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:

taplio