ASI Biont Blog — AI agents, automation and the digital twin
Practical articles, guides and analysis on AI agents, automation, development, integrations, IoT devices and science — how AI writes code, runs in a sandbox and connects any service, API or sensor
Cambridge Admissions: MAT (Mathematics Admissions Test) — Oxford: What the Exam Actually Tests, and Who Should Sit It
The MAT is not a test of how much maths you know. It is a test of whether you can reason under time pressure and show it. What the exam really measures, where preparation breaks, and who it suits.
Platform Engineering — Internal Developer Platforms: What the «Build It Like Spotify» Myth Gets Wrong
Platform Engineering isn't a portal you install — it's a product for your own engineers. A real walkthrough, the four ways IDPs break, and how to pick your path.
Startup — How to Create and Launch a Tech Startup: A Complete Course from Idea to Exit — What You Need to Know Before Starting
A complete course on creating a startup: from idea and validation to fundraising, growth, and exit. We cover how to test a hypothesis, calculate unit economics, avoid typical mistakes, and which learning paths to choose.
Credit History Under Control: How to Check Who Requested Your Report and Dispute Erroneous Data
Step-by-step guide: how to set a self-ban on credits through government services, which banks see it, what to do if fraudsters still take out a loan, and where an AI twin will save you time on paperwork.
ITIL 4 Managing Professional (MP): Why Your Sev-1 Outage Becomes a Four-Hour War Room — and How to Fix It
Why Sev-1 outages turn into four-hour war rooms, and the concrete runbook — severity matrix, incident commander, 15-minute comms, escalation triggers, blameless review — that actually cuts MTTR and repeat incidents.
Cloud Native — Microservices, Kubernetes and Cloud Technologies: Who This Path Fits, and Who It Will Frustrate
A practitioner's look at Cloud Native: how a real rollout breaks, where microservices cost you more than they save, and how to tell whether Kubernetes is your next step or a detour.
Data Science from Scratch: Why 500 Clean Rows Beat 50,000 Dirty Ones — Debunking the "More Data Is Better" Myth
Debunking the myth that "more data means a better model." Why 500 clean rows can beat 50,000 dirty ones: data cleaning, cross-validation, eliminating leakage, and overfitting control. A practical checklist.
Computer Vision and Image Processing: How to Go from CNN to Generative Models Without Getting Stuck in the Middle
How to master computer vision from scratch: a breakdown of a real defect detection task, model failure modes, comparison of learning paths, and a non-obvious conclusion about the importance of data. Practical tips and a link to a course for structured learning.
Embedded Linux & IoT: Where Beginners Stumble with Yocto, Buildroot, and Device Tree — and How to Avoid It
Where embedded Linux actually breaks — silent device tree mismatches, unpinned Yocto builds, RTOS timing surprises — and a concrete sensor walkthrough that shows what to check before writing any code.
Cambridge Admissions: PAT (Physics Aptitude Test) — Oxford: What You Need to Know Before You Start So You Don't Lose a Year
A breakdown of PAT: how the exam is structured, where strong applicants break down, failure modes, and a comparison of preparation paths. Practical examples and non-obvious takeaways.