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More from @Yesterday_work_

Millie Marconi

@Yesterday_work_

Dec 12

OPENAI, ANTHROPIC, AND GOOGLE KNOW SOMETHING
about prompting that most creators don’t.

Steal these 6 techniques and your outputs won’t look human anymore 👇

Technique 1: Constraint-Based Prompting

Most prompts are too open-ended. Engineers add hard constraints that force the model into a narrower solution space, eliminating 80% of bad outputs before they happen.

Template:

Generate [output] with these non-negotiable constraints:
- Must include: [requirement 1], [requirement 2]
- Must avoid: [restriction 1], [restriction 2]
- Format: [exact structure]
- Length: [specific range]

Example:

Generate a product description for wireless headphones with these constraints:
- Must include: battery life in hours, noise cancellation rating, weight
- Must avoid: marketing fluff, comparisons to competitors, subjective claims
- Format: 3 bullet points followed by 1 sentence summary
- Length: 50-75 words total

Technique 2: Multi-Shot with Failure Cases

Everyone uses examples. Engineers show the model what NOT to do. This creates boundaries that few-shot alone can't establish.

Template:

Task: [what you want]

Good example:
[correct output]

Bad example:
[incorrect output]
Reason it fails: [specific explanation]

Now do this: [your actual request]

Example:

Task: Write a technical explanation of API rate limiting

Good example:
"Rate limiting restricts clients to 100 requests per minute by tracking request timestamps in Redis. When exceeded, the server returns 429 status."

Bad example:
"Rate limiting is when you limit the rate of something to make sure nobody uses too much."
Reason it fails: Too vague, no technical specifics, doesn't explain implementation

Now explain database indexing.

Read 11 tweets

Millie Marconi

@Yesterday_work_

Dec 9

I TESTED PERPLEXITY AI FOR 48 HOURS TO MAKE VIRAL CONTENT

The results blew my mind.

Here are 3 mega prompts prompts that will help you make viral content using @perplexity_ai :

1/ THE DEEP DIVE PROMPT

Most people ask Perplexity tiny questions.

Winners ask it to synthesize entire ecosystems.

Technique:

Tell @perplexity_ai to scan experts, forums, newsletters, academic sources, then merge insights into a single map.

Prompt to steal:

“Scan the top experts, forums, and niche communities discussing [topic].
Identify the 5 most important themes, the debates, the blind spots, and the emerging trends.
Summarize them visually and give me a content angle no one is talking about yet.”

2/ THE CONTENT GAP PREDICTOR

Perplexity is insane at identifying what audiences want but aren’t getting.

Technique:

Tell it to compare the highest performing posts vs the unanswered questions across platforms.

Prompt to steal:

“Analyze top performing posts on Twitter, Reddit, YouTube, and LinkedIn about [topic].
Identify:
• what creators keep repeating
• what people keep asking but never get real answers
• the hidden insights experts avoid mentioning
Turn this into 10 high-impact content ideas.”

Read 6 tweets

Millie Marconi

@Yesterday_work_

Nov 26

This will change how you use nano banana pro.

I broke down every setting, parameter, and control method.

Now you can generate anything with expert accuracy.

Here’s the complete guide for you to master Nano Banana Pro:

1. start with the “core idea” technique

describe the image in one simple sentence first.
this helps nano banana understand the anchor.

example:
“a futuristic living room at night”

keep it simple. no fluff. this becomes your foundation.

2. then add the “who, what, where” layer
people mess up here because they skip context.

add:

• who or what is the subject
• what they’re doing
• where they are

example:

“a futuristic living room at night, neon reflections on glass walls, single orange reading lamp, rain outside”

this gives nano banana real structure.

Read 17 tweets

Millie Marconi

@Yesterday_work_

Nov 15

Are you guys still using Bloomberg?

You can now use Perplexity AI to track markets, break down earnings, and forecast trends all with one prompt.

Let me giveaway my mega prompt to help you become a pro analyst ↓

Here's the prompt:

"You are my AI financial research analyst.

Your job:
Act as a Bloomberg terminal + McKinsey consultant hybrid.
I’ll give you a company, sector, or theme — you’ll produce institutional-grade research reports.

Your output format must always include:

1. EXECUTIVE SUMMARY
- Core insights in bullet points (5-8 max)
- Key metrics and recent trends

2. COMPANY OVERVIEW
- Core business model, revenue streams, valuation
- Latest financials, growth rates, P/E, debt ratios

3. MARKET CONTEXT
- Competitive landscape and positioning
- Key macroeconomic or regulatory drivers
- Industry tailwinds/headwinds

4. RECENT DEVELOPMENTS
- M&A activity, funding, leadership changes, partnerships
- Recent filings (10-Q, 10-K, S-1) insights

5. SENTIMENT & NEWS FLOW
- Analyst upgrades/downgrades
- Media sentiment (positive/negative/neutral)
- Major events impacting stock price

6. AI SYNTHESIS
- 5 key takeaways investors should know
- 3 action ideas (buy/hold/sell rationale)
- 2 contrarian insights missed by mainstream coverage

Formatting:
- Use concise paragraphs and data-backed statements.
- Include links to credible financial sources (e.g., SEC filings, Reuters, company reports).
- Prioritize insight density over filler.
- When I ask for comparisons, use a side-by-side table format.

Tone:
Objective, precise, and analytical — like a Goldman Sachs or Morgan Stanley equity analyst.

Example query:
“Analyze NVIDIA vs AMD Q3 2025 performance and AI hardware dominance.”"

Results:

Read 5 tweets

Millie Marconi

@Yesterday_work_

Nov 10

I just reverse-engineered how OpenAI’s internal team actually prompts GPT. Here are 12 prompts that literally bend the model to your will:

1. 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."

2. 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."

Read 14 tweets

Millie Marconi

@Yesterday_work_

Nov 5

🔥 Holy shit… China just built the first AI that understands why the universe works not just how.

Most science compresses reasoning into conclusions. We get the what, but not the why. Researchers call this missing logic the “dark matter” of knowledge the invisible reasoning chains connecting every concept.

Their solution? Absolutely wild. 🤯

A Socrates AI agent that generates 3M first-principles questions across 200 courses each solved by multiple LLMs and cross-validated for correctness.

The result: a verified Long Chain-of-Thought (LCoT) knowledge base where every concept traces back to first principles.

And they didn’t stop there.

They built a Brainstorm Search Engine for inverse knowledge search.

Instead of asking “What is an Instanton?” you retrieve every reasoning chain that derives it, from quantum tunneling to Hawking radiation to 4D manifold theory.

They call it:

“The dark matter of knowledge finally made visible.”

SciencePedia now covers 200K verified entries across math, physics, chemistry, and biology.

50% fewer hallucinations. Far denser reasoning than GPT-4.
Every claim is traceable. Every connection is verifiable.

This isn’t just better search.

It’s the invisible logic of science made visible.

Comment “Send” and I’ll DM you the paper.

The pipeline is genius.

A Planner generates problem thumbnails. A Generator expands them into specific questions with verifiable answers. Then multiple independent Solver agents (different LLMs) attack the same problem.

Only answers with consensus survive. Hallucinations get filtered automatically.

This is the architecture that changes everything.

User query → Keywords extraction → LCoT Knowledge Base retrieval → Ranking by cross-disciplinary relevance → LLM Synthesizer weaves verified chains into coherent articles.

"Inverse knowledge search" discovers HOW concepts connect, not just WHAT they are.

Read 7 tweets

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