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Freemium
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Google Maps shows you where stores are; CornerScout helps you discover where your next store should be. Choosing the right retail location is one of the most important decisions a business can make. Yet traditional site selection relies on static demographics, expensive surveys, and incomplete estimates of foot traffic. CornerScout introduces a new source of intelligence: what is actually happening on the street. By transforming real-world street footage into actionable insights, CornerScout helps entrepreneurs, retail chains, and enterprise expansion teams identify promising locations based on observed activity, neighbourhood characteristics, and potential customer audiences. Instead of just showing where people live or where businesses already exist, CornerScout reveals how neighbourhoods actually function. **How it works:** 1. VASTData holds city neighborhood public footage indexed. CornerScout reads that index. 2. NVIDIA captioned those segments with Cosmos. The returned rows report nvidia/cosmos3-nano-reasoner. Those captions are the segment grid, and they drive the city report. 3. CoreWeave (Weights & Biases) looks at the pictures. Gemma4 W&B Inference counts and caches people, cars, cyclists, and dogs in those 180 frames. When you name a business, a text model on the same API picks the customer audiences 4. Cursor was used to speed-up development of the platform
Listed tools (7)
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SpaceXAI (Cursor)pendingAI Dev Platforms tool
CoreWeave (Weights & Biases)pendingAI Dev Platforms tool
Public-source project proof for Chat LangChain. The project demonstrates a documentation assistant agent, a public chat surface, LangChain agent patterns, and a Next.js frontend. Curated as public source evidence, not an ownership claim.
Public-source project proof for the Vercel AI Chatbot template. The repo and template page show a production-oriented AI chat app using Next.js, Vercel deployment primitives, model provider routing, and durable chat storage. Curated as public source evidence, not an ownership claim.
Listed tools (4)
Scroll for moreSource-labeled signals for cost, reliability, security, integration, and adoption proof. Missing compliance evidence is shown as a gap, not guessed.
Freemium profile signal; exact enterprise terms need buyer review.
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ESCAPE ROOM is an autonomous cyberdefense system for AI agents. It detects security breaches, investigates root causes, executes authorized remediation, and verifies recovery with real evidence. Built on Docker, it simulates unauthorized network access and synthetic canary exposure. Semgrep detects configuration drift, while real container probes confirm breaches. An autonomous Incident Commander selects allowlisted actions, remediates threats, and retests containment. Four recorded scenarios demonstrate its capabilities: STANDARD BREACH : Restores configuration and verifies recovery. FAILED VERIFICATION : Detects a failed repair, identifies a persistent override, quarantines it, and retests. OUT OF AUTHORITY : Refuses unauthorized actions and escalates to a human. REPEATED DRIFT : Blocks recurrence and generates a regression rule. Technologies: • Semgrep: Threat detection and prevention rules. • ClickHouse Cloud: 83 security events uploaded and verified. • OpenAI GPT-4o mini: AI-assisted incident analysis. • GitHub: Incident reports published and verified via API. • ElevenLabs: AI-generated cinematic voiceover and original demo soundtrack. Our recorded baseline achieved verified recovery in 6.9 seconds, with both security probes passing. The public dashboard provides interactive playback of real local Docker executions and transparent decision evidence. ESCAPE ROOM transforms cybersecurity from passive alerts into verifiable, autonomous defense.
Listed tools (9)
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ClickHousependingAI Dev Platforms tool
SemgreppendingAI Dev Platforms tool
GitHubpendingAI Dev Platforms tool
ElevenLabspendingAI Dev Platforms tool
CodexpendingAI Agents toolAI coding assistants forget everything between sessions, and the instruction files teams write for them (AGENTS.md, CLAUDE.md) go stale the moment a library changes. On long jobs like a major-version upgrade, they also drown in their own history and start repeating themselves. Evergreen is a long-running agent that does the upgrade and learns as it goes. It fixes one file at a time: known errors are fixed instantly by a saved rule; new ones get live official docs from Nimble, and Liquid's LFM2.5-8B-A1B model writes the patch entirely on the laptop, with no cloud AI. A fix is kept only if it passes safety guards and no previously passing test breaks; otherwise it's rolled back and retried, so progress never goes backwards. Every kept fix becomes a lesson with a confidence score, a source link and the library version it was proven on, and is written into the project's AGENTS.md so the next session with any assistant starts out knowing it. All history (attempts, patches, test results, lookups) lives in a Tinybird RawTree database instead of the prompt, so the prompt stays the same size from the first file to the last. In our run, it took a pandas 1.5 to 2.2 upgrade to all 21 tests passing. A live Next.js dashboard on Vercel reads RawTree and shows each run as it happens: tests passing over time, every patch and its diff, the rulebook, Nimble lookups, rollbacks, and zero human interventions. Built with Liquid, Nimble, Tinybird/RawTree, Python and Vercel.
Listed tools (5)
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Liquid AIpendingModel Hosting tool
NimblependingAI Dev Platforms tool
TinybirdpendingAI Dev Platforms toolNo security, trust, or compliance source attached
API profile signal with implementation source linked
View source10 recorded adoption events; verification is not established
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