Salacia – The First Runtime OS for Agentic Coding

Hacker News

All major AI coding agents (Cursor, Claude Code, Cline, etc.) share the same fatal flaw: they forget context the second the conversation changes. My goal was simple: make prompts reliable. So I built Salacia — a lightweight Runtime OS that sits underneath any agent. It does four things: Compiles raw prompts into structured Intent IR + verifiable specs Runs metamorphic testing to kill semantic drift Only asks one smart question when truly high-risk (Risk-first gate) Keeps full auditable journal (every change is traceable and replayable) Tested on 500+ SWE-bench tasks:with 3 SOTA models, +9pp pass rate uplift and 93% fault localization accuracy. Completely local, silent by default, works with Cursor, Claude Code, Cline, Antigravity, Gemini CLI and more. Open source, one command to install. GitHub: https://github.com/StartripAI/Salacia Website: https://startripai.github.io/Salacia/ Would love feedback, especially from people who have been frustrated with prompt drift and context loss.

Visit product View on Hacker NewsFeb 28, 2026

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

98%98% 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.

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

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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