Gusarich's thoughts
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TON Vanity
Meet the new blazingly fast vanity address generator for TON smart contracts!
The previous state-of-the-art solution was released 3 years ago and hasn't improved much since. We at TON Studio decided to develop a completely new solution from scratch, following the usage patterns the previous solution introduced.
Optimizations in both the smart contract and the kernel lead to extreme generation speedups. In realistic usage scenarios on an RTX 4090, the generator finds suffix patterns up to 5,100x faster, and prefix patterns up to 3,600x faster. In practice, this allows finding about 2 more letters than before in appropriate time.
Apart from the crazy speedups, there's a simple interface for TypeScript usage, like in smart contract tests and deployment scripts. Applying the generated vanity address requires writing just a few extra lines of code. The generator's output was improved too, storing all results in a structured JSON format with all useful metadata. Quality-of-life improvements include a polished CLI experience, comprehensive test coverage, and a benchmark script for comparing future optimizations.
The tool will be maintained and improved over time. The entire development process is open on GitHub, and contributions and feedback are welcome. A detailed write-up covering all optimizations and the development process will be published soon, for those who are interested.
Check it out: https://github.com/ton-org/vanity
707515
| 2 | My personal opinion based on experience:
* GPT-5.1 has the best instruction following, strong agentic capabilities, and very good skills in math, coding, and problem solving.
* GPT-5.1-Codex-Max has worse general capabilities than GPT-5.1, but is noticeably better for large and complex coding tasks.
* Opus 4.5 has the best implicit intent understanding, very good instruction following and agentic capabilities, but lacks depth in its reasoning that is required for complex problem solving.
* Gemini 3 Pro has the best raw intelligence, especially in math, and has good agentic capabilities, but lacks instruction following.
So, the choice becomes quite simple:
* For well-defined general tasks, go with GPT-5.1.
* For well-defined coding tasks, go with GPT-5.1-Codex-Max.
* For less defined or ambiguous tasks, as well as general agentic scenarios, go with Opus 4.5.
* For math and problem solving in general, go with Gemini 3 Pro, but either pair it with one of the above or work on extra scaffolding.
I'm personally using GPT-5.1 and its Codex variant mostly, but sometimes trying Opus 4.5 and Gemini 3 Pro when the task fits them. I got used to the way you have to use GPT-5.1 and it gives very good results in any task I throw at it, if it's defined properly. For vibe-coding and front-end, I'd go with Opus 4.5 for its intent understanding in more ambiguous cases.
All these models are roughly in the same pricing league, but OpenAI and Google also have stronger beasts: GPT-5.1 Pro and (upcoming) Gemini 3 Deep Think. These are only available in expensive subscriptions for $200/$250 a month, and are very slow. But I'm still using GPT-5.1 Pro almost daily for better results in tasks requiring reasoning.
A very common scenario in my work is to throw all the context about the task into GPT-5.1 Pro and ask it to write a detailed implementation plan, then give that plan to GPT-5.1-Codex-Max to implement. It works out very well, especially if you do a couple of follow-ups with GPT-5.1 Pro to refine the plan and sync it with your intent better.
Gemini 3 Deep Think is a similar thing, and it will probably be even better for complex reasoning tasks, but due to the lack of instruction following in Gemini models and the fact that it's behind another $250 paywall, I'll stick with ChatGPT Pro for now. | 1 573 |
| 3 | What LLM to use today?
Many major releases occurred in the past weeks. The current frontier consists of models that a couple of months ago were only rumors. And they are great.
OpenAI has GPT-5.1 and GPT-5.1-Codex-Max; Anthropic has Opus 4.5; Google has Gemini 3 Pro. I'm often working with code, and therefore I need a good coding model. I look at coding benchmarks, like SWE-bench, but scores there differ by just a couple of percent. Are there just no leaders for coding right now?
It's just that most benchmarks that companies show in release posts aren't really useful. Opus 4.5 having a 3% better score on SWE-bench doesn't mean that it's 3% better at all. In fact, it doesn't tell you pretty much anything that could translate even remotely to real-world usage. It could be much worse, or much better, or the same. But this benchmark is still one of the most referenced ones when talking about coding performance. There are more benchmarks like this, not only for coding. And they are also misleading.
So, how do you know which model is better then? Aren't benchmarks supposed to show that? Well, benchmarks show exactly what they test, and nothing else. SWE-bench (verified), for example, has 46% of tasks from a single Django repository, 87% of tasks are bugfixes, and 5-10% of tasks are just invalid. I wouldn't say that this benchmark somehow shows general capabilities in coding. And definitely it doesn't show capabilities in vibe-coding scenarios. What it mostly shows is the ability to locate a bug in a Python repository by being given a bug report, and fixing it in one shot.
What matters for real-world coding then? From my experience, instruction following skills and agentic capabilities are the two most important things. A model is useless if it cannot consistently do what you tell it, and it won't be helpful in complex scenarios if it is not agentic. But honestly, at the moment I don't know any *good* benchmarks that evaluate this in diverse environments.
How do you pick a model to use then? The first thing I'd recommend doing is to just try all of them in some real tasks you need to do. It doesn't necessarily have to be the same task, and not even the same complexity. You just have to be honest with yourself and think about how each model works with you in these tasks. Does the model understand what you want from it? Does it complete the task the way you want it to? Does it piss you off less than other models? Does it feel good? If the answers to most of the questions above are "yes", then just go with that model. At least it will feel better than others for you.
Models have different skill distributions and personalities. That's why many people have very different opinions about models even though *they are all good*. I believe that to take the most out of AI capabilities, you have to really understand what they are good and bad at, and use models based on use case. As I said earlier, the current state of coding benchmarks is bad, and therefore I'll be talking solely from my experience next. | 1 138 |
| 4 | https://openai.com/index/accelerating-science-gpt-5/ | 1 530 |
| 5 | There is nothing out-of-distribution
AI turned out to be very simple if you think about it. You just make a model that works with something generic, and feed as much training data as you can into it. The generic data I mean here is text. People had writing for thousands of years and the whole world is built on it — we write, we speak, we read, and we listen our entire lives. It's so deep in our brains that it's hard to think of something that cannot be described in text.
Some AI pessimists love to say that "LLMs do not generalize out-of-distribution", but if they are built to understand and write text — they can generalize to anything that can be described as text, so pretty much everything. What people actually refer to when pointing out to generalization problems is *intelligence*.
Why couldn't LLMs solve simple puzzles 2 years ago if they can generalize? The reason is simple: LLMs were stupid. Bad data, bad models, and bad training produced models with very limited intelligence. They simply lacked IQ to generalize well. Nothing was changed conceptually on a fundamental level in the past couple of years — we just gathered better data, designed better architectures, and improved training algorithms.
Things that LLMs fail right now usually aren't "boolean" in nature. If it only solves 1% of some specific tasks right now, it means that it *can* solve them sometimes and next generations of models will solve more. And it's hard to find a task where LLMs couldn't complete at least the simplest version of it today.
Inability to solve 100% of given tasks of some kind right away just means the lack of intelligence. In order to generalize some problem, you need the intelligence to understand and solve that problem well. There is no data limitation in LLMs in the sense of generalization ability, only an intelligence limitation that is being improved rapidly.
I believe that LLMs are one of the valid paths to ASI. There could be other paths, even better and more general ones, but LLMs can do the thing too. ASI is not far away already, and from what we'll see in 2026 we won't have to change much to reach a superhuman level of general intelligence. I don't see anything that could stop LLMs from progressing further at the current exponential pace. | 1 662 |
| 6 |  There is no singularity
When mentioning singularity, people often think of some "point" in time when AI progress starts to speed up exponentially very quickly with no human control and it all kind of converges to infinity and we don't know what will happen the second after. And I myself had a similar picture in my head too, until recently.
I was thinking that predicting anything after 2027 is impossible because of this "singularity" that I thought would happen in that period. I decided that I should not plan anything long-term and just focus on short-term decisions, while leaving the rest as is. And I kind of did not get why OpenAI is planning moves for several years ahead like nothing changes even though they seem to believe in ASI. But now I have changed my understanding of it. There will be no singularity. You can plan for years ahead, as before. Everything will go just as expected on the scale of humanity.
There is a 4-month-old post by Sam Altman, The Gentle Singularity. He talks about how the singularity won't be a certain point but gradual progress. And here's a quote I'd like to highlight right now:
We are climbing the long arc of exponential technological progress; it always looks vertical looking forward and flat going backwards, but it’s one smooth curve.
I kind of understood it on the first read, but not all the dots did connect in my head at the time.
The key is that you should not think of AI as some non-canonical event. This is just one of the many steps humanity takes while progressing. It will speed up the overall technological progress significantly, but it's just the same as other major advances did. From paper to the worldwide web. All major advances sped up the overall progress, but that is just how exponential progress works. New advances speed up the progress towards more advances. AI is not an exception to the overall trend here, even though it can feel like one.
We won't enter a singularity in a way some people think. It all will be just as usual. New advances will happen every day, same as now. The progress will speed up, same as it always did. The whole "self-evolving AI" thing is no different from, for example, how the existence of the internet allows improving the internet itself.
To make it all more clear, you can think of an actual exponent. You can pick three points and scale the chart in a way that makes it feel like the last point is "far away" from the previous two, and that the difference between these first two points is minimal compared to the last. We are at that middle point right now. And we are always on it. Whatever we imagine to happen 10 years from now feels much less realistic than whatever happened in the last 10 years. And that's normal.
Then, if you move through that exponent, what felt "impossible" now stays on the left tail and feels like it's not that significant. And again, new possibilities open for future advances that again feel much harder to achieve than before. But actually it's all just how it naturally works. And that is what humanity has always experienced.
And there is some chance an evil ASI kills humanity, for sure. But there was that chance with many major advances, like when humans made an atomic bomb. And it never stopped humanity from moving forward. There's no point in stopping. We should keep accelerating while considering all the risks. | 1 607 |
| 7 | Writing with AI
If you scroll up through my Telegram channel, or open my first blog posts, you will easily notice how they were completely written with AI. Those very obvious patterns that are very easy to spot, like "this isn't just X, it's Y". I was mostly writing drafts myself, but using AI to "finish the paragraph", and also to completely rewrite the whole draft in the end for the purpose of fixing grammar errors and "improving writing".
I'm now ashamed of that. A lot of time has passed and only recently I stopped using AI like this. Writing has to come fully from myself, otherwise it makes no sense. It also helps to actually think more about what I write. Before I could just drop a bunch of random thoughts into AI and ask it to write a nice post out of it, but as a result I skipped the whole stage of thinking in a structured way that happens when you write things yourself.
I think I changed my mind on all that after seeing hundreds of fully AI-generated posts on X for months. They all look the same. My first posts look this way too when I reread them now. It's soulless, feels cheap, and often provides less value to readers.
I'm not using AI this way anymore, at least for things that actually require some thinking from me. I'm now only using it to fact-check, spot grammar errors and give feedback, but never to rewrite whole chunks of text or to finish my thoughts. I'm writing everything myself, then asking AI to give feedback to me, then changing things myself. For random posts on X, I don't even do these last steps.
But I definitely use AI for boilerplate stuff, like prompts for AI itself or some Slack messages. Those things don't require much thinking from my side anyway. And I think for such cases, it's fine. | 1 461 |
