To kick off his participation at the World Leaders Climate Action Summit at the United Nations Climate Change Conference (COP29) in Baku, Azerbaijan, the Honourable Steven Guilbeault, Minister of Environment and Climate Change, provided the following statement to launch GAIA, a new finance platform ...

During the World Leaders Climate Action Summit at COP29, Minister Guilbeault announces major Canadian-led climate finance platform - Canada.ca

GAIA

gaia

GaiaNet offers a powerful AI-driven chat interface that makes it easy to interact with various models for different purposes. Whether you need assistance with coding, want to write an essay, or require help with a specific task, GaiaNet can help. Here’s a step-by-step guide on how to use GaiaNet Chat. Step 1: Accessing GaiaNet Chat To start using GaiaNet Chat, navigate to GaiaNet Chat on your web browser. You’ll be greeted with a clean, user-friendly interface. Step 2: Starting a New Chat Click on the “New Chat” button to initiate a conversation. This will open a new chat window where you can type your queries. Step 3: Selecting a Node GaiaNet uses various nodes for different types of tasks. For instance, if you need help with Python programming, you might want to select a Python-specific node. You can search for the desired node using the search bar. Click on the dropdown menu under “Select a Node.”
Type in the keyword related to your task (e.g., “Python”).
Choose the appropriate node from the list. Step 4: Asking a Question Type your question or request into the chat box. For example, if you want to convert temperatures in Python, you could ask: “Please write a Python script to convert Celsius to Fahrenheit.” Step 5: Receiving a Response GaiaNet will process your request and provide a detailed response. This might include code snippets, explanations, or step-by-step instructions depending on your query. Step 6: Interacting With the Response You can interact further with the response by asking follow-up questions or requesting modifications. For example, you could ask GaiaNet to refine the code or provide additional details. “Improve this script” Step 7: Start New Chat & Clearing Conversations If you want to start a new conversation or clear previous chats, click on the “Clear Conversations” button located on the left-hand side of the interface. Additional Tips Save Important Responses: You can save important responses by copying the text and storing it in a document. Explore Different Nodes: Experiment with different nodes to see which one best suits your needs. AI Agents are available such as Chemistry, Law, Medicine, Finance, and many more. Provide Clear Instructions: When asking for help, be as specific as possible to get the most accurate response. Using GaiaNet Chat is a straightforward way to leverage AI for a variety of tasks. With its intuitive interface and powerful nodes, you can get the assistance you need quickly and efficiently. Give it a try, and see how it can help you with your next project! Written by Zulfanbaswedan GaiaNet offers a powerful AI-driven chat interface that makes it easy to interact with various models for different purposes. Whether you need assistance with coding, want to write an essay, or require help with a specific task, GaiaNet can help. Here’s a step-by-step guide on how to use GaiaNet Chat. Step 1: Accessing GaiaNet Chat To start using GaiaNet Chat, navigate to GaiaNet Chat on your web browser. You’ll be greeted with a clean, user-friendly interface. GaiaNet Chat Step 2: Starting a New Chat Click on the “New Chat” button to initiate a conversation. This will open a new chat window where you can type your queries. Step 3: Selecting a Node GaiaNet uses various nodes for different types of tasks. For instance, if you need help with Python programming, you might want to select a Python-specific node. You can search for the desired node using the search bar. Click on the dropdown menu under “Select a Node.” Type in the keyword related to your task (e.g., “Python”). Choose the appropriate node from the list. Click on the dropdown menu under “Select a Node.” Type in the keyword related to your task (e.g., “Python”). Choose the appropriate node from the list. Step 4: Asking a Question Type your question or request into the chat box. For example, if you want to convert temperatures in Python, you could ask: “Please write a Python script to convert Celsius to Fahrenheit.” “Please write a Python script to convert Celsius to Fahrenheit.” Step 5: Receiving a Response GaiaNet will process your request and provide a detailed response. This might include code snippets, explanations, or step-by-step instructions depending on your query. Step 6: Interacting With the Response You can interact further with the response by asking follow-up questions or requesting modifications. For example, you could ask GaiaNet to refine the code or provide additional details. “Improve this script” “Improve this script” Step 7: Start New Chat & Clearing Conversations If you want to start a new conversation or clear previous chats, click on the “Clear Conversations” button located on the left-hand side of the interface. Additional Tips Save Important Responses: You can save important responses by copying the text and storing it in a document. Save Important Responses : You can save important responses by copying the text and storing it in a document. Save Important Responses Explore Different Nodes: Experiment with different nodes to see which one best suits your needs. AI Agents are available such as Chemistry, Law, Medicine, Finance, and many more. Explore Different Nodes : Experiment with different nodes to see which one best suits your needs. AI Agents are available such as Chemistry, Law, Medicine, Finance, and many more. Explore Different Nodes Provide Clear Instructions: When asking for help, be as specific as possible to get the most accurate response. Provide Clear Instructions : When asking for help, be as specific as possible to get the most accurate response. Provide Clear Instructions Using GaiaNet Chat is a straightforward way to leverage AI for a variety of tasks. With its intuitive interface and powerful nodes, you can get the assistance you need quickly and efficiently. Give it a try, and see how it can help you with your next project! Written by Zulfanbaswedan

How to Use GaiaNet Chat: A Step-by-Step Guide

SAN FRANCISCO, CA, June 4, 2024 — GaiaNet, the pioneering decentralized AI infrastructure project that aims to disrupt central AI inferencing servers like ChatGPT, today announced the launch of its Beta product. The launch follows the completion of the Alpha testing phase, during which the platform successfully handled a tremendous volume of daily requests on Telegram, Discord and X. The Beta release focuses on the ease of setting up a GaiaNet node, ensuring that users, from individuals to businesses, can effortlessly integrate into the network. With a user-friendly interface, GaiaNet simplifies the process, making its innovative solutions accessible to users of all technical levels. The commencement of beta testing marks a significant step forward in GaiaNet’s commitment to building a more inclusive and equitable AI ecosystem. With plans to roll out more functionalities in 2024, the platform is on the brink of redefining the boundaries of AI, making it more accessible, data bias-resistant, collaborative, and privacy-centric. Early Adopters Campaign To celebrate the Beta launch, GaiaNet invites the community to join the interactive campaigns on X, Discord, Telegram, and Warpcast. GaiaNet has a community and user-oriented philosophy; therefore, early users and community members can look forward to exclusive benefits. By actively setting up nodes and interacting with GaiaNet agents across different nodes, users can join the beta product testing and potentially unlock special rewards in the future. More details will be revealed soon. The Alpha phase demonstrated GaiaNet’s capability to decentralize AI interaction and application. With an impressive daily request volume, GaiaNet has validated its approach and is ready to scale with the Beta launch. This milestone underscores the demand for decentralized AI solutions and sets the stage for broader adoption. GaiaNet's progress has been marked by significant milestones, including a strategic partnership with UC Berkeley FHL Vive Center announced earlier this year. This collaboration aims to introduce decentralized AI teaching assistant technology into more education courses, leveraging AI inference power on a distributed Gaia Network. Additionally, GaiaNet raised $10 million in seed funding, announced on May 28, 2024. This funding fuels the mission to revolutionize AI interactions and applications across various sectors. Join the Revolution GaiaNet is dedicated to revolutionizing the AI landscape by decentralizing AI agent software and providing a fair, transparent, and secure environment. GaiaNet’s network of edge nodes enables decentralized AI interactions, empowering individuals and businesses to harness AI's power in innovative ways. The Beta launch represents a significant step in the mission to redefine AI interaction, and everyone is welcome to join the campaigns and be part of this transformative journey. For more information about the Beta launch and to participate in our community campaigns, visit www.gaianet.ai and follow us on X @GaiaNet_AI. About GaiaNet GaiaNet stands as a trailblazer in decentralized AI technology, dedicated to reshaping the tech industry with a focus on user privacy, transparency, and control. Our cutting-edge open network infrastructure empowers users by ensuring privacy and censorship resistance in daily AI activities through a dynamic network of peer-to-peer edge nodes. This framework fosters the creation and collaboration of innovative AI business ecosystems, benefiting both private data holders and developers. GaiaNet aims to revolutionize the user experience with AI agents, seamlessly introducing transformative changes while preserving the intuitive technology and satisfaction currently existing in the Web2 era. Website: www.gaianet.ai Github: https://github.com/GaiaNet-AI X: @GaiaNet_AI This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SAN FRANCISCO, CA, June 4, 2024 — GaiaNet, the pioneering decentralized AI infrastructure project that aims to disrupt central AI inferencing servers like ChatGPT, today announced the launch of its Beta product. The launch follows the completion of the Alpha testing phase, during which the platform successfully handled a tremendous volume of daily requests on Telegram, Discord and X. The Beta release focuses on the ease of setting up a GaiaNet node, ensuring that users, from individuals to businesses, can effortlessly integrate into the network. With a user-friendly interface, GaiaNet simplifies the process, making its innovative solutions accessible to users of all technical levels. The commencement of beta testing marks a significant step forward in GaiaNet’s commitment to building a more inclusive and equitable AI ecosystem. With plans to roll out more functionalities in 2024, the platform is on the brink of redefining the boundaries of AI, making it more accessible, data bias-resistant, collaborative, and privacy-centric. Early Adopters Campaign To celebrate the Beta launch, GaiaNet invites the community to join the interactive campaigns on X, Discord, Telegram, and Warpcast. GaiaNet has a community and user-oriented philosophy; therefore, early users and community members can look forward to exclusive benefits. By actively setting up nodes and interacting with GaiaNet agents across different nodes, users can join the beta product testing and potentially unlock special rewards in the future. More details will be revealed soon. The Alpha phase demonstrated GaiaNet’s capability to decentralize AI interaction and application. With an impressive daily request volume, GaiaNet has validated its approach and is ready to scale with the Beta launch. This milestone underscores the demand for decentralized AI solutions and sets the stage for broader adoption. GaiaNet's progress has been marked by significant milestones, including a strategic partnership with UC Berkeley FHL Vive Center announced earlier this year. This collaboration aims to introduce decentralized AI teaching assistant technology into more education courses, leveraging AI inference power on a distributed Gaia Network. Additionally, GaiaNet raised $10 million in seed funding, announced on May 28, 2024. This funding fuels the mission to revolutionize AI interactions and applications across various sectors. Join the Revolution GaiaNet is dedicated to revolutionizing the AI landscape by decentralizing AI agent software and providing a fair, transparent, and secure environment. GaiaNet’s network of edge nodes enables decentralized AI interactions, empowering individuals and businesses to harness AI's power in innovative ways. The Beta launch represents a significant step in the mission to redefine AI interaction, and everyone is welcome to join the campaigns and be part of this transformative journey. For more information about the Beta launch and to participate in our community campaigns, visit www.gaianet.ai and follow us on X @GaiaNet_AI. www.gaianet.ai About GaiaNet GaiaNet stands as a trailblazer in decentralized AI technology, dedicated to reshaping the tech industry with a focus on user privacy, transparency, and control. Our cutting-edge open network infrastructure empowers users by ensuring privacy and censorship resistance in daily AI activities through a dynamic network of peer-to-peer edge nodes. This framework fosters the creation and collaboration of innovative AI business ecosystems, benefiting both private data holders and developers. GaiaNet aims to revolutionize the user experience with AI agents, seamlessly introducing transformative changes while preserving the intuitive technology and satisfaction currently existing in the Web2 era. Website: www.gaianet.ai Github: https://github.com/GaiaNet-AI X: @GaiaNet_AI www.gaianet.ai https://github.com/GaiaNet-AI This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

GaiaNet Announces Beta Product Launch Following Successful Alpha Phase

Hello AI Enthusiasts! Welcome to the tenth edition of "This Week in AI Engineering"! "This Week in AI Engineering" Google released Gemma 3 with a powerful 27B parameter version, AI21 Labs launched Jamba 1.6 outperforming Mistral and Llama on long-context tasks, Manus AI emerged as a breakthrough autonomous agent with 95% task completion, and Sesame AI just made AI girlfriends real by achieving human-level speech quality with emotional intelligence. With this, we’ll also be talking about some must-know tools to make developing AI agents and apps easier. AI Girlfriends Just Got Real AI Girlfriends Just Got Real Sesame AI has introduced a voice-based AI companion focused on achieving genuine "voice presence" through emotional intelligence and contextual adaptation. The system moves beyond traditional text-to-speech by incorporating conversational dynamics to create more engaging and natural interactions. Seems like AI girlfriends just got real! Sesame AI Sesame AI Technical Architecture: Technical Architecture: Conversational Speech Model (CSM): End-to-end multimodal transformer with conversation history awareness
Split Tokenization System: Handles semantic meaning and acoustic details separately for high-fidelity output
Three-Tier Model Range: Tiny (1B backbone/100M decoder), Small (3B/250M), and Medium (8B/300M)
Training Scale: Processed approximately one million hours of predominantly English audio Conversational Speech Model (CSM): End-to-end multimodal transformer with conversation history awareness Conversational Speech Model (CSM): Split Tokenization System: Handles semantic meaning and acoustic details separately for high-fidelity output Split Tokenization System: Three-Tier Model Range: Tiny (1B backbone/100M decoder), Small (3B/250M), and Medium (8B/300M) Three-Tier Model Range: Training Scale: Processed approximately one million hours of predominantly English audio Training Scale: Performance Metrics: Performance Metrics: Word Error Rate: Small model matches human ground truth at 2.9% WER
Speaker Similarity: Achieves 0.938 score (vs 0.940 human baseline)
Homograph Disambiguation: 80% accuracy for Medium model vs 70% for PlayHT and OpenAI
Pronunciation Consistency: 90% for the Medium model, outperforming all competitors
Subjective Testing: 52.9% win rate against human references in blind no-context tests Word Error Rate: Small model matches human ground truth at 2.9% WER Word Error Rate: Speaker Similarity: Achieves 0.938 score (vs 0.940 human baseline) Speaker Similarity: Homograph Disambiguation: 80% accuracy for Medium model vs 70% for PlayHT and OpenAI Homograph Disambiguation: Pronunciation Consistency: 90% for the Medium model, outperforming all competitors Pronunciation Consistency: Subjective Testing: 52.9% win rate against human references in blind no-context tests Subjective Testing: Key Innovations: Key Innovations: Compute Amortization: Trains on only 1/16 of audio frames to improve efficiency
Contextual Adaptation: Performance increases to 66.7% human preference when evaluating with context
Novel Benchmarks: Introduces specialized tests for homograph disambiguation and pronunciation consistency
Multi-Stage Processing: Combines semantic understanding with acoustic reproduction Compute Amortization: Trains on only 1/16 of audio frames to improve efficiency Compute Amortization: Contextual Adaptation: Performance increases to 66.7% human preference when evaluating with context Contextual Adaptation: Novel Benchmarks: Introduces specialized tests for homograph disambiguation and pronunciation consistency Novel Benchmarks: Multi-Stage Processing: Combines semantic understanding with acoustic reproduction Multi-Stage Processing: The technology fundamentally reimagines voice assistants by focusing on the emotional and contextual elements that make human communication meaningful, addressing the "emotional flatness" problem that limits user engagement with current systems. Manus AI Agent Delivers 95% Task Completion With Full Autonomy Manus AI Agent Delivers 95% Task Completion With Full Autonomy Manus AI is an autonomous agent system that executes complex tasks end-to-end and achieves 95% task completion without continuous user supervision. The China-based platform is gaining significant attention as the "second DeepSeek moment" for AI, moving beyond conversation to focus on end-to-end task automation. Manus AI Manus AI Technical Architecture: Technical Architecture: Asynchronous Processing: Cloud-based task execution without requiring active user monitoring
Preference Learning: Adaptive system that optimizes outputs based on previous user interactions
File Management: Native capabilities for working with complex document types and compression formats
Output Customization: Flexible delivery formats including documents and spreadsheets Asynchronous Processing: Cloud-based task execution without requiring active user monitoring Asynchronous Processing: Preference Learning: Adaptive system that optimizes outputs based on previous user interactions Preference Learning: File Management: Native capabilities for working with complex document types and compression formats File Management: Output Customization: Flexible delivery formats including documents and spreadsheets Output Customization: Performance Metrics: Performance Metrics: GAIA Score: >65% benchmark rating on general AI agent intelligence assessments
Task Completion: 95% success rate across varied task types
Response Time: 2.5s average response latency during interactions
User Satisfaction: 92% positive rating from current users
Integration Capacity: 100+ external tool connections for expanded functionality GAIA Score: >65% benchmark rating on general AI agent intelligence assessments GAIA Score: Task Completion: 95% success rate across varied task types Task Completion: Response Time: 2.5s average response latency during interactions Response Time: User Satisfaction: 92% positive rating from current users User Satisfaction: Integration Capacity: 100+ external tool connections for expanded functionality Integration Capacity: Comparative Analysis: Comparative Analysis: Task Performance: Consistently outperforms competitors by 10-15% across key metrics
User Growth: Expanded from 1,000 to over 18,000 users between January and June 2025
Use Cases: Excels in data analysis, content creation, document processing, and decision support
Accuracy Rating: 95% vs approximately 80% for nearest competitors in comparative testing Task Performance: Consistently outperforms competitors by 10-15% across key metrics Task Performance: User Growth: Expanded from 1,000 to over 18,000 users between January and June 2025 User Growth: Use Cases: Excels in data analysis, content creation, document processing, and decision support Use Cases: Accuracy Rating: 95% vs approximately 80% for nearest competitors in comparative testing Accuracy Rating: The platform bridges the gap between conception and execution, enabling users to delegate tasks like resume screening, stock analysis, and educational content creation while receiving notification upon completion, without requiring continuous monitoring. Google Gemma 3 Open Model Family’s 27B Parameter Version Reached a 1338 ELO Score Google Gemma 3 Open Model Family’s 27B Parameter Version Reached a 1338 ELO Score Google has introduced Gemma 3, a collection of open-source AI models designed for efficient operation on consumer hardware while delivering frontier-level performance. The family includes four parameter sizes (1B, 4B, 12B, and 27B) with multimodal capabilities, 128K token context window, and support for over 140 languages. Gemma 3 Gemma 3 Technical Architecture: Technical Architecture: Vision Integration: Compatible with SigLIP vision encoder and Pan & Scan technology for handling varied image resolutions
Context Management: 5:1 ratio of local to global attention layers with 1024-token local span for optimized memory usage
Token Processing: Enhanced RoPE base frequency (1M for global layers, 10K for local layers) for extended context handling Vision Integration: Compatible with SigLIP vision encoder and Pan & Scan technology for handling varied image resolutions Vision Integration: Context Management: 5:1 ratio of local to global attention layers with 1024-token local span for optimized memory usage Context Management: Token Processing: Enhanced RoPE base frequency (1M for global layers, 10K for local layers) for extended context handling Token Processing: Performance Metrics: Performance Metrics: Chatbot Arena: 27B model achieves 1338 ELO score, ranking 9th overall ahead of models like DeepSeek-V3 (1318) and o3-mini (1304)
MATH Benchmark: 89.0% accuracy for the 27B model, significantly outperforming Gemma 2 27B (55.6%)
HumanEval: 87.8% pass rate for 27B model versus 51.8% for Gemma 2 27B
MMLU: 67.5% accuracy for 27B, compared to 56.9% for the previous generation Chatbot Arena: 27B model achieves 1338 ELO score, ranking 9th overall ahead of models like DeepSeek-V3 (1318) and o3-mini (1304) Chatbot Arena: MATH Benchmark: 89.0% accuracy for the 27B model, significantly outperforming Gemma 2 27B (55.6%) MATH Benchmark: HumanEval: 87.8% pass rate for 27B model versus 51.8% for Gemma 2 27B HumanEval: MMLU: 67.5% accuracy for 27B, compared to 56.9% for the previous generation MMLU: Key Innovations: Key Innovations: Architecture Optimization: Increased ratio of local to global attention layers reduces KV-cache memory requirements
Multimodal Processing: Vision capabilities using a 400M parameter SigLIP encoder with 256 image tokens
Language Coverage: Enhanced multilingual abilities through revised data mixture and tokenizer optimization
Knowledge Distillation: All models trained with knowledge distillation from larger teacher models Architecture Optimization: Increased ratio of local to global attention layers reduces KV-cache memory requirements Architecture Optimization: Multimodal Processing: Vision capabilities using a 400M parameter SigLIP encoder with 256 image tokens Multimodal Processing: Language Coverage: Enhanced multilingual abilities through revised data mixture and tokenizer optimization Language Coverage: Knowledge Distillation: All models trained with knowledge distillation from larger teacher models Knowledge Distillation: Google has also released ShieldGemma 2, a 4B parameter image safety classifier built on the Gemma 3 foundation, providing developers with ready-made solutions for content moderation across dangerous content, and violence categories. Jamba 1.6 Outperforms Mistral and Llama on Quality and Long Context Tasks Jamba 1.6 Outperforms Mistral and Llama on Quality and Long Context Tasks AI21 Labs has released Jamba 1.6, a new family of open-source models designed for enterprise deployments where data privacy and performance are critical requirements. The lineup includes Jamba Large 1.6 and Jamba Mini 1.6, both optimized for quality, speed, and extended context handling. Jamba 1. Jamba 1. 6 Technical Architecture: Technical Architecture: Hybrid Design: Combined Mamba-Transformer MoE architecture for efficient processing
Context Window: 256K tokens supported without performance degradation
Inference Speed: 165 tokens/second for Mini variant vs 125 for Ministral 8B and 100 for GPT-4o mini
Deployment Options: On-premises, in-VPC, or through AI21 Studio with batch processing capability Hybrid Design: Combined Mamba-Transformer MoE architecture for efficient processing Hybrid Design: Context Window: 256K tokens supported without performance degradation Context Window: Inference Speed: 165 tokens/second for Mini variant vs 125 for Ministral 8B and 100 for GPT-4o mini Inference Speed: Deployment Options: On-premises, in-VPC, or through AI21 Studio with batch processing capability Deployment Options: Performance Metrics: Performance Metrics: Arena Hard: Jamba Large 1.6 scores 75 vs Mistral Large 2's 65 and Llama 3.3 70B's 67
CRAG: 78 points for Jamba Large 1.6 vs 61 for Llama 3.3 70B and 47 for Command R+
HELMET RAG: 65 points for Jamba Large 1.6 vs 55 for Mistral Large 2
LongBench: 37 points for Jamba Large 1.6 vs 24 for Llama 3.3 70B and 18 for Command R+
HELMET LongQA: 57 points for Jamba Large 1.6 vs 50 for Mistral Large 2 Arena Hard: Jamba Large 1.6 scores 75 vs Mistral Large 2's 65 and Llama 3.3 70B's 67 Arena Hard: CRAG: 78 points for Jamba Large 1.6 vs 61 for Llama 3.3 70B and 47 for Command R+ CRAG: HELMET RAG: 65 points for Jamba Large 1.6 vs 55 for Mistral Large 2 HELMET RAG: LongBench: 37 points for Jamba Large 1.6 vs 24 for Llama 3.3 70B and 18 for Command R+ LongBench: HELMET LongQA: 57 points for Jamba Large 1.6 vs 50 for Mistral Large 2 HELMET LongQA: Enterprise Implementations: Enterprise Implementations: Retail (Fnac): 26% quality improvement with 40% better latency by switching to Jamba 1.6 Mini
Education (Educa Edtech): 90%+ retrieval accuracy and citation reliability for personalized chatbots
Digital Banking: 21% higher precision than previous solutions, matching GPT-4o quality
E-commerce: Automated conversion of inventory databases to structured product descriptions Retail (Fnac): 26% quality improvement with 40% better latency by switching to Jamba 1.6 Mini Retail (Fnac): Education (Educa Edtech): 90%+ retrieval accuracy and citation reliability for personalized chatbots Education (Educa Edtech): Digital Banking: 21% higher precision than previous solutions, matching GPT-4o quality Digital Banking: E-commerce: Automated conversion of inventory databases to structured product descriptions E-commerce: The new Batch API allows enterprises to handle large volumes of requests asynchronously, supporting high-volume data processing with faster turnaround times than traditional batch systems. Tools & Releases YOU Should Know About Tools & Releases YOU Should Know About N8n is a workflow automation platform designed for technical teams. It allows users to connect various apps and services to automate tasks without code. Technically, n8n operates through a node-based system where each node represents a specific action or integration. Users create workflows by linking these nodes together in a visual editor, defining the flow of data and operations. n8n can be self-hosted or used via a cloud version, offering flexibility and control. It's particularly useful for developers, IT professionals, and data engineers who need to streamline processes and integrate different systems.
The Eclipse Theia IDE is an extensible, open-source IDE available for both web and desktop use. Built on the Theia platform, it supports AI-powered features like chat, code completion, terminal assistance, and custom agents, leveraging arbitrary LLMs. Theia uses a modular architecture, allowing for custom extensions and tailored tool creation. While incorporating components like the Monaco editor from VS Code, it's independently developed and not a fork. Theia is ideal for developers seeking a customizable, vendor-neutral IDE with modern UX and AI integration, deployable across different environments.
Adrenaline is a platform engineered to streamline code-related inquiries by connecting directly to your GitHub repositories and documentation. It leverages advanced natural language processing (NLP) to understand questions about your codebase, offering precise answers and code snippets. It indexes your repositories and documentation, creating a searchable knowledge base. Users can then pose questions in natural language, which the platform processes to identify relevant code segments and documentation excerpts. This makes it particularly useful for developers, technical writers, and teams seeking rapid, context-aware answers within their existing projects, enhancing productivity and reducing search time.
Wren AI is an open-source SQL AI Agent that empowers users to obtain results and insights from databases faster by asking questions in natural language, eliminating the need to write SQL. Wren AI employs a semantic engine architecture, allowing users to establish a logical layer on their data schema using "Modeling Definition Language." This enables the LLM to understand the business context, generate accurate SQL queries, and provide AI-generated summaries with its GenBI feature, which converts data into actionable visuals. Wren AI supports various databases, LLMs, and analytics tools and can be deployed in any environment. N8n is a workflow automation platform designed for technical teams. It allows users to connect various apps and services to automate tasks without code. Technically, n8n operates through a node-based system where each node represents a specific action or integration. Users create workflows by linking these nodes together in a visual editor, defining the flow of data and operations. n8n can be self-hosted or used via a cloud version, offering flexibility and control. It's particularly useful for developers, IT professionals, and data engineers who need to streamline processes and integrate different systems. N8n N8n The Eclipse Theia IDE is an extensible, open-source IDE available for both web and desktop use. Built on the Theia platform, it supports AI-powered features like chat, code completion, terminal assistance, and custom agents, leveraging arbitrary LLMs. Theia uses a modular architecture, allowing for custom extensions and tailored tool creation. While incorporating components like the Monaco editor from VS Code, it's independently developed and not a fork. Theia is ideal for developers seeking a customizable, vendor-neutral IDE with modern UX and AI integration, deployable across different environments. The Eclipse Theia IDE The Eclipse Theia IDE Adrenaline is a platform engineered to streamline code-related inquiries by connecting directly to your GitHub repositories and documentation. It leverages advanced natural language processing (NLP) to understand questions about your codebase, offering precise answers and code snippets. It indexes your repositories and documentation, creating a searchable knowledge base. Users can then pose questions in natural language, which the platform processes to identify relevant code segments and documentation excerpts. This makes it particularly useful for developers, technical writers, and teams seeking rapid, context-aware answers within their existing projects, enhancing productivity and reducing search time. Adrenaline Adrenaline Wren AI is an open-source SQL AI Agent that empowers users to obtain results and insights from databases faster by asking questions in natural language, eliminating the need to write SQL. Wren AI employs a semantic engine architecture, allowing users to establish a logical layer on their data schema using "Modeling Definition Language." This enables the LLM to understand the business context, generate accurate SQL queries, and provide AI-generated summaries with its GenBI feature, which converts data into actionable visuals. Wren AI supports various databases, LLMs, and analytics tools and can be deployed in any environment. Wren AI Wren AI And that wraps up this issue of "This Week in AI Engineering." This Week in AI Engineering. Thank you for tuning in! Be sure to share this newsletter with your fellow AI enthusiasts and follow for more weekly updates. Until next time, happy building!

AI Girlfriends Just Got Real

Microbial Life on Venus Requires a Unique Life Cycle to Overcome Extreme Conditions

Multimodal AI for High-Fidelity Video Creation and Editing

Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Climate Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. The Startups in the Climate Tech industry nominated for Startups of The Year 2024 include: Matteco


IDEALERS B2B PVT LTD


Terrenity


HIJAU (c/o. PT Investasi Hijau Selaras)


Undesert Corporation


Waterly


Felicidad


Tracco


seeng LTD


Tree++ (Tree Plus Plus)


LEAP for Climate


Aatral ESP


ÁCARA CLIMATE


RenTech Global Solutions


VALKYRIE SOLAR SOLUTIONS


Alder Green Solutions


H2next


Eurasia Natural History Association


Climate Change Writers


Global Cerah


Tasteme


Virya Energy


Zetin Solar Technology Kenya


R.Flo


Ocergy


CENmat


Achelous Pure Metal Co. Ltd.


GoNetZero


Silogic Technology


Gaia First “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st, and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. Visit our FAQ page to learn more. Download our design assets here. Check out the Startups of The Year Merch Shop here. HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. Meet our sponsors: Wellfound: Join the #1 global, startup-focused community. At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool. Get your offer now! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free. Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. Climate Tech, Emerging Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Climate Tech startup ecosystem. Climate Tech , Emerging Tech - October 9, 2024 Tech - October 9, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “ Startups of The Year ” award, featuring the most promising and innovative companies in the Startups of The Year startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Climate Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Climate Tech industry here . here Startup of The Year in the Climate Tech industry here Startup of The Year in the Climate Tech industry here The Startups in the Climate Tech industry nominated for Startups of The Year 2024 include: Startups in the Climate Tech industry nominated Startups in the Climate Tech industry Startups in the Climate Tech industry Matteco IDEALERS B2B PVT LTD Terrenity HIJAU (c/o. PT Investasi Hijau Selaras) Undesert Corporation Waterly Felicidad Tracco seeng LTD Tree++ (Tree Plus Plus) LEAP for Climate Aatral ESP ÁCARA CLIMATE RenTech Global Solutions VALKYRIE SOLAR SOLUTIONS Alder Green Solutions H2next Eurasia Natural History Association Climate Change Writers Global Cerah Tasteme Virya Energy Zetin Solar Technology Kenya R.Flo Ocergy CENmat Achelous Pure Metal Co. Ltd. GoNetZero Silogic Technology Gaia First Matteco Matteco IDEALERS B2B PVT LTD IDEALERS B2B PVT LTD Terrenity Terrenity HIJAU (c/o. PT Investasi Hijau Selaras) HIJAU (c/o. PT Investasi Hijau Selaras) Undesert Corporation Undesert Corporation Waterly Waterly Felicidad Felicidad Tracco Tracco seeng LTD seeng LTD Tree++ (Tree Plus Plus) Tree++ (Tree Plus Plus) LEAP for Climate LEAP for Climate Aatral ESP Aatral ESP ÁCARA CLIMATE ÁCARA CLIMATE RenTech Global Solutions RenTech Global Solutions VALKYRIE SOLAR SOLUTIONS VALKYRIE SOLAR SOLUTIONS Alder Green Solutions Alder Green Solutions H2next H2next Eurasia Natural History Association Eurasia Natural History Association Climate Change Writers Climate Change Writers Global Cerah Global Cerah Tasteme Tasteme Virya Energy Virya Energy Zetin Solar Technology Kenya Zetin Solar Technology Kenya R.Flo R.Flo Ocergy Ocergy CENmat CENmat Achelous Pure Metal Co. Ltd. Achelous Pure Metal Co. Ltd. GoNetZero GoNetZero Silogic Technology Silogic Technology Gaia First Gaia First “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Climate Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st , and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Startups of The Year 2024 nominate their favorite startups until October 31st nominate their favorite startups Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. get free interviews startup-friendly packages startup-friendly packages Evergreen Tech Company News Evergreen Tech Company News In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Climate Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Climate Tech industry here . here Startup of The Year in the Climate Tech industry here Startup of The Year in the Climate Tech industry here About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. Startups of The Year 2024 Startups of The Year 2024 many stories many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview on HackerNoon Evergreen Tech Company News Evergreen Tech Company News Visit our FAQ page to learn more. FAQ FAQ Download our design assets here . here here Check out the Startups of The Year Merch Shop here . here here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. startup-friendly packages startup-friendly packages Meet our sponsors: Meet our sponsors: Meet our sponsors: Wellfound: Join the #1 global, startup-focused community . At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Join the #1 global, startup-focused community Join the #1 global, startup-focused community Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months , to build and scale your company with one powerful tool. Get your offer now ! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free . Hubspot: Get started for free Get started for free Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection , businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Bright Data’s scalable web data collection Bright Data’s scalable web data collection Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Algolia: AI end-to-end Search and Discovery Platform AI end-to-end Search and Discovery Platform

Startups of The Year 2024: 3,246 Startups Nominated in the Climate Tech Industry

የ2024 የአመቱ ጅምር፡ 3,246 ጅምር በአየር ንብረት ቴክ ኢንዱስትሪ ውስጥ ተመርጠዋል።

Стартапы 2024 года: 3246 стартапаў, намінаваных у індустрыі кліматычных тэхналогій

Startupy roku 2024: 3 246 nominovaných startupů v odvětví Climate Tech

Startups del año 2024: 3246 startups nominadas en la industria de la tecnología climática

Startups do ano 2024: 3.246 startups nomeadas na industria da tecnoloxía climática

Startupovi godine 2024.: 3246 startupova nominiranih u industriji klimatskih tehnologija

Startup dell'anno 2024: 3.246 startup nominate nel settore Climate Tech

2024年のスタートアップ・オブ・ザ・イヤー：気候技術業界で3,246社のスタートアップがノミネート

წლის სტარტაპები 2024: 3246 სტარტაპი ნომინირებულია კლიმატის ტექნოლოგიების ინდუსტრიაში

2024 жылдың стартаптары: климаттық технологиялар индустриясында 3 246 стартап ұсынылды

Startups of the Year 2024: 3 246 startupov nominovaných v odvetví klimatických technológií

Các công ty khởi nghiệp của năm 2024: 3.246 công ty khởi nghiệp được đề cử trong ngành công nghệ khí hậu

Thermodynamic Limits Around M-dwarf Stars: Acknowledgements

How AI and Blockchain are Converging: A Look into IVS Crypto 2024's Innovative Approach

এআই এবং ব্লকচেইন কীভাবে একত্রিত হচ্ছে: IVS ক্রিপ্টো 2024-এর উদ্ভাবনী পদ্ধতির দিকে একটি নজর

Wie KI und Blockchain zusammenwachsen: Ein Blick auf den innovativen Ansatz von IVS Crypto 2024

Cómo convergen la IA y Blockchain: una mirada al enfoque innovador de IVS Crypto 2024

Comment l'IA et la blockchain convergent : un aperçu de l'approche innovante d'IVS Crypto 2024

एआई और ब्लॉकचेन कैसे एक दूसरे से जुड़ रहे हैं: आईवीएस क्रिप्टो 2024 के अभिनव दृष्टिकोण पर एक नज़र

AIとブロックチェーンの融合: IVS Crypto 2024の革新的なアプローチを検証

AI와 블록체인이 융합되는 방식: IVS Crypto 2024의 혁신적인 접근 방식 살펴보기

Como a IA e o Blockchain estão convergindo: uma análise da abordagem inovadora do IVS Crypto 2024

Как ИИ и блокчейн объединяются: взгляд на инновационный подход IVS Crypto 2024

Yapay Zeka ve Blockchain Nasıl Birleşiyor: IVS Crypto 2024'ün Yenilikçi Yaklaşımına Bir Bakış

AI và Blockchain đang hội tụ như thế nào: Nhìn vào phương pháp tiếp cận đổi mới của IVS Crypto 2024

人工智能与区块链如何融合：了解 IVS Crypto 2024 的创新方法

Authors:
(1) Youn Kil Jung, Korea Astronomy and Space Science Institute, University of Science and Technology, and The KMTNet Collaboration;
(2) Cheongho Han, Department of Physics, Chungbuk National University and The KMTNet Collaboration;
(3) Andrzej Udalski, Warsaw University Observatory and The OGLE Collaboration;
(4) Andrew Gould, Korea Astronomy and Space Science Institute, Department of Astronomy, Ohio State University, Max-Planck-Institute for Astronomy and The KMTNet Collaboration;
(5) Jennifer C. Yee, Center for Astrophysics | Harvard & Smithsonian and The KMTNet Collaboration;
(6) Michael D. Albrow, University of Canterbury, Department of Physics and Astronomy;
(7) Sun-Ju Chung, Korea Astronomy and Space Science Institute and University of Science and Technology;
(8) Kyu-Ha Hwang, Korea Astronomy and Space Science Institute;
(9) Yoon-Hyun Ryu, Korea Astronomy and Space Science Institute;
(10) In-Gu Shin, Korea Astronomy and Space Science Institute;
(11) Yossi Shvartzvald, Department of Particle Physics and Astrophysics, Weizmann Institute of Science;
(12) Wei Zhu, Canadian Institute for Theoretical Astrophysics, University of Toronto;
(13) Weicheng Zang, Department of Astronomy, Tsinghua University;
(14) Sang-Mok Cha, Korea Astronomy and Space Science Institute and 2School of Space Research, Kyung Hee University;
(15) Dong-Jin Kim, Korea Astronomy and Space Science Institute;
(16) Hyoun-Woo Kim, Korea Astronomy and Space Science Institute;
(17) Seung-Lee Kim, Korea Astronomy and Space Science Institute and University of Science and Technology;
(18) Chung-Uk Lee, Korea Astronomy and Space Science Institute and University of Science and Technology;
(19) Dong-Joo Lee, Korea Astronomy and Space Science Institute;
(20) Yongseok Lee, Korea Astronomy and Space Science Institute and School of Space Research, Kyung Hee University;
(21) Byeong-Gon Park, Korea Astronomy and Space Science Institute and University of Science and Technology;
(22) Richard W. Pogge, Department of Astronomy, Ohio State University;
(23) Przemek Mroz, Warsaw University Observatory and Division of Physics, Mathematics, and Astronomy, California Institute of Technology;
(24) Michal K. Szymanski, Warsaw University Observatory;
(25) Jan Skowron, Warsaw University Observatory;
(26) Radek Poleski, Warsaw University Observatory and Department of Astronomy, Ohio State University;
(27) Igor Soszynski, Warsaw University Observatory;
(28) Pawel Pietrukowicz, Warsaw University Observatory;
(29) Szymon Kozlowski, Warsaw University Observatory;
(30) Krzystof Ulaczyk, Department of Physics, University of Warwick, Gibbet;
(31) Krzysztof A. Rybicki, Warsaw University Observatory;
(32) Patryk Iwanek, Warsaw University Observatory;
(33) Marcin Wrona, Warsaw University Observatory. Table of Links Abstract and Intro
Observation
Light Curve Analysis
Physical Parameters
Microlensing Planets in the (log s, log q) plane
Summary and Conclusions
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Authors: (1) Youn Kil Jung, Korea Astronomy and Space Science Institute, University of Science and Technology, and The KMTNet Collaboration; (2) Cheongho Han, Department of Physics, Chungbuk National University and The KMTNet Collaboration; (3) Andrzej Udalski, Warsaw University Observatory and The OGLE Collaboration; (4) Andrew Gould, Korea Astronomy and Space Science Institute, Department of Astronomy, Ohio State University, Max-Planck-Institute for Astronomy and The KMTNet Collaboration; (5) Jennifer C. Yee, Center for Astrophysics | Harvard & Smithsonian and The KMTNet Collaboration; (6) Michael D. Albrow, University of Canterbury, Department of Physics and Astronomy; (7) Sun-Ju Chung, Korea Astronomy and Space Science Institute and University of Science and Technology; (8) Kyu-Ha Hwang, Korea Astronomy and Space Science Institute; (9) Yoon-Hyun Ryu, Korea Astronomy and Space Science Institute; (10) In-Gu Shin, Korea Astronomy and Space Science Institute; (11) Yossi Shvartzvald, Department of Particle Physics and Astrophysics, Weizmann Institute of Science; (12) Wei Zhu, Canadian Institute for Theoretical Astrophysics, University of Toronto; (13) Weicheng Zang, Department of Astronomy, Tsinghua University; (14) Sang-Mok Cha, Korea Astronomy and Space Science Institute and 2School of Space Research, Kyung Hee University; (15) Dong-Jin Kim, Korea Astronomy and Space Science Institute; (16) Hyoun-Woo Kim, Korea Astronomy and Space Science Institute; (17) Seung-Lee Kim, Korea Astronomy and Space Science Institute and University of Science and Technology; (18) Chung-Uk Lee, Korea Astronomy and Space Science Institute and University of Science and Technology; (19) Dong-Joo Lee, Korea Astronomy and Space Science Institute; (20) Yongseok Lee, Korea Astronomy and Space Science Institute and School of Space Research, Kyung Hee University; (21) Byeong-Gon Park, Korea Astronomy and Space Science Institute and University of Science and Technology; (22) Richard W. Pogge, Department of Astronomy, Ohio State University; (23) Przemek Mroz, Warsaw University Observatory and Division of Physics, Mathematics, and Astronomy, California Institute of Technology; (24) Michal K. Szymanski, Warsaw University Observatory; (25) Jan Skowron, Warsaw University Observatory; (26) Radek Poleski, Warsaw University Observatory and Department of Astronomy, Ohio State University; (27) Igor Soszynski, Warsaw University Observatory; (28) Pawel Pietrukowicz, Warsaw University Observatory; (29) Szymon Kozlowski, Warsaw University Observatory; (30) Krzystof Ulaczyk, Department of Physics, University of Warwick, Gibbet; (31) Krzysztof A. Rybicki, Warsaw University Observatory; (32) Patryk Iwanek, Warsaw University Observatory; (33) Marcin Wrona, Warsaw University Observatory. Authors: Authors: (1) Youn Kil Jung, Korea Astronomy and Space Science Institute, University of Science and Technology, and The KMTNet Collaboration; (2) Cheongho Han, Department of Physics, Chungbuk National University and The KMTNet Collaboration; (3) Andrzej Udalski, Warsaw University Observatory and The OGLE Collaboration; (4) Andrew Gould, Korea Astronomy and Space Science Institute, Department of Astronomy, Ohio State University, Max-Planck-Institute for Astronomy and The KMTNet Collaboration; (5) Jennifer C. Yee, Center for Astrophysics | Harvard & Smithsonian and The KMTNet Collaboration; (6) Michael D. Albrow, University of Canterbury, Department of Physics and Astronomy; (7) Sun-Ju Chung, Korea Astronomy and Space Science Institute and University of Science and Technology; (8) Kyu-Ha Hwang, Korea Astronomy and Space Science Institute; (9) Yoon-Hyun Ryu, Korea Astronomy and Space Science Institute; (10) In-Gu Shin, Korea Astronomy and Space Science Institute; (11) Yossi Shvartzvald, Department of Particle Physics and Astrophysics, Weizmann Institute of Science; (12) Wei Zhu, Canadian Institute for Theoretical Astrophysics, University of Toronto; (13) Weicheng Zang, Department of Astronomy, Tsinghua University; (14) Sang-Mok Cha, Korea Astronomy and Space Science Institute and 2School of Space Research, Kyung Hee University; (15) Dong-Jin Kim, Korea Astronomy and Space Science Institute; (16) Hyoun-Woo Kim, Korea Astronomy and Space Science Institute; (17) Seung-Lee Kim, Korea Astronomy and Space Science Institute and University of Science and Technology; (18) Chung-Uk Lee, Korea Astronomy and Space Science Institute and University of Science and Technology; (19) Dong-Joo Lee, Korea Astronomy and Space Science Institute; (20) Yongseok Lee, Korea Astronomy and Space Science Institute and School of Space Research, Kyung Hee University; (21) Byeong-Gon Park, Korea Astronomy and Space Science Institute and University of Science and Technology; (22) Richard W. Pogge, Department of Astronomy, Ohio State University; (23) Przemek Mroz, Warsaw University Observatory and Division of Physics, Mathematics, and Astronomy, California Institute of Technology; (24) Michal K. Szymanski, Warsaw University Observatory; (25) Jan Skowron, Warsaw University Observatory; (26) Radek Poleski, Warsaw University Observatory and Department of Astronomy, Ohio State University; (27) Igor Soszynski, Warsaw University Observatory; (28) Pawel Pietrukowicz, Warsaw University Observatory; (29) Szymon Kozlowski, Warsaw University Observatory; (30) Krzystof Ulaczyk, Department of Physics, University of Warwick, Gibbet; (31) Krzysztof A. Rybicki, Warsaw University Observatory; (32) Patryk Iwanek, Warsaw University Observatory; (33) Marcin Wrona, Warsaw University Observatory. Table of Links Abstract and Intro Observation Light Curve Analysis Physical Parameters Microlensing Planets in the (log s, log q) plane Summary and Conclusions References Abstract and Intro Abstract and Intro Observation Observation Light Curve Analysis Light Curve Analysis Physical Parameters Physical Parameters Microlensing Planets in the (log s , log q ) plane Microlensing Planets in the (log s s , log q q ) plane Summary and Conclusions Summary and Conclusions References References REFERENCES Alard, C., & Lupton, Robert H. 1998, ApJ, 503, 325 Alcock, C., Allsman, R. A., Axelrod, T. S., et al. 1995, ApJ, 445, 133 Albrow, M. D. 2017, MichaelDAlbrow/pyDIA: Initial release on github, V1.0.0, Zenodo, doi:10.5281/zenodo.268049 Albrow, M. D., Horne, K., Bramich, D. M., et al. 2009, MNRAS, 397, 2099 Beaulieu, J.-P., Bennett, D. P., Fouqu´e, P., et al. 2006, Nature, 439, 437 Bennett, D.P., Rhie, S.H., Nikolaev, S. et al. 2010, ApJ, 713, 837 Bennett, D. P., Sumi, T., Bond, I. 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C., Zang, W., Udalski, A., et al. 2021, arXiv:2101.04696 Yoo, J., DePoy, D. L., Gal-Yam, A., et al. 2004, ApJ, 603, 139 This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Two Microlensing Planets through Planetary-Caustic Channel: References

প্ল্যানেটারি-কস্টিক চ্যানেলের মাধ্যমে দুটি মাইক্রোলেনসিং গ্রহ: তথ্যসূত্র

Zwei Mikrolinsenplaneten durch den planetar-kaustischen Kanal: Referenzen

Dos planetas con microlentes a través del canal cáustico planetario: referencias

Deux planètes à microlentille via un canal planétaire-caustique : références

ग्रहीय-कास्टिक चैनल के माध्यम से दो माइक्रोलेंसिंग ग्रह: संदर्भ

惑星-コースティックチャネルを通した2つのマイクロレンズ効果を持つ惑星: 参考文献

행성 가성 채널을 통한 두 개의 미세 렌즈 행성: 참고 자료

Dois planetas microlentes através do canal cáustico-planetário: referências

Две микролинзирующие планеты через планетарно-каустический канал: ссылки

Gezegensel-Kostik Kanal Üzerinden İki Mikro Mercekleme Gezegeni: Referanslar

Hai hành tinh vi thấu kính thông qua Kênh ăn da hành tinh: Tài liệu tham khảo

通过行星焦散通道观察两颗微透镜行星：参考文献

Authors:
(1) Youn Kil Jung, Korea Astronomy and Space Science Institute, University of Science and Technology, and The KMTNet Collaboration;
(2) Cheongho Han, Department of Physics, Chungbuk National University and The KMTNet Collaboration;
(3) Andrzej Udalski, Warsaw University Observatory and The OGLE Collaboration;
(4) Andrew Gould, Korea Astronomy and Space Science Institute, Department of Astronomy, Ohio State University, Max-Planck-Institute for Astronomy and The KMTNet Collaboration;
(5) Jennifer C. Yee, Center for Astrophysics | Harvard & Smithsonian and The KMTNet Collaboration;
(6) Michael D. Albrow, University of Canterbury, Department of Physics and Astronomy;
(7) Sun-Ju Chung, Korea Astronomy and Space Science Institute and University of Science and Technology;
(8) Kyu-Ha Hwang, Korea Astronomy and Space Science Institute;
(9) Yoon-Hyun Ryu, Korea Astronomy and Space Science Institute;
(10) In-Gu Shin, Korea Astronomy and Space Science Institute;
(11) Yossi Shvartzvald, Department of Particle Physics and Astrophysics, Weizmann Institute of Science;
(12) Wei Zhu, Canadian Institute for Theoretical Astrophysics, University of Toronto;
(13) Weicheng Zang, Department of Astronomy, Tsinghua University;
(14) Sang-Mok Cha, Korea Astronomy and Space Science Institute and 2School of Space Research, Kyung Hee University;
(15) Dong-Jin Kim, Korea Astronomy and Space Science Institute;
(16) Hyoun-Woo Kim, Korea Astronomy and Space Science Institute;
(17) Seung-Lee Kim, Korea Astronomy and Space Science Institute and University of Science and Technology;
(18) Chung-Uk Lee, Korea Astronomy and Space Science Institute and University of Science and Technology;
(19) Dong-Joo Lee, Korea Astronomy and Space Science Institute;
(20) Yongseok Lee, Korea Astronomy and Space Science Institute and School of Space Research, Kyung Hee University;
(21) Byeong-Gon Park, Korea Astronomy and Space Science Institute and University of Science and Technology;
(22) Richard W. Pogge, Department of Astronomy, Ohio State University;
(23) Przemek Mroz, Warsaw University Observatory and Division of Physics, Mathematics, and Astronomy, California Institute of Technology;
(24) Michal K. Szymanski, Warsaw University Observatory;
(25) Jan Skowron, Warsaw University Observatory;
(26) Radek Poleski, Warsaw University Observatory and Department of Astronomy, Ohio State University;
(27) Igor Soszynski, Warsaw University Observatory;
(28) Pawel Pietrukowicz, Warsaw University Observatory;
(29) Szymon Kozlowski, Warsaw University Observatory;
(30) Krzystof Ulaczyk, Department of Physics, University of Warwick, Gibbet;
(31) Krzysztof A. Rybicki, Warsaw University Observatory;
(32) Patryk Iwanek, Warsaw University Observatory;
(33) Marcin Wrona, Warsaw University Observatory. Table of Links Abstract and Intro
Observation
Light Curve Analysis
Physical Parameters
Microlensing Planets in the (log s, log q) plane
Summary and Conclusions
References 4. Physical Parameters The bulge VD is modeled based on stars in the Gaia catalog (Gaia Collaboration et al. 2016, 2018). That is, we first find red giant stars in the catalog within 2 arcmin centered on the event location. We then derive their mean velocity and its dispersion in Galactocentric Cartesian coordinates (x, y, z) as defined in Han & Gould (1995). This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Youn Kil Jung, Korea Astronomy and Space Science Institute, University of Science and Technology, and The KMTNet Collaboration; (2) Cheongho Han, Department of Physics, Chungbuk National University and The KMTNet Collaboration; (3) Andrzej Udalski, Warsaw University Observatory and The OGLE Collaboration; (4) Andrew Gould, Korea Astronomy and Space Science Institute, Department of Astronomy, Ohio State University, Max-Planck-Institute for Astronomy and The KMTNet Collaboration; (5) Jennifer C. Yee, Center for Astrophysics | Harvard & Smithsonian and The KMTNet Collaboration; (6) Michael D. Albrow, University of Canterbury, Department of Physics and Astronomy; (7) Sun-Ju Chung, Korea Astronomy and Space Science Institute and University of Science and Technology; (8) Kyu-Ha Hwang, Korea Astronomy and Space Science Institute; (9) Yoon-Hyun Ryu, Korea Astronomy and Space Science Institute; (10) In-Gu Shin, Korea Astronomy and Space Science Institute; (11) Yossi Shvartzvald, Department of Particle Physics and Astrophysics, Weizmann Institute of Science; (12) Wei Zhu, Canadian Institute for Theoretical Astrophysics, University of Toronto; (13) Weicheng Zang, Department of Astronomy, Tsinghua University; (14) Sang-Mok Cha, Korea Astronomy and Space Science Institute and 2School of Space Research, Kyung Hee University; (15) Dong-Jin Kim, Korea Astronomy and Space Science Institute; (16) Hyoun-Woo Kim, Korea Astronomy and Space Science Institute; (17) Seung-Lee Kim, Korea Astronomy and Space Science Institute and University of Science and Technology; (18) Chung-Uk Lee, Korea Astronomy and Space Science Institute and University of Science and Technology; (19) Dong-Joo Lee, Korea Astronomy and Space Science Institute; (20) Yongseok Lee, Korea Astronomy and Space Science Institute and School of Space Research, Kyung Hee University; (21) Byeong-Gon Park, Korea Astronomy and Space Science Institute and University of Science and Technology; (22) Richard W. Pogge, Department of Astronomy, Ohio State University; (23) Przemek Mroz, Warsaw University Observatory and Division of Physics, Mathematics, and Astronomy, California Institute of Technology; (24) Michal K. Szymanski, Warsaw University Observatory; (25) Jan Skowron, Warsaw University Observatory; (26) Radek Poleski, Warsaw University Observatory and Department of Astronomy, Ohio State University; (27) Igor Soszynski, Warsaw University Observatory; (28) Pawel Pietrukowicz, Warsaw University Observatory; (29) Szymon Kozlowski, Warsaw University Observatory; (30) Krzystof Ulaczyk, Department of Physics, University of Warwick, Gibbet; (31) Krzysztof A. Rybicki, Warsaw University Observatory; (32) Patryk Iwanek, Warsaw University Observatory; (33) Marcin Wrona, Warsaw University Observatory. Authors: Authors: (1) Youn Kil Jung, Korea Astronomy and Space Science Institute, University of Science and Technology, and The KMTNet Collaboration; (2) Cheongho Han, Department of Physics, Chungbuk National University and The KMTNet Collaboration; (3) Andrzej Udalski, Warsaw University Observatory and The OGLE Collaboration; (4) Andrew Gould, Korea Astronomy and Space Science Institute, Department of Astronomy, Ohio State University, Max-Planck-Institute for Astronomy and The KMTNet Collaboration; (5) Jennifer C. Yee, Center for Astrophysics | Harvard & Smithsonian and The KMTNet Collaboration; (6) Michael D. Albrow, University of Canterbury, Department of Physics and Astronomy; (7) Sun-Ju Chung, Korea Astronomy and Space Science Institute and University of Science and Technology; (8) Kyu-Ha Hwang, Korea Astronomy and Space Science Institute; (9) Yoon-Hyun Ryu, Korea Astronomy and Space Science Institute; (10) In-Gu Shin, Korea Astronomy and Space Science Institute; (11) Yossi Shvartzvald, Department of Particle Physics and Astrophysics, Weizmann Institute of Science; (12) Wei Zhu, Canadian Institute for Theoretical Astrophysics, University of Toronto; (13) Weicheng Zang, Department of Astronomy, Tsinghua University; (14) Sang-Mok Cha, Korea Astronomy and Space Science Institute and 2School of Space Research, Kyung Hee University; (15) Dong-Jin Kim, Korea Astronomy and Space Science Institute; (16) Hyoun-Woo Kim, Korea Astronomy and Space Science Institute; (17) Seung-Lee Kim, Korea Astronomy and Space Science Institute and University of Science and Technology; (18) Chung-Uk Lee, Korea Astronomy and Space Science Institute and University of Science and Technology; (19) Dong-Joo Lee, Korea Astronomy and Space Science Institute; (20) Yongseok Lee, Korea Astronomy and Space Science Institute and School of Space Research, Kyung Hee University; (21) Byeong-Gon Park, Korea Astronomy and Space Science Institute and University of Science and Technology; (22) Richard W. Pogge, Department of Astronomy, Ohio State University; (23) Przemek Mroz, Warsaw University Observatory and Division of Physics, Mathematics, and Astronomy, California Institute of Technology; (24) Michal K. Szymanski, Warsaw University Observatory; (25) Jan Skowron, Warsaw University Observatory; (26) Radek Poleski, Warsaw University Observatory and Department of Astronomy, Ohio State University; (27) Igor Soszynski, Warsaw University Observatory; (28) Pawel Pietrukowicz, Warsaw University Observatory; (29) Szymon Kozlowski, Warsaw University Observatory; (30) Krzystof Ulaczyk, Department of Physics, University of Warwick, Gibbet; (31) Krzysztof A. Rybicki, Warsaw University Observatory; (32) Patryk Iwanek, Warsaw University Observatory; (33) Marcin Wrona, Warsaw University Observatory. Table of Links Abstract and Intro Observation Light Curve Analysis Physical Parameters Microlensing Planets in the (log s, log q) plane Summary and Conclusions References Abstract and Intro Abstract and Intro Observation Observation Light Curve Analysis Light Curve Analysis Physical Parameters Physical Parameters Microlensing Planets in the (log s , log q ) plane Microlensing Planets in the (log s s , log q q ) plane Summary and Conclusions Summary and Conclusions References References 4. Physical Parameters The bulge VD is modeled based on stars in the Gaia catalog (Gaia Collaboration et al. 2016, 2018). That is, we first find red giant stars in the catalog within 2 arcmin centered on the event location. We then derive their mean velocity and its dispersion in Galactocentric Cartesian coordinates ( x, y, z ) as defined in Han & Gould (1995). x, y, z This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Two Microlensing Planets through Planetary-Caustic Channel: Physical Parameters

প্ল্যানেটারি-কস্টিক চ্যানেলের মাধ্যমে দুটি মাইক্রোলেনসিং গ্রহ: শারীরিক পরামিতি

Zwei Mikrolinsenplaneten durch den planetar-kaustischen Kanal: Physikalische Parameter

Dos planetas microlentes a través del canal cáustico planetario: parámetros físicos

Deux planètes à microlentille via un canal planétaire-caustique : paramètres physiques

ग्रहीय-कास्टिक चैनल के माध्यम से दो माइक्रोलेंसिंग ग्रह: भौतिक पैरामीटर

惑星-コースティックチャンネルを通した2つのマイクロレンズ惑星: 物理的パラメータ

행성 가성 채널을 통한 두 개의 미세 렌즈 행성: 물리적 매개변수

Dois planetas microlentes através do canal cáustico-planetário: parâmetros físicos

Две микролинзирующие планеты через планетарно-каустический канал: физические параметры

Gezegensel-Kostik Kanal Üzerinden İki Mikro Mercekleme Gezegeni: Fiziksel Parametreler

Hai hành tinh vi thấu kính thông qua Kênh ăn da hành tinh: Các thông số vật lý

通过行星焦散通道观察两颗微透镜行星：物理参数

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