Holy shit... Someone just built the world's largest AI image prompt library and made it free.

It's called MeiGen, it scrapes the most popular prompt posts from X each week and puts them all in one place.

No prompt engineering. No guessing. Just steal what's already working.

100% free. 100% Open Source. Here’s the problem MeiGen solves:

Some of the best AI image prompts are on X.

But they get lost in your bookmarks, likes, and the “I’ll come back to this” pile—until you never do.

MeiGen finds the best prompts each week and puts them in one place. They’re curated, organized, and easy to search.

🚨 BREAKING: Someone just leaked their full Claude Cowork setup and it compresses an entire workday into 90 seconds.

I scraped every power user workflow across X, Reddit, and private Slack groups to find out how.

99% of people are using it completely wrong.

Here's what the top 1% actually do 👇 Prompt 1: Inbox triage + summarization

"You are a Chief of Staff with 10 years of executive support experience.

I need you to process my inbox one email at a time using this exact chain of reasoning:

Step 1 → Classify: Is this urgent (needs reply today), important (needs reply this week), or noise (unsubscribe/archive)?
Step 2 → Extract: Pull out the sender, request, deadline, and any names mentioned.
Step 3 → Draft: Write a reply under 4 sentences. Match the sender's tone. Never use "I hope this email finds you well."
Step 4 → Flag: If it involves money, legal language, or a deadline under 24 hours, mark it [ESCALATE] before the reply.

Process every email in my inbox folder. Output in this format:
[CLASSIFICATION] | [EXTRACTED INFO] | [DRAFT REPLY] | [FLAG IF NEEDED]

Do not stop until every email is processed."

🚨BREAKING: This github has entire blueprint for building ChatGPT from scratch and put it on GitHub for free.

It's called LLMs-from-scratch and it walks you through building a GPT model in PyTorch line by line.

No black boxes. No hand-waving. Every single piece of the architecture explained and coded.

Here's why this changes everything for AI builders: ↓ Most people using ChatGPT every day have zero idea how it actually works under the hood.

Attention mechanisms. Tokenization. Pretraining. Finetuning. RLHF.

They're all magic words until you build one yourself.

This repo fixes that.

🚨BREAKING: GOODBYE POWERPOINT forever.

Claude just collapsed 10 hours of presentation building into 100 seconds completely free.

Here are 12 prompts to go from completely unprepared to completely untouchable in every meeting: (Save this): 1. Complete Presentation Blueprint

Act like a professional presentation consultant who has built decks for Fortune 500 boardrooms and billion-dollar pitch meetings. Create a complete presentation blueprint for [topic]. Define the objective, target audience, key message, emotional arc, and exact slide flow. Make every section earn its place and eliminate anything that doesn't move the audience toward one clear decision.

Holy shit... Someone just built the ultimate prompt collection for AI image creators.

It's called MeiGen, it scrapes the hottest prompt posts from X every week and curates them in one place.

No more bookmarking 50 tweets. No more losing that prompt you saw 3 days ago.

100% free. 100% Open Source. Here's the problem MeiGen solves:

The best AI image prompts live on X.

But they're buried in your bookmarks, your likes, your "I'll come back to this" pile that you never come back to.

MeiGen pulls the hottest ones weekly curated, organized, searchable.

Holy shit... Someone finally benchmarked AI code reviewers on REAL pull requests.

Not demos. Not vibes. Actual F1 scores across 8 tools.

The gap between #1 and #8?

34 percentage points.

Your eng team is probably using the wrong one right now: ↓ @entelligence benchmarked 8 tools on real pull requests.

Every "which code review tool should we use" convo ends the same way:

→ Someone shares a demo
→ Someone shares a vibe
→ Nobody has real numbers
→ Team picks based on brand recognition

That era is over.

I just ran 6 AI tools in one workflow.

No API keys. No dashboard switching. No glue code.

Just typed what I wanted and Claudcode + SkillBoss executed the entire thing.

Here's the breakdown: 👇 Most AI setups look like this:

→ ChatGPT tab for writing
→ Midjourney for images
→ Runway for video
→ Zapier to connect them
→ 4 API keys you'll inevitably break
→ 2 hours wasted before you even start

That's not a workflow. That's a second job.

RIP to every dev team charging $50K to build an internal dashboard.

UI Bakery just made every internal tool your dev team ever built look like a waste of time.

It's called UI Bakery, it builds and deploys a fully functional internal app in 2 minutes.

No sprint. No Jira ticket. No engineer bottleneck.

Here's how: ↓ Here's what it actually does:

→ Connect to 45+ databases (Postgres, MySQL, MongoDB, Snowflake, Redis, OpenAI...)
→ Describe the app you want in plain language
→ AI Agent generates and deploys a fully functional app
→ 2 minutes. Production-ready. SOC 2 compliant.

Not a prototype. A real app on live data.

🚨BREAKING: The open-source Alexa killer just dropped.

It's called OpenHome, a smart speaker dev kit you can run AI agents on.

No Amazon. No Google. No vendor lock-in. Your data stays local.

100% Opensource. OpenHome @OpenHome runs on Raspberry Pi 4 (8GB RAM) with:

→ Far-field AEC mic array (hears you across the room)
→ Full-range speaker with deep bass
→ Local LLMs + local STT/TTS (fully offline capable)
→ Smart home control via Matter/Thread & MQTT

Your agent finally has ears, a voice, and a home.

BREAKING: Someone quietly built the first AI assistant that runs on a $5 chip.

It’s called MimiClaw and it's a full @OpenClaw style agent running on an ESP32 microcontroller without @Linux

It’s built with local-first memory and privacy by default.

No Linux. No Mac mini. No Raspberry Pi. No VPS.

100% Opensource. MIT License. The setup is wild:

→ ESP32-S3 board ($5-8)
→ No Linux
→ 16MB flash, 8MB PSRAM
→ 0.5W power draw (runs 24/7 off USB)
→ Talks to you through Telegram
→ Uses Claude's API with full tool use (ReAct agent loop)

That's a full AI assistant for less than a coffee.

BREAKING: Someone just built an AI that codes and browses the web at the same time.

It's called Accomplish and it runs locally without burning through API credits.

No Claude Desktop. No Cursor. No monthly subscriptions.

100% Opensource. Accomplish is an open-source AI coding agent that gives Claude Sonnet 4.5 two superpowers simultaneously:

- Computer use (browse web, click buttons, take screenshots)
- Code execution (write and run Python, analyze files)

Both. At the same time. In one interface.

BREAKING: Claude is insane for market research.

I reverse-engineered how top consultants at McKinsey, Goldman Sachs, & JP Morgan use it.

The difference is night and day.

Here are 12 insane Claude Opus 4.6 prompts they don't want you to know (Save for later) 1. Market Sizing (TAM/SAM/SOM) from Scratch

Most founders pay consultants $3K just for a market sizing slide.

Claude does it in 30 seconds with actual logic:

Prompt:

You are a senior market research analyst at McKinsey.

Calculate the TAM, SAM, and SOM for [YOUR PRODUCT/SERVICE] in [TARGET MARKET].

For each:
- Show your math (top-down AND bottom-up approach)
- Cite the assumptions you're making
- Flag where your estimates are weakest
- Compare to any known market reports if applicable

Format as an investor-ready slide with numbers, not paragraphs. If my market is smaller than I think, tell me now.

Holy shit... your AI agent can now make real phone calls.

It's called ClawdTalk and it gives any Clawdbot or OpenClaw agent an actual phone number.

Not browser audio. Not a chatbot. Real calls over real telephony infrastructure.

Sub-200ms latency.

Here's why this changes everything: 🧵 Your agent can execute tools MID-CONVERSATION.

That means while it's on a live phone call, it can:
→ Book appointments
→ Pull customer data
→ Trigger workflows
→ Process orders

All while talking to a real human on a real phone line.

This isn't a demo. This is production-ready.

China is destroying every expensive AI model on OpenClaw.

Kimi K2.5 just dropped with Claude Opus 4.6 performance,
but 8x CHEAPER.

It's now #1 most used model for OpenClaw
& THE MOST USED MODEL OVERALL on OpenRouters.

Here's everything you need to know: 🧵 Look at the OpenRouters leaderboard right now:

1. Kimi K2.5 — 240B tokens
2. Trinity Large Preview — 141B tokens
3. Gemini 3 Flash Preview — 138B tokens
4. Claude Sonnet 4.5 — 54B tokens
5. Claude Opus 4.5 — 42.2B tokens

Kimi K2.5 isn't just winning. It's running away with it.

🚨BREAKING: Someone just built an AI mechanic for broken vibe-coded apps.

It's called FixMyApp AI and it fixes your broken AI-built MVPs without prompting, agencies, or rewriting anything.

Just connect your repo → describe the bug → get a PR in 48 hours.

Here's how:↓ Here's the problem nobody talks about:

Cursor, Bolt, Replit, Lovable they're incredible at building MVPs.

But the moment you push to production?

Auth breaks. APIs timeout. Edge cases everywhere.

You're stuck prompting in circles trying to fix code you didn't write.

Everyones using ChatGPT for content writing. Meanwhile Claude is quietly outperforming it on every platform.

I switched 3 months ago and my engagement jumped 340% across all socials.

The secret isnt Claude itself. Its the 10 prompts that make it write like an actual human, not a corporate robot.

Here they are: 🧵 1. The Coffee Shop Test

Prompt:

"Write this like you're explaining it to a friend over coffee. No marketing speak. No corporate jargon. Just straight talk about [topic]. If it sounds like a LinkedIn post, rewrite it."

Claude actually gets this. ChatGPT still sounds like it's pitching a SaaS product.

Holy shit... Someone just built an AI CFO that catches financial problems BEFORE they destroy your startup.

It's called Nume and it monitors your finances 24/7 without dashboards or prompting.

No finance degree. No spreadsheets. No "wait, where did the money go?"

Here's how it works: ↓ Most finance tools show you dashboards you'll never check.

@NumeAI does the opposite: it watches your money 24/7 and TELLS YOU when something's wrong.

Like a real CFO sending you a Slack message: "Your burn rate just spiked 34%."

No login. No navigation. Just alerts that matter.

Try it: nume.ai

This site is literally a prompt library with 1000+ engineered prompts.

100% free. Check it out: promptly.fyi/library

After 2 years of using AI for research, I can say these tools have revolutionized my workflow.

So here are 15 prompts across Claude, Gemini, ChatGPT, and Perplexity that transformed my research (and could do the same for you): 1/ LITERATURE REVIEW → Claude Sonnet 4.5

"Analyze these 30 papers on [TOPIC]. Find contradictions, research gaps, and emerging debates. Prioritize findings that challenge consensus."

Why Claude: 200K context window = reads entire papers, not just abstracts.

After spending $5,000 on prompt engineering courses, I realized they're all teaching outdated techniques.

Here are 6 powerful prompts that actually matter in 2026 (copy & paste into Grok, Claude, or ChatGPT): 1. Deep researcher

Prompt:

"I'm researching [topic]. First, break down this topic into 5 key questions that experts would ask. Then for each question: 1) Provide the mainstream view with specific examples, 2) Identify 2-3 contrarian perspectives that challenge this view, 3) Explain what data or evidence would prove each side right. Finally, synthesize this into a framework I can use to evaluate new information on this topic."

Researchers waste weeks reading scattered sources.

This structures your entire research process upfront. I used this to write a market analysis that landed a $50k client.

After 6 months of testing, Gemini 3.0 is the most underrated AI for financial analysis.

It's completely free and outperforms GPT-5.2 on market research.

Here are 8 prompts for investment research that actually work: 1. Earnings Call Decoder

Prompt:

"Analyze the last 3 earnings calls for [company ticker].

Don't summarize what they said - tell me what they're NOT saying.

Focus on:

1) Questions the CEO dodged or gave vague answers to,
2) Metrics they stopped reporting compared to previous quarters,
3) Language changes - where they went from confident to cautious or vice versa,
4) New talking points that appeared suddenly,
5) Guidance changes and the exact wording they used to frame it. Then connect this to their stock performance in the 2 weeks following each call.

What pattern emerges?"

Gemini can process multiple transcripts simultaneously and catch subtle language shifts. I caught a revenue recognition issue 3 weeks before the stock tanked because the CFO changed how he talked about "bookings." Made 34% shorting it.