Gusarich's thoughts

2. We love the community, and the community loves us The community truly appreciates Tact—especially newcomers. Although we've faced some resistance from long-time developers, attitudes are positively shifting as we continue to improve and enhance our language. All past developer feedback polls and objective analyses of smart contracts deployed on the mainnet clearly illustrate Tact's steadily increasing adoption rate. At the beginning of 2024, only 8.7% of unique smart contracts on the mainnet were written in Tact. By the end of the year, this figure grew to 32.9%. Essentially, every third unique smart contract deployed on the mainnet today is written using Tact. This upward trend shows no signs of slowing down, particularly after our recent major release—the largest in Tact's history. It introduced numerous new features and improvements, including significant gas optimizations, making Tact even more appealing to developers for their projects. An essential part of this growth is our genuine love for the community. We welcome all kinds of feedback—whether through comments, chat messages, or GitHub issues. We actively implement features requested by our users and prioritize resolving the issues they report. Last but certainly not least, our documentation is outstanding. When covering blockchain-specific topics, our documentation is frequently more comprehensive and accurate than even the official TON documentation, which has yet to catch up with ours. Regarding Tact itself, every feature is thoroughly documented, complete with numerous examples, specifications, details, and important warnings. And we're constantly expanding and refining it!

1. We have an excellent and consistently expanding team. A team is always more effective than an individual, especially when dealing with complex and broad projects such as developing a programming language. Tact is not limited to just the compiler itself; it encompasses multiple tools, each of which must be regularly maintained to keep up with the latest language features and user feedback. Managing all these components is significantly easier and more efficient with a larger team. Additionally, comprehensive code reviews, which are crucial in compiler development, become more manageable with more team members involved. Since we're developing a language intended for smart contracts, even a minor error can result in substantial financial losses. Therefore, we cannot simply introduce new features or modify existing functionalities without thorough validation and extensive testing. Multiple engineers carefully reviewing each change greatly reduce the risk of mistakes. We have continuously hired new engineers to address weaknesses and expand our team's expertise and capabilities. Our team consists of skilled engineers with extensive knowledge in various fields, combining their expertise to strengthen our overall capability. This growth strategy will be instrumental in Tact's success. As evidenced by recent statistics, our productivity has significantly increased over the past few months. This month alone, we averaged merging five pull requests per day. This figure only includes the main Tact monorepo—comprising the compiler and documentation—and does not account for numerous additional tools.

In addition to my previous post, I want to share my thoughts on why Tact will become the ultimate language for TON, surpassing Tolk, its primary competitor. Below, I outline three key points in separate posts.

In the upcoming version of Tact, we've introduced over 100 updates, including new features, dozens of bug fixes, and more than 20 optimizations that improve gas consumption. I want to highlight these optimizations because, until now, the most common reason for not using Tact has been its inefficiency. For this release, we benchmarked our optimizations using the most common smart contract on TON—the Jetton. We compared our standard implementation against the reference FunC version, which is currently the most widely used. I'm happy to say that we’ve actually outperformed it! On average, a Jetton compiled with this upcoming Tact version consumes just 95% of the gas used by the reference FunC implementation. This isn’t a cherry-picked result—these optimizations apply universally to all smart contracts, impacting them in almost the same way. Essentially, Tact is now on par with FunC in terms of gas efficiency—or even surpasses it in many cases—all while offering a much higher level of abstraction, enabling developers to build scalable, maintainable, and extensible projects with ease. And this is just the beginning. We have a lot more to share in the coming months.

I recently joined X. https://x.com/GusarichOnX

💡️️️️️️ Openfiles: Bringing Simplicity to Decentralized Storage In the world of blockchain and decentralized technologies, there’s a powerful tool called IPFS (InterPlanetary File System). It’s widely used in the crypto space for hosting images, metadata for NFTs, and various types of data that need to be reliably accessible from anywhere in the world. IPFS has become a cornerstone for decentralized storage solutions, and its adoption has been supported by user-friendly services like Pinata Cloud, which make it easy for non-technical users to manage and host files on IPFS. TON Storage is a similar technology within the TON blockchain ecosystem. It offers decentralized and secure file storage with unique features tailored to the TON network. However, unlike IPFS, TON Storage lacks the robust ecosystem of accessible tools and services that simplify its use. Currently, there’s only one service that I know for hosting files on TON Storage, but it’s limited in functionality and offers a subpar user experience. ✨ This is where Openfiles comes in. Over the past few weeks, my team and I have been developing Openfiles to address this gap. Our goal is to create a platform that’s not only as easy to use as Pinata Cloud but even more accessible and user-friendly, aiming to provide the best experience across all blockchains and services. Openfiles will allow users to upload, manage, and share files on TON Storage with a clean, intuitive interface, while also offering extensive customization and automation options for advanced users. We’re committed to keeping Openfiles as straightforward and beginner-friendly as possible for a quick start, while ensuring it meets the needs of power users with advanced features and flexibility. ⏰ We’re in the final stages of preparing Openfiles for its beta release, and we’re aiming to launch it this month. For updates, follow our channel: @openfiles. This is the project I hope will bring tangible benefits to the TON community.

🎄 Happy New Year 2025 has arrived, and it’s the perfect moment to reflect on the past year and look ahead to the future. Looking back at 2024, I see it as a year filled with growth, learning, and self-discovery. I’m proud to say that I achieved all the goals I set for myself and, in some cases, even exceeded my expectations. This year has proven how consistent effort and dedication can lead to remarkable results. One of the most important lessons I’ve learned is the value of balance. Taking care of both physical and emotional well-being is essential for sustainable progress and happiness. Small steps toward self-improvement — whether in health, discipline, or skills — can lead to meaningful changes over time. Another key insight from this year is the role of failure in personal growth. Setbacks are inevitable, but they are not reasons for self-criticism. Instead, they’re opportunities to learn and improve. Every mistake provides valuable experience and helps reduce the likelihood of similar errors in the future. I’ve also come to understand that happiness is built from small, daily moments. If each day is filled with activities you dislike, happiness will remain out of reach. Finding joy in your everyday routine is crucial for a fulfilling life. As we step into 2025, it’s a great time to think about new dreams and goals. While achieving them is important, I’ve realized that the journey matters just as much, if not more, than the destination. Most of our time is spent working toward our goals, not enjoying the fleeting reward of reaching them. By focusing on the process and celebrating small victories along the way, every step becomes more meaningful. I appreciate everyone who has been with me this year, offering support and encouragement. Let’s make 2025 a year full of opportunities, challenges, and growth together. Happy New Year!

From o1 to o3: Path to AGI? Just a few months ago, we were amazed by the capabilities of OpenAI’s o1 model, a "reasoner" that brought a new level of thoughtfulness to AI performance. Now, with the showcase of o3, we’re witnessing a leap that feels nothing short of revolutionary. Here’s what makes o3 so remarkable: - 2700+ Codeforces Rating: o3 operates at a level equivalent to the top 0.2% of competitive programmers in the world—Grandmaster territory. To put this into perspective, this is akin to an IQ level of over 150 which is typically considered as "genius". - 96.7% on AIME: It absolutely crushed the AIME math benchmark, solving nearly all problems correctly. - 25% on FrontierMATH: On the PhD-level math benchmark, it scored 25%, a staggering improvement from o1’s mere 2%. These aren’t just incremental improvements—they’re quantum leaps in capability. The Secret to o-series Models Success: Scaling Test-Time Compute The o3 model proves how scaling test-time compute can dramatically boost performance without changing the underlying architecture or parameter count. This shows that the same model, given more time and resources to reason, can achieve far greater results. For example: - Tasks that required hundreds of retries with o1 can now be solved in just a few attempts by o3. - Complex problems that were almost unreachable for o1 are now solved with confidence. This isn’t just a technical upgrade; it’s a glimpse into how far we can push existing AI technologies. The Wild Cycle of Iterative Improvement Here’s where things get crazy 🤪 We might not need entirely new architectures to reach AGI. Instead, OpenAI has unlocked a potential self-improvement loop: 1. Start with a "base" model like GPT-4. 2. Develop a "reasoner" model (o1, o3) that scales test-time compute for higher quality results. 3. Use the reasoner to generate a massive, high-quality synthetic dataset, including data far more complex than what exists publicly. 4. Train a new base model (e.g., GPT-5) on this enriched dataset, making it significantly smarter than the previous one. 5. Build an even stronger reasoner model using the new base—and repeat the cycle. With enough resources, this approach could lead to AGI much sooner than anyone anticipated. Imagine: every iteration of this cycle produces an AI that’s smarter, more capable, and better at generating even more complex data for the next round. It’s a compounding effect, and there’s no obvious limit to how far it can go. What’s Next? This isn’t just about AI setting new records in competitive programming or math. The implications are staggering. We’re seeing proof that AGI might be achievable with the architectures we already have today. AI has already "solved" chess, Go, and other games once thought to be the ultimate tests of intelligence. Now, it’s poised to "solve" fields like mathematics. By leveraging iterative reasoning and scaling, AI could soon provide solutions and insights that were previously beyond human reach. Much like how DeepBlue's victory in chess marked a turning point for AI in strategy games, o3’s success could be the beginning of a new era where AI dominates abstract, technical domains. My Prediction for 2025 By 2025, I believe AI will surpass humans in an incredibly diverse range of technical fields, much like how DeepBlue surpassed humans in chess. We’ll also witness a surge of scientific discoveries made entirely by AI, pushing the boundaries of human knowledge in ways we can’t yet imagine. It’s astonishing how quickly we’ve progressed from o1 to o3. The question now is: how far can this iterative improvement cycle take us? Could this be the final sprint to AGI?

In the next few days, Joi will get exciting updates: better user experience, new ways to earn Stars, detailed stats, and more. Joi may be just an experiment, but we want to make it as exciting as possible to achieve better results. In the meantime, join our chat: @joi_on_ton_chat

Recent Social AI Experiment 📰 You might have heard about Freysa AI, a recent experiment in the Ethereum community. The concept was simple but intriguing: players could send messages to an AI agent with access to a crypto wallet, trying to convince it to release the funds. If successful, the player would win the prize pool. Freysa’s purpose was to explore how well AI agents can follow rules and resist manipulation. The game gained some buzz, attracting 200 participants and a prize pool of around $50,000 in just one week. On paper, it sounds exciting. 😞 But here’s the catch: each message cost hundreds of dollars, which meant only a small group of people could participate. Worse, the system prompt and code were open from the start, allowing players to replicate the agent locally, test inputs without paying, and exploit the system. This likely explains how the game ended so quickly. Despite these flaws, I loved the idea. It got me thinking: what if there was a version of this game that was more engaging and accessible? 💭 That’s how I came up with Joi. In just a day, with help from a friend, I developed a game inspired by Freysa but with refined mechanics: • Messages are affordable, costing just a couple of dollars, and the first message is free. • The system prompt is hidden, making the experiment more realistic and challenging. However, if a round lasts too long, the prompt will be published to speed up the process. • The game doesn’t end after one win. When someone convinces AI agent to release the prize, the system prompt is updated in an attempt to fix the vulnerability, and the game restarts with a new prize pool. 🤑 The first round of Joi is live now, starting with a $2,500 prize pool funded from my personal money. Every paid message grows the pool further. It’s just an experiment, but one that might teach us something valuable. Let’s see what we can discover.

A new cool mini app with mining is hyping right now 😳 t.me/memhash_bot/start?startapp=1uQqQX

Why Are Security Audits Important in Crypto? 🛡 Recently, I talked about how the Tact programming language for smart contracts on the TON Blockchain is growing and attracting new developers to build innovative projects. In this post, I’ll explain what security audits are and why they matter for smart contracts and TON-based projects. Smart contracts are programs that often control large sums of money, like in token exchange platforms. Security is extremely important in this area. Some may think hiring skilled developers who "just write code without bugs" is enough to make a project secure. However, even the best developers can make mistakes or miss rare edge cases that can lead to vulnerabilities. This is why it’s common to bring in external specialists to conduct security audits. Here’s how it usually works: the project is developed, code is written, most bugs and issues are found and fixed during testing, and then, when everything is ready to launch, the project gets auditors (like CertiK) to carefully review the code and try to find any vulnerabilities or bugs the developers may have missed. However, it's important to remember that an audit does not guarantee that all issues are gone. Auditors, like developers, can also miss something. For big projects, it’s a good idea to have multiple audits by different experts or teams and to run a public bug bounty program. This reduces risks and makes the project as secure as possible. Who Benefits from Security Audits? 🤩 1. Developers and founders. Audits help make sure the code is safe and doesn’t have critical vulnerabilities that could put users' funds or the project’s future at risk. It’s not just a formality — it protects the project's reputation. A mistake in the code could cause major problems, and fixing it before launch can save the project from failure. 2. Investors. For investors, an audit is a sign that the project is reliable and well-built. But they don’t just care about whether an audit was done — they also look at who did the audit. If a well-known and trusted team carried out the audit, it increases confidence in the project and can help attract bigger investments. 3. Users. People using DeFi protocols or other blockchain projects want to know their funds are safe from hacks and exploits. A project that has passed a strong audit gives users more confidence, making them feel more secure. My Experience with Security Audits 👨‍💻 Recently, I’ve audited multiple DeFi projects, including Hipo (a liquid staking protocol), PixelSwap (a decentralized exchange), Emmet (a cross-chain bridge), and several smaller projects. In each project, I found many issues, ranging from minor to critical. After being fixed, they made the system more reliable. These audits didn’t just find problems — they also helped improve the overall quality of the code. If you're preparing to launch a project, doing a security audit is a key step to gaining trust from both users and investors. If you want to discuss your project’s security, feel free to contact me — @Gusarich.

Новая модель от OpenAI решает задачи по спортивному программированию быстрее людей Наверняка многие из вас уже слышали о недавнем релизе новой модели от OpenAI под названием "o1", которая умеет думать перед тем, как выдать ответ. В отличие от GPT-4o, которую позиционируют как модель общего назначения, o1 значительно лучше справляется со сложными задачами, требующими тщательного обдумывания. Однако, из-за этого стоимость её использования значительно возрастает, как и время отклика. OpenAI представили множество бенчмарков с впечатляющими цифрами и графиками. Но как модель показывает себя на практике? Я написал простую программу, которая брала задачу по спортивному программированию и просила o1 решить её. Причём генерация запускалась параллельно в нескольких потоках, чтобы, даже если модель иногда ошибалась, хотя бы часть решений была правильной. На каждую задачу уходит всего пара минут, что очень быстро. Однако с долей правильных решений всё не так однозначно. Я протестировал модель на задачах разной сложности: от самых простых, которые она решала правильно в 100% случаев, до очень сложных, где даже из нескольких сотен попыток не удавалось получить ни одного верного решения. В результате тестирования я пришёл к выводу, что модель лучше всего работает на простых или средних по сложности задачах — благодаря своей скорости по сравнению с человеком. Для сложных задач её тоже можно использовать, но для получения результатов потребуется доработать способ взаимодействия с моделью и выделить значительно больше средств на API. Ведь эта модель дорогая: на каждую попытку уходит от $0.30 до $1.00 в зависимости от сложности задачи, а для сложных задач требуется десятки или даже сотни попыток. Теперь конкретнее: если рассматривать задачи уровня Division 2 на платформе спортивного программирования Codeforces, то модель решает их примерно на уровне рейтинга ~1800 (топ 10%). Однако на более простых задачах, таких как в соревнованиях Division 3, модель абсолютно доминирует над людьми, решая задачи правильно всего за пару минут 🤯 Для сравнения, лучшим программистам обычно требуется не менее 20-30 минут, чтобы решить все задачи в таком соревновании, а o1 нужно всего 2-3 минуты, если генерировать решения параллельно для всех задач, или 10-20 минут, если решать их по очереди. Удивительно, как за меньше чем два года мы перешли от того времени, когда LLM не могли написать даже небольшой фрагмент кода без ошибок, если их не переспрашивать по 10 раз, к тому, что видим сейчас. И это ещё далеко не предел возможностей — будущие модели будут ещё умнее.

😏 Telegram updated? Try foundation.ton

TON обошёл Ethereum по количеству ежедневных активных пользователей. Источник: https://cointelegraph.com/news/telegram-ton-daily-active-addresses-surpass-ethereum-june Что-то я опоздал с новостью на 10 дней 😄 Но ситуация за это время не поменялась.

Tact обновился до версии 1.4.0, и я хочу рассказать о том, что это такое и как с этим связан я. Если вы интересовались разработкой смарт-контрактов в TON, то наверняка слышали о языке программирования FunC. Он используется почти во всех ключевых смарт-контрактах, таких как токены, кошельки и стейкинги. Но дело в том, что FunC довольно низкоуровневый и зачастую требует от разработчика знаний о том, как всё работает "под капотом". В этом нет ничего плохого — низкоуровневость помогает в оптимизациях и в целом позволяет разработчику почти полностью контролировать то, как будет работать получившийся смарт-контракт. Но помимо низкоуровневых языков полезно иметь в экосистеме и высокоуровневые аналоги — для новичков, для более быстрой разработки MVP проектов, для понижения порога входа и, как следствие, привлечения ещё большего количества разработчиков в TON. Поэтому в 2022 году зародилась идея нового языка "Tact". В течение следующих месяцев его по большей части разрабатывал один человек — Стив Коршаков. Это тот же человек, что основал TON Whales и потом внёс большой вклад в развитие инструментов для разработчиков в TON, разработав удобную TypeScript библиотеку для работы с блокчейном и ещё некоторые полезные вещи. Стив реализовал большую часть компилятора и довёл Tact до состояния, когда его уже можно было начинать использовать для написания небольших смарт-контрактов. Но через какое-то время Стив прекратил работу над компилятором (я не узнавал по какой причине), и ещё несколько месяцев Tact не получал никаких обновлений, и никто над ним не вёл работу. Это продолжалось, пока в ноябре 2023 года к TON Foundation не присоединился Антон Трунов в качестве ведущего разработчика этого языка. Антон до этого занимался разработкой компиляторов и формальной верификацией в других блокчейнах, поэтому опыта и знаний в этой сфере у него точно достаточно. Какое-то время ушло на то чтобы возобновить процесс разработки и понять куда языку следует двигаться дальше. Так совпало, что в этот же момент и мне захотелось внести какой-то вклад в этот язык. Я сам являюсь разработчиком смарт-контрактов и написал много кода на FunC, поэтому видел, что в Tact очень много чего не хватает и им местами бывает неудобно пользоваться. Я решил добавить в Tact несколько новых фич, которые считал очевидно необходимыми. Антон помог правильно их реализовать, и в итоге в феврале этого года они попали в версию 1.2.0. После этого мне предложили присоединиться к команде разработки, где я продолжил работу над компилятором и занимаюсь этим до сих пор. За последние 4 месяца мы выпустили ещё три релиза: 1.3.0, 1.3.1 и 1.4.0. Была проделана огромная работа как над самим компилятором, так и над его документацией и внешними инструментами. Суммарно за это время нашей командой из нескольких человек было реализовано почти сто новых фич, исправлений багов и всякого рода изменений и улучшений. Благодаря всему этому сейчас Tact можно гораздо комфортнее использовать для разработки смарт-контрактов, так что если вы хотели научиться этому, но не знали с чего начать - вперёд читать документацию 😃 Очень надеюсь что наш труд по улучшению Tact позволит большому количеству новых разработчиков войти в экосистему TON и появится ещё больше интересных проектов!