Idowu Adigun @mike2509

Two simulated SO-101 arms cooperate to set a dinner table in MuJoCo. One arm opens a drawer and retrieves cutlery, the arms perform a contact-based spoon hand-off, and a plate, cup, spoon and fork are placed on the table. Object poses, scene conditions and approach paths are randomized per seed, so each run requires the system to solve a slightly different task rather than replay a fixed trajectory. A cooperative scripted expert, built with inverse kinematics and contact-driven manipulation primitives, generates demonstrations for training. Across ten randomized evaluation seeds, the expert completes the full workflow with no monitored unintended collisions or numerical warnings. Demonstrations are exported as versioned LeRobot datasets and published as pinned Hugging Face releases for reproducibility. We fine-tuned SmolVLA on an Azure Tesla T4. The final multi-task dinner model was trained for up to 5,600 steps and early-stopped at the best validation checkpoint, step 4,749, with validation loss 0.0100. An earlier 20,000-step run on a 50-episode cup dataset established the training and evaluation pipeline. Policies are evaluated closed-loop on held-out seeds, with predicted actions validated before execution. Learned task success is not yet consistently reliable, and the scripted expert is used as the demonstration source and baseline rather than presented as the learned VLA. For Intel deployment, the fine-tuned policy exports to OpenVINO in FP16 and INT8 using static-shape prefix and denoise graphs. FP16 output differs from PyTorch by a maximum absolute action error of 0.00029, and the exported policy runs closed-loop in simulation. The pipeline supports CPU, iGPU and NPU benchmarking. Final Intel Core Ultra measurements are the remaining deployment step; current latency results are build-machine measurements only.

xRobotics is building AI-powered autonomous drones for high-risk, real-world operations, starting with fire response. Fire scenes are dynamic, dangerous, and information-heavy. Today, most drone solutions provide raw video feeds that still require human interpretation under pressure. Our system combines scene intelligence with goal-based autonomous navigation. The drone can analyze visual input, detect fire, generate structured insights, and reposition itself intelligently to capture better vantage points. We separate perception intelligence (vision-language reasoning) from control intelligence (reinforcement learning-based navigation), enabling modular development and scalable deployment. Fire detection is our initial use case, but the broader vision is a general-purpose autonomous aerial robotics platform capable of operating in emergency response, industrial inspection, and delivery logistics, starting in Africa and scaling globally.

Financial Assistant Bot that helps you with your finances. This bot has contextual awareness of user information capable of building users to make actionable financial decisions that will cost a lot more than a professional advisor. In addition to that, it helps to navigate around the app to take action like sending funds, setting a savings plan, taking a loan or any other action that the user needs to achieve. This solves a lot of problem like the time you take to carry out an action, improve your experience on the application and also ensure that your financial actions are guided. In addition to these it will also provide insight into your finances if you want.

"In the workplace, conflicts and issues can occasionally arise that are not always promptly addressed with the HR department, as both employees and HR professionals may be concerned about the potential impact on the company's reputation. These issues can encompass a range of topics, including but not limited to matters such as sexual harassment, bullying by peers or superiors, and disputes related to Garden Leave. LawMinds serves as a preliminary overview of the workplace disputes that our AI chatbot solution aims to address. Our goal is to create a supportive chatbot that employees can turn to when facing everyday workplace stress, heavy workloads, team dynamics challenges, or instances of managerial feedback. This chatbot will serve as a trusted ally, assisting individuals in mapping out their plans and setting achievable goals, resolving disputes within specified timeframes."

🌎 Online Hackathon | 💻 Online Build: Sept 26 - Oct 5, 2026 A virtual, global hackathon. Build from anywhere in the world. Dark Factory is the official hackathon of WeAreDevelopers World Congress North America, presented by BAND, but you don't need to be in San Jose to take part. For one week, you build a software factory in BAND Desktop: a band of coding agents that plans work, implements it and checks its own results. 🎟️ Bonus for sign-ups: you are eligible for a free ticket to WeAreDevelopers World Congress North America in San Jose (Sep 23–25). If you do go, stop by the BAND booth: a surprise is waiting for you.

🌎 Hybrid Hackathon | 💻 Online Build: September 10–16, 2026 | 📍 On-site: September 15–17, 2026 The official hackathon of AI Infra Summit — co-organized by Kisaco Research and lablab.ai. Build production-grade AI applications on the modern AI infrastructure stack, alongside 8,000+ attendees, researchers, founders, and infra leaders. 📍 On-site Venue (September 15–17): Santa Clara Convention Center 5001 Great America Pkwy, Santa Clara, CA 📍 On-site participation is by invitation only. Travel and accommodation expenses will not be covered.

🌎 Online Hackathon | 💻 Month-long challenge | Dates: Sep 1–30, 2026 | 🏆 $10,000 Prize Pool ($5k cash + $5k in AAI credits) A month-long online challenge run by lablab.ai together with AssemblyAI, the Voice AI infrastructure company for builders. Every participant builds on AssemblyAI. You can join at any point — registration stays open for the whole build window, so you can start on day one or pick the project up halfway through and still submit. 🌍 Fully online hackathon. Join and build from anywhere in the world.