Key Points

References

Reference_description_with_linked_URLs_______________________	Notes______________________________________________________________

Key Concepts

AI Frameworks and their use cases

Dan Lee - AI frameworks and their use cases

Want to build an AI App? Choosing the right framework is crucial.

Here’s a quick breakdown:

𝟭. 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 – 𝘉𝘦𝘴𝘵 𝘧𝘰𝘳 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 & 𝘢𝘨𝘦𝘯𝘵𝘴
✓ Large ecosystem, modular, strong community
✗ Debugging can be difficult

2. 𝗟𝗹𝗮𝗺𝗮𝗜𝗻𝗱𝗲𝘅 – 𝘉𝘦𝘴𝘵 𝘧𝘰𝘳 𝘥𝘰𝘤𝘶𝘮𝘦𝘯𝘵 𝘴𝘦𝘢𝘳𝘤𝘩 & 𝘤𝘩𝘢𝘵𝘣𝘰𝘵 𝘮𝘦𝘮𝘰𝘳𝘺
✓ Optimized search, easy database integration
✗ Fewer tools compared to LangChain

3. 𝗖𝗿𝗲𝘄𝗔𝗜 – 𝘉𝘦𝘴𝘵 𝘧𝘰𝘳 𝘈𝘐 𝘵𝘦𝘢𝘮𝘸𝘰𝘳𝘬 & 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯
✓ Simple setup, task delegation
✗ Limited integrations

4. 𝗦𝘄𝗮𝗿𝗺 – 𝘉𝘦𝘴𝘵 𝘧𝘰𝘳 𝘴𝘦𝘭𝘧-𝘰𝘳𝘨𝘢𝘯𝘪𝘻𝘪𝘯𝘨 𝘈𝘐 𝘴𝘺𝘴𝘵𝘦𝘮𝘴
✓ Distributed execution
✗ Low adoption, niche use

𝟱. 𝗣𝘆𝗱𝗮𝗻𝘁𝗶𝗰𝗔𝗜 – 𝘉𝘦𝘴𝘵 𝘧𝘰𝘳 𝘴𝘤𝘩𝘦𝘮𝘢 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘰𝘯 & 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘥𝘢𝘵𝘢
✓ Enforces structured LLM outputs
✗ Limited scope

𝟲. 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 – 𝘉𝘦𝘴𝘵 𝘧𝘰𝘳 𝘤𝘰𝘮𝘱𝘭𝘦𝘹 𝘓𝘓𝘔 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦𝘴
✓ Graph-based control, scalable
✗ Steep learning curve

𝟳. 𝗔𝘂𝘁𝗼𝗴𝗲𝗻 – 𝘉𝘦𝘴𝘵 𝘧𝘰𝘳 𝘈𝘐 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩 & 𝘵𝘢𝘴𝘬 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯
✓ Self-improving, automation
✗ Complex setup

⤷ Which AI framework is your go-to? Drop one ↓

Found it insightful?
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Opensource Coding AI Agents. linkedin

Opensource alternatives have become the go-to answer for even businesses trying to build their products.
So, if you have a competitive Model LLM provider like Claude and GPT - you can pretty much use this tech for free.

📌 Command Line Tools

1. Aider
- Local auto code completion using CLI.
- Aider is noted for being more economical in token usage than Cline.
- Not beginner-friendly.

https://lnkd.in/gubh-Xr3

2. Codename Goose by Block
- Full codebase migrations between frameworks/languages.
- Performance benchmarking automation.
- Using Model Context Protocol (MCP), Goose integrates natively with your Enterprise codebases

https://lnkd.in/gKj-dzBX

📌 Full Stack Applications

1. Bolt Dev (Previously oTTodev)
- Create an entire app with just a prompt.
- Support to choose between different models.
- Even supports Open-Router for free API access for a few models.

https://lnkd.in/g_MDh8Wt

2. Src Book

- Similar workflow to Bolt.dev
- Based on Typescript
- Supports Major LLM providers
- Two versions available:
a- Opensource Local download and setup
b- Use their hosted app with pricing options
- ❌ Their application is best suited for non-opensource LLMs like Claude Sonnet 3.5 and GPT-4o.

https://lnkd.in/gitfkQ2m

3. Zed

- A fully fledged AI code editor.
- A multiplayer code editor from the creators of Atom and Tree-sitter.
- Highly flexible in different languages.
- ❌ Not available for Windows at the moment

https://lnkd.in/gfJvrZin

VSCode Extensions

1. Cline (Previously Claude Dev)
- Very similar workflow to Cursor.
- It was built with Anthropic's MCP(Model Context Protocol) feature.
- Cline can launch a site in a headless browser, click, type, scroll, and capture screenshots + console logs.

https://lnkd.in/g9ZYvAY9

2. Roo-Cline (Personal Favourite)
- Forked version of Cline.
- It is a better version of Cline with a faster response time and more experimental features.
- Utilizes coding and thinking concepts from Aider.

https://lnkd.in/gAFtgyjB

3. Continue
- It has a workflow similar to that of early versions of GitHub Copilot.
- Best if you want simple auto-code completion and suggestions.

https://lnkd.in/gN-ykqNJ

4. Llama Coder
- Build your code bases locally
- Best for simple code completions without depending on external LLM APIs.

Check it out here: https://lnkd.in/gBiirEur

Note: This is not an exhaustive list, these are tools I've found to have the best workflow, but they may differ from yours.

You don't need expensive LLMs to make your AI Agents better
- Here's what I mean: https://www.linkedin.com/posts/rakeshgohel01_you-can-build-better-reasoning-ai-agents-activity-7292912659585282050-uwzI?utm_source=share&utm_medium=member_desktop

Check out a comparison of Paid Coding Agents
- https://www.linkedin.com/posts/rakeshgohel01_coding-agents-were-the-most-used-ai-agent-activity-7292187675921367040-CDBH?utm_source=share&utm_medium=member_desktop

QuantaLogic is a ReAct (Reasoning & Action) framework for building advanced AI agents.

It seamlessly integrates large language models (LLMs) with a robust tool system, enabling agents to understand, reason about, and execute complex tasks through natural language interaction.

The cli version include coding capabilities comparable to Aider.

https://github.com/quantalogic/quantalogic

Python tools

Scikit

Pytorch

Spring Ai

https://spring.io/projects/spring-ai

https://docs.spring.io/spring-ai/reference/

Spring AI is a project within the Spring Framework that helps Java developers incorporate artificial intelligence (AI) into their applications. It's an application framework that aims to use Spring's design principles, like modularity and portability, in the AI domain. Spring AI provides a Spring-friendly API and abstractions for developing AI applications, and it supports many AI models, including:

OpenAI, Azure OpenAI, Amazon Bedrock, Hugging Face, Google VertexAI, Mistral AI, Stability AI, Ollama, PostgresML, and Transformers (ONNX).

Spring AI simplifies the integration of AI functionality into Spring applications by streamlining interactions with AI models.It uses familiar Spring concepts and patterns, which can help with learning and adoption.For example, prompt templates can be used to populate placeholders within a model object, and the resulting string becomes the prompt for the AI model.

Spring AI is an application framework for AI engineering. Its goal is to apply to the AI domain Spring ecosystem design principles such as portability and modular design and promote using POJOs as the building blocks of an application to the AI domain.

Features

Portable API support across AI providers for Chat, text-to-image, and Embedding models. Both synchronous and stream API options are supported. Dropping down to access model-specific features is also supported.

Chat Models

Amazon Bedrock
- Anthropic
- Cohere's Command
- AI21 Labs' Jurassic-2
- Meta's LLama
- Amazon's Titan
Anthropic Claud
Azure Open AI
Google Vertex AI
- PaLM2
- Gemini
Groq
HuggingFace - access thousands of models, including those from Meta such as Llama
MistralAI
MiniMax
Moonshot AI
Ollama - run AI models on your local machine
OpenAI
QianFan
ZhiPu AI
Watsonx.AI

Text-to-image Models

OpenAI with DALL-E
StabilityAI

Transcription (audio to text) Models

OpenAI

Embedding Models

Azure OpenAI
Amazon Bedrock
- Cohere
- Titan
Azure OpenAI
Mistral AI
MiniMax
Ollama
(ONNX) Transformers
OpenAI
PostgresML
QianFan
VertexAI
- Text
- Multimodal
- PaLM2
ZhiPu AI

The Vector Store API provides portability across different providers, featuring a novel SQL-like metadata filtering API that maintains portability.

Vector Databases

Azure AI Service
Apache Cassandra
Chroma
Elasticsearch
GemFire
Milvus
MongoDB Atlas
Neo4j
OpenSearch
Oracle
PGvector
Pinecone
Qdrant
Redis
SAP Hana
Typesense
Weaviate

Spring Boot Auto Configuration and Starters for AI Models and Vector Stores.

Function calling You can declare java.util.Function implementations to OpenAI models for use in their prompt responses. You can directly provide these functions as objects or refer to their names if registered as a @Bean within the application context. This feature minimizes unnecessary code and enables the AI model to ask for more information to fulfill its response.

Models supported are

OpenAI
Azure OpenAI
VertexAI
Mistral AI
Anthropic Claude
Groq

ETL framework for Data Engineering

The core functionality of our ETL framework is to facilitate the transfer of documents to model providers using a Vector Store. The ETL framework is based on Java functional programming concepts, helping you chain together multiple steps.
We support reading documents in various formats, including PDF, JSON, and more.
The framework allows for data manipulation to suit your needs. This often involves splitting documents to adhere to context window limitations and enhancing them with keywords for improved document retrieval effectiveness.
Finally, processed documents are stored in the Vector Database, making them accessible for future retrieval.

Extensive reference documentation, sample applications, and workshop/course material.

Future releases will build upon this foundation to provide access to additional AI Models, for example, the Gemini multi-modal model just released by Google, a framework for evaluating the effectiveness of your AI application, more convenience APIs, and features to help solve the “query/summarize my documents” use cases. Check GitHub for details on upcoming releases.

Getting Started

You can get started in a few simple steps

Install the Spring CLI, and then, in your shell, run the command.

spring boot new --from ai --name myai

This command creates a new application for you to start basic interactions with ChatGPT, just follow the instructions in the generated README file to get your API KEY and then

Run the application

./mvw spring-boot:run

And curl the endpoint:

curl localhost:8080/ai/simple

Want to get started in another way? View the Getting Started section in the reference documentation.

HuggingFace

huggingface-notes1

The platform where the machine learning community collaborates on models, datasets, and applications.

Hugging Face provides access to state-of-the-art AI models, focusing on NLP and transformers. The platform includes comprehensive, open-source libraries that simplify tasks like model training, data processing, and tokenization

Hugging Face is an open-source platform that serves as a central hub for accessing, building, and sharing pre-trained machine learning models, primarily focused on Natural Language Processing (NLP), allowing developers and researchers to easily incorporate state-of-the-art AI capabilities into their applications by providing a readily accessible collection of models and datasets, simplifying the process of creating AI and AML solutions across various tasks like text generation, translation, sentiment analysis, and more, all within a collaborative environment where users can contribute and share their own models and research findings.

Key features of Hugging Face:

Extensive Model Library:
Offers a vast repository of pre-trained models from leading researchers, covering a wide range of NLP tasks, which can be readily fine-tuned for specific use cases.
Transformers Library:
A core component providing efficient implementation of transformer architectures, enabling high-performance NLP tasks with ease.
Community-Driven Collaboration:
Fosters a collaborative environment where users can share their models, datasets, and research findings, accelerating AI development.
User-Friendly Interface:
Provides intuitive tools and APIs for accessing and utilizing models, making it accessible to a wide range of users, from experienced researchers to beginner developers.

How Hugging Face is used in AI and AML solutions:

Rapid Prototyping:
Quickly experiment with different pre-trained models to identify the best fit for a specific NLP task.
Fine-tuning Models:
Adapt existing pre-trained models to specific datasets and requirements for customized performance.
Model Deployment:
Easily integrate pre-trained models into applications, allowing for fast development and deployment of AI-powered features.
Research Acceleration:
Access cutting-edge AI models and research findings to stay updated with the latest advancements in the field.

Overall, Hugging Face significantly simplifies the process of building AI and AML solutions by providing a comprehensive platform for accessing, utilizing, and contributing to a vast collection of high-quality pre-trained models, making advanced NLP capabilities readily available to a wider community of developers and researchers.

Getting-started-with-generative-ai-using-hugging-face-platform-on-aws/

Hugging Face Platform provides no-code and low-code solutions to train, deploy and publish state-of-art generative AI models for production workloads on managed infrastructure. In 2023, the Hugging Face Platform became available on AWS Marketplace, allowing AWS customers to directly subscribe to connect their AWS account with their Hugging Face account.

This allows customers to directly pay for Hugging Face usage with their AWS account, a new integrated billing method that makes it easy to manage payment for usage of all managed services by all members of your organization. Hugging Face has provided step-by-step guidance for customers how to subscribe and connect their Hugging Face account with their AWS account.

In this post, we will delve into the premium features and managed services provided by the Hugging Face Platform, elucidating the value they to customers.

Hugging Face is an AWS Specialization Partner with Competencies in Generative AI and Machine Learning. Its mission is to democratize machine learning through open source, open science, and Hugging Face products and services.

TIP - Fine-Tuning, Deploying, and Training Machine Learning Models With Hugging Face

users can also leverage it to create interactive model demos, access research resources, develop business applications, and evaluate ML models.

1. Chatbot Applications: Hugging Face provides a suite of chatbot applications, including Chatty, Talking Dog, Talking Egg, and Boloss. These chatbots are designed to engage and entertain you through AI-powered conversations. Additionally, they showcase the company’s NLP capabilities.

2. HuggingChat: HuggingChat is an open-source chatbot model developed with an impressive 30 billion parameters. It’s based on the latest LLaMa model from the OpenAssistant project. HuggingChat is designed to be lightweight and efficient, making it suitable for running on consumer hardware. What sets it apart is its strong commitment to data privacy, ensuring that messages are stored solely for user display and not used for research or identification purposes.

3. Expert Acceleration Program: This program will connect you with ML experts who provide dedicated support throughout the development and implementation of ML models. From research to production, these experts provide assistance, answer questions, and will help you find solutions to specific ML challenges. The program also promotes collaborative learning and guidance.

4. Private Hub: Similar to the public hub, the private hub will enable you to collaborate, experiment, train, and develop ML models. However, it provides a private group setting, ideal for businesses or teams that want to work on ML projects within a secure and restricted environment.

5. Inference Endpoints: Hugging Face’s Inference Endpoints service streamlines the deployment of models. You can deploy various models, including transformers and diffusers, on dedicated and managed infrastructure. This service provides production-ready APIs, eliminating the complexities of infrastructure management and MLOps. It operates on a pay-as-you-go structure and provides secure offline endpoints via a direct connection to your Virtual Private Cloud (VPC).

6. AutoTrain: AutoTrain is an automated solution for training, evaluating, and deploying ML models, even for those without extensive coding expertise. Leveraging it, you can define your tasks and upload the necessary data. Furthermore, AutoTrain takes care of model selection and training. This feature also simplifies and accelerates the ML model development process.

7. StarCoder: In collaboration with ServiceNow, Hugging Face developed StarCoder, an open-source language model tailored for code generation. StarCoder is trained in over eighty programming languages and can generate code based on descriptions or sample audio. It also acts as an alternative to other AI code-generating systems.

8. Models: Hugging Face hosts a vast library of ML models, boasting over 300,000 models as of the latest data. These models cover various domains and use cases, making them a valuable resource for researchers and developers looking for pre-trained models to accelerate their projects.

9. Data Sets: Effective ML model training relies on high-quality data sets. Hugging Face provides access to a range of community-uploaded data sets. These data sets cover diverse topics, including books, Wikipedia data, human preferences related to AI outputs, movie reviews from IMDb, and more.

10. Spaces: Hugging Face Spaces is a user-friendly environment that simplifies the showcasing of ML models. It provides computing resources that are necessary for hosting model demonstrations. Spaces is designed to be accessible even to users without extensive technical knowledge, making it easy to share and present ML models to a wider audience.

Collaborative and Open-Source Approach

1. Accessibility: Hugging Face democratizes AI development by lowering entry barriers. It provides pre-trained models, fine-tuning scripts, and deployment APIs, reducing the need for extensive computing resources and specialized skills. This accessibility accelerates the creation of Language Model (LLM) applications.

2. Integration: Hugging Face facilitates seamless integration with various Machine Learning (ML) frameworks. For instance, its Transformer library smoothly integrates with popular frameworks like PyTorch and TensorFlow, ensuring flexibility and compatibility in model development.

3. Rapid Prototyping: Hugging Face empowers developers with rapid prototyping and deployment capabilities for NLP and ML applications. This agility enables swift experimentation and testing of new AI-driven functionalities.

4. Community Support: Hugging Face boasts a vibrant community of AI enthusiasts and professionals. This collaborative ecosystem continually updates models, provides extensive documentation, and includes tutorials. Additionally, access to this community fosters knowledge-sharing and problem-solving.

5. Cost-Effectiveness: Building large ML models from scratch can be financially daunting. Hugging Face provides a cost-effective alternative. Leveraging its hosted models and resources, businesses can save significant costs while benefiting from AI-driven solutions that meet their specific needs.

Fine-Tuning, Deploying, and Training Machine Learning Models With Hugging Face

1. Enhancing Web Interactions: Hugging Face plays a pivotal role in web development by enabling the integration of AI-powered chatbots and NLP models into websites. This enhances user interactions and provides real-time responses, improving the overall user experience.

2. Chatbot Integration: Hugging Face facilitates the seamless integration of chatbot applications into websites. These chatbots can analyze user queries, understand context, and respond intelligently, making websites more engaging and interactive.

3. Content Curation: Hugging Face can assist in automating content curation on websites. AI models can analyze trending topics and user preferences to curate and recommend relevant content, keeping websites fresh and engaging.

4. Content Generation: Hugging Face’s NLP models can assist in web content generation. It can automatically generate articles, product descriptions, or other textual content, saving time and effort for web developers and content creators.

5. Search Functionality: Hugging Face’s NLP capabilities can enhance website search functionality. It enables more accurate search results by understanding user queries in natural language, leading to improved user satisfaction.

6. Personalization: Hugging Face can be used to implement personalized user experiences on websites. AI models can analyze user preferences and behavior to recommend relevant content or products, increasing user engagement and conversion rates.

7. Data Enrichment: In web development, data enrichment is crucial for improving user profiles and targeting. Hugging Face’s NLP models can help extract valuable insights from user-generated content, enabling better user segmentation and personalized marketing.

8. Natural Language Understanding: Hugging Face’s NLP models aid in understanding and processing user feedback, reviews, and comments on websites. This helps businesses gain valuable insights into customer sentiments and opinions.

9. Chat Support: Implementing Hugging Face chatbots on websites provides instant customer support. Users can get answers to their queries 24/7, leading to improved customer satisfaction and reduced support workload.

10. Multilingual Support: Hugging Face’s NLP models excel in multilingual applications, making websites accessible to a global audience. They can handle content translation, language detection, and localization, expanding a website’s reach.

11. Voice Search: Voice search functionality on websites can be enhanced using Hugging Face’s NLP capabilities. Leveraging its capabilities, users can interact with websites using voice commands, making navigation more convenient.

12. Analytics and Insights: Hugging Face can help web developers gain insights into user behavior, preferences, and engagement patterns through NLP-driven analytics. This data can be used for informed decision-making and website optimization.

Impacts from AI solutions

Hugging Face has had a profound impact across various industries by revolutionizing NLP, Computer Vision, Multimodal, and other AI applications. In healthcare, it has improved patient-doctor interactions through chatbots, while in Finance, it enhances fraud detection and customer support. Leveraging it, the education industry benefits from personalized learning, and e-commerce gains improved recommendation systems.

What is Hugging Face? - TT

Hugging Face is known for its Transformers Python library, which simplifies the process of downloading and training ML models. The library gives developers an efficient way to include one of the ML models hosted on Hugging Face in their workflow and create ML pipelines.

The Hugging Face Hub is where to find some of the main features of Hugging Face, including the following:

Models. Hugging Face hosts a large library of models that users can filter by type. As of this writing, there are more than 300,000 models on Hugging Face. Hugging Face also hosts some of the top open source ML models on the platform. Some of the models on the leaderboard at the time of this writing include the following:

Data sets. Data sets help train models to understand patterns and relationships between data -- and creating a good data set can be difficult. Hugging Face provides access to data sets uploaded by the community that users can access. Some example data sets in the Hugging Face library include the following:

the_pile_books3, which contains all data from Bibliotik in plain text. Bibliotik is a repository of 197,000 books.
wikipedia, which contains data from Wikipedia.
Anthropic/hh-rlhf, which contains human preference data about the helpfulness and harmlessness of AI outputs.
imdb, which contains a large collection of movie reviews.

Spaces. Machine learning models on their own typically require technical knowledge to implement and use. Spaces packages models in a user-friendly experience that lets users showcase their work. Hugging Face provides the computing resources necessary to host demos. Spaces doesn't require any technical knowledge to use. Some examples of Hugging Face Spaces include the following:

LoRA the Explorer image generator. Users can generate images in a variety of different styles based on a prompt.
MusicGen music generator. MusicGen lets users generate music based on a description of the desired output or sample audio.
Image to Story. Users can upload an image, and a large language model uses text generation to write a story based on it.

Hugging Face is free to sign up for as a community contributor. Users get a Git-based repository where they can store Models, Datasets and Spaces. After creating an account, users can do the following:

Check the activity feed.
Access the Hugging Face Hub.
Create organizations or private repositories.
Explore their profile and adjust settings.
Initiate a new Model, Dataset or Space.
Discover the latest trends within the Hugging Face community.
Review the organizations the user is a part of and access their specific sections.
Access useful ML resources and documentation.

Hugging Face also offers a paid pro account that gives users access to more features, and an enterprise account at a slightly higher rate.

Hugging Face SLM SmolLM2

https://github.com/huggingface/smollm

Smol Models, a family of efficient and lightweight AI models from Hugging Face. Our mission is to create powerful yet compact models, for text and vision, that can run effectively on-device while maintaining strong performance.

SmolLM2 is our family of compact language models available in three sizes:

SmolLM2-135M: Ultra-lightweight model for basic text tasks
SmolLM2-360M: Balanced model for general use
SmolLM2-1.7B: Our most capable language model, available at 🤏 SmolLM2-1.7B-Instruct here.

All models have instruction-tuned versions optimized for assistant-like interactions

SmolVLM is our compact multimodal model that can:

Process both images and text and perform tasks like visual QA, image description, and visual storytelling
Handle multiple images in a single conversation
Run efficiently on-device

Documentation

Pretrained Models

Datasets

SmolTalk - Our instruction-tuning dataset
FineMath - Mathematics pretraining dataset
FineWeb-Edu - Educational content pretraining dataset

Some more AI tools

Glasp - social AI tagging tool

Glasp is designed to help researchers, learners, and anyone who wants to capture and share publicly their knowledge and insights from the web

Glasp

Glasp is a social web and PDF highlighter, allowing users to collect, organize, and share insightful ideas from the web, and build an AI clone based on their highlights and notes, fostering a collective knowledge-sharing experience.

Here's a more detailed explanation:
Functionality:
Highlighting and Annotation: Glasp lets you highlight text, images, and videos on websites and PDFs, and add notes to your highlights.
Organization: You can organize your highlights and notes with tags and authors.
Sharing: Glasp is a social tool, meaning your highlights and notes are public on your profile, allowing others to see what you're reading and what resonates with you.
AI Clone: You can build an AI clone from your highlights and notes, which can generate personalized insights and recommendations based on your learning history.

Summarization: Glasp can summarize YouTube videos, web articles, and PDFs.
Quoteshots: You can generate quoteshots from webpages.

Discover Content: Discover more content from other curators.

Sync Highlights: Sync all your highlights and notes from Kindle eBooks.

Export Highlights: Export highlights to note-taking apps like Notion, Roam Research, and Obsidian.

Mission:
Glasp aims to democratize access to other people's learning and experiences, creating a shared proof of knowledge that can be visualized and accessed by others.
Browser Extension:
Glasp is a free browser extension available for Chrome and Safari.
Purpose:
Glasp is designed to help researchers, learners, and anyone who wants to capture and share their knowledge and insights from the web

Eclipse Theia IDE integrates with your AI agents of choice - 2025

https://devops.com/eclipse-foundation-to-release-open-source-ide-infused-with-ai-agents

Potential Value Opportunities

AI Agent Development without Coding

Panjuta

Steps to Build AI Agents Without Coding

1. Define the Purpose & Goal
Decide what your AI agent will do, such as answering queries, automating workflows, or generating content.

2. Choose the Right AI Model
Select a model based on the task—GPT for conversations, AutoGPT for automation, or LangChain for data processing.

3. Select a No-Code AI Platform
Use platforms like Make.com, Zapier, or Pipedream to build and deploy AI workflows without coding.

4. Integrate APIs & Data Sources
Connect APIs like Google Search, Notion, or Scrapy to fetch, process, and store real-time data for AI use.

5. Automate Task Execution
Enable triggers, pre-built AI blocks, and custom actions to automate responses, scheduling, and follow-ups.

6. Build Logic & Memory
Enhance intelligence with memory storage, decision-making loops, and goal-oriented execution for dynamic responses.

7. Deploy & Host Your AI Agent
Launch AI agents on cloud services or no-code platforms, embedding them in chatbots, websites, or apps.

Image preview

some common 2024 AI Personal tools

Potential Challenges

Candidate Solutions

Some AI Design Tools

ai-design-tools-list-2025.pdf. GD

ai-design-tools-list-2025.pdf file

Step-by-step guide for Example

sample code block

sample code block

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