utopian tech bro, crunchin numbers. Research Engineer @PAM/HarmixAI
Madrid/London
Joined March 2015
- Tweets70
- Following345
- Followers43
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Vitalii Ratushnyi retweeted
The context a long-running agent works over across days easily runs to millions of tokens and the field still has no consensus on what memory should even look like.
@Misreadable is the Lead Research Engineer at HarmixAI working on long-term memory for agents with inputs of 1–10 billion tokens, and a former quant, now based in Madrid.
He'll compress two years of research and production experience into practical recipes for scalable agent memory.
Vitalii Ratushnyi retweeted
We are giving away 40 water-tight contract templates. This would cost >$10K to any agency, startups, or freelancer, to create from scratch.
Retweet this tweet and comment 'Monk' and we'll send them to you.
Vitalii Ratushnyi retweeted
$3 Trillion is currently stuck in unpaid invoices.
An average invoice takes 59 days to clear.
If you're doing >$10M in revenue, getting paid in 30 days instead of 59 days will literally make you millions over 2 years.
Introducing Monk. It collects your money faster and pays for itself in 30 days, guaranteed.
Fast-growing companies like ElevenLabs and Profound rely on Monk.
The Problem: 39% of the $3T stuck is due to two stupid reasons:
1. "Please fix this comma in the invoice and then we'll pay you'" (this happens 2-3 times per transaction)
2. "Sorry the payment reminder got buried in my inbox" (automated emails get ignored)
A payment that should take 2 days, takes 10 days. Your cash on hand is embarrassingly behind "recorded revenue".
To fix this, we raised $4M led by @btv_vc with participation from @gtmfund and @danonanthony.
For our first product, we had to innovate on 3 dimensions:
1. Monk turns signed contracts into invoices with near-perfect precision
- We leverage frontier models to extract key terms from your deals and turn them into invoices.
2. Monk collects payments agentically with a 24% better reply rate than automated emails:
- Tuned to write emails that feel like a human request, not spam. Your invoice is competing with everything in their inbox.
- We know how to find an alternate point-of-contact when someone is OOO, when to reach out, and what tonality yields a higher response rate.
If you had someone on your team whose invoice emails got answered 24% more than others - that would be a big deal. You’d make them the head of revenue collection.
3. Granular visibility into your cashflow:
- Understand your cash position, aging, or expansion/contraction at customer level.
Book a demo at monk.com/book-a-demo-form and we guarantee that Monk will pay for itself within 30 days.
If you've read this far, we are giving away 40 water-tight contract templates. This would cost >$10K to any agency, startups, or freelancer, to create from scratch.
Retweet this and comment 'Monk' and we'll send them to you.
Vitalii Ratushnyi retweeted
AI ASMR is blowing up
This account was started three days ago with just 11 videos…but has racked up 82k followers 👇
Vitalii Ratushnyi retweeted
An attempt to explain (current) ChatGPT versions.
I still run into many, many people who don't know that:
- o3 is the obvious best thing for important/hard things. It is a reasoning model that is much stronger than 4o and if you are using ChatGPT professionally and not using o3 you're ngmi.
- 4o is different from o4. Yes I know lol. 4o is a good "daily driver" for many easy-medium questions. o4 is only available as mini for now, and is not as good as o3, and I'm not super sure why it's out right now.
Example basic "router" in my own personal use:
- Any simple query (e.g. "what foods are high in fiber"?) => 4o (about ~40% of my use)
- Any hard/important enough query where I am willing to wait a bit (e.g. "help me understand this tax thing...") => o3 (about ~40% of my use)
- I am vibe coding (e.g. "change this code so that...") => 4.1 (about ~10% of my use)
- I want to deeply understand one topic - I want GPT to go off for 10 minutes, look at many, many links and summarize a topic for me. (e.g. "help me understand the rise and fall of Luminar"). => Deep Research (about ~10% of my use). Note that Deep Research is not a model version to be picked from the model picker (!!!), it is a toggle inside the Tools. Under the hood it is based on o3, but I believe is not fully equivalent of just asking o3 the same query, but I am not sure.
All of this is only within the ChatGPT universe of models. In practice my use is more complicated because I like to bounce between all of ChatGPT, Claude, Gemini, Grok and Perplexity depending on the task and out of research interest.
Vitalii Ratushnyi retweeted
Introducing Eureka, the world’s first AI doctor.
Eureka can order labs and give care in the real world. It is covered by health insurance just like a healthcare provider and is already working with thyroid patients in the US.
It’s 90x faster than most care in the US, and 9 out of 10 users want to continue with Eureka’s recommendations.
Eureka thinks like a doctor and reasons like a detective. Before any care starts, a board-certified physician reviews Eureka’s recommendations to make sure everything is in order.
Eureka currently specializes in endocrine conditions like thyroid and diabetes. Here’s what users think about Eureka 🧵 (1/5)