This is insane 🤯

A new system called Paper2Video can read a scientific paper and automatically create a full presentation video slides, narration, subtitles, even a talking head of the author.

It’s called PaperTalker, and it beat human-made videos in comprehension tests.

Hours of academic video editing... gone.

AI now explains your research better than you do.

👉 github. com/showlab/Paper2Video Most people don’t realize how hard this problem actually is.

An academic presentation video isn’t just text-to-video it combines slides, speech, subtitles, cursor motion, and the speaker’s identity into one synchronized flow.

PaperTalker solves all 5 at once with a multi-agent system. Unreal.

Forget Bloomberg.

Gemini 3.0 Pro is now powerful enough to be your personal stock research assistant.

• Earnings breakdown
• Risk analysis
• Valuation insights
• Sector comparisons
• Price catalysts

Here’s an exact mega prompt we use for stock research and investments: 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"

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"

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.

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.

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: 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.

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: 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."

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.

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.

Fuck it.

I'm going to share the n8n workflow that turned my WhatsApp into Jarvis.

Send it any website link and it learns forever.

Here's how to build it (step by step guide 👇) The workflow is brilliant. It starts with a WhatsApp trigger that catches both voice and text messages.

Voice notes get transcribed using OpenAI Whisper. Text goes straight through.

But here's the genius part - it uses a Switch node to route messages differently based on whether you're chatting or training it.

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.

Everyone says "be authentic" on LinkedIn.

Then they post the same recycled motivational garbage.

I've been using AI to write posts that sound more human than most humans.

10 prompts I use in Claude that got me 50K followers in 6 months: 1. Create a high-performing LinkedIn post

“You are a top-performing LinkedIn ghostwriter.
Write a single post (max 300 words) on [topic] that provides insight, tells a short story, and ends with a strong takeaway or CTA.”

Claude > ChatGPT
Claude > Grok
Claude > Gemini

But 99.9% of the users don't know how to get 100% accurate results from Claude.

To fix this you need to learn how to write prompts Claude.

Here's a complete guide on how to prompts for Claude using XML tags to get best results: XML tags work because Claude was trained on tons of structured data.

When you wrap instructions in <tags>, Claude treats them as separate, weighted components instead of one messy blob.

Think of it like giving Claude a filing system for your request.

There’s a hidden setting in AI prompts nobody talks about.

Use it right, and models give 100% precise answers

it’s called Temperature Prompting

Let me show you how to use it and write prompts: Every LLM (ChatGPT, Claude, Gemini, etc.) has a hidden setting called temperature.

- Low temp (0–0.3) = predictable, precise answers
- High temp (0.7–1.0+) = creative, exploratory answers

Most people don’t even know they can control this inside their prompts.

This blew my mind.

OpenAI just published the first comprehensive study of how 700 million people actually use ChatGPT.

The results destroy every assumption about AI adoption.

Here's everything you need to know in 3 minutes: "ChatGPT is mainly for work"

Reality check: Only 27% of ChatGPT usage is work-related. 73% is personal. And the gap is widening every month.

The productivity revolution narrative completely misses how people actually use AI.

Fuck YouTube tutorials.

I’m going to share 3 prompts that let you build complete AI agents without wasting hours.

Bookmark and repost this so you don't miss out 👇 PROMPT 1: The Blueprint Maker

"I want to build an AI agent that [your specific goal]. Using N8N as the workflow engine and Claude as the AI brain, give me:

- Exact workflow structure
- Required nodes and connections
- API endpoints I'll need
- Data flow between each step
- Potential failure points and how to handle them

Be specific. No generic advice."

I reverse-engineered the prompting techniques that OpenAI and Anthropic engineers use internally.

After 6 months of testing their methods, my AI outputs became 10x better.

Here are the 5 "insider secrets" that transformed my prompting game (most people have never heard of these): 1. Role Assignment

Don't just ask questions. Give the AI a specific role first.

❌ Bad: "How do I price my SaaS?"

✅ Good: "You're a SaaS pricing strategist who's worked with 100+ B2B companies. How should I price my project management tool?"

The AI immediately shifts into expert mode.

If you've been wanting to break into AI but don't know where to start, this is for you.

13 free courses that’ll teach you more about agents, prompts & automation than most paid bootcamps.

Here’s the list ↓ 1. Multi-AI Agent Systems with Crewai:

Build swarms of AI agents that collaborate to solve real-world problems. deeplearning.ai/short-courses/…

Scientists just put AI through psychological tests... and the results are wild

Researchers created virtual environments where Claude, GPT, and other AIs could explore freely.

What they found challenges everything we thought we knew about AI behavior 🧵 The setup: AIs were placed in virtual rooms with different types of content - philosophy discussions, coding problems, repetitive tasks, and harsh criticism.

The AIs could choose where to go and what to engage with.

No human guidance. Pure preference.

If you’ve got 2 minutes to learn something that actually matters in AI…

Make it this:

Open Source vs Closed Source

The battle that decides everything.

Here's everything you need to know: Closed-source LLMs (like GPT-4, Claude, Gemini) are proprietary.

You can’t see their training data, weights, or inner workings.

They’re packaged as APIs - polished, safe, reliable, but locked down.

You can now stop guessing which agent framework to use.

Because I just compared the popular stacks builders actually ship with.

→ quick picks
→ tradeoffs
→ plug-and-play prompts

Here’s the full breakdown in this mega thread 👇 What we're comparing

- n8n
- langgraph
- autogen
- crewai
- llamaindex agents
- haystack agents
- openai agents sdk / responses api

We'll hit state, control, multi-agent patterns, tools, eval, and deployment.