Russian-language engineering talks, posts and research, summarised in English.
13 sources filed · RU 13
Architecture: tools are thin adapters; a shared core handles one HTTP pool per process, API key injection, logging, tracing and error mapping.
5 of 44coffee shops returned for 'nearest coffee shop' with no point given, all in Vorkuta (latitude about 67.5)
The original hypothesis, about 18 months before the talk: cut average customer handling time by at least 30 seconds without lowering quality.
12,000active users, mostly contact-centre operators, out of 75,000+ with access
An AI pre-analysis of about 26 critical components, checked by component owners, produced a database of scenarios and test cases with priorities; the video description cites 28 components in five languages and more than 11,000 test cases, the talk more than 13,000.
70%pipeline runs that produced a successful merge request, out of about 1,500
Interface: start "task name" opens a session and returns the current SDK description; run "Python script" executes code on the server in a sandbox with the user's permissions.
2commands in the whole agent interface: start a session, run a Python script
By August the weekly limit ran out in about 3 days; friends hold top subscriptions to Claude and GPT at once.
~8xcost of the same actions in an 800K-token session versus a 100K-token session
Skills chosen by GitHub stars on 28 September 2026, from superpowers (292,189) to compound-engineering (25,299); domain skills, memory systems and orchestrators excluded.
2 of 9popular Claude Code skills that beat a length-matched placebo, both on cost only (12% and 5% cheaper)
Earlier stages of the experiment: git plus Obsidian (8 February 2026), Personal OS structure (27 February), comparison with Karpathy's LLM Wiki (5 April), an agent persona (23 May).
~1,500documents in the Personal OS knowledge base moved from a git repo to a server
Agents use two tools: search (query to documents) and fetch (URL to page text).
>80%share of GPT-5.6 Luna's search queries that use quotation marks in Artificial Analysis runs
Starting point: Lehman's second law of software evolution.
~7xreduction in cost per evolution cycle the agent achieved on its own, starting from about 30% cache hit rate
10 skills built in about two months with Anthropic's skill-creator: acceptance criteria, Bamboo deployment, WireMock stubs, task testing, OpenSearch logs, bug reports, Allure test cases, daily test report and others.
10Claude Code skills built over about two months, covering the full QA workflow
Topic, from the title and abstract: training a VLM-based agent with reinforcement learning to carry out tasks in a real web browser for a user.
About 4,000 PRs a day pass through NeuroReview, roughly 40% of PRs created in Arcadia; use is voluntary and internal only.
~4,000pull requests a day reviewed by NeuroReview, about 40% of all PRs in Yandex's Arcadia monorepo
The specification is the single source of truth; code is treated as secondary and in principle regenerable in another language.
about 90%share of reviewer blockers caused by missing edge-case tests