Si

Sigil – FIDO2 key-derived P2P remote desktop (agentic workflow retro)

Hacker News

Sigil – FIDO2 key-derived P2P remote desktop (agentic workflow retro)

I built a remote desktop app where your FIDO2 security key acts as the address. The hmac-secret produces the same Iroh P2P node identity on both ends. That means there are no accounts, pairing, or VPN required. I documented an agentic development workflow retrospective, including PoC spikes, spec driven design, agentic roadbumps, and what "shipping v0.1" means to me. This writeup covers the agentic development process-changes I am making to adapt to various evolving DevEx tools; focusing on ergonomics, efficiency, and elegance. cargo install --git https://args.io/cat/sigil sigil

Visit product View on Hacker NewsJun 30, 2026

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, agentic · Missing: agents, macos, cursor

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: vpn, including · Missing: supports, reddit linkedin, podcasting

36%36% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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