A static scanner for LLM app code
Code scanner to check for issues in prompts and LLM calls - kereva-dev/kereva-scanner
A static scanner for LLM app code launched on March 17, 2025 with 6 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Kereva LLM Code Scanner is a static analysis tool designed to identify potential security risks, performance issues, and vulnerabilities in Python codebases that use Large Language Models (LLMs). It analyzes your code without execution to detect problems like hallucination triggers, bias potential, prompt injection vulnerabilities, and inefficient LLM usage patterns. # Clone the repository git clone https://github.com/rbitr/kereva-scanner.git cd kereva-scanner # Install dependencies pip install -r requirements.txt 📊 Usage Basic Scanning # Scan a single file python main.py path/to/file.py # Scan a Jupyter notebook python main.py path/to/notebook.ipynb # Scan an entire directory python…from github.com
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An open-source code scanner to find issues in prompts and LLM calls2025 · github.com · ▲5Code scanner to check for issues in prompts and LLM calls - kereva-dev/kereva-scanner
Why write code if the LLM can just do the thing? (web app experiment)Nov 2025 · github.com · ▲436I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Letting LLMs Run a Debugger2025 · github.com · ▲147Hey HN, I just built an experimental VSCode extension called LLM Debugger. It’s a proof-of-concept that lets a large language model take charge of debugging. Instead of only looking at the static code, the LLM also gets to see the live runtime state—actual variable values, function calls, branch decisions, and more. The idea is to give it enough context to help diagnose issues faster and even generate synthetic data from running programs. Here’s what it does: * Active Debugging: It integrates with Node.js debug sessions to gather runtime info (like variable states and stack traces). *…
PromptTools – open-source tools for evaluating LLMs and vector DBs2023 · github.com · ▲211Hey HN! We’re Kevin and Steve. We’re building PromptTools (https://github.com/hegelai/prompttools): open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. Evaluating prompts, LLMs, and vector databases is a painful, time-consuming but necessary part of the product engineering process. Our tools allow engineers to do this in a lot less time. By “evaluating” we mean checking the quality of a model's response for a given use case, which is a combination of testing and benchmarking. As examples: - For generated…


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