🤝 Top Collaborators
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🤓 Latest Submissions
Loop
The problem ADHD executive dysfunction rarely shows up as "not knowing what to do." It shows up as an inability to organize racing, unstructured thoughts fast enough to act on them. Someone can know they need to email their boss, pay rent, and stop worrying about a project — all in the same breath — but converting that into a clean, prioritized action plan is exactly the step that stalls. The solution Loop removes that step. Open the app, tap the mic, and talk freely — no structure required. Loop listens, transcribes live, and organizes the mess into a clear, triaged list: Tasks, Reminders, and Open Loops. It distinguishes real actionable items from vague recurring worries, so nothing gets force-fit or lost. How it works Tap to record — talk freely, with a live transcript streaming on screen as proof it's listening Tap to stop — a brief "sorting…" moment while AssemblyAI's LeMUR structures the ramble See a triaged list: Tasks / Reminders / Open Loops Act immediately, or hand the decision to Guide Me mode Guide Me — the core differentiator Most task apps stop at "here's your organized list" and leave the deciding to you — which is exactly the step that stalls an ADHD brain. Guide Me asks two quick-tap questions (how much time, how much energy you have right now), then picks one task, explains briefly why it fits, and gives a concrete first physical step to start it. It solves task-paralysis, not just organization. Built on AssemblyAI Real-time streaming transcription captures speech live, showing captions as the user talks — this is what makes capture feel immediate and trustworthy rather than a black box. LeMUR handles every structuring step: turning a raw ramble into categorized items, detecting when a new thought duplicates an existing task, and deciding when a follow-up ramble should update an existing task rather than create a new one.
30 Sep 2026
TrustShield
**TrustShield AI** is an explainable and accountable AI governance layer for cybersecurity. It addresses a critical gap in AI-powered security systems: they can detect threats and make decisions, but often fail to clearly explain why a decision was made, what evidence supports it, and when human oversight is required. Our MVP focuses on **suspicious login detection**. TrustShield analyzes signals such as unknown devices, suspicious IP reputation, failed login attempts, unusual locations, and unusual login times. It generates a risk score, confidence level, supporting evidence, and a human-readable explanation. The decision then passes through an **Accountability Engine** that determines whether AI can act autonomously: • Low Risk → Automatically Allow • Medium Risk → Analyst Review • High Risk → Human Approval Required • Low Confidence → Escalate to Human For high-risk events, AI cannot directly execute the recommended action. A security analyst can **Approve, Reject, or Escalate** the recommendation. Every decision is recorded in a complete audit trail, including the security event, AI analysis, evidence, explanation, accountability policy, human action, and final outcome. ### Core Workflow **DETECT → EXPLAIN → EVIDENCE → GOVERN → HUMAN CONTROL → AUDIT** What makes TrustShield different is that it focuses on **what happens after AI makes a cybersecurity decision**. Instead of simply saying "AI detected a threat," TrustShield shows why the AI made the decision, what evidence supports it, how confident it is, what policy governs the action, whether human approval is required, and what ultimately happened. TrustShield AI demonstrates a practical approach to making AI-powered cybersecurity more **explainable, auditable, accountable, controllable, and honest about uncertainty**. **TrustShield AI: The trust and accountability layer for AI-powered cybersecurity.**
10 Aug 2026
PakAI Capital | AI Investment Intelligence for PSX
PakAI Capital is an enterprise-grade autonomous AI Investment Intelligence Platform that replaces traditional chartered financial analysts for Pakistan Stock Exchange (PSX) investors. Built with Google Gemini 2.0 Flash, the system deploys a coordinated team of six specialized agents: Research Analyst Agent — Performs fundamental analysis using RAG over company filings Macro Economist Agent — Evaluates SBP policy, PKR, inflation, and global signals Sentiment Analyst Agent — Processes real-time news from Dawn, Business Recorder, and more Risk Manager Agent — Calculates volatility, position sizing, and risk scores Portfolio Manager Agent — Synthesizes all signals into clear BUY/HOLD/SELL recommendations Governance & Compliance Agent — Audits every recommendation for hallucinations, bias, and missing disclosures Key Highlights: Real-time PSX data ingestion + news scraping ChromaDB / Pinecone RAG pipeline for grounded analysis LangGraph orchestration for reliable multi-agent workflow Beautiful Next.js dashboard with live agent reasoning feed Strong focus on explainability and AI governance — directly aligned with DeepMind’s enterprise AI priorities This submission targets Track 2: AI Agents with Google AI Studio and Track 1: Agent Security & AI Governance.
19 May 2026
