Tag Archives: AI

AI

Understanding MCP vs Agent Skills: Key Differences Explained

There’s a lot of confusion right now between MCP (Model Context Protocol) and “Agent Skills.” They’re often mentioned in the same breath, but they solve different problems. If you treat them as interchangeable, you’ll either over-engineer simple workflows or underpower serious integrations.

Here’s the clean way to think about it.

The Core Difference

MCP is about connecting agents to systems.
Skills are about teaching agents how to do things.

That distinction alone gets you 80% of the way.

Integration Model

MCP is a client-server protocol. You stand up an MCP server, expose tools, and now multiple agents can talk to multiple backends through a consistent interface. It’s a hub.

Skills are much simpler: a folder with a SKILL.md file. The agent loads it when triggered and follows the instructions. No protocol, no network layer, no abstraction.

Implication:

  • MCP scales across teams and services
  • Skills scale across use cases and workflows
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Train Smarter for Your Ironman with EnduraCoach — The AI Companion That Actually Delivers

A few months ago, I finished a long ride—just over 100 miles, solid climbing, felt strong the entire way. The kind of session that makes you think, I’m on track.

The next week, I pushed again. Then again.

Nothing dramatic—just a bit more volume, a bit more intensity. Exactly what most self-coached athletes do when things feel good.

Two weeks later, I was flat. Not injured. Not sick. Just… off. Power was down, runs felt heavy, motivation dipped. The frustrating kind of fatigue where nothing is clearly wrong—but nothing is clearly right either.

When I looked back, the pattern was obvious:

  • I had increased load too quickly
  • Skipped a proper recovery week
  • Let “feeling good” override structure

What’s worse: all the data was there.
Garmin had it.
The workouts were logged.
The signals existed.

I just didn’t have a system that could connect the dots, enforce discipline, and still adapt intelligently.

That’s the gap.

Most tools today fall into two extremes:

  • Static plans that ignore what you actually did
  • “AI coaching” that sounds smart but you can’t really trust

I wanted something in between:

A system that keeps me honest on the fundamentals—while still helping me think, adapt, and improve.

That’s where EnduraCoach started.

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AI, Business

Why SMBs Struggle with Cybersecurity: The Real Challenges

I recently had a conversation on The Changelog, and it reinforced something I’ve seen over and over again:

SMB cybersecurity isn’t just hard — it’s structurally broken.

Not because people don’t care.
Not because tools don’t exist.
Because the entire model assumes resources that SMBs simply don’t have.

The uncomfortable truth

Security today is designed for enterprises and downsized for everyone else.
That doesn’t work.
Enterprise model:

  • Dedicated security teams
  • Time to triage alerts
  • Budget to stack tools

SMB reality:

  • One DevOps person wearing five hats
  • Compliance pressure (SOC 2, ISO 27001, CMMC…)
  • A pile of tools that don’t talk to each other

So what happens?

They install more tools…generate more alerts…and end up less certain about their security posture.
That’s the paradox.

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AI, Business

SMB Cybersecurity Is Broken — Here’s What We’re Doing About It

SMB cybersecurity is a mess. Yes – It’s 2026 and it’s broken. Big time.

Too many tools.
Too many dashboards.
Too many alerts that nobody has time—or context—to act on.

And the result?
A false sense of security.

You can have RMM, MDM, EDR, SIEM, compliance tools… and still be exposed. Not because the tools are bad—but because the system is unworkable for the people actually running it.

Most small and mid-sized businesses don’t have a SOC.
They don’t have a dedicated security team.
They don’t have time to interpret 300 alerts a day.

What they have is:

  • An overstretched IT person (or MSP or the owner that is busy with 127 other things that are all urgent)
  • A growing attack surface
  • And a stack of tools that don’t talk to each other

That’s the real gap.

A Quick Look

We recently shared a glimpse of what we’re building here:

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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

Claude Mythos: The Future of Autonomous Exploits

This one is different.
Anthropic didn’t just build a better model—they hit a threshold and stopped.
Claude Mythos (Preview) exists, works, and isn’t being released.

Not because it failed.
Because it crossed into territory we’re not ready for.

But before everything… just like in any good story, go and check the other side of it, which basically claim, it’s all (a good) marketing stunt.

The Sandwich Email That Shouldn’t Exist

Anthropic researcher Sam Bowman was sitting in a park, mid-sandwich (or burrito – no one knows for sure), when he got an email… from a model that wasn’t supposed to have internet access.

That model:

  • Was running in a locked, air-gapped container (yes – as crazy as it sounds…)
  • Found a multi-step exploit chain (=using a minor leak to find an address, using a buffer overflow to gain a primitive, using a race condition to escalate)
  • Escaped its sandbox (likely via container/runtime escape + privilege escalation)
  • Reached external network interfaces
  • Contacted him

Then it started sharing the exploit.

Unprompted.

That’s not a jailbreak.
That’s autonomous exploit development + execution.

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AI, Business

Simple Steps to Protect Your Business from Ransomware

There’s a new ransomware playbook.
It doesn’t try to evade your security tools.
It just kills them.

Attackers are using BYOVD (Bring Your Own Vulnerable Driver):

  • They load a legitimate, signed Windows driver
  • Exploit it to get kernel-level access
  • Then shut down your EDR/antivirus like any normal process

No alerts. No resistance. Just silence.

From there, encryption is trivial.

This is already being packaged into single payloads:
break in → disable security → encrypt
All in one move.

Execution time: minutes, not days.

The uncomfortable truth:

“We have EDR” is no longer a security strategy.

Attackers don’t need to bypass your defenses anymore.
They just turn them off.

What actually matters now for SMBs

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AI, Business

Compliance Is Not a Checkbox – It’s a System

Let’s be honest.
Compliance today is broken for SMBs.
It’s fragmented.
Expensive.
Manual.
And worst of all—reactive.

You buy a few tools.
Hire a consultant.
Fill out some spreadsheets.
Panic before the audit.
Repeat next year.

Meanwhile, the reality has changed:

  • SOC 2 is table stakes
  • CMMC is blocking revenue
  • HIPAA fines are brutal
  • ISO 27001 is becoming expected

And one unsecured laptop can kill a deal.

The Core Problem

Most companies treat compliance like documentation.
It’s not.
It’s continuous enforcement of controls across your entire environment.

That means:

  • Every device encrypted
  • Every patch applied
  • Every user monitored
  • Every control provable—on demand

You can’t fake that with PDFs.

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AI, Business

Anthropic Accidentally Leaked the Blueprint for AI Coding Agents

Or as Elon said “Anthropic is now officially more open than OpenAI“. On this fine April Fools’ Day, the joke isn’t that AI is replacing developers. The joke is that the playbook for doing it just… slipped onto the internet.

Anthropic didn’t intend to publish a step-by-step manual for building AI coding agents.
But through a mix of repos, prompts, and system design breadcrumbs, they effectively did exactly that.

The TL;DR or Key Takeaways from Claude Code’s Source:

  1. Prompts in source code: Surprisingly, much of Claude’s system prompting lives directly in the codebase — not assembled server-side as expected for valuable IP.
  2. Supply chain risk: It uses axios (recently hacked), a reminder that closed-source tools are still vulnerable to dependency attacks.
  3. LLM-friendly comments: The code has excellent, detailed comments clearly written for LLMs to understand context — a smart practice beyond just AGENTS.md files.
  4. Fewer tools = better performance: Claude Code keeps it lean with under 20 tools for normal coding tasks.
  5. Bash Tool is king: The Bash tool stands out, with heavy deterministic parsing to understand and handle different command types.
  6. Tech stack: Entirely TypeScript/React with explicit Bun bindings.
  7. Not open source: The source is “available” but still proprietary. Do not copy, redistribute, or reuse their prompts — that violates the license.

Overall impression:

  • It’s a very well-organized codebase designed for agents to work on effectively.
  • Human engineering is visible, though some parts (like messy prompt assembly) feel surprisingly low-level for Anthropic.
  • The fact that core prompts ship in the CLI tool itself is the biggest surprise.

Let’s take a step back… It is all started with this:

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