Choosing a graph backend is less about finding the "best" tool and more about matching the tool to your scale, query style, and deployment constraints. This is a survey of the main options across three tiers: in-process libraries, embedded file-based engines, and full graph database servers. Each entry below gives …

Most mainframe troubleshooting RAGs fail at the same place: retrieval. An abend code like S0C7 is a near-exact lookup, not a fuzzy semantic match — but vector search happily returns the S0C4 chunk because the embeddings sit close together. And job dependencies are graph-shaped: an abend in step 3 cascades to …

A startup employee equity cliff period is the minimum amount of time an employee must stay with the company before earning any ownership rights to their stock options or equity grants.

Typical Structure

4-Year Vesting with 1-Year Cliff The most common arrangement: equity vests over four years, with nothing earned …

Self-learning courses promise freedom — learn anytime, anywhere, at your own pace. But that same freedom is what kills most of them. The completion numbers tell the story: median completion rates for online courses sit around 12.6%, with the worst performers near 0.7% and even the best topping out …

In 2014, Masayoshi Son stood on a Tokyo stage and unveiled Pepper — a wide-eyed white humanoid he called the world's first robot that could read human emotions. He was not pitching a gadget. He was pitching a new era, one where SoftBank would lead the way as robots moved from …

In November 2017, a five-foot robot named Mitra greeted Ivanka Trump and chatted with Narendra Modi at the Global Entrepreneurship Summit in Hyderabad. It was the kind of moment a founder cannot buy — national press, a viral clip, India's own humanoid robot sharing a stage with world leaders.

Nine years …

Sashiko (刺し子, "little stabs") borrows its name from a Japanese reinforcement-stitching technique — fabric repaired and strengthened at its points of wear. The metaphor is the whole pitch: an agentic system that stitches over the weak spots in proposed Linux kernel patches before they land. It's written in Rust …

Training & Adaptation Strategy

1. What approaches exist for training or adapting an LLM? — Pretraining, fine-tuning, instruction tuning, prompt engineering, RAG.

2. Base model vs instruction-tuned model? — Pure next-token predictor vs one aligned to follow instructions.

3. When would you choose fine-tuning over RAG? — Stable domain knowledge, style/format control, latency sensitivity.

4. When would …

A starter question bank for screening entry-level GenAI engineers. Grouped by theme, covering fundamentals through production concerns.

Fundamentals

1. What is generative AI vs discriminative AI? — Generative models learn to produce new data; discriminative models learn decision boundaries to classify or predict.

2. What is a large language model (LLM)? — A neural …

When I was in 11th grade, my school — a Government Higher Secondary School — got its first color computer.

Even though I was a Bio-Maths student, the school management made a great call: one mandatory computer session every Friday for all 11th and 12th graders.

Our computer teacher had a tradition …