Artem Zaitsev
CTO · Idealogic Development OÜ
CTO and Co-founder at Idealogic. Directing engineering, systems architecture, security, and full-stack delivery across web, mobile, and blockchain.
Articles by Artem Zaitsev
Decentralized Exchange Development: Architecture and Cost
A build guide for decentralized exchange development: how AMM, order-book and intent designs differ, what the architecture really contains, what a fork costs you in licence terms, and a cost model with its assumptions written down.
Custom Blockchain Development: When to Build vs Buy
Custom blockchain development means four very different jobs, from writing your own contracts on a public chain to standing up a base layer. This guide separates them, prices each route against published figures, and runs the build or buy call in order.
Blockchain Game Development: Build a Web3 Game Like Pixels
Blockchain game development turns in-game items and currency into player-owned tokens on a blockchain. This guide explains what a blockchain game is, how on-chain ownership works, the web3 tech stack, how a game like Pixels is built, and how to design play-and-earn that lasts.
Types of Digital Wallets: Closed, Open, Crypto, and MPC
No two lists of digital wallet types agree, because wallets sort along four axes: the loop they spend in, the form they take, who holds the credential, and what asset sits inside. This guide runs all four, with the regulatory definitions and the adoption data behind them.
Smart Contract Audit Cost: 2026 Prices, Drivers, and Scope
Almost nobody publishes a price list for smart contract audits. This guide collects the references that do exist, dated and linked: Sherlock's 2026 bands, public contest award pools, and a vendor calculator that returns a form instead of a number. Then what moves the figure.
How to Create a Cryptocurrency: Coins, Tokens & Costs
Creating a cryptocurrency is three different projects wearing the same name. This guide separates them: a token on an existing chain, your own appchain, or a no-code launcher. With a working ERC-20, the 2026 rulebook in the EU and the US, and a costed model of each route.
Enterprise Blockchain: Use Cases, Platforms & Adoption
Most enterprise blockchain pilots still stall before production. This guide names the projects that are actually live in 2026 and the ones that were shut down, compares the platforms worth evaluating, and shows how to scope a proof of concept that has a real chance of shipping.
Blockchain dApp Development: A Step-by-Step Build Guide
A working dApp is three layers plus the wiring between them. This guide walks blockchain dApp development the way a team sequences it: chain and stack choice, contract design, wallet integration, indexing, testing, the audit gate, deployment, and what it costs to run.
Smart Contract Development: Lifecycle, Patterns, Cost
Smart contract development is mostly verification, not typing. This guide walks the lifecycle from specification to monitoring, with Solidity code, a language and chain comparison, exploit losses measured from DeFiLlama, and what an audited build costs.
DeFi Protocols Explained: 7 Main Types, 2026 TVL List & Risks
DeFi protocols are the financial building blocks behind every dApp. This guide covers what one is, the main types, a dated list of the largest protocols by value locked, and the risks that decide whether one is safe to build on.
dApp Examples: Decentralized Apps That Work in 2026
A dApp runs its core logic in smart contracts on a public blockchain instead of a company's servers. Here is what that buys you, what it costs, where the users actually are as of August 2026, and which of the confident predictions made two years ago turned out to be true.
Crypto Wallet App Development: Architecture, Security, Cost
A wallet holds keys, not coins, so key handling decides the architecture, the security budget and whether you need a licence. Custody models, the component architecture, measured theft data, the MiCA and FinCEN thresholds, and a cost model in engineer-weeks.
Blockchain Security: Threats, Controls, and Audits
Blockchain security is where the money gets lost, and it is almost never the chain. This guide walks the threat model layer by layer, reads the measured loss data from 2020 to 2026, and covers the contract, key, bridge, and operational controls that hold.
Web3 Development Guide (2026): Full Stack, Costs & 7 dApp Layers
Web3 development swaps the central server for a blockchain, and that one change rewrites the whole stack. Here is the 2026 version: chains, contracts, indexers, wallets, the build sequence, what it costs, and the on-chain data behind each decision.
What Is a Fractional CTO? Role, Cost & When to Hire
A fractional CTO gives you senior technical leadership a few days a week, without the full-time hire. Here's what the role actually covers, what it costs, and the signs you need one, or don't yet.
Technical Due Diligence: A Checklist for Investors & Founders
Before an acquisition or a funding round, technical due diligence is what tells you whether the software is an asset or a liability. Here's what it covers, how it runs, and the checklist we work through.
Model Context Protocol (MCP) for Developers
The Model Context Protocol is an open standard for connecting AI applications to your data and tools. A practitioner's guide to how MCP works, the host-client-server model, its tools, resources, and prompts, building an MCP server, and the security part most demos skip.
Claude Code Skills: Teaching Your AI Coding Agent Your Stack
Claude Code skills are folders of instructions a coding agent loads only when relevant. A practitioner's guide to the SKILL.md model, progressive disclosure, how skills differ from MCP, and how to author ones that encode your stack's conventions instead of bloating context.
The AI Software Engineer: What the Role Actually Is Now
An AI software engineer is an engineer who builds software with AI in the loop, directing agents and owning the result. A grounded look at how the role changed, the skills that now matter, how it differs from ML and prompt engineering, and how to grow into it.
AI Pair Programming: How Senior Engineers Work With AI
AI pair programming is working with an AI as your coding partner: it drives or navigates while you hold judgment. A grounded look at the day-to-day practice, a real session, where it shines, where it quietly fails, and the habits senior engineers use to keep it honest.
AI Coding Tools in 2026: The Landscape, Honestly
There is no single best AI coding tool, and any ranking is stale within a month. This is a vendor-neutral map of the categories that hold their shape, the selection criteria that actually matter, and the trade-offs nobody puts in the demo. A dated mid-2026 snapshot.
Agentic Coding: Building Software With AI Agents in the Loop
Agentic coding is directing an AI agent through real engineering work: it plans, acts, and checks itself in a loop while you set direction and review. A grounded guide to how the loop runs, where agents earn their keep, where they thrash, and the guardrails that keep it safe.
Vibe Coding: A Practitioner's Guide to Shipping With AI
Vibe coding is describing what you want in plain language and letting an AI write the code. A grounded guide to the workflow, where it breaks on security and maintainability, and how senior teams ship with it without shipping garbage.
RAG vs Fine Tuning: Which One Your LLM Actually Needs
RAG changes what a model knows. Fine tuning changes how it behaves. Teams pick wrong because they never ask which of the two is broken. Here's what the published benchmarks actually show, what each approach costs, how they stack, and how to diagnose your own case.
LLM Architecture: From Transformer Block to Production
Ask about LLM architecture and you get two different answers: how the model works inside, and how the system around it is built. Both matter, but only some of the internals change a decision you will actually make. Here is the whole stack, layer by layer.
Generative AI Implementation: From Demo to Production
What a generative AI implementation actually involves: hosted models versus self-hosting, prompt and context design as engineering, where retrieval belongs, guardrails and content safety, evaluation before and after launch, cost per request at scale, and the integration surface.
Fine Tuning LLM: A Practical Guide to Doing It Well
Most teams reach for fine-tuning when prompting or RAG would have shipped faster and cheaper. This guide covers the real decision: when fine tuning an LLM pays off, how to prep data, LoRA vs QLoRA, evaluation, cost, and the pitfalls we see in production.
AI API Integration: From First Call to Production
Calling an AI API is four lines of code. Keeping that call standing under real traffic is the actual work. Here's what AI API integration takes in production: keys, rate limits, latency budgets, streaming, retries, cost ceilings, fallback, and evals.
Agentic AI vs AI Agents Explained: The Real Difference
Agentic AI vs AI agents is not a synonym pair. Agentic names a property a system has by degree; an agent is a countable thing you deploy. Where the boundary sits, where the industry disagrees, why vendors blur it, and how the distinction changes what you build.
AI Agents for Business: Use Cases, Cost, and Build vs Buy
Most AI agent pitches are a demo with a business case bolted on. What agents actually automate, which functions pay back first, when to buy instead of build, what a real deployment costs, and how to spot a use case that will not survive production.
Agentic AI in Production: How to Evaluate and Prove Your Agent Works
Everyone ships agents. Almost nobody can say whether theirs works. AI agent evaluation is the discipline that answers it: task success versus step accuracy, eval sets that decay, LLM-as-judge and its measured biases, trajectory analysis, and cost as a first-class metric.