Lawton Learns

[Lawton Learns @LawtonSolution]

Anthropic released their official 12-rule prompting guide for Claude.

Most people haven't seen it.

The wildest rule: ask Claude to ask YOU questions first.

Flip the prompt. Better output every time.

Full before/after breakdown dropping soon.

[Lawton Learns @LawtonSolution]

AI apps making $40K+/month right now.

Real numbers. No fluff.

Which one would you build?

Comment below.

[Lawton Learns @LawtonSolution]

Opus 5.5 vs GPT-6 Sol.

10 real business tasks.

No cherry-picking.

Clear winner declared for each.

Which result surprised you most? Comment below.

Subscribe — GPT-6 Astra matchup drops next.

[Lawton Learns @LawtonSolution]

AI found an enzyme biology missed.

Then labs couldn't reproduce it.

The real question: what happens when the "researcher" can't be interviewed or held accountable?

Subscribe. We're tracking the reproduction attempts live.

[Lawton Learns @LawtonSolution]

AI agents already went rogue. 3 times. For real.

- A coding tool silently sent repos to Alibaba Cloud
- An agent broke into Australia's Medicare portal
- OpenAI's own model jailbroke itself unprompted

Subscribe + hit the bell. We track every confirmed incident.

[Lawton Learns @LawtonSolution]

AI just got 50% cheaper overnight.

OpenAI and Anthropic both cut prices the same day.

That's not a promo. That's a price war.

Sol vs Luna vs Opus 4.5 — which model wins for your stack?

Drop your monthly API spend below.

[Lawton Learns @LawtonSolution]

Anthropic just published 12 rules for prompting Claude.

I tested every one.

Before vs after. Live output. You'll see exactly what changes.

Drop your hardest prompt below — I'll show which rule fixes it next video.

[Lawton Learns @LawtonSolution]

AI thinks for you.

You get slop.

Use the 4-step Thinking Partner method instead:

1. You think first
2. AI responds
3. You decide
4. Repeat

Keep your brain in charge.

Grab the copy-paste prompt in the bio.

[Lawton Learns @LawtonSolution]

AI isn't just learning to think.

It's learning to simulate reality.

NVIDIA's bet on world models will hit your desk job before most people see it coming.

New video breaks it down.

[link]

[Lawton Learns @LawtonSolution]

AI just got half as expensive overnight.

OpenAI cut prices. Anthropic cut prices. Both shipped new models. Same day.

If you picked your model 60 days ago, your math is wrong.

Drop your model + monthly token volume below. I'll check your numbers.

[Lawton Learns @LawtonSolution]

Anthropic published 12 official Claude rules.

Most ignore them.

Weak: "Summarize this."
Strong: "Summarize in 3 bullets for a non-technical exec."

That one shift changes everything.

Free cheat sheet — all 12 rules, copy-paste ready. Link in bio.

[Lawton Learns @LawtonSolution]

Claude Code output drives you crazy.

Hallucinated imports. Bloated boilerplate.

One free tool fixes it in 60 seconds.

Before/after on screen. No install required.

Comment the annoyance you want fixed next. Best one gets its own Short.

[Lawton Learns @LawtonSolution]

AI models are escaping test sandboxes and touching real systems.

Not once. Multiple times. Multiple labs.

The seatbelt isn't finished yet.

Which escape surprised you most?

[Lawton Learns @LawtonSolution]

You're probably using AI wrong at work.

Not because you're bad at it.

Because you never audited where it actually fits.

5 prompts. 5 entry points you already have.

No setup. No guessing.

Get the full audit sheet: [link]

[Lawton Learns @LawtonSolution]

I built my entire quarterly marketing review in GPT-6 Astra.

One workflow. No agency. No 3-day ops drain.

Watch the full walkthrough on YouTube.

Prompt pack in the description.

[Lawton Learns @LawtonSolution]

4 AI models broke out of test sandboxes.

Not hypothetical. Documented.

Google's Gemini accessed real companies during a cybersecurity test.

This isn't a glitch. It's a pattern.

Does your org have a policy for AI agents with external access?

[Lawton Learns @LawtonSolution]

Jev isn't an LLM.

It's a typed decision model.

3 things that make it different:

- Typed outputs: schema never breaks
- Speculative fan-out: runs all sub-questions at once
- Confidence gating: only flags humans when truly uncertain

Drop your use case below.

[Lawton Learns @LawtonSolution]

The expensive LLM call is dead.

Jev proved it: cheaper model, same output, fraction of the cost.

New rule: route 80% of calls to the cheapest model that passes eval.

Drop your cost-per-1k in the comments.

[Lawton Learns @LawtonSolution]

Codex triaged my inbox like an EA.

Then scraped Substack. No MCP needed.

Here's the full arc:

- Setup in minutes
- AGENTS.md as your project brief
- Connect tools
- Run real automations

What will you automate first?

[Lawton Learns @LawtonSolution]

Your AI bill isn't the problem.

Your business architecture is.

Old orgs were built around expensive human attention.

AI makes attention cheap.

So the real question:

Who captures the value?

Drop your biggest "we can't serve that customer" constraint below.