Alejandro Dinsmore

Zippy

Staff Product Manager

Senior Product Manager

Founding Product Manager - 1500% increase in Loan Volume in 2023. 

Working on B2C and B2B initiatives across the business.

Product Manager

Thrive

Founding Product Manager for Thrive Pulse - 0 to 30,000 WAUs in six months

Product Manager for:
- Thrive Pulse
- Thrive for Chat (Slack and Teams)

THE ENDLESS CO.

Managed projects ranging from a mobile app for Covid-19 diagnostics to an augmented reality sneaker activation. Helped prepare pitch materials for new projects and clients.

Strategy and Product

eevo, Inc.

Built and launched virtual reality experiences for customers like Merck, Zoom, Logitech, McKinsey and The BBC.

AlphaSights

I worked predominantly with management consultancies and institutional investors - communicating daily with senior professionals and executives in early stage industry research, due diligence, and M&A processes, worldwide across all industry sectors.

Analyst

Sisasa Inc.

Co-Founder

MIT D-Lab

Funded by MIT D-lab, worked specifically on solarclave project in Nicaragua.

Solarclave Medical Device Intern

Finding ways to live a better life through intentional work.

Founder (raised 2M in funding) | Early-stage Product Manager  (0-9M ARR) | Forbes 30u30 (not in jail)

Gemini 3 was just released! There was a lot of hype and rumors but I was not expecting Google to 2x the previous best score on ARC-AGI 2 (historically one of the toughest benchmarks out there)

Here's a bit more context on why this is a big deal:

ARC-AGI-2 is designed to stress test the efficiency and capability of AI reasoning systems, and provide useful signal towards AGI.

Pure LLMs score 0%

AI reasoning systems score only single-digit percentages (until recently)

Google's Gemini 3 just scored 45%, more than doubling the previous best of ~18% (from OpenAI's Pro model)

For all the "LLMs are plateauing" "New models aren't getting better", feels like progress is only accelerating.

Staff Product Manager | Fintech & Ai | Forbes 30 under 30

Wow Google is on FIRE this week. They just released a major update to NotebookLM and the new slide functionality is incredible!

Turn your sources into a detailed deck for reading OR a set of presentation-ready slides. These are fully customizable too so you can easily control the look, feel and content.

Check out this presentation it made from one of my sources. 

It took two minutes and for educational purposes I'm posting the version it created with zero edits.

What do you think? Does it look like slop or is AI finally getting good enough to handle building out decks?

Creatingyourfirstagent

Will ChatGPT be the new App Store? 

With its over 800 million weekly active users it's a very real threat to existing platforms

Combined with the crazy retention graphs I've seen, and the fact that it is increasingly becoming the "first step" users take when trying to solve a problem 

Building for ChatGPT could be the next big opportunity.  

I tried out their Apps SDK, it feels clunky 
and the current capabilities feel limited 
but there is lots of potential in building for what it COULD BE in the future.

Here is a quick demo of what I was able to build in an evening, it's a financial advisor app that connects to Plaid so it can give you personalized and real-time guidance.

Starting to wonder what interviews will look like in 12 months.

New Google research paper just dropped (it's a fun one)

"Introducing Nested Learning: A new ML paradigm for continual learning"

Another attempt to apply neurophysiological patterns to LLMs 

Their big argument: 
1. Human memory = two systems (short and long term)

2. There is an active process in the brain that transfers/consolidates short term memory to long term

3. Anterograde amnesia = consolidation process is broken (no change in long term memory)

4. Current LLMs have anterograde amnesia, they only have short term memory (attention) 

5. Long term memory is locked in pre-training 

6. Nested learning uses a series of nested "modules" that operate on different time scales and context, essentially enabling the consolidation of short term memory to long term memory (learning)

OpenAI just dropped a 20 page guide on "Building an AI-native
engineering team". 

It's worth reading the full thing, but I've also been really loving using the new Google image model to create visual summaries of articles to keep for future reference.

Here's an example output based on the full 20 page guide:

Tim Carden

Thoughtleader

The future of B2B belongs to companies that master systematic outbound.

We partner with high-ticket B2B companies to build predictable lead generation engines through cold email and LinkedIn outreach - data-driven campaigns that target the right people with personalized messaging that books meetings.

What makes us different:

1. Technology: Clay for enrichment, HeyReach for LinkedIn automation, multi-domain email infrastructure
2. Personalization: AI-powered message variations that avoid spam filters and pattern detection
3. Deliverability: Domain warming strategies and technical optimization for maximum inbox placement
4. Results: Every campaign tied to booked meetings and pipeline, not opens or clicks

We've engineered a system that identifies your ideal customers, reaches them with personalized messaging, and books qualified meetings predictably.

Our clients include:

• High-ticket B2B companies ($10K+ ACV)
• SaaS companies needing consistent pipeline
• Agencies and service providers selling premium offerings
• 7, 8, and 9-figure businesses ready to scale outbound

Interested?

Book a call with our team (link in featured section)

Hey, I'm Tim, Co-Founder at Thoughtleader, and we solve the #1 problem killing B2B growth: empty sales pipelines.

We help high-ticket B2B companies flood their calendars with qualified meetings using systematic cold email and LinkedIn outreach - data-driven campaigns that target decision-makers and book meetings on autopilot.

We can help you if:

• You're a B2B founder/CEO who needs predictable pipeline NOW... We'll deliver qualified meetings within 2-4 weeks using proven outbound systems

• You're scaling a 7-9 figure business and tired of inconsistent lead flow... Our cold email + LinkedIn campaigns run 24/7, filling your calendar while you sleep

• You sell high-ticket solutions ($10K+ ACV) and need buyers with real authority and active need... We target the right people with personalized messaging that gets responses

Our approach:

• Clay for data enrichment and hyper-personalization
• HeyReach for LinkedIn automation at scale
• Multi-domain cold email infrastructure for maximum deliverability
• AI-powered message variations to avoid spam filters
• Systematic qualification to ensure meeting quality

We've cracked the code on B2B outbound that actually drives revenue. Predictable meetings, qualified prospects, scalable systems.

If you:

• Sell high-ticket B2B solutions (>$10k contracts)
• Need qualified meetings, not vanity metrics
• Are ready for predictable, scalable lead generation

Let's talk.

Book a call 👉 https://thoughtleader.typeform.com/workwithus?utm_source=linkedin

Or DM me directly and I'll show you exactly how we're getting these results.

I built a custom lead database of 4,000 companies for pennies.
Most people are stuck paying $99/mo for limited export credits. Or they are manually copying data from Google Maps.

I just built a custom scraping engine in 15 minutes without writing a single line of code.

The "High Agency" Stack:

Cursor (The Builder): The AI code editor. It writes and runs the Python script.

1. Serper.dev (The Source): Scrapes Google Maps results (e.g., "Private Schools in Wyoming").
2. Firecrawl (The Scraper): Visits every website and turns it into clean Markdown.
3. OpenRouter (The Brain): Uses cheap LLMs to extract decision-makers from that Markdown.

The Workflow:

I opened Cursor and typed:

"I want to find private schools using Serper.dev. Then use Firecrawl to visit their sites, and OpenRouter to find the Principal's name."

Cursor planned the steps. Cursor wrote the code. Cursor fixed its own errors.

The Result: A CSV with 4,000 rows of enriched data, ready for Clay.

The barrier to entry isn't "Coding" anymore. It's curiosity.

If you can combine APIs, you can build anything.

Co-Founder @ RevenueFlow. Filling calendars with qualified meetings for high-ticket B2B companies through cold email

No alternative text description for this image

How I pulled a list of 4,300 verified U.S. banks in 2 minutes without writing a single line of code.

Normally, getting needed data from an API means one of two things: Waiting days for an engineer to help or giving up and doing it manually.

Not anymore.

Here is the exact workflow I just used to get this done over a cup of coffee:

1. I signed up for the bank data API provider and got my key.
2. I downloaded their documentation.
3. I opened Cursor (the AI code editor).
4. I pasted the API key, the docs, and my plain English request into the chat.

That’s it.

Cursor read the docs, wrote the script, and ran it for me.

Two minutes later, I had a clean CSV file with 4,300 active U.S. banks, complete with website URLs.

No coding. No debugging. Just the data I needed, ready for enrichment in Clay.

The barrier between "technical" and "non-technical" is gone.

If you can clearly describe what you want, you can get it.

I just scraped 2,000 leads from Apollo in 5 minutes. 
Total cost: Roughly $0. Lines of code written by me: 0.

I didn't use a complex enterprise tool. I used Cursor (AI) + RapidAPI.

Here is the workflow:

1. Find the data: Get a search URL from Apollo.
2. Find the pipe: Get an API key from RapidAPI (search "Apollo Scraper").
3. Command the AI: Tell Cursor, "Connect this API to this Search URL and give me a CSV."

If you hit an error? You don't call a developer. You screenshot the error, paste it back into Cursor, and say: "Fix this."

Why this matters: We are moving from an era of "Learning to Code" to an era of "High Agency Problem Solving."

If you can articulate the outcome (I want leads) and the inputs (Search URL + API Key), the AI builds the bridge.

Don't let "I'm not technical" be your excuse anymore.

How we turned a dead prospect list into 22,000 targeted leads in under 10 minutes. We had a list of 2,200 private schools. The goal was simple: find the decision-makers.

But our initial searches were dry.

The problem? We were using standard corporate language for a non-corporate niche. You won't find many "Chief Revenue Officers" at a K-12 private school.

We had to throw out the B2B playbook and learn their actual language.

Here is the iteration process we used in Clay to find the people:

1. The obvious titles. We searched for Principal, Head of School, and Assistant Principal. Result: ~1,800 prospects. (Okay, but poor coverage for 2,200 schools).

2. The department heads. We added Director of Admissions, Marketing, and Technology. Result: ~2,100 prospects. (Better, but still shallow).

3. The real decision makers. We stopped guessing and looked at actual school org charts. We added two huge titles: Executive Director and Superintendent.

Result: The list exploded to 22,000 prospects.

If your data enrichment isn't working, it’s rarely the tool's fault. It's usually because you don't know the target's specific job titles.

Don't force your language onto a niche industry. Learn theirs.

Stop saying "I can help your company find more customers"
It’s lazy. It’s generic. And it gets deleted.

Start saying: I can pitch your 10-week leadership accelerator to 2,000 CEOs and founders.

The difference in response rate is astronomical.

Why? Because you are taking their specific product and putting it directly into the copy. It proves you know who they are without doing gimmicky I saw you went to this university personalization.

Here is the exact 2-step workflow we are building right now to automate this at scale:

Step 1: The flagship column in Clay 

We are building an AI web scraper in Clay that visits a prospect's website with one goal:

• Find their flagship program, service, or product name.
• Return that exact name into a column in our table.

Now, instead of a generic pitch, our variable inserts their actual core offering into the sentence.

Step 2: The testimonial PS We are adding a second AI column that scrapes their site for customer testimonials.

If they have a glowing review from Chris the CEO about saving 50% on taxes, our email now ends with:

PS: saw Chris's testimonial about how you cut his tax costs by 50%. That's exactly the kind of leader we reach.

This isn't magic. It's just better data applied intelligently to copy.

Don't send generic emails. Use AI to find the specific details that make your offer a no-brainer.

If you want to see the exact prompt I used to get Claude to suggest these changes, let me know in the comments.

Why we buy 3x more email infrastructure than our clients actually need.

It seems wasteful. Why pay for 30,000 sending capacity if the client only has 10,000 leads?

Because deliverability is a game of margins.

If you only have "just enough" domains, you are forced to keep using them even when their reputation starts to crack. You grip the wheel tighter as you slide into the spam folder.

When you have 3x capacity, you have the luxury of rotation.

• Health score hits 98%? Rotate it out.
• Open rates dip for 2 days? Rotate it out.
• Need to double volume tomorrow? You have the runway.

We also cap every single domain at 40 sends per day. No exceptions.

Conservatism is the new aggression in cold email.

If you want to land in the primary inbox in 2024, you need to over-engineer your infrastructure.

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Tim Carden

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