Microsoft unveils agent-based GitHub Copilot app at Build 2026, enhancing AI-assisted dev for productivity. #githubcopilot #microsoftbuild #aidevelopment #agentbased

New Contribution: "Why Object-Oriented Programming is not the best method to implement Agent-Based models" by Martin Hinsch. rofasss.org/2026/02/03/oop #oop #abm #objectoriented #agent-based #agentbased #rofasss #simulation #modelling

Global AI will follow permissioned, agentbased designs

$GENSYN The Decentralized AI Engine for Internet Scale Training Understanding Gensyn in the Easiest Way:- Training powerful AI models normally requires expensive GPUs and huge cloud infrastructure. Only big companies can afford it. $Gensyn changes this completely. It connects thousands of idle GPUs around the world from laptops, servers, data centers and turns them into one giant decentralized supercomputer for AI training. Whenever someone submits a job:- ➡️ $Gensyn finds available compute ➡️ Splits the training tasks ➡️ Verifies every computation cryptographically ➡️ Returns a correct fully trained model No AWS bills. No central control. Just decentralized GPU power. 🔵 How $Gensyn Works (Easy to Understand) Gensyn = task manager trustless verification layer You upload a model training job $Gensyn distributes the work across global GPUs Each device computes its assigned part Blocklevel proofs verify that every step is correct You receive the final trained model Its like turning the entire internet into one giant $GPU cluster. CURRENT FEATURES 1) RL-Swarm Distributed Training Layer The engine that lets many devices work together ✔ Splits tasks ✔ Handles coordination ✔ Removes central servers ✔ Supports large ML workloads 2) $BlockAssist - Trustless Compute Verification Gensyn’s cryptographic verification system. ✔ Ensures GPUs cannot cheat ✔ Every result is validated ✔ Fraud-proof training ✔ Accurate output guaranteed 3) $CodeAssist - Easy Developer Integration Makes connecting ML models simple. ✔ Quick job setup ✔ Works with PyTorch / TensorFlow ✔ Auto dataset handling ✔ Minimal configuration 4) $CodeZero - ZeroTrust Execution Layer Ensures training is transparent and decentralized. ✔ No central authority ✔ Full-proof logs ✔ Secure execution ✔ True decentralization These four components are already functional in the network. UPCOMING FEATURES 🔜 1) Containerized Model Training (Docker support for any custom ML environment) 🔜 2) AutoScaling Global Compute Network Network capacity grows automatically when workload increases. 🔜 3) $Gensyn SDK for Developers Fewline integration for startups & tools. 🔜 4) Advanced Proof-of-Compute Upgrades Faster verification stronger correctness proofs. 🔜 5) ML Agent Training Framework Agentbased learning tools built on decentralized compute. 🔜 6) Dataset Marketplace Open ecosystem for datasets needed for model training. These are confirmed future features in the roadmap. 🟡Why Gensyn Is Useful (Real Benefits) 1) Cheaper AI Training No cloud pricing. No GPU renting. 2) Massive Scaling Thousands of GPUs → faster training. 3) Accurate & Verified Results $BlockAssist ensures correctness without trust. 4) Permissionless Access Anyone can contribute compute or train a model. 5) Truly Decentralized AI No single company controls the network. 🟢 Where Gensyn Can Be Used Gensyn can power:- ✔ LLM training ✔ Fine-tuning ✔ Reinforcement learning ✔ Robotics simulations ✔ #AI agents ✔ Onchain AI systems ✔ #Gaming & simulation models ✔ Research labs & universities Basically anyone needing GPU compute. Simple, Direct Example (No Extra Story) A small AI startup wants to train a speech-to-text model but : They dont have GPUs Cloud providers are too expensive Training would take weeks With $Gensyn:- They upload the training job The network connects them to global idle GPUs RL-Swarm splits the job across all devices $BlockAssist verifies every computation The model is trained faster and at a much lower cost To the startup the entire process feels:- 1) Fast 2)Cheap 3)Smooth 4)Reliable Even though hundreds of devices worked behind the scenes. Final Summary:- $Gensyn is building the world’s first decentralized trustless AI training network a global compute engine that makes powerful AI accessible to everyone. The next revolution in AI infrastructure begins with $Gensyn

Big moves from @Infinit_Labs The AI-powered DeFi revolution is getting stronger! • Smarter agentbased strategies • Faster automation • Seamless cross-chain execution The future of autonomous finance is being built right here. #InfinitLabs #DeFi #AI #Web3

Big moves from @Infinit_Labs The AI-powered DeFi revolution is getting stronger! • Smarter agentbased strategies • Faster automation • Seamless cross-chain execution The future of autonomous finance is being built right here. #InfinitLabs #DeFi #AI #Web3

🚀 Exciting News! Our latest blog post is now live and is a must-read for anyone in the M&A space! It talks about agent-based AI systems for M&A drkarlpopp.com/karl-michael-… #MandA #AgentBased #AI

Điểm mạnh và tiềm năng nổi bật của @almanak 1. Mô hình Agent-Based cho DeFi thông minh Almanak giúp người dùng xây dựng, kiểm thử và triển khai các AI Agent tài chính theo quy trình 4 bước từ ý tưởng đến giám sát — bao gồm phân tích, chọn chiến lược, mô phỏng/đánh giá và triển khai, tất cả đều trong môi trường non-custodial an toàn. 2. Hỗ trợ người dùng từ chuyên gia đến cá nhân Dự án hướng đến việc cung cấp công cụ tương đương cấp độ quỹ phòng hộ (quant) nhưng dễ sử dụng, mở rộng tới người dùng bán lẻ trong tương lai. 3. Đa dạng nguồn doanh thu tiềm năng Theo tính toán từ các kịch bản thực tế: Phí giao dịch: Nếu Almanak chiếm 5% khối lượng giao dịch DeFi và thu 0.05% phí, có thể đạt ~$146 triệu doanh thu/năm. Phí hiệu suất trên TVL: Với giả định TVL là $1 tỷ và APY 10%, phí 5% trên lợi nhuận có thể tạo ra $5 triệu/năm. Phí tính toán mô phỏng: Nếu có 5,000 người dùng và mỗi người thực hiện 10 mô phỏng giá $50/lượt, với phí dịch vụ 5%, Almanak có thể thu ~$125k/tháng. Ngoài ra, dự án có thể khuyến khích các giao thức DeFi khác mua token ALMANAK để tăng tính ưu tiên cho chiến lược của họ — mở thêm nguồn động lực tăng trưởng hệ sinh thái. 4. Tiến trình triển khai rõ ràng Platform dự kiến ra mắt vào Quý 1/2025, bắt đầu với nhóm người dùng là các quants chuyên nghiệp, sau đó mở rộng ra thị trường bán lẻ. Phí TVL ban đầu được giới hạn ở mức $25 triệu để kiểm soát và tối ưu hóa chất lượng chiến lược — giúp đảm bảo kiểm soát rủi ro khi ra mắt. Hướng đến phát triển một “marketplace” các chiến lược đầu tư (vaults), nơi người dùng có thể đầu tư vào mà không cần tiết lộ chi tiết chiến lược, hoặc cho thuê lại chiến lược với tham số tùy chỉnh. 5. Dân chủ hóa tiếp cận AI trong DeFi Dự án giúp người dùng cá nhân sở hữu công cụ như quỹ lớn: xây dựng, huấn luyện và triển khai các chiến lược phức tạp mà trước đây chỉ các tổ chức mới làm được. Đây là một bước tiến đáng kể để “dân chủ hóa” quyền kiểm soát tài chính thông qua AI Agent. Tổng hợp nhanh: Tiềm năng trong 3 từ khóa Công nghệ Agent-Based Nguồn doanh thu đa dạng Dân chủ hóa DeFi-AI #DeFAI, #AgentBased, #Web3AI

Gm @almanak a web3 simulation platform, optimizes DeFi & gaming projects with AIdriven agentbased modeling. It maximizes protocol profitability while ensuring economic security. With $8.45M raised and $25M TVL, it’s a next-gen tool for portfolio management. #Almanak #DeFi #Web3

This is a game changer for DeFi. The move towards protocol-native finance with automated, agentbased execution is exactly what the space needs. Excited to see how V2 shapes the future of decentralized strategies.

Great partnership between @Zoro_ai and @NeurochainAI! Neurochain is building a decentralized AI inference network and Forknet.io, a custom Layer 2 on AggLayer. Together, they're enabling agentbased workloads to access on demand GPU inference and move seamlessly across chains, linking verifiable AI tasks with highperformance compute and real time crosschain liquidity.

🔥 Why It Matters $30M raised by Recall Labs to build the first unstoppable intelligence network Testnet live with agentbased AI and on-chain knowledge sharing Cookie.fun is the new frontier for social airdropswhere your voice is your wallet.@recallnet @cookiedotfun

Yes,Magic Newton UX is a breath of fresh air. Simplifying token swaps and eliminating unnecessary complexity is exactly what crypto needs. Their agentbased approach could be a gamechanger.

gCamp @campnetworkxyz an AInative Layer1 blockchain,introduces a Proof of Provenance protocol for onchain IP registration,enabling traceable origin, usage rights,and attribution.Its testnet features gasless IP registration,royalty distribution, and supports agentbased workflows

New contribution by Peter Steiglechner and Marijn Keijzer: "Make some noise! Why agent-based modelers should embrace the power of randomness" #RofASSS #ABM #noise #randomness #robustness #socialsimulation #taxonomy #simulation #science #method #agentbased rofasss.org/2025/05/31/noise…

What Makes Up Network’s AI Glasses Different? 👉Privacy First, On Device AI: Data is processed locally without being sent to the cloud. 👉Web3 Integration & Ownership: Users have true ownership of their digital assets. 👉Context-Aware Intelligence: Understands the user’s environment for smarter responses. 👉Powered by Google Gemini & XR Technology: Cutting edge AI and immersive tech combined. 👉AgentBased Operating System: Interactions are handled by intelligent, task focused agents.

The slides from my talk at the "Culture of the artificial" workshop in Copenhagen. Available at cfpm.org/slides/Culture-of-A… #narrative #simulation #socialscience #ABM #agentbased #stakeholders #policy #culture #LLM

Less than a week to apply for our positions in @GeoDSGlasgow team @UofGlasgow Find them all here: gdsglasgow.github.io/opportu… Apply for our #research Positions & join our wonderful #team 😊 While you can do amazing research, we want to #support each other to become the best in whatever you wish to be in your #career. You can develop your own team & lead, work with our partners, & supervise PhD and masters projects with a lot of flexibility #Postdocs #MissingData #Statistics #geospatial #UrbanAnalytics #AgentBased #modelling #RSE in #Geo_AI #AI

5/9 If we want to build #inclusive #cities & #society we need to know more about under-represented people as much as the majority of citizens. Apply for this 2y research associate/ fellow in #AgentBased #modelling if you have interest or expertise in #reinforcement #learning #computational #SocialScience or similar areas that help us understand their behaviour and needs jobs.ac.uk/job/DJN741/resear…

🚨New publication! @GustavoHdezM et al. from our group and beyond recently used #agentbased modelling to look at the impact of architectural interventions on the spread of SARS-CoV-2 in a hospital emergency department. Find out more 👉doi.org/10.1016/j.jhin.2024.…