Pinned Tweet
I quit my cushy tech job after working as a Data Analyst for the past 16 years. I was able to do this, because I spent those years building a solid financial foundation and learnt how to make money work for me, so the job became optional.
I started from almost 0, ten years ago when I moved to the US from India.
If you want to learn how I did it and what I learnt, follow along.
It is weeks like these, when risk tolerance is truly tested.
Anyone losing sleep because Nasdaq is down 8% from it's peak, is risking too much capital.
BREAKING: The Nasdaq 100 closes down -2.9%, posting its biggest daily drop since June 5th.
The index erased nearly -$1.2 trillion in market cap today.
Have any of you built your own AI harness to run your local agentic AI workflows?
How has it worked so far?
What were the wins and challenges?
Would love to connect and share notes.
Drop me a DM.
I like working with local AI.
The upside: privacy, and no per-token bill.
The downside: you need the hardware, and you give up some capability. (Even with 128 GB of memory on my Mac, the models I can run reliably are under 100B
parameters.)
But the good ones are fully capable of a lot of everyday work.
One recent project: making my 12.5k photos and videos searchable. I wanted a description of what's in each file, so I could search for things I remembered: my cat Chandi, a place, a street sign etc
For that I needed a model that was small, fast, and good enough. It had to get through the whole library overnight. Anything slower was a waste of my time.
So I tested four local vision models under 100B parameters on the same ten public photos. Here is what I found:
• Qwen3.6 35B-A3B was the fastest at 30 seconds. But it invented two seated people and a burning fire in a living room. Search for a fire, and that photo would show up.
• Gemma 4 31B wrote the best living-room description, including a person the faster Gemma missed. But it took 95 seconds and missed a stop sign hanging upside down.
• Qwen3.8 27B caught the finest details, a black tail tip and a shirt brand, then read "United Nations" off a flag too blurry to read. 87 seconds.
• Gemma 4 26B-A4B noticed the upside-down sign and finished in 35 seconds.
For what I needed, Gemma 26B and Qwen3.8 were neck and neck. Gemma was about 2.5x as fast, so it's the one that now describes my photos every night.
Two surprises from separate checks: all four miscounted a group of 12 people. And all four could read a Hindi sign correctly
Which local model are you running, and what has worked well for you?
I wrote the full comparison with the test photos in my essay. Link in the reply below 👇🏾
Here is the full essay: aayushgoenka.com/essays/ai-p…
You can pay a medical bill today and reimburse yourself from your HSA years later, tax-free.
We leave ours invested and save the receipts. Here's how it works, and why we do it.
Another thing that is interesting with Opus 5.5 is that it last way longer than what Opus used to be.
Maybe I got more efficient at using it (that is what I’d like to think lol)
So my cost per token has reduced.
Are you seeing that too?
From non-engineer vibe coders: What was your first AI workflow that you built yourself using AI and that actually worked?
Mine was a photo siphoning workflow (using local AI) that checked and copied iCloud photos/videos to my Mac every hour (incrementally) and sorted them into a desktop folder.
This way I didn’t have to pay to increase my iCloud storage (lol, saved $2).
I've been using Opus 5.5 as my daily workhorse, and Codex (Sol, Astra) has become mostly a reviewer.
Codex does a better job with language, but Claude still shines at structuring content more logically.
Overall, they complement each other very well.
Thoughts?
Can you find a photo from five years ago in under a minute?
I couldn't. I had about 12.5k photos and videos on my phone and had never deleted or organized a single one. Finding a good picture of our cat Chandi meant scrolling for ten minutes.
Last week I shared that I was turning all of it into a searchable database with local AI.
It works! 🙌🏾
Now I type one search: "good quality, just Chandi, not posted yet" and my AI workflow spits out over 600 photos of her.
The part I care about most: it all runs on my own Mac locally. None of our personal photos had to be uploaded to cloud AI servers.
What's the one photo search you wish you could do easily from your large collection of media files?
I wrote about how I built it. Sharing the link in comments below 👇🏾
Here is how I did it: aayushgoenka.com/essays/ai-p…
100 followers: 23 days
200 followers: 14 days (Yay! It feels like a milestone on X)
How long until we reach 300?
Here's a 🥔 and my cat’s video because it's Sunday evening and time to chill.
We talk AI and Money on Monday.
Today we watch Chandi play.
😀😃🐈😽
Be prepared to have intense discussion on psychology with your spouse if you plan to watch the new Elizabeth Holmes documentary ‘You can see everything’
🥲🥲🥲
Looking to connect with non-software engineers building their own AI solutions.
How have you been doing? What challenges are you facing?
An ex-colleague told me something that resonated: she struggled to figure out GitHub.
When I started, I had no clue either.
Building with AI is fun, but it's a steep learning curve. Agree?
As a non-engineer, I felt I accomplished a lot using AI workflows for my first automated Instagram post for my cat Chandi.
Having a full pipeline work and run on schedule feels like a child's first steps. Long journey ahead, but walking and running aren't far off.
By the end of this quarter, I want so many automated workflows that the first one working seamlessly already excites me. All built with a local AI setup.
My goal is to stretch the limits of what I can do with my Mac and local AI. Frontier is a must, but a lot doesn't need that level of intelligence.
If you're on a similar journey, come say hi. I'd love to hear what you're working on, share notes, and maybe partner on a project.







