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
TensorFlow + Data Science Professional: Why Your Model Fails on New Data and How to Fix It
Why does a model with 95% test accuracy fall apart in production? We break down the causes: data leakage, overfitting, data drift, incorrect splitting. Learn to spot problems in advance and set up monitoring.
Open Source Contribution: The Mistakes Beginners Make, the PR That Actually Gets Merged, and the Licences Everyone Misreads
Why good pull requests get rejected, how licences and CLAs actually work, where open source breaks structurally, and how to learn contribution without wasting months on guesswork.
WebAssembly (WASM) — From Browser to Server: Where WASM Pays Off in Production and Where You Pay for Performance with an Extra Layer
We break down how WASM actually works in production: the compilation pipeline, WASI, Wasmtime and WasmEdge runtimes, where WASM pays off in plugins and edge functions, and where you pay for performance with an extra layer of abstraction.
Terraform & IaC: The Myths That Cost You Money and How Infrastructure as Code Actually Works
Terraform & IaC myths debunked: learn how infrastructure as code really works, where state management breaks, and how to avoid costly mistakes.
Serverless Architecture: Where Lambda and Cloud Functions Break, and How to Design Around It
Serverless is honest about the pitch but quiet about the bill. A practitioner's walkthrough of an image pipeline, the four ways the model breaks on its own terms, and how to learn it without a production incident.
Vector Databases — From Search to RAG: Why Your RAG Chatbot Gives Wrong Answers and How to Fix Retrieval
Your RAG bot gives wrong answers because retrieval fails, not the LLM. A practical walkthrough: chunk sizes, hybrid BM25+vector search, metadata filters, reranking, and how to measure recall@5 before blaming the model.
Cambridge Admissions: ENGAA (Engineering Admissions Assessment) — What to Choose and Why: Alternatives and Pitfalls Explained
We break down how the ENGAA exam is structured, what traps it contains, and how to choose a preparation path: self-study, tutor, or course. Comparison of alternatives and practical examples.
Technical Writer: Why Product Documentation, References and Standards Break — and How to Learn It
The documentation exists and the answer still isn't there. A practitioner's look at technical writing: how a webhook reference actually gets written, where doc systems break, and how to learn it.
Customer Support Myths: Why "The Customer Is Always Right" Fails and How to Handle Angry Customers Without Inflating Repeat Contacts
Debunking the 'customer is always right' myth: why agreeing with everything inflates repeat contacts, and how to de-escalate with a three-step script while tracking FCR and repeat-contact rate.
A/B Testing That Survives Scrutiny: Prompts That Make an LLM Your Statistician, Not Your Cheerleader
Two ways to run an A/B test: watch the dashboard and hope, or compute power, fix the metric, and refuse to peek. Four battle-tested prompts turn an LLM into the statistician who catches peeking, multiple comparisons, and premature wins.