Muhammad Ibraheem Ashraf@Ibrahimjatt

Tablekeeper is an advanced Executive Tablekeeper & Concierge Suite engineered to modernize live restaurant and hospitality operations. Built around a robust Flask backend and a responsive front-end architecture, the platform coordinates five core operational zones—Drive-Thru Lanes, Front Counter, Self-Order Kiosks, Global Dispatch Hub, and the VIP Executive Lounge—providing real-time visibility and instant table allocation. Developed through a rigorous 4-stage autonomous pipeline, Tablekeeper achieves exceptional reliability with 575 passing cumulative execution checks, thorough UI optimization (scoring 5.47:1 contrast ratios), and dynamic theme persistence (Dark Glass, Light Clean, Editorial Cream). It streamlines high-velocity dining queues, integrates secure user authentication with comprehensive error handling, and features an instant CSV reporting module for operational audits. Designed for modern hospitality venues, Tablekeeper bridges automated agent workflows with practical commercial execution, delivering seamless multi-zone coordination and elevated guest experiences.

Modern engineering teams operating complex distributed systems face a critical challenge: high Mean Time to Resolution (MTTR) during unexpected production incidents. When alerts trigger, Site Reliability Engineers spend valuable time manually correlating logs, analyzing stack traces, and navigating complex source code to isolate root causes. This manual diagnostic bottleneck often leads to extended downtime and increased operational costs. Our solution, OpsPilot AI, introduces an autonomous SRE assistant embedded right into the developer and reliability workflow. The platform ingests error traces and source code snippets through an interactive web workspace hosted live on Vercel, applies advanced Gemini LLM reasoning to parse syntax trees and runtime context, and instantly delivers an evidence-backed diagnostic summary alongside a secure, production-ready code patch. The platform is purpose-built for SREs, DevOps engineers, and backend developers. Users interact with OpsPilot AI via a sleek, dark-mode enterprise web console where they input error histories or buggy source code snippets and execute autonomous triaging with a single click. What makes OpsPilot AI unique is its combination of real-time SRE telemetry formatting, automated confidence scoring (98.7%+), and robust fault-tolerant handling. By automating the tedious preliminary phase of root-cause analysis, OpsPilot AI reduces median patch-ready time down to minutes, ensuring engineering teams maintain maximum uptime with complete human control.

OpsPilot AI is an advanced, enterprise-grade autonomous SRE and incident remediation platform designed to eliminate alert fatigue, drastically reduce mean time to resolution (MTTR), and automate infrastructure diagnostics safely. By deploying a specialized multi-agent mesh—including Telemetry, Diagnostic, Adversary, Risk, and Allocator agents—OpsPilot AI doesn't just surface alerts; it actively investigates root causes, challenges its own diagnostic conclusions through adversarial validation, and executes verified mitigation workflows. Engineered for modern cloud environments, the platform features a dynamic model routing engine supporting both Gemini and Claude backends, live incident scenario simulation, and an Obsidian-style topology graph for deep architectural visibility. Security and compliance are built into its core through zero-trust guarantees, PII data scrubbing prior to model inference, TLS 1.3 encryption, and alignment with enterprise compliance standards. Furthermore, OpsPilot AI integrates real-time speech capabilities via Speechmatics to enable voice-driven automation, seamless transcriptions, and interactive incident management teams can talk to in real time. Backed by verified performance metrics achieving an 84 percent reduction in MTTR, OpsPilot AI bridges the gap between raw telemetry and autonomous, fail-safe infrastructure resilience.

Teams often have many meetings where the same topic is discussed again and decisions change over time. Finding the final decision manually requires opening several recordings or transcripts and comparing them. This project solves that problem by creating a searchable voice memory for meetings. 1. Search multiple meetings instead of one transcript at a time. 2. Understand the meaning of discussions using semantic retrieval. 3. Track how a decision changed from one meeting to another. 4. Give a final answer instead of simply returning raw transcript text. 5. Provide evidence with exact timestamps. 6. Allow the user to jump directly to the original audio.

AlphaPilot AI is an end-to-end automated options paper-trading platform built to deliver transparent, rule-backed execution with institutional capital discipline. The core engine continuously monitors SPY using a multi-factor technical matrix—combining SMA20, SMA50, RSI, MACD, and volume telemetry—to generate deterministic BUY or NO-TRADE signals paired with a real-time confidence score. Upon issuing a validated BUY signal, the platform’s algorithmic contract scanner searches, filters, and ranks option chains based on Days to Expiration (DTE), strike proximity, open interest, and liquidity metrics. Before any order is routed, the Entry Manager enforces strict safety controls, including technical confidence verification, active position collision checks, duplicate order protection, option quote validation, and strict portfolio risk limits. Once a paper position is active, the Position Monitor continuously tracks real-time price action, unrealized P&L, Stop Loss, and Take Profit thresholds. The engine automatically triggers paper exits when pre-established risk limits are met. All executed trades are permanently logged to a CSV audit ledger and analyzed through a comprehensive Performance Intelligence module to calculate win rates, risk metrics, and cumulative P&L. AlphaPilot AI is strictly configured for paper trading via the Alpaca API, allowing the entire algorithmic trading workflow to be thoroughly evaluated in real-time market conditions with zero capital risk.

🌎 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.

💻 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

🌎 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.