VT

VT Code – LLM-agnostic coding agent with MCP/ACP and sandboxed tools

Hacker News

VT Code – LLM-agnostic coding agent with MCP/ACP and sandboxed tools

VT Code is a Rust CLI/TUI coding agent that works semantically on code (Tree-sitter + ast-grep), routes across multiple LLMs (incl. local via Ollama), and integrates with editors via ACP and external tools via MCP. It focuses on safety (workspace boundaries, per-tool Allow/Prompt/Deny, sandboxed commands/timeouts) and reproducibility (TOML config, caching, summarization). What’s different: - AST-aware search/refactors with preview-before-apply - Provider-agnostic routing with failover and cost/latency-aware caching - Policy-gated tools and strict workspace boundaries - Terminal-first ergonomics you can script Install: cargo install vtcode # or brew install vinhnx/tap/vtcode npm install -g vtcode Quick start: export OPENAI_API_KEY=... # or another provider key vtcode vtcode ask "Find md5 usages across the repo and propose SHA-256 replacements (show diff)" vtcode ask "Search for .unwrap() on Result and suggest safer handling" Status: research preview (MIT). Feedback welcome on ergonomics, ACP/MCP DX, safety defaults, and real-world AST refactor recipes.

Visit product View on Hacker NewsOct 23, 2025

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

83%83% 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 · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

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

23%23% 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

14%14% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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