Muhammad Muavia Ateeq@muaviaateeq

Small companies receive many supplier invoices and delivery notes by email, in different formats and languages. Staff retype them by hand, miss mistakes, and sometimes pay the wrong amount. Supplier Document Desk is an AI agent that does this check automatically. A supplier document arrives by email or upload. An Evolus workflow reads the file and turns it into structured data. An open model, served by us with vLLM, checks the document, explains problems in plain words, and writes a polite reply to the supplier in the supplier's language. Our own code does all the math and matching, so the AI never guesses a number. It checks line totals, tax, purchase order match, price and quantity differences, duplicate invoices and changed bank details. Personal data is hidden before anything is shared. Clean documents are approved automatically. Unclear or risky cases go to a human review queue. [AMD SENTENCE: see below] We tested the system on [N] fake documents in [languages]. Results: [X]% field accuracy, [Y]% of planted errors caught, and about [Z] seconds per document compared with [W] minutes by hand. The product saves staff time and reduces payment mistakes. Companies would pay a small monthly fee per team.

Tablekeeper is an autonomous software engineering project built for the Dark Factory Hackathon by WeAreDevelopers and BAND. Under strict dark factory rules, zero lines of application code were written by humans. Our team acted solely as factory builders, defining generic mandates and orchestrating three autonomous AI agent seats inside BAND Desktop: Coordinator, Developer, and Reviewer. The Coordinator parsed the official track specification into structured work items, managed room handoffs, and maintained strict stage isolation. The Developer autonomously implemented the complete Stage 1 REST JSON API service, resolving complex domain requirements such as concurrency locking to prevent double-booking, safe idempotent write replays, atomic reservation transactions, state export and import, and IANA timezone daylight-saving transitions. The Reviewer independently inspected commits and verified test runs. The completed backend was verified in an isolated container environment against the official hackathon test harness, achieving a clean pass across all 120 test cases. The project repository includes generic mandate files passing the swap test, the complete room.json collaboration log, and container deployment files.

AI coding tools now generate code faster than humans can review it safely. A developer can ship hundreds of lines in minutes, but speed creates new risks: hardcoded secrets, hallucinated packages, missing auth checks, prompt injection attacks, and spec violations that slip through human review undetected. TrustGate is an automated merge gate built to catch these AI-specific failure modes before code reaches production. When a pull request is submitted, five specialized IBM watsonx-powered checkers run in parallel on the code and deliver a clear PASS, REVIEW, or BLOCK verdict with exact cited evidence file path, line number, and quoted snippet before the code is allowed to merge. The five checkers are: Security Reviewer (finds hardcoded secrets, SQL injection, missing auth), Spec Conformance (reads the PRD and compares it against code using document understanding), Injection Scanner (finds prompt injection attempts in comments and README), Dependency Verifier (catches hallucinated PyPI packages), and License Checker (detects GPL violations in MIT projects). Every finding must cite exact evidence or it is not reported. This eliminates hallucinated false positives that make AI security tools untrustworthy. The verdict engine scores findings (high=5, medium=2, low=1) and forces a BLOCK on any single high-severity finding regardless of total score. TrustGate integrates directly into GitHub via a GitHub Action that fires on every pull request and posts the verdict as a PR comment. The Explainable Trust Dashboard shows judges and reviewers exactly why a PR was blocked, with a human-approval-with-reason override flow that demonstrates responsible AI deployment. Built in 48 hours for the IBM Bob 2.0 Hackathon.

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🌎 Hybrid Hackathon | 💻 Online Build: 12-17 October 2026 | On-site phase: 17-18 October 2026 To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. The AMD Developer Hackathon: ACT III is a hands-on event for developers, founders, engineers and builders who want to push what’s possible with AI on real infrastructure. 📍 If approved for the on-site experience, you'll also have the opportunity to showcase your project live. Travel and accommodation will not be 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