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Gusarich's thoughts
15 Feb, 20:00
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 hasGusarich's thoughts
27 Jan, 08:04
Things got too easy with AIAI provides incredible value to me and to many other people in our daily lives and work. It's now possible to do more, better, and faster than ever. But I realized that the progression of AI capabilities outpaced the progression of my goals.I recently started thinking in retrospect about what I was doing and what I was thinking about in the past months, and it feels like I didn't really face any hard challenges in many months.I can't remember the last time I was thinking about a problem for a week straight myself because it was so nontrivial. In most cases AI just one-shots stuff or does so after a couple of days of low-effort prompting iterations.I was thinking a lot about different stuff, but it was more of a meta-analysis or idea generation of some kind. I didn't really struggle with the execution part for a long time.Very often when I seeGusarich's thoughts
8 Jan, 21:26
I gave Codex its own Mac MiniI was playing around with Codex CLI a lot over the holidays, and apart from making it run 30 instances of itself as "subagents" (actually just doing codex exec runs in background terminals) I also decided to buy a fresh Mac Mini and give it to Codex.I quickly implemented a pretty simple setup, that consists of a Codex caller that controls Codex process and that forces it to run in an endless loop, and a Telegram bot through which I interact with the agent.The setup was the easiest part to implement and I actually spent about 20x more time on writing first working versions of prompts with Claude. Prompts are everything here.I decided to use Codex here with GPT-5.2 model on Extra High reasoning effort, as that is the best model available in terms of instruction following and long context. Earlier I saw it run for 3 hours doing 4 compactions in the
Gusarich's thoughts
2 Jan, 20:21
A detailed write-up covering all optimizations and the development process will be published soonDaniil SedovTON Vanity: 286,000x faster vanity addressesRebuilding vanity address generator for TON from scratch with AI, achieving up to 286,000x speedup through a series of domain-specific optimizations.Gusarich's thoughts
31 Dec 2025, 22:56
Happy new year 🕺Gusarich's thoughts
31 Dec 2025, 15:25
A new blog post with my predictions on AI progress in 2026.https://gusarich.com/blog/ai-in-2026/Gusarich's thoughts
11 Dec 2025, 22:05
forwarded from @tondev_news
TON VanityMeet 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
Gusarich's thoughts
28 Nov 2025, 07:04
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,Gusarich's thoughts
28 Nov 2025, 07:04
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 talkingGusarich's thoughts
20 Nov 2025, 20:20
AI would discover a lot of new science.Gusarich's thoughts
20 Nov 2025, 13:08
There is nothing out-of-distributionAI 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: LLMsGusarich's thoughts
12 Oct 2025, 22:22
There is no singularityWhen 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
Gusarich's thoughts
12 Oct 2025, 12:29edited
Writing with AIIf 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 thinChannel photo updated
11 Oct 2025, 19:00
Gusarich's thoughts
17 Sept 2025, 18:55
This February I made a prediction that AI would surpass top human performers in competitive programming by the end of the summer.For context, at that point, the best models we had were o1, Sonnet 3.6, and Gemini 2.0. They couldn't reliably solve even simple tasks, and the only hint we had was the late-December preview of o3 (which eventually turned out to be a different version from what we got in April). This "o3-preview", when benchmarked on Codeforces contests, ranked in the 99.8th percentile, which is extremely good, but it was still far behind top performers, positioned at about ~130th place.In the past couple of months, Google and OpenAI have been showing off their experimental reasoning models' skills by participating in various math and coding olympiads. Just today, they both posted about their results at ICPC, which is the most prestigious competitive programming olympiad,Gusarich's thoughts
11 Aug 2025, 19:38
Why did many people have a bad first impression of GPT-5?Actually, the reason behind that is absurdly stupid. OpenAI fucked up with UX. That's it. The model is actually good; all variants of it are. But OpenAI rushed the release for some reason, and their goal of making the UX better made it worse for a lot of users.The key detail here was the model router that they added to ChatGPT so that users don't have to manually choose a model, and it can just choose the appropriate one on its own. For example, if you ask it how to pronounce a word, that can easily be answered with a non-thinking model, with lower latency and the same accuracy. But if you give it a math problem, ask something about coding, or just generally give it a task that requires more reasoning - it is better processed by a thinking variant of the model.And the idea is good, especially for the average user who doesn'tGusarich's thoughts
11 Aug 2025, 19:38
My impression of GPT-5This was an extremely anticipated release. Literally the whole AI bubble waited for it and watched closely. It's been 2 years since GPT-4, and people expected something extraordinary. Me too.I raised my expectations for GPT-5 in the past few months - hoping that it would basically be "o4" but under a new name. And I expected a capability jump similar to the jump from o1 to o3.I was also watching the whole rollout extremely closely and had tried out GPT-5 before the official release for a few days. First, when it was being tested on LMArena under the codenames "Zenith" and "Summit", and another time when it was available on Perplexity due to a bug.I didn't try it out heavily on real tasks in those days, but I still sent many prompts for testing purposes. And I had a "taste" of it at that time. It felt similar to o3 in vibe, but smarter, more precise, andGusarich's thoughts
7 Aug 2025, 17:05
https://www.youtube.com/live/0Uu_VJeVVfo?si=_01lfBGSyzd_7hoAYouTubeIntroducing GPT-5Sam Altman, Greg Brockman, Sebastien Bubeck, Mark Chen, Yann Dubois, Brian Fioca, Adi Ganesh, Oliver Godement, Saachi Jain, Christina Kaplan, Christina Kim, Elaine Ya Le, Felipe Millon, Michelle Pokrass, Jakub Pachocki, Max Schwarzer, Rennie Song, Ruochen…Gusarich's thoughts
29 Jul 2025, 07:21
The Complexity Threshold of AIWe see dozens of new LLMs heavily tuned for software engineering tasks, and they're becoming very good at it, very quickly. As models evolved, I started using them more and more for writing code, eventually reaching a point where I almost completely stopped writing code myself. The last time I wrote code manually (or rather, with AI-assisted tab completions) was around four months ago. However, once tasks become larger and more complex, these models quickly become inefficient. They seem to have a certain complexity threshold, beyond which their efficiency rapidly declines.I was mostly using AI either to quickly take projects "from 0 to 1" by iterating on MVPs, or to build small Python scripts for working with LLMs and data. About a week ago, I needed to rapidly build another MVP while iterating on ideas, so I used Claude Code and completed the wholeGusarich's thoughts
18 Jul 2025, 07:10
✍️ New Blog Post: "Billions of Tokens Later: Scaling LLM Fuzzing in Practice"I've spent months running LLM-powered fuzzing at production scale—processing billions of tokens, discovering practical scaling laws, and developing effective deduplication strategies. Here’s what I learned along the way:https://gusarich.com/blog/billions-of-tokens-later/
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