Fiery streams of data converting into a green neural network grid
Business, AI

Using LLMs to Find Security Bugs: A Practitioner’s Playbook

TL;DR

LLMs won’t replace AppSec.
They will dramatically compress the search space.

If you use them right:

  • Run multi-model analysis (Opus + GPT + Gemini)
  • Structure prompts around attack surfaces, not “find bugs”
  • Require PoCs or tests for validation
  • Trust only cross-model consensus or reproducible exploits

If you don’t do this, you’ll drown in false positives.


Security research has always been asymmetric.
Attackers need one bug; defenders need zero.
Historically, scale worked against defenders.

LLMs start to rebalance that—not by magically finding zero-days, but by acting as a fast, always-on analyst that can:

  • Read entire subsystems in seconds
  • Connect logic across files
  • Generate realistic attack paths

Used correctly, they don’t replace expertise—they let you spend it where it matters.
Used incorrectly, they produce confident nonsense.
This is a practitioner’s workflow that actually works.

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AI, Business

Building Continuous AI Agents with OpenClaw and Ollama

Most people are still using AI like it’s 2023:
prompt → response → done.

That’s not where things are going.
The real shift is toward agents that run continuously and do work for you. And one of the most interesting ways to get there today is:

OpenClaw + Ollama

Before diving in, quick grounding.

What OpenClaw and Ollama Actually Are

OpenClaw is an open-source agent framework.
It’s not a chatbot—it’s a system that can:

  • plan tasks
  • call tools (browser, APIs, files)
  • maintain memory
  • run loops without constant input

Think: a programmable worker, not a Q&A interface.

Ollama is the simplest way to run large language models locally.
It handles:

  • downloading models (Llama, Gemma, etc.)
  • running them efficiently on your machine
  • exposing them via a clean API

Think: Docker for LLMs.

Put them together and you get:

A local, autonomous agent system with zero API costs and full control.

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AI, Business

OpenClaw: Redefining Productivity with Autonomous Skills

OpenClaw isn’t interesting because it chats.
It’s interesting because it acts.

If you haven’t internalized that yet, you’re still thinking in “LLM as assistant” mode. OpenClaw is closer to a junior operator with insomnia and root access.
In early 2026, the ecosystem around OpenClaw (which evolved from Clawdbot and Moltbot) has exploded with community-built “skills.” The real shift? These skills run locally and have a heartbeat. They wake up. They check things. They move.

Let’s break down the most popular ones — and more importantly, how to actually build and use them without turning your machine into a chaos engine.

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