
product lead @trendlefi
Lisbon, Portugal
Joined April 2007
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- Following669
- Followers472
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Vlad Rafeev retweeted
meet @mostik_ai!
what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can.
everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning?
we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched.
how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance.
we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst
WIRED has the first external account of the company and the work: wired.com/story/russian-star…
full writeup, the setup, and all the numbers: mostik.ai/read-more
Vlad Rafeev retweeted
Introducing the Monastery for AI-native founders.
A single builder can now outperform a publicly traded company.
$2 million. 12 weeks. Do the impossible.
I gave my Claude Code agent a search engine across all my communications.
It can now search Slack, Telegram, Gmail, WhatsApp, Linear, Discord, Markdown — all at once, one query. Everything synced into a local SQLite database on my machine.
github.com/dandaka/traul
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The moment it clicked: I asked "how are we progressing on cross-marketing with X" — and it pulled chats I wasn't even remembering about, highlighted specific next steps and who's responsible for each. Full picture of a cross-team initiative in one query.
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So I built traul. CLI that syncs all your messaging channels into one local database. FTS5 for keyword search, Ollama for semantic search. Claude just searches mid-conversation without you doing anything.
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Here's how I got here. I kept feeding my agent more context — docs, call transcripts, project specs. Each piece made it noticeably smarter. But the most important context was missing — the actual conversations where decisions happen. Slack threads, Telegram chats, emails.
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Vlad Rafeev retweeted
A few random notes from claude coding quite a bit last few weeks.
Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.
IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.
Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.
Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.
Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.
Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.
Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.
Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.
Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?
TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.
Vlad Rafeev retweeted
Trendle on @monad SOON!
Attention markets needed a home with actual horsepower
We stress-tested everything
Only one survived
Gmonad
Vlad Rafeev retweeted
Trendle Beta is NOW LIVE!
We started it with one belief: attention moves markets
200+ invites already out. Each one mints another.
Vlad Rafeev retweeted
The attention markets are waking up.
Big week ahead for Trendlefi.
Attention and prediction markets are about to have their perps moment.
Trendle is a new attention market platform, where you can bet on social attention movements. Check it app.trendle.fi/i/1/1/dandaka…
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We are organizing the second padel tournament around #lisbonaiweek and #websummit, check it out luma.com/3krpigjw
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Vlad Rafeev retweeted
Before, you’d just admire Sydney’s dress in silence — maybe send it to a friend or two.
Soon, your first instinct will be to sprint to @TrendleFi and go 50,000x long on Sydney before the world catches on.
That’s what the attention economy really looks like.
Sydney Sweeney and Christy Martin pose together at Variety's #PowerOfWomen event ❤️
Vlad Rafeev retweeted
🚨 Closed Beta Incoming 🚨
We’re opening the doors to the very first wave of users.
The closed beta is about to launch – and you can be among the early testers.
We’ll be picking the first cohort of pioneers straight from this list.
Fill out the Typeform with all the necessary info 👇
Vlad Rafeev retweeted
Website for STEREOTYPE is featured on Site of Sites🏆
siteofsites.co/websites/ster…
#webdesign #design
Vlad Rafeev retweeted
"Differentiable Logic Cellular Automata" is here!🎉 End-to-end differentiable, discrete-state NCA using logic gates! Self organizing systems, coming to hardware.
Check it out:
google-research.github.io/se…
work done with @eyvindn, @RandazzoEttore and @zzznah
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Vlad Rafeev retweeted
Breaking #crypto design stereotypes 🎨
We just launched the brand identity and #website for @certain_fi – a #stablecoin that reimagines financial flexibility. Everyday yield + freedom of use = a new kind of #fintech.
Check it out: certain.fi/
@offgriddesign 👋














