A daily practice for scanning the horizon — surfacing unusual events, papers, projects, and announcements that hint at possible futures before they become trends.
Signals is a personal futures intelligence project. Every day, four AI systems independently scan the internet for weak signals — outliers, edge cases, and early indicators that suggest where things might be heading, typically 6–18 months before they become obvious trends.
The reports you see here are the unified output: a human-curated synthesis of what those AI scanners found, filtered for the weird, the surprising, and the potentially consequential.
Unexpected capabilities, new frameworks, tools fighting AI side effects
Error correction breakthroughs, new applications, accessibility milestones
Garage projects, open source tools, one-person projects punching above their weight
Legislative shifts, digital sovereignty moves, infrastructure vulnerabilities
Health-tech crossovers, robotics, bio-computing interfaces
Retro-computing revivals, bizarre experiments, things that make you go "wait, what?"
Routine product updates. Funding announcements (unless the structure is novel). Obvious hype cycles. Mainstream news that everyone already knows. If most of the signals come from Nature, TechCrunch, or Wired — we haven't dug deep enough.
Each signal passes through a five-stage intelligence pipeline, from raw collection to actionable forecasting.
The raw collection. Every day, four AI systems scan independently and their outputs are unified into a single report. Cross-source signals (appearing in multiple AI scanners) are given extra attention since independent discovery suggests something real is happening.
When a pattern of weak signals points toward a potential future, we ask: what specific, measurable thing would need to change for this future to arrive? That becomes a tracking signal — a concrete threshold we monitor over time. When a threshold is crossed, the signal "fires" and we know that future is closer than we thought.
The synthesis layer. Eight axes map the landscape of possible futures, with positions based on accumulated evidence from daily signals and tracking data. These aren't predictions — they're a visualization of which directions the evidence currently points.
Each source uses a specialized prompt tuned for weak signal detection, with mandatory requirements for weird/delightful signals, counter-movements, and indie projects.
Not all evidence is created equal. We use a three-tier system to assess how much weight to give each data point — and actively watch for hype.
| Tier | Type | Examples |
|---|---|---|
| Tier 1 | Primary measurement | Peer-reviewed paper, benchmark result, SEC filing, shipped product, enacted law |
| Tier 2 | Official claim | Company blog, press release, pre-print, announced partnership |
| Tier 3 | Secondary report | News article, analyst note, social media post, community discussion |
When a topic has lots of Tier 3 coverage but little Tier 1 evidence, that's a hype signal — many people talking, few verifiable results. Conversely, steady Tier 1 evidence accumulating quietly is a substance signal. We flag the difference.
Each morning, a Claude agent visits all four AI sources, extracts their reports, and synthesizes them into a single unified document. The process prioritizes: