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Earlier this month I shipped two premium templates — a SaaS landing page and a developer portfolio. Not a startup, not a SaaS, just templates. This post is about the two constraints I built them under, why they made the code better, and a few things I learned launching as a solo dev with zero audience. Constraint 1: zero dependencies beyond next, react, and tailwind Open the package.json of most templates and you'll find 20+ packages: icon libraries, animation libraries, carousel plugins, UI kits, utility libraries. Every one of them is a version conflict waiting to happen for the buyer, and most are replaceable with a few lines of code in 2026. What I used instead: Icons → inline SVG components. An icon component is ~10 lines. You need maybe 15 icons for a landing page. Animations → plain CSS. Scroll-blur navbars, gradient glows, an animated "typing" terminal — all doable with keyframes and transitions. No framer-motion. The dashboard mockup in the hero → pure CSS. Divs, borders, gradients. It looks like a product screenshot but it's ~80 lines of JSX and weighs nothing. Result: both templates land at ~100KB first-load JS, npm install takes seconds, and there is nothing to break when Next.js 16 arrives. Constraint 2: every piece of content in ONE typed config file The thing I hated most about templates I've used: content is smeared across 30 components. Changing a headline means hunting through JSX. So both templates keep all content in a single file — lib/content.ts for the landing page, site.config.ts for the portfolio. Headlines, nav, pricing tiers, testimonials, project lists, even the lines that animate in the fake terminal. Components are pure renderers of that config's TypeScript type. Two things surprised me here: TypeScript becomes your content linter. Forget an alt text, malform a link, give a pricing tier three features when the type expects a non-empty array — the build fails. Content mistakes surface at compile time. It forces better component design. W

Автор: Владислав Штер, соло-фаундер экосистемы SovereignПоследнее обновление: Июль 2026 года Поиск критических уязвимостей через нейросетевой консилиум Egregor. Десктопное приложение Egregor находит критические уязвимости в смарт-контрактах с помощью одновременной работы нескольких ИИ-моделей. Этот инструмент создан для Web3-разработчиков, которым необходимо проверять сложный код без риска пропустить ошибки, свойственные одиночным нейросетям. В ходе тестирования консилиум Egregor обнаружил 4 критические проблемы (включая уязвимость Reentrancy и вечные права деплоера) в смарт-контрактах SovereignBank Web3, тогда как 13 ручных проверок одиночными топовыми ИИ (Claude, Gemini, ChatGPT, DeepSeek, Grok) назвали код полностью чистым. Используйте платформу Egregor для проведения глубокого аудита кода, чтобы получать верифицированные решения вместо догадок одной модели. Защита от эхо-камеры и слепых зон алгоритмов в программе Egregor Система Egregor устраняет эффект эхо-камеры и систематические слепые зоны нейросетей за счет встроенных механизмов Anti-Groupthink и "Адвоката дьявола". При анализе сложной логики одиночные нейросети часто вежливо соглашаются друг с другом, но алгоритмы Egregor запрещают моделям принимать чужие выводы без подтвержденных в коде фактов. Во время аудита смарт-контракта механизм перекрестной проверки в Egregor отсеял неподтвержденные гипотезы и позволил 5 моделям в разных ролях перекрыть слепые зоны друг друга. Запускайте локальный консилиум Egregor, чтобы система сама отделяла реальные баги от шума и выдавала финальный вердикт Модератора с оценкой уверенности от 1 до 5. Стоимость многоуровневого анализа кода на платформе Egregor Программа Egregor кардинально снижает финансовые затраты на профессиональный аудит кода до нескольких центов. Данное решение идеально подходит для инди-разработчиков и участников хакатонов, у которых нет бюджетов в тысячи долларов на заказ проверок у специализированных аудиторских компаний. Полноценный комплексный прогон мо

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

OrbitLens Ace → ace.orbitlens.io A busy quarter is easy to stage. Code that's still there in two years isn't. Pick any metric a team has ever used to judge people, and someone has quietly figured out how to move it without doing the underlying thing. Lines of code rewarded typing, so people typed. Commit counts rewarded committing, so commits got smaller and more frequent. Velocity rewarded closed points, and points drifted upward until a "3" meant nothing. DORA measured how often you deploy, so teams shipped trivial changes just to move it. Even churn — the number the "code health" tools lean on — is something you can lower on purpose, which means you can manage the number instead of the mess underneath it. None of that requires dishonest engineers. It's Goodhart's law doing what it always does. Every one of those numbers is a measure of activity , and activity is cheap to produce. Once you're paid for activity, the fastest way to get paid more is to produce more of it — not more of whatever the activity was supposed to be a sign of. So the question worth asking isn't which activity metric is least bad. It's whether a git history contains anything at all that you can't move just by being busier. It turns out there's one. And it's not because we were clever — it's because of what the thing is actually made of. What lasts isn't something you do Take everything a person wrote, wait a while, and ask a smaller question than "did they work hard." Ask whether the specific lines are still there. Not reverted, not rewritten, not quietly swallowed by someone else's refactor. Still holding weight at HEAD. That's survival. We read it with time-decayed git blame : a line's weight fades month by month unless the line keeps existing, and it counts for more once other people have built on top of it instead of leaving it as a private island. Survival that others have built on is what we call gravity — the structural pull that outlives the person who created it. Try to game it and w

I'm a experienced/senior developer which is frequently using ai, guiding coding agents, etc. I wonder, how does my job look like in a few years? Which skills might be the best ones to have? Currently, having business knowledge, development experience helps greatly with guiding coding agents, creating MVPs/PoCs in "no time", improving code, etc. But what if coding agents/ai would overtake this job?

@kungfufk Since the dawn of computing, we have built Artificial Intelligence on a flawed premise: perfect rationality. We brute-force algorithms to find the optimal solution, assuming infinite time and infinite capacity. But humans don't work like that. As Herbert Simon famously coined, we operate on Bounded Rationality. We make decisions based on limited time, limited cognitive capacity, and limited information. What if, instead of forcing AI to be perfectly rational, we created a mathematical equivalent for human processing? What if we modeled human cognition using the laws of physics — wave theory, thermodynamics, and mechanical energy equations — to build a heavy, complex, but highly probabilistic AI engine? Here is a blueprint for a new field of research: Computational Cognitive Mechanics . 1. The Core Equations of Cognitive Processing To model bounded rationality mathematically, we first need to define the relationship between Knowledge ($K$), Cognitive Capacity ($C$), and Processing Time ($T$). Based on human observation, we can establish these foundational proportions: Knowledge vs. Time — The more knowledge you possess, the faster you can generate a decision. $$T \propto \frac{1}{K}$$ Capacity vs. Time — High cognitive capacity (skills, processing power) inversely relates to the time required to solve a problem. $$T \propto \frac{1}{C}$$ Knowledge vs. Capacity — This is the most fascinating limit. Knowledge does not scale linearly with capacity. Gaining true knowledge requires exponential capacity (effort/skill). Therefore, knowledge is roughly proportional to the square root of capacity. $$K \propto \sqrt{C}$$ By integrating these, we can build a baseline processing algorithm for an AI. Instead of giving an AI unlimited time to compute, we cap its computing time based on a synthetic "Knowledge and Capacity" matrix, forcing it to use heuristics — just like a human. 2. Cognitive Wave Theory & FFT: Information as Interference In physics, waves interact throug