want to tailor their PKM experience.

2. Logseq

Pros:

Graph-Based: Similar to Obsidian, Logseq emphasizes bi-directional linking.
Task Management: Supports task lists and checklists.
Open Source: Community-driven development.
Block-Based: Organizes content in blocks.

Cons:

Smaller Community: Fewer users compared to Obsidian.
Learning Curve: Graph-based systems may take time to grasp.

Best Suited For:

Outliner Lovers: Logseq's block-based structure suits those who prefer outlining.
Open Source Advocates: Users who appreciate community-driven projects.

3. Notion

Pros:

Versatility: All-in-one workspace for notes, tasks, wikis, and databases.
Rich Media: Supports embedded media (images, videos, etc.).
Collaboration: Ideal for team collaboration.
Templates: Offers pre-built templates for various use cases.

Cons:

Complexity: Feature-rich interface can be overwhelming.
Pricing: Free version has limitations; paid plans can be expensive.

Best Suited For:

Team Collaboration: Notion excels in collaborative environments.
Structured Workflows: Users who need databases, project management, and documentation.

4. Visual Studio Code (VSCode)

Pros:

Extensibility: Rich ecosystem of extensions.
Markdown Support: Supports Markdown editing.
Coding and Notes: Ideal for developers who want a unified environment.

Cons:

Not Focused on PKM: VSCode is primarily a code editor.
Learning Curve: May be complex for non-developers.

Best Suited For:

Developers and Coders: VSCode users who want to integrate note-taking with coding.

Note-Taking Types and Archetypes

Types of Notes: All tools support plain text, code snippets, images, and attachments.
Archetypes: Each tool can accommodate various archetypes (e.g., meeting notes, research summaries, personal reflections).

Productivity, Time Management, and Knowledge Management

Productivity Systems: Implement GTD (Getting Things Done), Pomodoro, or custom workflows using tags, folders, and reminders.
Time Management: Use calendars, reminders, and time-blocking techniques.
Knowledge Management: Organize notes with tags, links, and folders.

Events, Task Management, and Retrieval

Events: Use calendars or dedicated event notes.
Task Management: Create task lists, set deadlines, and track progress.
Notes Retrieval: Leverage search, tags, and linking.
Notes Organization: Use folders, hierarchies, and categories.

Conclusion

Choose based on your preferences, workflow, and specific needs. Remember, there's no one-size-fits-all solution—each tool has its strengths. Experiment and find what resonates with you! 🚀

Source:
(1) Logseq vs Obsidian - which PKM tool should you use?. https://www.logseqmastery.com/blog/logseq-vs-obsidian/.
(2) Logseq vs. Notion vs. Obsidian Comparison - SourceForge. https://sourceforge.net/software/compare/Logseq-vs-Notion-vs-Obsidian/.
(3) Obsidian vs Logseq: which PKM tool is right for you?. https://www.compsmag.com/vs/obsidian-vs-logseq/.
(4) The Lion's Den | Why Obsidian is now my preferred PKM tool. https://aires.fyi/blog/logseq-vs-obsidian/.
(5) People who use Notion or Logseq and Obsidian: what do you use each for .... https://www.reddit.com/r/ObsidianMD/comments/1058yw2/people_who_use_notion_or_logseq_and_obsidian_what/.
(6) Obsidian vs Logseq vs Notion - papereditor.app. https://papereditor.app/obsidian-vs-logseq-vs-notion.
(7) Logseq vs Obsidian: Which Note-Taking App is Better?. https://theprocesshacker.com/blog/logseq-vs-obsidian/.
(8) Choosing between Logseq and Obsidian | by Mark McElroy | Medium. https://medium.com/@markmcelroydotcom/choosing-between-logseq-and-obsidian-1fe22c61f742.
(9) Obsidian Vs Logseq: Which Note-Taking Tool Do You Need?. https://imviews.com/obsidian-vs-logseq/.
(10) Battle Royale: Obsidian vs Logseq — Which is Better? - Medium. https://medium.com/obsidian-observer/battle-royale-obsidian-vs-logseq-which-is-better-42cfa1a66d8d.
(11) Joplin vs. Logseq vs. Notion vs. Obsidian Comparison - SourceForge. https://sourceforge.net/software/compare/Joplin-vs-Logseq-vs-Notion-vs-Obsidian/.
(12) Notion vs. Obsidian: Comparing Productivity Tools for Task Management .... https://eightify.app/summary/productivity-tools/notion-vs-obsidian-comparing-productivity-tools-for-task-management-and-knowledge-management.
(13) Notion Vs Obsidian: Which Tool Wins for Note-Taking and Knowledge .... https://imviews.com/notion-vs-obsidian/.
(14) Logseq vs. Obsidian - Which is the Best Note-Taking Tool. https://www.mksguide.com/logseq-vs-obsidian/.
(15) Three Phase Balanced vs unbalanced system / load - TheElectricalGuy. https://www.theelectricalguy.in/tutorials/three-phase-balanced-vs-unbalanced-system-load/.
(16) OnePlus Buds Z2 | OnePlus India. https://www.oneplus.in/oneplus-buds-z2.
(17) What Is the Time Lag in Monetary or Fiscal Policy? | Sapling. https://www.sapling.com/8087404/time-lag-monetary-fiscal-policy.
(18) Compare Logseq vs. Microsoft OneNote vs. Notion vs. Obsidian - Slashdot. https://slashdot.org/software/comparison/Logseq-vs-Microsoft-OneNote-vs-Notion-vs-Obsidian/.

Dosa

charudatta — Thu, 11 Apr 2024 11:10:38 +0000

Dosa, a popular South Indian delicacy, comes in various delightful varieties. Let's explore some of them:

Plain Dosa:
- A classic favorite, plain dosa is a thin crepe made with fermented rice and lentil batter. It's usually served with coconut chutney and sambar.
- Thickness: Thin and crispy.
- Texture: Crispy on the outside and soft on the inside.
Masala Dosa:
- A filled dosa, masala dosa is typically stuffed with a spiced potato filling (masala aloo). It's a flavorful and satisfying option.
- Thickness: Similar to plain dosa.
- Texture: Crispy on the outside, with a flavorful potato filling.
Neer Dosa:
- Neer dosa has a batter consistency comparable to water. It's made with just three ingredients: water, salt, and rice.
- Thickness: Extremely thin.
- Texture: Light and delicate.
Cheese Dosa:
- A fusion delight, cheese dosa combines the goodness of dosa with melted cheese. It's like a pizza-dosa hybrid!
- Thickness: Similar to plain dosa.
- Texture: Crispy with gooey cheese.
Pesarattu (Moong Dal Dosa):
- Made from green gram (moong dal), pesarattu is a unique dosa variety from Andhra cuisine.
- Thickness: Thin.
- Texture: Earthy and flavorful.
Ragi Dosa:
- Ragi (finger millet) dosa is the healthiest option. It's beneficial for health-conscious individuals and those with diabetes.
- Thickness: Similar to plain dosa.
- Texture: Nutty and wholesome. Absolutely, let's expand the list with a few more varieties of dosa:
Set Dosa:
- Originating from Karnataka, set dosa is known for its softness and sponginess. It's thicker than the regular dosa and is served in a set of three, usually with coconut chutney and vegetable sagu[^20^].
- Thickness: Thick, small in size.
- Texture: Soft and spongy.
Paper Dosa:
- As the name suggests, paper dosa is an extremely thin and crisp variety. It's often large in size and can be made with just a ladle or two of batter¹⁵.
- Thickness: Very thin, almost like paper.
- Texture: Light and crispy.

These dosas add even more variety to the already diverse world of dosas, each with its own unique taste and texture. Enjoy exploring these delicious options! 😊🍴.

Source:
(1) Types of Dosa: Delightful 15 Varieties of Dosa Explained | Seema. https://www.seema.com/the-different-types-of-dosas-you-should-make/.
(2) Why Sabudana Dosa is better than Rice Dosa; recipe inside. https://timesofindia.indiatimes.com/life-style/food-news/why-sabudana-dosa-is-better-than-rice-dosa-recipe-inside/photostory/104642941.cms.
(3) Dosa Recipe (With Homemade Dosa Batter) - Dassana's Veg Recipes. https://www.vegrecipesofindia.com/dosa-recipe-dosa-batter-recipe/.
(4) Dosa Recipe, How to Make Dosa Batter - Swasthi's Recipes. https://www.indianhealthyrecipes.com/dosa-recipe-dosa-batter/.
(5) 22 Dosa varieties | South Indian dosa varieties for breakfast. https://www.indianhealthyrecipes.com/dosa-recipes-dosai-dosa-varieties/.
(6) Dosa (food) - Wikipedia. https://en.wikipedia.org/wiki/Dosa_%28food%29.
(7) Variety Dosa | Masala Dosa | Instant Dosa - Archana's Kitchen. https://www.archanaskitchen.com/dosa-recipes-you-will-love-variety-dosa-masala-dosa-instant-dosa.
(8) 10 types of Dosa one can't resist | Times of India - Times Food. https://recipes.timesofindia.com/web-stories/10-types-of-dosa-one-cant-resist/photostory/105483023.cms.
(9) Getty Images. https://www.gettyimages.com/detail/photo/masala-dosa-south-indian-food-royalty-free-image/909906350.
(10) Set Dosa Recipe (Sponge Dosa) - Swasthi's Recipes. https://www.indianhealthyrecipes.com/set-dosa-recipe/.
(11) Paper Dosa » Dassana's Veg Recipes. https://www.vegrecipesofindia.com/paper-dosa-recipe/.
(12) https://www.indianhealthyrecipes.com/dosa-recipes-dosai-dosa-varieties/.
(13) https://www.seema.com/the-different-types-of-dosas-you-should-make/.
(14) https://en.wikipedia.org/wiki/Dosa_%28food%29.
(15) https://www.archanaskitchen.com/dosa-recipes-you-will-love-variety-dosa-masala-dosa-instant-dosa.
(16) https://recipes.timesofindia.com/web-stories/10-types-of-dosa-one-cant-resist/photostory/105483023.cms.
(17) https://www.vegrecipesofindia.com/dosa-recipe-dosa-batter-recipe/.
(18) https://www.indianhealthyrecipes.com/dosa-recipe-dosa-batter/.
(19) https://timesofindia.indiatimes.com/life-style/food-news/why-sabudana-dosa-is-better-than-rice-dosa-recipe-inside/photostory/104642941.cms.
(20) Paper Dosa Recipe: How to make Paper Dosa Recipe at Home | Homemade Paper Dosa Recipe. https://recipes.timesofindia.com/us/recipes/paper-dosa/rs53239433.cms.
(21) Ghee Roast Dosa Recipe - Crispy Paper Dosa Recipe. https://www.archanaskitchen.com/homemade-crisp-ghee-roast-dosa-s.
(22) paper dosa recipe | crispy paper dosa | South Indian paper dosa. https://www.tarladalal.com/crispy-paper-dosa-1699r.
(23) Paper Dosa | Traditional Pancake From Tamil Nadu, India - TasteAtlas. https://www.tasteatlas.com/paper-dosa.
(24) set dosa recipe | sponge dosa | how to make set dose recipe. https://hebbarskitchen.com/set-dosa-recipe-sponge-dosa/.
(25) Set Dosa, How to make South Indian Set Dosa - Cooking With Sapana. https://cookingwithsapana.com/set-dosa/.
(26) Set Dosa Recipe-Karnataka Style Set Dosa - Padhuskitchen. https://www.padhuskitchen.com/2018/08/set-dosa-recipe-karnataka-style-set-dosa.html.
(27) Getty. https://media.gettyimages.com/photos/masala-dosa-south-indian-food-picture-id909906350?b=1&k=6&m=909906350&s=612x612&w=0&h=3YiP149HNcAE4kUH8-CHS45PGaxwtCggN7VKEaXn1-8=.

Notes templates

charudatta — Sun, 07 Apr 2024 07:35:40 +0000

Notes templates can significantly enhance your note-taking workflow by providing consistent structures for different types of notes. Here are some useful templates you can create:

Daily Notes (Journaling):
- Use this template for daily reflections, thoughts, and events.
- Include sections like "Highlights," "Challenges," and "Gratitude."
- Capture your mood, accomplishments, and any significant moments.
Weekly Review:
- Reflect on the past week's achievements, setbacks, and learnings.
- Consider adding sections for goal progress, lessons learned, and upcoming priorities.
Monthly Review:
- Similar to the weekly review but on a monthly basis.
- Evaluate your overall progress, adjust goals, and plan for the next month.
Article Writing:
- Organize your thoughts when writing articles or blog posts.
- Include sections like:
  - "Type of post" (opinion piece, guide, list, etc.)
  - "Rapid fire thoughts" (raw ideas to explore)
  - "Tags"
  - "Headlines"
  - "Outline"
  - "Intro"
  - "Main points"
  - "References/Resources"
People Notes:
- Create a template for capturing information about individuals.
- Include fields for name, contact details, background, and any relevant notes.
Book Notes:
- Use a structured table to record book details:
  - Author
  - Published date
  - Theme
  - Keywords
  - Date read
  - Status (finished/abandoned/ongoing)
- Add sections for:
  - "The Book in 3 Sentences"
  - "Relevant Quotes"
  - "My Notes"
  - "Related Books"
Movie Notes:
- Customize a template for movie reviews:
  - Title
  - Release year
  - Genre
  - Plot summary
  - Personal rating
  - Favorite scenes or quotes
Cornell Notes:
- Set up a Cornell Notes template with sections for:
  - "Main ideas" (key points)
  - "Notes" (detailed information)
  - "Summary" (brief overview)
Bullet Notes:
- Create a simple template for quick, concise notes.
- Use bullet points or numbered lists to capture information efficiently.

Remember that these templates are adaptable to your specific needs. Feel free to modify or create additional templates based on the types of notes you frequently add to your vault. Happy organizing! 📝✨

For more inspiration, you can explore other users' templates and adapt them to your workflow. 😊

Source:
(1) 7 Obsidian Templates to Streamline Your Notes - Medium. https://medium.com/@dianademco/7-obsidian-templates-to-streamline-your-notes-ab3a3c8757be.
(2) 6 Useful Templates for Obsidian - Filipe Donadio. https://filipedonadio.com/6-useful-templates-for-obsidian/.
(3) Free Obsidian Templates - Alphr. https://www.alphr.com/free-obsidian-templates/.
(4) Practically Paperless with Obsidian, Episode 8: Note Templates for .... https://jamierubin.net/2021/11/30/practically-paperless-with-obsidian-episode-8-note-templates-for-consistency/.

Vscode setup with Foam and Logseq for Digital Note Taking

charudatta — Tue, 02 Apr 2024 17:00:11 +0000

Setting up Visual Studio Code (VSCode) with Foam and Logseq for digital note-taking involves a few steps to integrate these tools into a powerful personal knowledge management system. Here's a guide to get you started:

Install Visual Studio Code: If you haven't already, download and install VSCode from the official website.
Install Foam:
- Foam is a personal knowledge management system inspired by Roam Research, built on VSCode and GitHub¹.
- Use the foam-template to generate a new Foam workspace. You can find this template on the Foam GitHub page².
- Clone the repository locally and open it in VSCode.
Configure Foam Settings:
- After setting up the repository, open .vscode/settings.json and edit, add, or remove any settings you’d like for your Foam workspace¹.
- Foam provides features like graph visualization, backlinking, and wikilinks to help you organize your thoughts and notes.
Install Logseq Compatibility Extension (Loam):
- Loam is a fork of Foam that enables better compatibility with Logseq, allowing you to open your Logseq folder in VSCode and get a comparable note-taking experience³.
- Install Loam from the VSCode Marketplace to integrate Logseq into your workflow.
Use Logseq:
- Logseq is a privacy-first, open-source knowledge base that works on top of your local files.
- You can use Logseq alongside Foam to manage your notes, tasks, and knowledge graph.
Customize Your Workflow:
- Both Foam and Logseq are highly customizable, so take the time to adjust the settings and extensions to fit your personal workflow.
- Explore additional VSCode extensions that can complement your note-taking, such as Markdown All in One, Markdown Preview Enhanced, and others.

By following these steps, you'll have a robust setup for digital note-taking in VSCode, leveraging the strengths of both Foam and Logseq to manage your knowledge effectively. Happy note-taking! 📚✨

Source:
(1) A personal knowledge management and sharing system for VSCode - Foam. https://foambubble.github.io/foam/.
(2) A personal knowledge management and sharing system for VSCode. https://github.com/foambubble/foam.
(3) Loam - Visual Studio Marketplace. https://marketplace.visualstudio.com/items?itemName=ciceroisback.loam.