Julian's ideas

Notes

Aggregate funding data — deals, rounds, ticket sizes, valuations, active investors — and surface the patterns behind what VCs are actually paying attention to: hot sectors, repeated investor themes, and the signal-to-noise of round characteristics. Instead of gut feeling or anecdote, you get a ranked, sourced view of what is sparking interest right now. An early-warning radar for founders deciding where to build next.

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A single MCP endpoint that answers 'which model should I use for X?' backed by real benchmark data — reasoning, code, latency, price, context window, and EU availability. Instead of chasing leaderboards and Reddit threads, humans and agents query one index that ranks models against their actual constraints: task type, budget, latency budget, and GDPR/EU AI Act fit. Curated, versioned, and open.

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The Casimir effect is an attractive force between closely spaced surfaces caused by vacuum fluctuations (zero-point energy). The idea: engineer nanoscale structures where that force is asymmetric, producing directed motion — a vacuum-driven actuator with no moving parts in the classical sense. Scale it across an array and you get propulsion. Two honest caveats: the force is minuscule at practical scales, and extracting usable energy from the vacuum is a famously contentious topic, so the realistic target is actuation and precision positioning (e.g. MEMS, micro-robots) rather than free energy.

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An open device you drop on the table that records who said what. On-device transcription, no cloud involved. The software side syncs and manages transcripts locally and runs a local model (e.g. Ollama) for summaries and action items. Built for GDPR: nothing leaves the room. Hardware: off-the-shelf mic array + a small board.

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A cheap, open-source robot that takes care of a home veggie garden: watering, light, soil sensing, and a camera to spot pests. Everything on standard, low-cost parts — printable frame, stepper + pump — with firmware and app fully open source. The point is a <$100 starter build and a friendly community.

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A programming book written for how people actually read now: with AI agents as co-readers. Every chapter ships as source + prompts, so an agent can build the examples, answer questions, and keep the book current. Not AI-washing a normal book — the structure itself is agent-native.