Technology & AI

Practical takes on software, hardware, and AI — what actually changes for users and builders, minus the hype.

AI Recommendation Systems: How They Work and When They Break

Recommendation systems are a sequence of concrete choices about algorithm, feedback signal, metric, and guardrail. Most production failures trace to one misjudged step, not the model itself.

Editorial AI ·4 min read

Technology & AI Edited

Bounded AI for Personal Productivity: Email, Calendar, Tasks

AI assistants can cut coordination overhead across email, calendar, and tasks, but only when scoped to those surfaces and paired with approval gates and a review loop.

Editorial AI ·4 min read

Technology & AI Edited

AI for A/B Testing: Automating Experiment Design and Analysis

AI can propose hypotheses, size samples, and run Bayesian sequential reads of product experiments. The metrics, guardrails, and stopping rules that make a test trustworthy still belong to humans.

Editorial AI ·4 min read

Technology & AI Edited

AI for Model Interpretability: Beyond SHAP and LIME

SHAP and LIME have well-documented limitations on complex models due to unstable local approximations. Concept-based methods like TCAV and attribution-based interpretable networks offer more robust alternatives, but each has trade-offs. Here's how to choose.

Editorial AI ·4 min read

Technology & AI Edited

Why AI Co-Pilots Are Replacing Autonomous Visions in Circuit Design

Circuit design exemplifies the shift from autonomous AI agents to practical co-pilots that augment engineer expertise, reflecting a broader normalization of artificial intelligence as a human-controlled tool.

Editorial AI ·3 min read

Technology & AI Edited

From Model-Centric to Data-Centric AI: What Developers Need to Know

Data-centric AI treats dataset quality as the primary lever for model performance. Developers who audit labels, balance classes, and curate edge cases often gain more than those who keep tuning architectures.

Editorial AI ·4 min read

Technology & AI Edited

AI Feature Ship Checklist: 10 Must-Haves for Production

Shipping an AI feature to production requires more than model integration. This checklist covers success metrics, data protection, monitoring, rollback, edge-case testing, human oversight, and documentation.

Editorial AI ·4 min read

Technology & AI Edited

What the Model Context Protocol Actually Standardizes (and What It Leaves Open)

MCP is the open protocol that lets AI agents plug into tools once instead of building N×M custom integrations. It standardizes the connection, not the intelligence: context costs and security stay on you.

Editorial AI ·4 min read

Technology & AI Edited

Keyword Search vs. Semantic Search: Why Good Search Uses Both

Keyword and semantic search both score relevance as a vector inner product. Each fails where the other succeeds, so production search, including AI assistant retrieval, fuses them with hybrid search and reciprocal rank fusion.

Editorial AI ·4 min read

Technology & AI Edited

Why AI Coding Benchmark Scores Don't Predict Real-World Speed

SWE-bench Verified scores look impressive, but the benchmark measures narrow, partly memorized tasks. Field evidence shows developers can feel faster while measurably slowing down.

Editorial AI ·4 min read

Technology & AI Edited

Tokenization Demystified: How LLMs Process Text and Why It Matters

Tokenization converts raw text into the numerical units LLMs process, driving context limits, API cost, and latency. Learn how subword tokenizers work and how to write token-efficient prompts.

Editorial AI ·4 min read

Technology & AI Edited

AI-Assisted API Design: Generating OpenAPI Specs from Plain English

AI tools can draft OpenAPI specs from plain-English descriptions, but validation, edge-case handling, and human review turn a draft into a reliable contract.

Editorial AI ·4 min read