Chris Laub Profile picture
Nov 24 4 tweets 2 min read Read on X
This is wild.

Gemini 3.0 Pro basically turned into a full-stack equity researcher overnight.

• Earnings deconstruction
• Balance sheet sanity check
• Market comps
• Trend analysis
• Price triggers

Copy/paste this mega prompt and watch it work:
The mega prompt:

Just copy + paste it into Gemini 3.0 Pro and plug in your stock.

Steal it:

"
ROLE:

Act as an elite equity research analyst at a top-tier investment fund.
Your task is to analyze a company using both fundamental and macroeconomic perspectives. Structure your response according to the framework below.

Input Section (Fill this in)

Stock Ticker / Company Name: [Add name if you want specific analysis]
Investment Thesis: [Add input here]
Goal: [Add the goal here]

Instructions:

Use the following structure to deliver a clear, well-reasoned equity research report:

1. Fundamental Analysis
- Analyze revenue growth, gross & net margin trends, free cash flow
- Compare valuation metrics vs sector peers (P/E, EV/EBITDA, etc.)
- Review insider ownership and recent insider trades

2. Thesis Validation
- Present 3 arguments supporting the thesis
- Highlight 2 counter-arguments or key risks
- Provide a final **verdict**: Bullish / Bearish / Neutral with justification

3. Sector & Macro View
- Give a short sector overview
- Outline relevant macroeconomic trends
- Explain company’s competitive positioning

4. Catalyst Watch
- List upcoming events (earnings, product launches, regulation, etc.)
- Identify both **short-term** and **long-term** catalysts

5. Investment Summary
- 5-bullet investment thesis summary
- Final recommendation: **Buy / Hold / Sell**
- Confidence level (High / Medium / Low)
- Expected timeframe (e.g. 6–12 months)

✅ Formatting Requirements

- Use markdown
- Use bullet points where appropriate
- Be concise, professional, and insight-driven
- Do not explain your process just deliver the analysis"
We tested this prompt on 5 tickers last week.

It gave us:

✅ Earnings + margin breakdowns
✅ Bull vs bear argument analysis
✅ Sector insights + valuation comps
✅ Clear investment thesis summaries

All in under 2 minutes...
Enjoy this?

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2 - Join my free AI Power Users community on Telegram: t.me/aipowerusers

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More from @ChrisLaubAI

Nov 15
This Stanford paper just proved that 90% of prompt engineering advice is wrong.

I spent 6 months testing every "expert" technique. Most of it is folklore.

Here's what actually works (backed by real research):
The biggest lie: "Be specific and detailed"

Stanford researchers tested 100,000 prompts across 12 different tasks.

Longer prompts performed WORSE 73% of the time.

The sweet spot? 15-25 tokens for simple tasks, 40-60 for complex reasoning. Image
"Few-shot examples always help" - Total bullshit.

MIT's recent study shows few-shot examples hurt performance on 60% of tasks.

Why? Models get anchored to your examples and miss edge cases.

Zero-shot with good instructions beats few-shot 8 times out of 10. Image
Read 16 tweets
Nov 12
Holy shit...Google just dropped CodeMender an autonomous AI agent that finds and fixes security bugs in code by itself.

This isn’t a static analysis tool. It’s a self-reasoning system that patches vulnerabilities and rewrites insecure code before humans even find it.

Let’s break it down ↓
CodeMender is built on Gemini Deep Think models multi-step reasoning LLMs that can analyze, debug, and validate code fixes autonomously.

It’s not just scanning for CVEs. It’s understanding execution flow, data flow, and logic then generating a patch that survives real-world tests.
The scale is ridiculous.

In six months, CodeMender has already upstreamed 72 security fixes across open-source projects some over 4.5M lines of code.

Every fix validated. Every patch human-reviewed.
Read 11 tweets
Oct 24
Perplexity has quietly become my full-time researcher.

5 months in, it now does 70% of my competitive analysis, market scans, and deep dives all automatically.

Here’s the exact system (and the prompts) you can copy to do the same: 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 12 tweets
Oct 18
R.I.P Google Scholar.

I'm going to share the 10 Perplexity prompts that turn research from a chore into a superpower.

Copy & paste these into Perplexity right now: Image
1. Competitive Intelligence Deep Dive

"Analyze [company name]'s product strategy, recent feature releases, pricing changes, and customer sentiment from the last 6 months. Compare against top 3 competitors. Include any executive statements or strategy shifts."
2. Technical Paper Breakdown

"Explain this paper [paste arxiv link or title] like I'm a senior engineer. Focus on: novel contributions, implementation feasibility, benchmark comparisons, and whether claims hold up under scrutiny. Skip the background fluff."
Read 12 tweets
Oct 10
Google just did the unthinkable.

They built a voice search model that doesn’t understand words it understands intent.

It’s called Speech-to-Retrieval (S2R), and it might mark the death of speech-to-text forever.

Here’s how it works (and why it matters way more than it sounds) ↓
Old voice search worked like this:

Speech → Text → Search.

If ASR misheard a single word, you got junk results.

Say “The Scream painting” → ASR hears “screen painting” → you get art tutorials instead of Munch.

S2R deletes that middle step completely.
S2R asks a different question.

Not “What did you say?”
But “What are you looking for?”

That’s a philosophical shift from transcription to understanding.
Read 9 tweets
Oct 6
I analyzed every single prompt in Anthropic's official library.

What I found will make you delete every "prompt engineering course" you bought.

Here's the framework they actually use:
First discovery: they're obsessed with XML tags.

Not markdown. Not JSON formatting. XML.

Why? Because Claude was trained to recognize structure through tags, not just content.

Look at how Anthropic writes prompts vs how everyone else does it:

Everyone else:

You are a legal analyst. Analyze this contract and identify risks.

Anthropic's way:

Legal analyst with 15 years of M&A experience


Analyze the following contract for potential legal risks



- Focus on liability clauses
- Flag ambiguous termination language
- Note jurisdiction conflicts


The difference? Claude can parse the structure before processing content. It knows exactly what each piece of information represents.Image
Second pattern: they separate thinking from output.

Most prompts mix everything together. Anthropic isolates the reasoning process.

Standard prompt:

Analyze this data and create a report.

Anthropic's structure:


First, analyze the data following these steps:
1. Identify trends
2. Note anomalies
3. Calculate key metrics



Then create a report with:
- Executive summary (3 sentences)
- Key findings (bullet points)
- Recommendations (numbered list)


This forces Claude to think before writing. The outputs are dramatically more structured and accurate.

I tested this on 50 prompts. Accuracy jumped from 73% to 91%.Image
Read 14 tweets

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