n":"1mo ago"}]]}],null,false]}]]}] 16:T464,I built an open source desktop AI assistant after getting frustrated with how brittle most tools feel once questions go beyond basic Q and A.

The goal was to explore whether an assistant could reliably handle interview style interactions such as system design discussions, multi step coding problems, and deeper follow up questioning without hiding behavior behind a closed SaaS.

The assistant supports both cloud and local LLMs, uses a bring your own API key model, and is intentionally opinionated so behavior stays predictable under pressure.

Most of the work went into managing context, follow ups, and failure cases rather than optimizing for fast single shot answers.

I used Antigravity heavily during development to iterate quickly, then refined and validated the behavior manually.

Repository https://github.com/evinjohnn/natively-cluely-ai-assistant

Happy to answer questions about design tradeoffs, local versus cloud inference, or what worked and failed while building this.17:T574,Hey HN,

I'm a solo founder running a service business through an Estonian e-Residency company (OÜ). Every time I invoiced a client in another EU country, I had to manually check if reverse charge applies, validate their VAT number on VIES, and figure out the correct tax treatment. I got tired of Googling the same rules over and over, so I built the tool I wanted.

Invoxo automatically determines the correct VAT treatment for each invoice based on seller/buyer countries, VAT registration status, and buyer type. It covers:

- VIES VAT number validation (real-time, with proof retention) - Reverse charge auto-detection for EU B2B - OSS handling for B2C digital services - Immutable tax records locked at issuance

The tax engine routes every invoice through a regime-specific decision system — no hardcoded rules in the UI layer. Tax payloads are vers

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