🔥 tw93 / Pake - 🤱🏻 Turn any webpage into a desktop app with one command.
GitHub热门项目 | 🤱🏻 Turn any webpage into a desktop app with one command. | Stars: 50,088 | 72 stars today | 语言: Rust
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Building a Deterministic Security Scanner for AI-Generated Code TL;DR: I built TruffleKit , a CLI security scanner that catches 22 vulnerability classes in under 2 seconds with zero false positives. Here's how the scanning engine works under the hood. AI code generation is producing more production code than ever. But AI models are trained on public code — which means they reproduce the same security mistakes the open-source ecosystem has been making for decades. In my tests, 73% of AI-generated code snippets contain at least one security vulnerability that a standard linter would completely miss. I couldn't find a tool that was fast, deterministic, and had zero false positives. So I built one. The Architecture The scanner is a rule-based deterministic engine written in Python. Each rule is a self-contained module that pattern-matches against a file's AST or raw content. scanner/ ├── __init__.py ├── engine.py # Orchestrator ├── reporter.py # Output formatting ├── rules/ │ ├── __init__.py │ ├── secret_detection.py │ ├── sql_injection.py │ ├── path_traversal.py │ ├── weak_encryption.py │ ├── cors_misconfig.py │ └── ... (22 rules total) └── models.py Key Design Decisions 1. AST-Based Pattern Matching For languages like Python and JavaScript, we parse the file into an AST and match against structural patterns — not regex. This eliminates false positives from strings that happen to look like code. import ast class SQLInjectionRule ( BaseRule ): def check ( self , tree : ast . AST , filename : str ) -> list [ Finding ]: findings = [] for node in ast . walk ( tree ): # Match: cursor.execute(f"...{variable}...") if isinstance ( node , ast . Call ): func_name = self . _get_call_name ( node ) if func_name in ( ' cursor.execute ' , ' db.execute ' , ' connection.execute ' ): for arg in node . args : if self . _is_f_string_or_concat ( arg ): findings . append ( self . _make_finding ( severity = ' high ' , message = ' SQL injection: parameterized query required ' , line = node .
There's a pattern I've noticed with every new AI coding tool that comes out: they all want you to switch editors. Or open a new terminal. Or context-switch into some standalone app. I DON'T WANT TO DO THAT My entire dev workflow lives in VS Code. My keybindings, my split panes, my snippets, my extensions — all of it. When Google released the Antigravity CLI ( agy ), an agentic coding assistant, I genuinely liked what it could do. But to use it properly, I had to live in a terminal window, manually managing sessions, typing slash commands from memory, and losing my editor context entirely. So I built a VS Code extension for it instead. What is Antigravity? Google Antigravity is Google's agentic coding CLI — think of it as a Gemini-powered dev assistant that can read your project, run tools, execute terminal commands, and help you build. It's the kind of tool that can handle complex multi-step tasks, not just autocomplete. The CLI is called agy , and it's genuinely capable. The problem was the workflow: terminal-first, session management by hand, and no visual layer over the context you're already in. The Extension: Antigravity for VS Code Install it on the VS Code Marketplace Source on GitHub The core idea is simple: the extension is a UI layer. It never bundles or replaces the agy binary — it shells out to whichever version you have installed locally. Same philosophy as the Claude Code VS Code extension: the editor provides the surface, the CLI does the work. Here's what it actually does: Sessions List The sidebar panel opens to all your saved sessions. You can open an existing one, delete it, or start fresh. New sessions can be launched in sandboxed mode or with permissions bypassed — accessible right from the "New Session" overflow menu, without memorizing CLI flags. Any session with an active turn shows a loading indicator in its row, so you always know what's in flight. Chat Panel (Material 3 Expressive) This is the main surface. Each session runs its own live,