Ahmad Ali Arshad@ahmadaliarshad0_0

InfraForge VLA is a language-controlled robotic manipulation pipeline built for Intel's Bimanual VLA Manipulation Online Challenge, part of the AI Infra Summit Hackathon. How it works: a plain-language instruction such as pick up the mug with arm A is parsed by a rule-based language interface into a structured action plan. That plan drives a simulated bimanual robot, built on MuJoCo and LeRobot's ALOHA environment, through a real pick-and-lift sequence using end-effector control. What is verified, not just claimed: pick-up succeeds and is reproducible, reaching reward 2 out of 4 for the right arm and 4 out of 4 for the left arm, both confirmed on real hardware. The pick-up mechanic is position-relative, computed from the object actual position using a measured geometric offset rather than hardcoded coordinates. We validated that this generalizes with a 10-seed randomized placement test, succeeding on 9 out of 10 seeds within an empirically measured safe range. Inference is benchmarked with Intel OpenVINO on real CPU and integrated GPU hardware, including an honest finding that for this small policy model, CPU outperforms GPU, since GPU dispatch overhead dominates at this model size. What is honestly still in progress: two-arm hand-off, passing the object between grippers, is not yet reliable. We root-caused the actual blocker through direct physics measurement rather than guesswork, documented in full in the repository technical notes. We prioritized a smaller, genuinely working, well-documented pipeline over a larger scenario attempted unreliably, given the online-only timeline.

AI Options Wheel Agent is an auditable autonomous trading system combining LLM-based reasoning, deterministic risk controls, and Alpaca paper trading infrastructure. The project explores a key question in AI trading: how much autonomy should an AI agent have, and where should deterministic controls take over? The agent implements an Options Wheel strategy across two portfolio states: CASH and SHARES. In CASH, it evaluates cash-secured put opportunities. After assignment, it transitions to SHARES and evaluates covered-call opportunities. When shares are called away, the cycle returns to CASH. Claude acts as the reasoning layer, evaluating pre-filtered candidates and proposing structured trading actions. The model cannot bypass risk controls — every proposal passes through deterministic validation before execution. The risk layer validates collateral, exposure, contract validity, option type, quantity, quote quality, liquidity, duplicate orders, portfolio state, and covered-call cost-basis constraints. Invalid or malformed output fails closed instead of reaching the broker. Alpaca provides market data and paper-trading execution via limit orders, with an auditable decision and transaction ledger. A Streamlit dashboard provides visibility into decisions, portfolio state, performance, orders, and trading history. The system includes replay-based evaluation, baseline-versus-agent comparison, adversarial testing, and live paper-trading validation backed by an 88-test automated suite. Live validation exposed and corrected real API response-shape assumptions around option-chain data, open interest, and order parsing. The core principle: AI reasons. Deterministic systems enforce safety. Alpaca executes. The goal is not unrestricted AI control, but a verifiable, testable, auditable boundary where AI reasoning operates within deterministic safety constraints.

Every company runs on knowledge scattered across policies, SOPs, and the heads of whoever's been there long enough to remember the exceptions. AI agents don't have that instinct. Most "enterprise AI" tools just search documents and hand back a paragraph — which isn't enough for an agent to safely act on its own. BrainOS reads uploaded company documents and builds a structured company brain: entities, rules, and reusable Skill Cards — an auditable, executable definition of a business process, with its inputs, conditions, and action. Ask BrainOS a real question, and it retrieves the matching skill, checks each condition against the situation, and returns an answer with a full step-by-step reasoning trace, not a black-box response. The pipeline runs document upload, text extraction, LLM-based entity and skill extraction, vector storage in Qdrant, and agent reasoning, all through a provider-agnostic API layer. During development we used the Google AI Studio (Gemini) API; for judging, the same code path points at Fireworks AI, which serves inference on AMD Instinct GPUs, via a one-line environment swap. The working prototype: upload a policy document, watch entities and skills extract in real time on the dashboard, inspect the generated Skill Card, then ask a live question and watch the reasoning trace resolve. Not a search tool wrapped in a chat window — the structured knowledge layer AI agents need to act safely inside a real company.

VisionPay is a next-generation Agentic Commerce system built on the Arc Layer-1 blockchain, showcasing the future of the Machine-to-Machine (M2M) economy. It enables AI agents to autonomously understand user intent, reason over purchasing decisions, and execute payments using USDC—without manual intervention. The Problem: Modern commerce is fragmented and manual. AI systems can recognize products, and blockchains can process payments, but there is no unified system that allows an AI agent to autonomously connect visual understanding with onchain transactions. The Solution: VisionPay bridges this gap by combining Google Gemini 1.5 multimodal vision with Arc’s EVM-compatible settlement layer, enabling end-to-end autonomous purchasing driven by visual input. How It Works (Agentic Flow)? • Multimodal Perception: Users upload an image of a product (live camera or gallery). Gemini 1.5 Flash analyzes the image to identify the product, brand, and context. • Autonomous Reasoning: Using a LangGraph-based agent, the system acts as an independent broker—searching for comparable products and selecting the best available price. • USDC-Native Settlement: Once a decision is made, the agent constructs and signs a transaction via Web3, executing payment directly on the Arc Testnet using USDC as native gas, ensuring stable and deterministic settlement. Why Arc & Circle? Arc’s native USDC architecture removes volatility and currency-swap friction, allowing AI agents to hold and spend a stable, programmable currency. Circle’s USDC enables secure, transparent, and trust-minimized autonomous transactions for goods, APIs, and services. Key Technologies: AI: Google Gemini 1.5 (Multimodal Vision & Reasoning) Blockchain: Arc Layer-1 (Testnet) Currency: USDC (Native Settlement) Backend: FastAPI, LangGraph, Web3.py

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🌎 Hybrid Hackathon | 💻 Online Build: 3–8 November 2026 | On-site Phase: 8 November 2026 📍 On-site Venue: Pierpont Place 163 W Pierpont Ave, Salt Lake City, UT 84101, USA ✨ Please note that travel and accommodation are not covered.

🌎 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.