AI agents lack cross-application real-time context of user screen activity because existing video/OCR recording solutions drain CPU/battery and leak private personal data to remote servers.
AI agents lack continuous, cross-application real-time context of what users are doing on their screens without high resource consumption, micro-management, or privacy risks.
Launch HN: Screenpipe (YC S26) – Power your agents by your 24/7 screen recording
Launch HN: Screenpipe (YC S26) – Power your agents by your 24/7 screen recording
if this doesn't run fully local its a no go for enterprise let alone ordinary users
commentzero chance im trusting any cloud or third party SaaS if this doesn't run fully local its a no go for enterprise let alone ordinary users
Engineers and power users looking to equip AI agents with cross-application screen context without sacrificing privacy or CPU resources.
Repeated complaints focus on severe resource drain ('space heater') and privacy fears surrounding cloud or video-based screen capture.
Unlike heavy video OCR apps, LocalPipe uses OS accessibility APIs and diff-based local indexing, providing zero-latency agent context with sub-1% CPU usage and strict local data sovereignty.
An open-source, highly optimized local native daemon that converts accessibility tree events and diff-based screen snapshots into structured, privacy-filtered local context vectors for AI agents.
Developers and knowledge workers spend hours manually curating Obsidian notes and building custom screen scrapers; a lightweight local-first daemon solves a clear operational pain point explicitly noted by privacy-conscious users.
“Continuous local AI screen context without battery drain or privacy leaks.”
An open-source, highly optimized local native daemon that converts accessibility tree events and diff-based screen snapshots into structured, privacy-filtered local context vectors for AI agents.
Launch open-source daemon on GitHub, Hacker News, r/LocalLLaMA, and Reddit's Obsidian/AI developer subreddits.
Continuous capture on non-M-series hardware or Linux environments can still trigger CPU spikes if event debouncing is unoptimized.
Modern OS security controls (macOS Screen Recording/Accessibility prompts) require explicit user setup, raising onboarding friction.
Users might fear hidden telemetry or unverified outbound network requests in local agent tools.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis opportunity scores well above the median for ideas surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "strong" rating in this band typically means the pain signal is consistent and recurring across multiple discussions, but one of the three pillars (severity, willingness to pay, or competitor weakness) is somewhat softer than top-tier opportunities. Founders evaluating this should focus customer discovery on the softest pillar first — confirming the gap before committing engineering time to a build.
It sits at the intersection of "ai-powered", "automation", "data-management", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. Opportunities in this category typically reward founders who can describe the pain in the user's own language — both because that's the basis of effective marketing, and because it's the strongest signal that the founder has done the upfront listening. The MonetScope pipeline surfaces this category alongside other other signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most other opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
