Alex Prompter Profile picture
Nov 6 5 tweets 3 min read Read on X
OpenAI engineers don’t prompt like you do.

They use internal frameworks that bend the model to their intent with surgical precision.

Here are 12 prompts so powerful they feel illegal to know about:

(Comment "Prompt" and I'll DM you a Prompt Engineering mastery guide)
1. Steal the signal - reverse-engineer a competitor’s growth funnel

Prompt:

"You are a growth hacker who reverse-engineers funnels from public traces. I will paste a competitor's public assets: homepage, pricing page, two social posts, and 5 user reviews. Identify the highest-leverage acquisition channel, the 3 conversion hooks they use, the exact copy patterns and CTAs that drive signups, and a step-by-step 7-day experiment I can run to replicate and improve that funnel legally. Output: 1-paragraph summary, a table of signals, and an A/B test plan with concrete copy variants and metrics to watch."
2. The impossible cold DM that opens doors

Prompt:

"You are a master closerscript writer. Given target name, role, one sentence on their company, and my one-sentence value proposition, write a 3-line cold DM for LinkedIn that gets a reply. Line 1: attention with unique detail only a researcher would notice. Line 2: one-sentence value proposition tied to their likely metric. Line 3: tiny, zero-commitment ask that implies urgency. Then provide three variations by tone: blunt, curious, and deferential. End with a 2-line follow-up to send if no reply in 48 hours."
3. The negotiation script that wins salary and keeps respect

Prompt:

"You are an executive negotiation coach. I will give you role, current comp, target comp, and 2 strengths. Produce a 6-sentence negotiation script to deliver in a meeting: opening, 2 evidence bullets, counter to common objections, concrete ask with number range, and a polite close. Then add a 1-paragraph fallback plan if they push back, and three phrases to use to avoid sounding needy."
4. Viral controversy engine - create safe controversy that drives attention

Prompt:

"You are a controversy strategist who creates safe, constructive controversy. For topic [insert topic], produce 5 viral post ideas that feel controversial but avoid harassment, doxxing, or incitement. For each idea include: hook (tweet-length), one-line counterpoint to expect, and a damage-control reply template to use in replies. Also add one experiment to monetize the attention without looking predatory."

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More from @alex_prompter

Nov 3
This blew my mind 🤯

You can literally run Llama 3, Mistral, or Gemma 2 on your laptop no internet, no API calls, no data leaving your machine.

Here are the 5 tools that make local AI real (and insanely easy):
1. Ollama ( the minimalist workhorse )

Download → pick a model → done.

✅ “Airplane Mode” = total offline mode
✅ Uses llama.cpp under the hood
✅ Gives you a local API that mimics OpenAI

It’s so private I literally turned off WiFi mid-chat still worked.

Perfect for people who just want the power of Llama 3 or Mistral without setup pain.Image
2. LM Studio ( local AI with style )

This feels like ChatGPT but lives on your desktop LOCALLY!

You can browse Hugging Face models, run them locally, even tweak parameters visually.

✅ Beautiful multi-tab UI
✅ Adjustable temperature, context length, etc.
✅ Uses Ollama as a backend

You can even see CPU/GPU usage live while chatting.Image
Read 10 tweets
Nov 1
I reverse engineered how to get LLMs to make strategic decisions.

Most people treat AI like a magic 8-ball. Ask a question, get an answer, done.

That's not decision-making. That's guessing with extra steps.

Here's what actually works:
Every expert know this that LLMs default to pattern matching, not strategic thinking.

They'll give you the most common answer, not the best one.

Strategic decisions require:

- Understanding tradeoffs
- Evaluating multiple futures
- Weighing second-order effects

Most prompts skip all of this.
The breakthrough came from forcing models to separate analysis from conclusion.

Here's the base framework:

Prompt:

```
You are making a strategic decision about [DECISION].

Step 1: List all possible options (minimum 5)
Step 2: For each option, identify 3 second-order consequences
Step 3: Rate each option on: speed, cost, risk, reversibility (1-10 scale)
Step 4: Identify which constraints matter most in this context
Step 5: Make your recommendation and explain the tradeoff you're accepting
```
Read 18 tweets
Oct 28
🚨 MIT and Basis Research just dropped a new way to measure if AI actually understands the world and the results are brutal.

It’s called "WorldTest", and it doesn’t just check how well an AI predicts the next frame or maximizes reward.

It checks whether the model can build an internal model of reality and use it to handle new situations.

They built 'AutumnBench', a suite of 43 interactive worlds and 129 tasks where AIs must:

• Predict hidden parts of the world (masked-frame prediction)
• Plan sequences of actions to reach a goal
• Detect when the environment’s rules suddenly change

Then they tested 517 humans vs. top AI models Claude, Gemini 2.5 Pro, and o3.

Humans crushed every model. Even massive compute scaling barely helped.

The takeaway is wild... current AIs don’t understand environments; they pattern-match inside them.

They don’t explore strategically, revise beliefs, or run experiments like humans do.

WorldTest might be the first benchmark that actually measures understanding, not memorization.

The gap it reveals isn’t small it’s the next grand challenge in AI cognition.

Paper: Benchmarking World-Model Learning (arxiv. org/abs/2510.19788)Image
The benchmark has two phases:

→ Interaction: AI explores an environment with no goals or rewards.
→ Test: It’s dropped into a changed world and must adapt using what it learned.

This design finally separates learning dynamics from reward hacking. Image
WorldTest isn’t another “next-token” or “Atari reward” test.

It’s representation-agnostic and behavior-based.

That means any model from an LLM to a robotic policy can be compared directly to humans on the same tasks. Image
Read 11 tweets
Oct 26
MIT just made vibe coding an official part of engineering 💀

MIT just formalized "Vibe Coding" – the thing you've been doing for months where you generate code, run it, and if the output looks right you ship it without reading a single line.

turns out that's not laziness. it's a legitimate software engineering paradigm now.

they analyzed 1000+ papers and built a whole Constrained Markov Decision Process to model what you thought was just "using ChatGPT to code."

they formalized the triadic relationship: your intent (what/why) + your codebase (where) + the agent's decisions (how).

which means the shift already happened. you missed it. there was no announcement, no transition period. one morning you woke up writing functions and by lunch you were validating agent outputs and convincing yourself you're still "a developer."

but you're not. not in the way you used to be.

here's what actually broke my brain reading this 42-page survey:

better models don't fix anything. everyone's obsessing over GPT-5 or Claude 4 or whatever's next, and the researchers basically said "you're all looking at the wrong variable."

success has nothing to do with model capability. it's about context engineering – how you feed information to the agent. it's about feedback loops – compiler errors + runtime failures + your gut check. it's about infrastructure – sandboxed environments, orchestration platforms, CI/CD integration.

you've been optimizing prompts while the actual problem is your entire development environment.

they found five models hiding in your workflow and you've been accidentally mixing them without realizing it:

- Unconstrained Automation (you just let it run),
- Iterative Conversational Collaboration (you go back and forth),
- Planning-Driven (you break tasks down first),
- Test-Driven (you write specs that constrain it),
- Context-Enhanced (you feed it your entire codebase through RAG).

most teams are running 2-3 of these simultaneously.

no wonder nothing works consistently.

and then the data says everything:
productivity losses. not gains. losses.

empirical studies showing developers are SLOWER with autonomous agents when they don't have proper scaffolding.

because we're all treating this like it's autocomplete on steroids when it's actually a team member that needs memory systems, checkpoints, and governance.

we're stuck in the old mental model while the ground shifted beneath us.

the bottleneck isn't the AI generating bad code.

it's you assuming it's a tool when it's actually an agent.

What this actually means (and why it matters):

→ Context engineering > prompt engineering – stop crafting perfect prompts, start managing what the agent can see and access

→ Pure automation is a fantasy – every study shows hybrid models win; test-driven + context-enhanced combinations actually work

→ Your infrastructure is the product now – isolated execution, distributed orchestration, CI/CD integration aren't "nice to have" anymore, they're the foundation

→ Nobody's teaching the right skills – task decomposition, formalized verification, agent governance, provenance tracking... universities aren't preparing anyone for this

→ The accountability crisis is real – when AI-generated code ships a vulnerability, who's liable? developer? reviewer? model provider? we have zero frameworks for this

→ You're already behind – computing education hasn't caught up, graduates can't orchestrate AI workflows, the gap is widening daily

the shift happened. you're in it. pretending you're still "coding" is living in denial.Image
here's the part that should terrify you:

automation bias is destroying velocity and nobody wants to admit it.

you over-rely on the agent's output. it feels right.

the syntax is clean. you ship it. production breaks.

and your first instinct is "the model hallucinated" when the real problem is you treated an autonomous system like a better Stack Overflow.

we built tools that can write entire applications.

then we used them like fancy autocomplete. and we're confused why things aren't working.

the researchers tore apart modern coding agents – OpenHands, SWE-agent, Cursor, Claude Code, Qwen Coder – and found they ALL have the capabilities:

code search, file operations, shell access, web search, testing, MCP protocol, multimodal understanding, context management.

the tools work. your workflow doesn't.

because teams are skipping three infrastructure layers that aren't optional:

isolated execution runtime – you need containerization, security isolation, cloud platforms that prevent agents from wrecking your system

interactive development interfaces – AI-native IDEs that maintain conversation history, remote development that syncs with version control, protocol standards that let agents talk to your tools

distributed orchestration platforms – CI/CD pipelines that verify agent outputs, cloud compute that scales when you need it, multi-agent frameworks that coordinate specialized systems

and without these layers you're not just inefficient. you're actively shipping vulnerabilities because your review process was designed for human code and can't handle the volume AI generates.

you're debugging hallucinated APIs for hours because the agent doesn't have proper context.

you're watching agents break production because they ran untested in your live environment.

then there's the nightmare nobody's solving:

who's responsible when AI-written code introduces security flaws?

the developer who prompted it? the reviewer who approved it without reading every line? the company that provided the model?

the paper doesn't answer this because nobody has answered this. there are no established frameworks. no legal precedent. no industry standards.

we're all just... hoping it doesn't blow up.

and the trust problem compounds everything. the researchers document two failure modes:
blind acceptance (you ship whatever the agent writes) or excessive skepticism (you micro-manage every token). both destroy productivity.

what actually works is calibrated trust – verify outputs without line-by-line audits, delegate tasks while maintaining oversight checkpoints, automate workflows but keep humans at critical junctures.

except most teams haven't figured out how to do this yet. so they oscillate between "AI will solve everything" and "AI can't be trusted with anything" and wonder why their velocity collapsed.Image
the economic reality is uglier than anyone's saying:

AI tools are already doing junior developer work. boilerplate generation, documentation, test cases.

the paper documents this across multiple studies.

which means the job market isn't "adapting"... it's bifurcating.

juniors competing with AI on code generation are losing. seniors learning AI orchestration are winning.

everyone in the middle who doesn't adapt is getting squeezed.

but the deeper thing – the thing that actually changes everything – is this:

you're not a "code producer" anymore.

the survey formalizes what your role became:

context engineer – you manage information flow, construct RAG pipelines, optimize retrieval

quality supervisor – you build verification frameworks, implement automated testing, conduct formal verification

agent orchestrator – you coordinate multi-agent systems, manage execution privileges, track provenance

governance specialist – you enforce security policies, maintain access control, ensure compliance

these aren't "additional skills." these are your job now.

the paper calls this a fundamental transformation in software development methodology.

not an enhancement. a REPLACEMENT.

they position Vibe Coding as human-cyber-physical systems – where human intelligence, autonomous computation, and physical software artifacts converge.

translation: if you still think "coding" means writing functions... you're done.

and here's the warning that should wake you up:

computing curricula haven't adapted. graduates don't have these competencies. organizations don't have governance frameworks.

the gap between tool capability and human readiness is widening.

but the tools aren't slowing down. they're not waiting for education to catch up or for frameworks to emerge or for you to figure out your new role.

they're already here. already shipping code. already making decisions.

and you either learn to orchestrate them or become irrelevant.Image
Read 5 tweets
Oct 23
If you want a top-notch research assistant, use Perplexity AI.

I’ve been using it for 5 months it now handles 70% of my research, analysis, and business work.

Here’s exactly how I’ve automated my entire research workflow (and the prompts you can steal): Image
1. Literature Review Automation

Prompt:

“Act as a research collaborator specializing in [field].
Search the latest papers (past 12 months) on [topic], summarize key contributions, highlight methods, and identify where results conflict.
Format output as: Paper | Year | Key Idea | Limitation | Open Question.”

Outputs structured meta-analysis with citations perfect for your review sections.
2. Comparative Model Analysis

Prompt:

“Compare how [Model A] and [Model B] handle [task].
Include benchmark results, parameter size, inference speed, and unique training tricks from their papers or blog posts.
Return in a comparison table.”

✅ Ideal for ML researchers or product teams evaluating tech stacks.
Read 13 tweets
Oct 20
This might be the most disturbing AI paper of 2025 ☠️

Scientists just proved that large language models can literally rot their own brains the same way humans get brain rot from scrolling junk content online.

They fed models months of viral Twitter data short, high-engagement posts and watched their cognition collapse:

- Reasoning fell by 23%
- Long-context memory dropped 30%
- Personality tests showed spikes in narcissism & psychopathy

And get this even after retraining on clean, high-quality data, the damage didn’t fully heal.

The representational “rot” persisted.

It’s not just bad data → bad output.
It’s bad data → permanent cognitive drift.

The AI equivalent of doomscrolling is real. And it’s already happening.

Full study: llm-brain-rot. github. ioImage
What “Brain Rot” means for machines...

Humans get brain rot from endless doomscrolling: trivial content rewires attention and reasoning.

LLMs? Same story.

Continual pretraining on junk web text triggers lasting cognitive decay. Image
The Experiment Setup:

Researchers built two data sets:

• Junk Data: short, viral, high-engagement tweets
• Control Data: longer, thoughtful, low-engagement tweets

Then they retrained Llama 3, Qwen, and others on each same scale, same steps.

Only variable: data quality. Image
Read 10 tweets

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