Sana Adeel@SanaAdeel

Problem Flaky tests — tests that pass or fail inconsistently with no underlying code change — are a quiet but expensive problem in software engineering. They erode trust in CI pipelines until "just rerun it" becomes standard practice, they hide genuine bugs behind statistical noise, and diagnosing them usually means a developer manually re-running a suite dozens of times while guessing at root causes from stack traces. There's no standard tooling that treats flakiness itself as a first-class problem to detect, explain, and fix. Solution FlakyTestHunter automates the entire flaky-test workflow using a 3-stage IBM Bob 2.0 subagent pipeline: Detector — runs the target test suite 10 times in a row and reports the raw pass/fail pattern, flagging any test with inconsistent results. Diagnosis — reads the actual source code behind the failing test and explains, in plain English with exact line references, the root cause of the non-determinism (unseeded randomness, timing dependencies, etc.). Fix & Proof — applies a deterministic fix (typically mocking the non-deterministic source), then re-runs the suite 10 more times to prove the fix works, reporting a final pass/fail ratio. Every stage is a genuine, live Bob Shell task — invoked headlessly via bob run --accept-license from a FastAPI backend, with zero human approval needed mid-run. Nothing is scripted or faked. What makes this a complete, working product (not just a proof of concept): Live deployed backend on Render, running inside a Docker container with Node.js, Python, and the Bob Shell CLI all installed non-interactively — solving a genuine installer/PATH challenge to get Bob running unattended in a cloud container. Live deployed frontend on Netlify, offering three ways to see the pipeline in action: an instant cached demo (real, previously-captured Bob output), a live "Run Live" button that triggers a fresh Bob run on the deployed backend in real time (~1-2 minutes, real Bobcoins spent), and an Upload & Fix feature where anyone can upload their own Python file and watch Bob diagnose and fix it live, with the corrected file downloadable immediately. Proven generalization: we validated the pipeline not only against our seeded demo app (a function with an intentional ~33% failure rate from unseeded random.uniform), but also against a completely different, previously-unseen flaky pattern (time/latency-based) submitted through the live upload feature — confirming the approach isn't overfit to one example. Real, transparent cost data: every run reports its actual Bobcoin cost (typically ~0.3–0.7 Bobcoins per full pipeline), all well within the 40-Bobcoin trial budget, and all of it is visible in the raw Bob session output shown directly on the site. Full transparency: 10 real, unedited screenshots from actual Bob Shell terminal sessions are included in the repo and displayed in-app, so nothing about the Bob usage is staged or summarized after the fact. FlakyTestHunter is built for any engineering team that has flaky tests sitting in their CI right now and wants an AI reviewer that doesn't just flag the problem, but explains it and proves the fix.

FailSim AI is a multi-robot failure analysis platform that helps robotics companies discover why their robots fail before building physical prototypes. Instead of weeks of manual testing, teams can run 1000+ physics-based simulations overnight using PyBullet with real robot specifications (Kuka iiwa7, Franka Panda, UR5, or custom robots). Our platform uses Gemini 3 Flash AI to automatically analyze failure clusters and explain the physical mechanisms causing failures. The system provides interactive features including experiment configuration, real-time 5-frame simulation visualization, failure distribution charts, and click-to-analyze AI insights for individual runs. Built on Vultr infrastructure with a React dashboard, FastAPI backend, and deployed 24/7 at http://80.240.20.49. FailSim AI reduces testing time by 10x, cuts costs by 90%, and enables teams to fix issues in simulation rather than expensive hardware iterations. Perfect for robotics startups, QA teams, and research labs validating designs before real-world deployment.

ComplyFlow AI is an AI-powered compliance and workflow automation copilot designed to help organizations review documents against internal policies quickly and accurately. It uses multi-agent AI to analyze policy alignment, assign risk scores, and recommend workflow decisions such as approve, flag, or escalate. By automating complex compliance checks, ComplyFlow AI reduces manual effort, minimizes human error, and accelerates decision-making in regulated environments. The system combines document retrieval, intelligent reasoning, and explainable audit trails, making it suitable for real-world use cases in finance, operations, HR, and risk management while remaining transparent and easy to integrate into existing workflows

**Autonomy when you want it, Control when you need it, Billing infrastructure for AI agents** AgentInvoice is a dual-mode agentic invoicing and escrow system designed for the next generation of autonomous commerce on Arc. It enables AI agents and humans to create, approve, escrow, and settle USDC invoices securely using trustless smart contracts and Circle infrastructure. The core innovation of AgentInvoice is its dual-mode payment architecture. In Autonomous Mode, AI agents can independently negotiate terms, generate invoices, lock USDC into escrow, and release payments once predefined conditions are met — without human intervention. This is ideal for agent-to-agent commerce, API monetization, and automated service marketplaces. In Manual Mode, enterprises and users can require human approval before escrow funding or release, providing compliance, safety, and governance while still leveraging the same on-chain infrastructure. AgentInvoice uses USDC as the settlement asset and executes all transactions on Arc, ensuring deterministic finality, transparent verification, and predictable fees. Smart contract escrow guarantees trustless execution, automatic refunds, and deadline-based releases. To support adoption, AgentInvoice includes a developer-first toolkit: SDK for integrating agentic billing into applications REST APIs for invoice creation, escrow management, and settlement CLI tools for developers and operators Monitoring dashboards for invoice and escrow lifecycle tracking This allows builders to easily add agentic payments to AI systems while giving enterprises the controls they need to safely adopt autonomous workflows. AgentInvoice bridges the gap between fully autonomous AI commerce and real-world business requirements, making agentic payments practical, secure, and production-ready.

Qubic Sentinel is a real-time blockchain monitoring and alerting system built for Track 2 – EasyConnect Integrations. It continuously polls Qubic’s RPC endpoints, detects whale-sized token transfers, and instantly forwards alerts to any webhook-enabled platform—such as Discord, Slack, Make, Zapier, n8n, or Google Sheets. Designed for traders, analysts, and automation builders, Qubic Sentinel turns raw blockchain activity into actionable, real-time intelligence without requiring smart contracts. Key Features Real-Time Whale Detection Detects high-value QU transfers in live ticks and triggers alerts in under a second. EasyConnect Webhook Integration Works seamlessly with any EasyConnect-compatible webhook, enabling no-code automation workflows. Multi-Deployment Architecture React dashboard (UI for demos & visualization) PowerShell sentinel (Windows server monitoring) Node.js service (cloud deployment or Docker) How It Uses EasyConnect Qubic Sentinel sends structured webhook payloads directly to EasyConnect-compatible URLs. These payloads can be used to: Create Discord or Slack notifications Log whale movements into Google Sheets Trigger trading bots or AMM automation Update dashboards or market monitors Send Telegram or email alerts Chain workflows in Zapier, Make, or n8n This makes Qubic Sentinel a zero-code blockchain monitoring engine that bridges Qubic’s live blockchain data with the broader automation ecosystem. Why It’s Useful Traders monitor whale movements for sentiment and trend shifts DeFi teams track treasury or contract activity Analysts log big transfers for research Communities get real-time notifications Builders use it as a trigger engine for more complex automations The app turns raw blockchain events into instant, automated signals, empowering both developers and non-developers.

The SME Loan Risk Meta-Validator is an end-to-end intelligent workflow designed to streamline and modernize SME credit assessment. It automatically ingests loan applications from spreadsheets, portals, and supporting documents, then extracts and normalizes key information using OCR and AI-driven field extraction. The system enriches each application with external credit bureau and sanctions screening data, evaluates data quality and consistency, and applies transparent rule-based checks to enforce lending policies. For eligible cases, an AI risk model produces nuanced risk and eligibility scores, which are combined with rules to determine whether to auto-approve, auto-reject, or escalate for human review. An agentic policy reviewer ensures alignment with credit policies, fairness guidelines, and regulatory expectations. Every application generates a complete audit artifact—including inputs, risk reasoning, rules fired, and review actions—providing full traceability for risk teams and regulators. This workflow improves decision accuracy, reduces manual workload, ensures compliance, and delivers a scalable, consistent framework for SME lending.

AI Subscription Payment Agent on Arc is an autonomous, blockchain-based subscription system designed to simplify micropayments for content creators. Built on the Arc blockchain, it leverages native USDC for stable, on-chain recurring payments while minimizing transaction fees through Arc’s deterministic fee model. The system integrates Solidity smart contracts for subscription registration and automated payment execution, along with an off-chain AI agent that dynamically adjusts subscription pricing based on user engagement metrics. A web frontend allows users to manage subscriptions, monitor payments, and interact seamlessly with the AI-powered system. This project demonstrates the fusion of blockchain, AI automation, and real-world financial utility, offering a scalable solution for content monetization.

QAaaS-MVP (Quality Assurance as a Service) implements the “Internet of Agents” concept by connecting multiple specialized AI agents into a cohesive system. Each agent performs distinct tasks—repository cloning, automated testing, code aggregation, and unit testing—while communicating through a central server for orchestration. This project demonstrates real-time agent registration, heartbeat monitoring, and task execution, allowing developers to automate QA pipelines efficiently. The modular architecture supports easy addition of new agents, integration with GitHub repositories, and optional Coral Studio monitoring. QAaaS-MVP showcases scalable AI-driven automation, making software development faster, more reliable, and smarter.

CoachAI is an AI-powered personal assistant designed to simplify learning new applications. Users provide natural language instructions, and CoachAI automatically executes the actions on a virtual canvas while logging every step in an interactive transcript. Each step can be replayed on hover for better understanding, and the entire session can be exported as PDF or Word for later reference. By leveraging GPT-5 Agent Mode, CoachAI reduces the time and frustration associated with mastering new apps, empowering users to learn faster, more efficiently, and confidently. It’s scalable to any app, providing a seamless learning experience.

💻 Online Hackathon | ⏳ Build: 48 hours | 📅 September 25–27, 2026 The official IBM Bob 2.0 hackathon. Build what's next in AI-assisted development, alongside the developers and experts shaping it. ⭐ Get help from experts throughout the event 🤝 Join a team or build solo 💰 Prize pool: $12,000 🧑🏻‍💻 Register before the Kick-Off Stream to start building with Bob

⏱️ Design, build, and deploy trustless AI trading agents. 📅 March 30 – April 12, 2026 🌐 Build AI trading agents that execute strategies using ERC-8004 or Kraken CLI. 🤝 Build solo or in teams to create autonomous trading, risk, yield, or protection agents. 💰 $55,000 prize pool, allocated and shared via Surge and Kraken. Your team should have registered project at early.surge.xyz in order to be eligible for the prize. Get access in your team profile or our discord 🔑 Access to early.surge.xyz If prompted for login credentials, use: • Username: admin • Password: JBRv2xWG7AzwVrLz88

Teams work together with multiple AI agents in shared workspaces to design, build, and launch complete AI products — from initial idea to market-ready app. 📅 February 23– March 1, 2026 ⏱️ 6 days to build and ship an end-to-end agentic AI product 🤝 Work together in shared AI workspaces where your team and AI agents collaborate in real time 🔧 Build with multiple AI agents that handle development, design, testing, and documentation together 🎯 Create everything in one place — from ideation to product to marketing materials, all by prompting and observing 🚀 Focus on real-world products that teams could actually ship and use 🏆 $3,500 in prizes + premium 3-month Complete.dev access for top teams 🧑‍💻 Join your team workspace and start building together