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Engineering

A collection of 23 posts

Online Exam Proctoring as a Chrome Extension: Integrity Without the Spyware Reputation
AI development

Online Exam Proctoring as a Chrome Extension: Integrity Without the Spyware Reputation

Students will smear lotion on a webcam to blind the proctoring AI. That's the real state of the art on the cheating side, and it's exactly why verified practice is worth more, not less, as AI makes cheating trivial. How to build integrity as signals plus a human, not surveillance theater.

  • 2muchcoffee
    2muchcoffee
7 min read
RAG Over Messy PDFs: OCR, Structure-Preserving Chunking, and Incremental Re-indexing
AI development

RAG Over Messy PDFs: OCR, Structure-Preserving Chunking, and Incremental Re-indexing

Every RAG demo works on a clean PDF. Then you point it at a decade of scanned contracts and it returns confident nonsense. The chunk is the unit of retrieval, and three moves decide whether yours are any good before the model ever runs.

  • Oleg Logvin
8 min read
Build vs White-Label for an SAT Prep Platform: When Each One Wins
EdTech

Build vs White-Label for an SAT Prep Platform: When Each One Wins

The most expensive mistake in this market is building the wrong half. The real question isn't build or buy, it's what the platform is to you: a channel for your tutoring, or the product itself. One answer is a few weeks, the other is the right call only if the hard engine is your actual edge.

  • 2muchcoffee
    2muchcoffee
5 min read
Agentic RAG: Why Retrieval Is the Layer Under the Agent
AI development

Agentic RAG: Why Retrieval Is the Layer Under the Agent

Classic RAG is a straight line: retrieve once, then answer. Agentic RAG is a control loop, an agent that decides what to fetch, whether it was enough, and whether the question is answerable at all. Here is the real graph, and when the loop is worth its cost.

  • Oleg Logvin
6 min read
The 80% Problem: Your AI-Built MVP Is a Verdict, Not a Draft
AI

The 80% Problem: Your AI-Built MVP Is a Verdict, Not a Draft

AI gets you to 80% of a product in a weekend, then stalls on the 20% that decides whether it survives real users. Most founders try to finish it. The smarter move is to read it: that 80% is a cheap verdict on whether the idea earns the expensive part.

  • 2muchcoffee
    2muchcoffee
10 min read
Bun vs Node.js: I Swapped a Real TypeScript Project to Bun
Bun

Bun vs Node.js: I Swapped a Real TypeScript Project to Bun

I swapped a real React and TypeScript project from Node.js to Bun and timed what changed. The runtime swap barely moved the build. The real speedup came only when Bun replaced more of the toolchain.

  • 2muchcoffee
    2muchcoffee
15 min read
The 2muchcoffee Angular Manifesto: Modern Angular in 2026 (Signals, Standalone, Zoneless)
Angular

The 2muchcoffee Angular Manifesto: Modern Angular in 2026 (Signals, Standalone, Zoneless)

The Angular that made you write NgModules, decorate every input, and trust zone.js is gone. Signals, standalone by default, and as of v21 no zone.js at all. This is how we actually write Angular in 2026, rule by rule, with the code.

  • 2muchcoffee
    2muchcoffee
13 min read
How to Actually Evaluate Your RAG: Golden Sets, Retrieval Precision, and the Eval Loop
AI development

How to Actually Evaluate Your RAG: Golden Sets, Retrieval Precision, and the Eval Loop

It feels better is not a number, and you cannot improve retrieval you cannot measure. How to build a golden set, measure the four things that matter, and run it as a regression so the next change to your RAG is a result, not a hunch.

  • Oleg Logvin
7 min read
How to Build an SAT Prep Platform in 2026 (and What's Still Standing in 2028)
AI development

How to Build an SAT Prep Platform in 2026 (and What's Still Standing in 2028)

If you sell SAT prep, you spent 2026 watching Google give it away free. The niche survives because the easy half is free and the hard half, the part a junior or an AI gets wrong, is the whole business. A builder's map to a digital-SAT platform, and what's still standing in 2028.

  • 2muchcoffee
    2muchcoffee
15 min read
Pinecone vs Qdrant vs pgvector: Choosing a Vector Database in 2026
AI development

Pinecone vs Qdrant vs pgvector: Choosing a Vector Database in 2026

There is no best vector database. There is the one that fits your hardest constraint. A practitioner who shipped one scores Pinecone, Qdrant, Weaviate, pgvector, Milvus, and Chroma on the questions that actually decide.

  • Oleg Logvin
9 min read
Your Agent Is Only as Smart as Its Search: Inside Our Hybrid RAG on Qdrant
AI development

Your Agent Is Only as Smart as Its Search: Inside Our Hybrid RAG on Qdrant

Our matching agent answered a continuous stream of developer requests with confident, well-rounded, wrong shortlists, until we fixed the search under it. The architecture, the reasoning, and an interactive fusion demo you can tune yourself.

  • Oleg Logvin
18 min read
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