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
Time Series Analysis: Why Prophet and ARIMA Break Where You Least Expect It
We break down how Prophet and ARIMA/SARIMA are structured, where they honestly work and where they break: short series, trend shifts, multiple seasonality, backtesting, and drift in a production pipeline.
AI Tester (AI Auto-Tests): What Lies Behind Test Automation and Where It Breaks
AI testing is not magic but engineering with trade-offs. We break down how test generation, AI assertions, and visual checks work, where the mechanism breaks, and how to choose a learning path.
Cambridge Admissions: STEP Mathematics (STEP 2 & 3) — What Nobody Warns You About Before You Start
What actually separates a grade 2 from a grade 1 on STEP 2 and 3 — the selection strategy, the proof-rigour trap, and the honest trade-offs between self-study, tutoring and a structured course.
Data Science and Machine Learning: A Realistic Step-by-Step Plan — Python, Statistics, Models and Getting Them Into Production
A practitioner's walkthrough of Data Science and Machine Learning: one churn task solved end to end, the four ways the standard pipeline breaks, and an honest comparison of learning paths.
Skydiving Theory: Why Landing and Malfunction Planning Decide Whether You Get Hurt
Most skydiving injuries happen under canopy, not in freefall. A practical look at canopy control, landing-pattern mistakes, AAD and reserve limits, and the malfunction sequence that actually works.
Multimodal AI: How Working with Images, Audio and Video Actually Works — and Where the Myths Break
Multimodal AI demos look magical; the systems behind them are narrower than they seem. A practical walkthrough of working with images, audio and video — plus where the architecture itself breaks.
AI Book Editing vs. a Professional Literary Editor: An Honest Comparison of Two Paths in 2026
We compare two real paths for editing a manuscript in 2026: an AI assistant and a professional literary editor. We break down when AI wins, where it breaks down, and how to choose the right tool for your manuscript's stage and budget.
Excel Formulas or Apps Script? How to Choose Your LLM Prompt Stack for Spreadsheet Data Analysis
A practical prompt stack for spreadsheet work: when Excel formula prompts beat Apps Script, when the reverse is true, and the exact prompts to clean exports, build pivots, and explain inherited formulas.
MCP Servers and Tools for AI: How We Build Tools That Truly Extend Agent Capabilities — and Who This Path Is For, and Who It Isn't
How MCP servers and tools for AI agents are structured: a breakdown of tool design, transports, and real failure modes. Who this path is for, who it isn't, and how the learning approaches differ.
AI Security (Guardrails): How to Protect AI Systems from Hacking — Prompt Injection, Jailbreaks, and Red-Teaming in Practice
We break down AI security in practice: how to protect LLMs from prompt injection and jailbreak, which guardrails work, where protection mechanisms break, and how to choose a learning path.