Software design automation

Various tools for automating the design, estimation and documentation of any IT-projects at all stages of their life cycle.

Products

Products

tekBlueprint.infra is an infrastructure modeling, load simulation, and metric collection tool available as CLI for CI/CD integration and a web service for team collaboration. Includes an AI assistant with 15 specialized agents for natural language form filling.

Software metrics forecasting
Manual or automatic distribution of services across servers
Report generation and topology diagrams
Hardware and software components library
AI assistant with 15 agents
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tekBlueprint.plans is a tool for estimating tasks within a project with subsequent planning by development stages and required resources. The tool allows you to automate the process of estimating tasks and planning, reducing the amount of manual work and routine operations repeated from project to project.

Task decomposition
Resource planning
Planning of development and implementation stages
Integration with tekBlueprint.specs
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tekBlueprint.specs — specification and documentation management tool, allows you to document projects at various stages. Using predefined templates for specification blocks, reference books can help significantly speed up and simplify the creation of project documentation.

Output documentation template management
Specification block typification
Documentation generation
Different template types
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tekBlueprint.kbase is the service of a common knowledge base of unifying components from various sources used in tekBlueprint apps.

Sync components from different repositories
Integration with tekBlueprint applications
User interface for database search
MCP support for connecting to AI agents
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Blog

Blog

Porting an MCP Server from Go to Rust Using LLMs

A while ago there was a post about agentic development for a project. This is a follow-up, but in a slightly different, more practical direction. The task was an MCP service / KAG / GraphRAG in Go. After completion, it “unexpectedly” turned out that the project contained a fatal flaw in the shape of the letter G. So a deliberate decision was made to migrate to Rust. This venture required changing the tech stack, the libraries, the algorithms, implementing an LSM-like mechanism for the vector database, and so on. In other words, it was not a mechanical port but a migration with substantial rework.

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Agentic Development: A Personal Experience

A spontaneous article on agentic development, written after some time and attempts at using this approach. Below are general theses, some of which I probably already touched on about a year ago. Overall, things turned out as expected. There was no revolution, and the bubble is deflating here and there, even though everyone pretends everything is “on plan”. The promised AGI, for some reason, still hasn’t taken over the world :)

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Agent Orchestration for Process Automation

Chances are many of you already use various tools to automate your work – some still at L1, others maybe reached L5 by now but haven’t told anyone about it. This article covers the L3 → L4 transition, where full automation is not very practical and frankly doesn’t make much sense. Otherwise this whole thing would devolve into yet another blog churned out by neural slop – endless streams of articles nobody asked for.

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Application with Wails 3 and ReactJS

Another variation on the theme of combining backend and frontend in a single application. There have already been examples of Spring Boot with ReactJS, Go with HTMX and Electron with ReactJS. This time, the alternative to Electron is Wails v3, which combines Go and ReactJS to create native desktop applications.

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Production LLM Inference — Part 4: Frameworks and Operations

Part 3 covered parallelism and speed techniques. This is the final part (4 of 4) of the Production LLM Inference series. It covers inference frameworks, production operations, and a practical decision framework for choosing the right stack.

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Production LLM Inference — Part 3: Parallelism and Speed

Part 2 covered quantization and KV cache optimization. This is Part 3 of 4 — it focuses on the techniques that accelerate inference: multi-token prediction and distributed parallelism strategies.

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