Protect GenAI and UGC platforms with end-to-end trust, safety & security with Alice, formerly ActiveFence.
Based on the provided information, I cannot generate a meaningful summary of user opinions about ActiveFence. The social mentions you've shared only show YouTube video titles that simply repeat "ActiveFence AI" without any actual user feedback, reviews, or commentary about the tool's performance, features, or user experience. To provide an accurate summary of user sentiment, I would need actual review content, user comments, ratings, or detailed social media discussions that express opinions about ActiveFence's strengths, weaknesses, pricing, or overall value.
Mentions (30d)
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Reviews
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Based on the provided information, I cannot generate a meaningful summary of user opinions about ActiveFence. The social mentions you've shared only show YouTube video titles that simply repeat "ActiveFence AI" without any actual user feedback, reviews, or commentary about the tool's performance, features, or user experience. To provide an accurate summary of user sentiment, I would need actual review content, user comments, ratings, or detailed social media discussions that express opinions about ActiveFence's strengths, weaknesses, pricing, or overall value.
Features
Funding Stage
Series B
Total Funding
$100.0M
Where’s Larry? Result of a ~12 hour autonomous Claude loop experiment.
My dog, Larry, wanders a lot and I’m often wondering where he is (I live off-grid surrounded by forest). I’ve been experimenting with a custom built autonomous Claude loop and thought I’d test it by asking it to build a system that could simply answer the question, “Where’s Larry?”. I provided the system with an initial direction prompt, access to my Home Assistant installation, Unifi camera API, and Larry’s airTag. Then I let the system run autonomously over ~12 hours and 133 Claude sessions. This video shows just a small preview of what the autonomous system created. This was just a fun experiment, to explore the potential and limits of a pure Claude Code automated build pipeline (no Open Claw). Happy to answer any questions! Features created: Real-time dog tracking using UniFi Protect camera AI detections + Apple AirTag location fusion · Claude Vision-powered photo analysis to distinguish between two visually similar dogs · Interactive satellite property map with camera FOV cones, geo-fence zones, and live position trails · Behavioral model that learns daily patterns and predicts current zone when no live data exists · Signal fusion engine combining camera detections, AirTag GPS, behavioral predictions, and spatial triangulation into one confidence-scored location answer · "Where's Larry?" natural language query API accessible via iPhone Shortcuts · Presence/away detection using AirTag home/away as a gate · Bedroom inference from negative evidence when AirTag says home but no camera has seen him · Sleep session tracking with nap detection and daily sleep budget · Self-improving recognition pipeline with profile refinement, confidence calibration, and reference photo gallery · Spatial self-tuning with per-camera bias correction and multi-camera triangulation · Auto-generated geo-fence zones from camera field-of-view data · Web dashboard with live location, zone heatmaps, activity timeline, day replay, photo journal, and movement flow visualization · Daily digest, weekly intelligence report, and morning briefing auto-generation · Smart notifications to iPhone via Home Assistant · Weather and solar correlation tracking for outdoor behavior prediction · Fully autonomous — 133 sessions, 97 sprints, ~67K lines of code, built in ~12 active hours across 4.4 days with minimal human direction About the automation system Autonomous orchestration system ("conductor") that runs Claude Code sessions back-to-back without human intervention · Three operating modes: creative (imagines and builds new features), refine (audits and improves existing code), and alternating (switches between both automatically) · Each session reads project state, proposes a sprint with 4 tasks, executes them, commits, and hands off to the next session · Sprint proposal system with quality gate — low-risk sprints auto-approve, high-risk ones pause for human approval · Suggestion inbox where the human drops ideas in plain English and the next session picks them up as priority tasks · Creative values and refine values files that guide autonomous decision-making priorities · Guardrails file that defines constraints the conductor must never violate · History deduplication log that prevents the conductor from re-proposing already-completed work · Push notifications to iPhone via Home Assistant on start, every 5 sessions, stop, and when blocked · Graceful stop signal (touch a file) that lets the current session finish before halting · Full audit trail with per-session markdown notes, sprint proposals, conductor logs, and git commits · Ran 133 sessions across 97 sprints over 4.4 days, averaging 1.2 sessions/hour (peaking at 7.3/hour overnight) · Produced 200 git commits, 160 Python modules, and ~67K lines of code from ~15 human-written suggestions submitted by /u/mrgulabull [link] [comments]
View originalActiveFence uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Exposing the Hidden Risks of AI Toys, AI Product Launch Checklist, Demystifying AI Red Teaming, How Your Agentic Systems Can Fail and How to Prevent It, Regulations in the GenAI Era: What Enterprises Need to Know, Essential AI Red Teaming Tools Techniques for Product Teams, Alice AI Security Benchmark, Guide to Guardrails.
