15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T422,What it is: A hacking agent you run from a web UI. It enumerates, exploits, validates, and drafts reports.

Why we built it: manual pentests are slow and scarce. We want repeatable coverage between human-led tests.

What is different: a validator agent replays PoCs to cut false positives. You can lock the agent to your own methodology to control scope and steps. You can also give it tasks to complete.

How to try: sign up, download the docker container, add a target, set rules, then run. Free credits on signup and no card required. Usage based pricing after that.

What it can do today: Pretty much any asset type including AD, web applications, and cloud.

Limits today: noisy targets and complex auth flows may need manual setup. It does not have the tooling for Wi-Fi pentesting or mobile apps.

What we want feedback on: where it breaks, missing integrations, and what would make this useful for your team.

For more information the website is: https://www.vulnetic.ai17:T451,I built Artifex, a Python library to create task-specific LLMs for NLP and Text Classification, without training data. At the moment, only Intent Classification and Guardrail models are supported, but I'll be adding more soon based on users' feedback. The reason I made this is that general-purpose LLMs are overkill for simple Text Classification tasks, and LLM APIs can get expensive quickly. On the other hand, fine-tuning an LLM for a specific task requires labelled data, which not everybody has.

That's why I created a library to train small, task-specific LLMs WITHOUT training data. You simply describe how the model should behave, and it will be trained on synthetic data generated for that purpose.

The models can run locally (without a GPU) or on small servers, offloa

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