AI product leader at Google, Author, and community builder making complex AI concepts simple for technical and business audiences. Published author of two books on GPT-3 and Neural Search. Lead and manage an active community of 200K+ AI developers across my social media channels and Unwind AI platform. Creator of Awesome LLM Apps, a #1 top-trending GitHub repository with 90k+ stars featuring practical AI Agents and RAG tutorials.

Building a community of 1M+ AI Developers at Unwind AI, an open AI education platform where 200k+ AI developers today learn to build with AI in just 3 minutes a day:
→ Daily AI news that actually matters
→ Code-first tutorials on AI Agents, RAG and LLMs
→ Open-source AI Agent and RAG apps you can use today with full code implementation

Author of books on: 
→ "GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API"
→ "Neural Search - From Prototype to Production with Jina"

Previously:
→ Built Tenstorrent's developer community from scratch & launched first generation of AI hardware products from AI Accelerator cards to AI Desktop Workstations.
→ Built AI systems for Wall Street hedge funds @Arcesium (DE Shaw)
→ Led data initiatives at 256 Network
→ Tech Leaders Fellow @Plaksha

I share daily tips and tutorials on:
→ Building production-ready LLM apps
→ Implementing RAG systems
→ AI Agents and opensource agentic frameworks

🤝 Let's connect if you're:
→ Building with AI and need practical guidance 
→ Looking for ready-to-use LLM implementations
→ Want to stay ahead of AI developments and grow your career in AI

Follow me for daily AI insights and tutorials on AI Agents, RAG and LLMs.

"Clawdbot is an overnight success."

No. I just looked at the creator's GitHub profile.

50+ projects. Years of building. Before anyone noticed.

Here's what Peter shipped before Clawdbot blew up:
🧑‍💻 Claude Code MCP - An agent inside your agent
🚇 VibeTunnel - Turn any browser into your terminal for remote agent control
🎚️ CodexBar - Track your agent token limits in real time
📸 Peekaboo - macOS screenshots and GUI automation for MCP
🧲 mcporter - Call MCPs from TypeScript or package them as CLI
🍭 sweetlink - Playwright vibes in your browser to close the agent loop
📖 llm codes - Transform docs for AI agents
🧮 tokentally - LLM token and cost math
🧿 oracle - Token management for AI workflows
📜 agent-rules - Shared rules and knowledge for coding with agents
🛑 Terminator MCP - Terminal output for agents
🪢 Conduit MCP - File ops and web prowling for agents

Every single one solving a real problem in agentic engineering.

Then Clawdbot happens and people ask "how did he build this so fast?"

He's been building the pieces for years.

The MCP servers. The token trackers. The browser automation. The CLI tools.

Clawdbot wasn't a lucky first try.

It was project number 50+.

Everyone sees the viral launch. Nobody sees the 49 projects before it.

Ship beats perfect. Consistently. For years.

That's the whole secret.

Link to Peter's GitHub in comments.

Shubham Saboo

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 90k+ stars) | 3x AI Author | 100k+ on X | Views are my Own

No alternative text description for this image

Big News: Claude Code agent can now run locally for free 🔥

Ollama just added official support for Claude Code.

Run it with open source models. No API costs. 100% local.

Here's what just happened:

Claude Code is Anthropic's agentic coding tool. It reads, modifies, and executes code in your working directory.

Until now, you needed Anthropic API credits.

Not anymore.

In just three commands.

You get Claude Code agent harness. Running locally. Free forever.

Models that work great:

Local:
→ qwen3-coder (built for coding)
→ gpt-oss:20b (strong general purpose)
→ gpt-oss:120b (complex tasks)

One requirement: You need at least 32K context window. Ollama handles this.

Why this matters:
→ No API bills
→ No rate limits
→ No data leaving your machine
→ Full Claude Code experience with open models

Cursor costs $20/month.
GitHub Copilot costs $10/month.
Claude Code + Ollama costs $0/month.

The agentic coding era just got accessible for all.

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 91k+ stars) | 3x AI Author | 100k+ on X | Views are my Own

Open source Claude Cowork just dropped 🔥

It runs locally and that too for free with Ollama local models.
You bring your own API keys. 
Your files never leave your machine.

Introducing Openwork.

Think Claude Cowork, but:
• 100% Open Source
• Runs entirely on your machine
• Works with any model (OpenAI, Anthropic, Google, xAI, Ollama)
• No subscription. No cloud. No trust required.

Most AI desktop agents are cloud services. Your files get uploaded. Your data gets processed somewhere else. You pay a subscription. You hope they don't change the terms.

Here's what it actually does:

📁 File Management
Sort, rename, and move files based on content or rules you define.

✍️ Document Creation
Draft, summarize, rewrite docs and reports.

🌐 Browser Automation
Research workflows. Form entry. Repetitive web tasks.

🔗 Tool Connections
Works with Notion, Google Drive, Dropbox through local APIs.

Setup takes 2 minutes:
1. Download the app
2. Add your API key (or use Ollama)
3. Choose which folders it can access
4. Start working

Link to the GitHub Repo in the comments.

🥑 AI Product Manager at Tenstorrrent focused on bridging the gap between state-of-the-art software and the specialized hardware built for AI. 

📕 Co-author of "GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API" and "Neural Search - From Prototype to Production with Jina"

🗞️ Run a daily AI newsletter "Unwind AI" that delivers the latest AI developments and best AI tools directly to your inbox.

🤝 Senior AI Evangelist at Jina AI responsible for building the next generation of AI ecosystem for developers to build and deploy multi-modal/cross-modal applications in the cloud.

🤖 Co-founded Kairos Data Labs to minimize the gap between state-of-the-art AI innovations and applied AI applications within organizations. Our mission is to accelerate innovation with the widespread adoption of AI.

👨‍💻 Founding Data Scientist responsible for creating organization-wide data strategies and infrastructure to build & scale the data science and machine learning vertical within Arcesium (The DE Shaw Group). Built state-of-the-art AI solutions in the area of post-technology services for the biggest hedge funds of the world.

💵 Lead the data initiative at 256 Network, to build a data-driven ecosystem of global decision-makers investing in the startup ecosystem.

📈 Part of the founding cohort at Plaksha's Tech Leaders Fellowship (https://plaksha.org/techleaders)

🤝 Let's Connect: Follow me to stay on top of what's happening in the AI world. Your next insight or inspiration is just a connection away.

Samuel A.

JUST SHIPPED: Open Source multimodal language learning app using Google Gemini Live API 🔥

And it's wild.

You pick a scenario. Ordering coffee in Paris. Buying a train ticket in Tokyo. Bargaining with a shopkeeper. 

Then an AI character shows up. Not a chatbot. A persona that stays in character and speaks back to you in real-time audio.

And here's what got me: each character has emotions.

The bus driver gets impatient if you fumble your words. The cafe owner warms up when you nail the pronunciation. The neighbor gets excited when you actually keep up with the conversation.

It feels like talking to a real person. Because it reacts like one.

The "Immersive Mode" is brutal (in a good way):

No translations. No hints. No safety net.

You either speak the language or the conversation doesn't move forward.

This is how you actually learn. By getting uncomfortable.

Built with Gemini Live API. Full WebSocket audio streaming. Sub-second latency.

The best part?

It's 100% open source. Fork it. Remix it. Build your own.

Open the repo in Google Antigravity and vibe code your own features. Want a "Mars Colony Arrival" mission where the AI plays a customs officer? Just describe it. Antigravity writes the code. You see it update live.

When you're happy, one click deploys it to Cloud Run.

Built by Google Cloud DevRel team (Zack Akil, G. Hussain Chinoy)

Try the app now: https://immergo.app/

GitHub repo: https://lnkd.in/ge3KSEBr

Google

Full-stack ML/AI Engineer - Educator - Public Speaker - Developer Advocate - Google

Creating delightful experiences through great AI technology | Generative AI solutions

Diego Arturo Bustos Aponte

JUST SHIPPED: Developer’s guide to multi-agent patterns in Google Agent Development Kit (ADK) 🔥

Here's why I wrote it:

I kept seeing the same mistake everywhere.

Developers building ONE agent to do EVERYTHING.

Then wondering why it hallucinates. Why it breaks. Why it's impossible to debug.

We solved this problem in software engineering 15+ years ago.

Monoliths don't scale. Micro-services do.

AI agents are no different.

So I documented the 8 agentic design patterns that actually work in production:

• Sequential Pipeline Pattern: Like a relay race. Each agent runs their leg, passes the baton. Clean handoffs, zero chaos.

• Coordinator/Dispatcher Pattern: Your AI receptionist. Listens, understands, routes to the right expert every time.

• Parallel Fan-Out Pattern: Send 5 agents to do 5 tasks at once. What took 5 minutes now takes 1.

• Hierarchical Decomposition Pattern: Can't solve a massive problem in one go. Break it down. Delegate down.

• Generator & Critic Pattern: Write. Review. Rewrite. The feedback loop that catches errors before users do.

• Iterative Refinement Pattern: First satisfactory answer is never the best. Polish until it shines.

• Human-in-the-Loop Pattern: Automate everything except the decisions that keep you up at night.

• Composite Pattern: Real systems aren't textbook examples. Mix and match patterns as needed.

Full guide includes pseudocode for every pattern.

My biggest lesson after writing this?
Start simple. One pattern. Debug it.
Complexity should be earned, not assumed.

Full developer's guide: https://lnkd.in/g2XhQkAN

Agentic Design Patterns

Google quietly dropped a 71-page prompting guide 🔥

And it's a masterclass in getting better outputs from AI.

Here's what most people miss:

The 4-part prompt framework that changes everything:

• Persona: Tell AI who you are
• Task: Be specific about what you need
• Context: Give background that matters
• Format: Describe your desired output

Simple? Yes.
But 90% of people skip at least 2 of these.

The guide breaks down prompts by role:
• Executives
• Marketers
• Sales teams
• HR professionals
• Customer service reps

Each with copy-paste examples you can steal today.

My 3 favorite takeaways:
1. "Make it a conversation" – iterate, don't one-shot
2. Use @file references to ground outputs in YOUR data
3. Ask AI: "What questions do you have for me?"

That last one? Game-changer for complex projects.

The best part: Google says prompting is a skill anyone can learn.

No engineering degree required.

Prompting Guide 101

Introducing the Universal Commerce Protocol for AI Agents by Google 🔥

UCP lets AI agents discover products, fill carts, and complete purchases autonomously.

And it's 100% Open Source.

Here's the problem with AI shopping today:

User: "Find me a lightweight suitcase under $200"

Agent: "Here are some options..." *links to 12 different websites*

Now you're opening tabs. Comparing prices manually. Re-entering your payment info. Again. And again.

The "AI shopping assistant" just became a glorified search engine.

UCP fixes this entirely.

Your agent doesn't just find products. It checks inventory. Applies your discounts. Completes checkout. All without leaving the conversation.

Here's how it works:

🔍 Dynamic Discovery
Businesses publish capabilities in a standard manifest. Agents auto-discover what's possible. No hardcoded integrations.

🛒 Full Commerce Lifecycle
From browsing to checkout to order tracking. One protocol. One integration.

💳 Universal Payments
Works with Google Pay, Shop Pay, Stripe, any handler. Every transaction backed by cryptographic proof of consent.

🔌 Transport Agnostic
REST APIs. MCP. A2A. Your infrastructure, your choice.

The old way:
Business builds 47 integrations → Maintains 47 integrations → Breaks 47 integrations

The UCP way:
Business implements once → Every agent surface just works

Backed by Shopify, Stripe, Visa, Mastercard, Target, Walmart, Wayfair, Etsy, and 20+ partners.

Chat-based shopping was step one.
Agentic commerce is step two.

Stop building bespoke integrations that don't scale.
Start speaking the universal language of commerce.

Link to the GitHub repo in the comments.

Claude Code can now create videos 🤯

Remotion just dropped Agent Skills for Claude Code.

One prompt. 2 minutes. Full video. Done.

This is the ChatGPT moment for video creation.

🌟 Opensource Awesome LLM Apps Repo: https://lnkd.in/dW6b_dEn

Here's what just happened:

I typed a single prompt. Claude Code generated a complete animated video for my open source repo.

No After Effects. No Premiere. No learning curve.

Just words → video.

But wait. It gets crazier.

I generated a rap song with Suno. Added it to the video.

Sound on for fun 🎵

The entire thing took minutes. Video animation + custom AI rap track.

Install it with one command:

`npx skills add remotion-dev/skills`

Then prompt Claude Code like:

"Create a 30-second animated explainer for Opensource Awesome LLM apps repo (get the content from: repo link) "

And watch it build the entire thing.

Why this hits different:
→ Videos that used to take hours? Minutes.
→ Animations that needed a designer? Just describe them.
→ Custom soundtrack? Suno. 30 seconds.
→ Explainers, demos, social content? One-shot.

I've been playing with this for a few hours.

It's way too addictive.

The barrier between "I have an idea" and "here's a full video with music" just disappeared.

Developers making videos. AI writing the soundtrack.

We are so cooked.

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 89k+ stars) | 3x AI Author | 100k+ on X | Views are my Own

Google just dropped interactive text-to-world generation AI model  🤯

Type a prompt. Get an entire world. Walk around inside it.

Project Genie is now live for you to try.

Here's what this actually means:

You write a text prompt. Genie 3 creates a full navigable environment. You move through it. The world generates ahead of you in real time.

Not a static image. Not a video you watch. A world you explore.

Three core capabilities:

🎨 World Sketching
Text + image = living environment. Define your character. Choose your perspective. Walk, ride, fly, drive. Nano Banana Pro lets you preview and fine-tune before jumping in.

🚶 World Exploration
Move through your world. The path generates based on your actions. Physics and interactions simulated on the fly.

🔄 World Remixing
Take existing worlds. Remix into new interpretations. Download videos of your explorations.

What this enables:
• Robotics simulation
• Film previsualization
• Historical settings you can walk through
• Game prototyping
• Training environments for AI agents

This is an early research prototype. 
Generations currently run up to 60 seconds. 

But the trajectory is clear.

Text-to-image → Text-to-video → Text-to-world

We're watching foundation models learn to simulate reality.

Available now for Google AI Ultra subscribers in the U.S. (18+).

Izek Chen

I just built multi-Agent automated VC diligence team using Google ADK 🔥

VCs typically spend 40+ hours analyzing a single startup. 

This multi-agent AI team does it in minutes.

7 AI Agents work together as a team using Gemini 3 Pro, Gemini 3 Flash, and Nano Banana Pro.

100% Open Source Code.

Here's what this multi-agent team does:

Give it a startup name or URL. Watch 7 specialized agents collaborate to deliver a complete investment analysis.

🔍 Agent 1: Company Research
Searches the web for founders, funding history, product details, traction signals, recent news. Works even for early-stage unknowns.

📊 Agent 2: Market Analysis
TAM/SAM sizing, competitor mapping, positioning analysis, market trends. All from live data.

💰 Agent 3: Financial Modeling
Revenue projections with Bear/Base/Bull scenarios. Unit economics. Return analysis. Auto-generates charts.

⚠️ Agent 4: Risk Assessment
Deep analysis across 5 categories: Market, Execution, Financial, Regulatory, Exit. Severity ratings. Mitigation strategies.

📝 Agent 5: Investor Memo
Synthesizes everything into a structured investment thesis. Executive summary. Recommendation. Next steps.

📄 Agent 6: Report Generator
McKinsey-style HTML report. Professional typography. Print-ready.

🎨 Agent 7: Infographic Generator
Visual TL;DR using Gemini's multimodal image generation. Key metrics, risk score, investment recommendation—all in one graphic.

The agents pass context using ADK's SequentialAgent pattern. Each stage builds on the last. Nothing gets lost.

This does it in minutes.

The best part? The code is 100% Open Source.

📌 Step-by-step tutorial on Unwind AI: https://lnkd.in/gZgWV_Nx

🌟 GitHub Repo: https://lnkd.in/dW6b_dEn

Unwind AI

NEW RESEARCH: What if your LLM could call itself recursively to handle infinite context 🤯

Researchers from MIT just dropped Recursive Language Models (RLMs).

And the results are wild.

GPT-5-mini with RLMs outperforms GPT-5 on long-context benchmarks.

By more than double. At lower cost.

Here's why this matters:
2025 gave us plenty of evidence that "context rot" is real.

Long-running Claude Code sessions get dumber. 
ChatGPT forgets instructions after 50 messages. 
Your agent loses its personality halfway through a workflow.

Critical instructions disappear into the noise.

Everyone knows this happens. Nobody could benchmark it. Until now.

Here's how RLMs work:

Instead of stuffing everything into one massive context window:
🔄 The model recursively calls itself (or other LLMs) over chunks of context
🐍 It operates in a Python REPL environment with your context as a variable
🔍 It can peek, grep, partition, and summarize before answering
📊 The root LLM never sees the full context, so it never rots

The model decides how to break down the problem. Not you.

The results speak for themselves:
• RLM(GPT-5-mini) beats GPT-5 by 114% on the long-context benchmarks
• 49% better performance at lower cost per query
• Zero degradation even with 10M+ tokens
• Model autonomously decides how to decompose tasks at inference time

Agents decompose problems based on human intuition.

RLMs let the model decide how to decompose problems for itself.

This could be the next milestone after CoT reasoning and ReAct agents.

The era of "just make the context window bigger" is ending.

The era of recursive reasoning is beginning.

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 87k+ stars) | 3x AI Author | 100k+ on X | Views are my Own

Just SHIPPED: A visual guide to AI agent protocols 🔥

Here’s why I wrote this:
People keep comparing protocols like they’re rivals.

MCP vs A2A. AG-UI vs A2UI.

This framing is completely wrong.

These aren't competing standards. They're layers in a stack.

New protocols are dropping every month. MCP. A2A. AG-UI. A2UI. AP2. UCP.

Most developers are confused about when to use what.

Here's the mental model that makes it click:
Think of it like the networking stack. TCP/IP handles packets. HTTP handles requests. HTML handles content. Each layer does one thing well.

Agent protocols work the same way.

🔧 MCP: Agent → Tools 
USB-C for AI. One standard connector for all external resources. Databases, APIs, services. Build once, works everywhere.

🤝 A2A: Agent → Agent 
How agents discover and coordinate with each other. Agent Cards advertise capabilities. Tasks get delegated. Results flow back.

🖥️ AG-UI: Agent → Frontend 
Why your agent demo feels janky while Cursor feels like magic. Streaming, tool calls, human approval, state sync. All standardized.

🎨 A2UI: Agent → Interfaces 
Why should agents only return text? Generate forms, charts, dialogs, comparison cards. The agent outputs structure, the frontend renders it.

💳 AP2: Agent → Payments 
Cryptographic proof of user intent. Mandates that prove authorization. Built with Mastercard, PayPal, Stripe, Coinbase.

🛒 UCP: Agent → Commerce 
Standardized shopping across every retailer. Discovery, inventory, checkout, returns. One integration for all merchants.

Here's how they work together:
You ask your agent for running shoes under $150.
→ MCP pulls your size and purchase history 
→ A2A coordinates with price-comparison agents 
→ UCP queries merchants for real-time inventory 
→ AG-UI streams results to your phone 
→ A2UI renders a comparison card 
→ You tap buy 
→ AP2 signs the payment cryptographically

Six protocols. One flow. Each layer did exactly one job.

What to actually use:
Simple agent with tools? MCP only. 
Multiple agents? Add A2A. 
User-facing UI? Add AG-UI.
Rich interfaces? Add A2UI. 
Handling money? Add AP2. 
Commerce features? Add UCP.

Start simple. Add layers as complexity grows.

Agent Protocol 101

Build an AI Business Agent with Google ADK, Agent2Agent (A2A) and the newly launched Universal Commerce Protocol🔥

Your AI agent can now handle the entire shopping experience. Search to checkout to payment to order confirmation.

100% Open Source code.

Here's why this matters:

Every e-commerce platform speaks a different language. Different APIs. Different data formats. Different payment flows.

Building an AI shopping assistant meant custom integrations for every platform. Every merchant. Every payment provider.

That changes now.

Meet the Universal Commerce Protocol (UCP) for AI Agents.

An open standard that lets AI agents communicate with any commerce platform using a common vocabulary.

Here's what the sample AI agent does:

🛒 Product Discovery
"Show me cookies available in stock" → Agent returns real inventory

🛍️ Checkout Management
Add items. Collect shipping address. Calculate totals. Handle the entire session lifecycle.

💳 Payment Processing
Multiple payment methods. Mock processor simulates real payment flows.

📦 Order Confirmation
Complete the transaction. Return order details. Full lifecycle handled.

The architecture:
• Google ADK for the AI agent (Gemini + 8 shopping tools)
• A2A Protocol for agent discovery and communication
• UCP for standardized commerce data types
• React chat client that renders checkout, payments, and orders

The magic? Capability negotiation.

Before any transaction, the client and merchant exchange UCP profiles. They agree on supported features. Then they transact using a shared vocabulary.

Your agent talks to any UCP-compatible platform. Any merchant. Any payment provider.

One protocol. Many integrations.

Link to the GitHub repo in the comments.

Stop describing UI bugs to your AI agent. Just point at them. 🔥

Agentation just dropped.

Here's the problem we all have:

You see a bug in your app. You open Claude Code. You type:

"The blue button in the sidebar isn't working"

Agent guesses wrong. Fixes the wrong button. You try again.

"No, the other blue button. The one below the nav."

Back and forth. Wasted time. Frustration.

Agentation fixes this.

Click on any element in your running app. Add a note. Copy. Paste into your agent.

The output includes:
→ Exact class names
→ CSS selectors
→ Element positions
→ Your feedback

It greps your codebase. Finds the exact file. Makes the fix. Done.

How it works:
1. Click the icon in the bottom corner
2. Hover over elements to see their names
3. Click any element to annotate
4. Write your feedback
5. Copy the markdown output
6. Paste into Claude Code, Cursor, Windsurf, whatever

Works with any AI coding tool. Zero dependencies beyond React.

The key insight:

Agents find and fix code much faster when they know exactly which element you mean.

Stop playing guessing games with your agent.

Start pointing at problems.

Link to the tool in the comments.

Airtable just dropped Superagent and it spins up a team of AI agents on the fly.

One prompt. Multiple specialized agents. Working simultaneously.

I asked it to do a competitive analysis of AI web agent infrastructure companies. Browserbase, Browser Use, Tinyfish, and others from a VC point-of-view.

Then I sat back and watched.

One agent pulling company backgrounds.
One agent digging through funding rounds.
One agent mapping technical moats.
One agent analyzing GTM strategies.
One agent synthesizing the investment thesis.

All at once. Like watching a research team speedrun due diligence.

But here's where it got crazy.
The output wasn't a text dump I'd spend 3 hours reformatting.

It was a Super Report.

• Interactive competitor matrices I could click through
• Scrollable visualizations
• Expandable sections with full citations
• Shareable URL ready to send to anyone

This would normally take me a weekend.

Superagent did it in just ~10 mins.

The part that surprised me: it didn't flatten a hard question into generic fluff.

It actually broke down the problem. Worked each dimension. Gave me something I'd actually present to a partner.

If you do competitive analysis, market research, investment memos, strategy decks - anything that needs depth + polish.

This is for YOU.

Try it at: https://lnkd.in/gejqXS3a

#SuperagentPartner

Google just dropped a free course on Gemini CLI with DeepLearning AI🔥

An open-source AI agent that lives in your terminal.

Here's why this matters:

Most AI coding tools work in isolation. They help you write code. That's it.

Gemini CLI is different. It has access to your local files, your dev tools, AND cloud services. All from the command line.

You give it high-level instructions. It plans. It executes. Multiple steps. Autonomously.

What you'll actually build in this course:
• A conference website session catalog
• An interactive data dashboard combining local CSVs with cloud data
• Social media content generated from video recordings
• Automated PR review workflows with GitHub Actions

The real power? Tool orchestration.

Connect MCP servers. Use Gemini CLI extensions. Coordinate across:
• Local filesystem
• GitHub
• Google Workspace
• Canva

All from one terminal command.

The course also covers:

🧠 How Gemini CLI reasons through tasks and calls tools
📁 Context management with gemini MD files
🔌 Connecting MCP servers and extensions
🔍 Web search grounding for accurate, cited responses

Who should take this:
• Developers who want to automate workflows
• Anyone prototyping apps faster
• People learning agentic AI patterns

Basic command line and Python helps. That's it.

Free. Hands-on. Available now.

Taught by our rockstar DevRel Jack Wotherspoon

Link to the free course in the comments.

STOP SCROLLING: The best startup idea of 2026 already died in 2017 🤯

925 dead startups. $32.5 billion burned to ashes.

Someone just analyzed over 900 startup post-mortems to find out why.

The patterns will surprise you.

Here's the thing most people get wrong:

"No market need" is NOT the top killer.

Everyone repeats this stat. It's outdated.

The real data tells a different story:
🥇 85.6% - Product problems (quality, tech, UX)
🥈 82.7% - Outcompeted / strong competition
🥉 62.6% - Bad unit economics
4️⃣ 52.8% - Lost focus / pivot problems
5️⃣ 50.7% - Marketing / distribution issues
6️⃣ 45.4% - Ran out of cash
7️⃣ 44.2% - Operational / scalability issues
8️⃣ 41.8% - Legal / regulatory challenges
9️⃣ 36.2% - No market need
🔟 32.0% - Team conflicts

"No market need" ranks 9th. Not 1st.

Here's what actually happens: product issues lead to poor unit economics, which drains cash. It's rarely one thing. It's everything compounding.

The 2026 playbook? AI Agents.

Find a Series B failure from 2015-2018 that died from bad unit economics.

Rebuild it with:
→ Serverless infrastructure (pay per millisecond)
→ AI agents replacing human operations
→ Generative content instead of static creation

Flip margins from -40% to +20%.

Webvan burned $800M in 2001.
Instacart is worth $10B+ today.

Same idea. Better timing. Modern AI stack.

The founders who win in 2026 aren't inventing new ideas.

They're resurrecting old ones with agents.

What dead startup would you rebuild with Agents? 👇

taplio

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