explainx.ainewsletter3.5k
ai newstrendingpathwaysworkshopsskills
pricing
workshops ↗
explainx.ai

Upskill in AI — 16 free pathways, live workshops & bootcamps, and 50+ courses from practitioners. Plus the skills, tools, and MCP servers to practice on.

follow us

custom AI agents

[email protected]

get started

Join · $29/mo

learn

pathways — start freeworkshopsbootcampscoursescertificationsmock testsexplainx universitycorporate traininglearn skills & mcp

discover

skillsmcp serverstoolsagentsllmsdesignsagi trackerranks

company

aboutvisionmissionteaminstructorscommunityhackathonscareers

content

daily AI newsblogreleasespromptsgeneratorsresource librarydemofor LLMs

solutions

all solutionsdeveloper upskillingmarketing upskillingproduct manager upskillingleadership upskilling

Sister Products

Infloq

Infloq

Influencer marketing

BgBlur

BgBlur

Privacy-first blur

Olly Social

Olly Social

Social AI copilot

Ceptory

Ceptory

Video intelligence

BgRemover

BgRemover

Background removal

newsletter · weekly

Get AI news, tools, and insights in your inbox.

contactsupportprivacytermsdata rightssubmission guidelines

© 2026 AISOLO Technologies Pvt Ltd

home/skills/tag/semantic
skill tag

semantic▌

4 indexed skills · max 10 per page

skills (4)

semantic-release

terrylica/cc-skills · Productivity

0

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

semantic-versioning

aj-geddes/useful-ai-prompts · Productivity

0

Establish semantic versioning practices to maintain consistent version numbering aligned with release significance, enabling automated version management and release notes generation.

semantic-kernel

github/awesome-copilot · Productivity

0

Use this skill when working with applications, plugins, function-calling flows, or AI integrations built on Semantic Kernel.

pgvector-semantic-search

timescale/pg-aiguide · AI/ML

0

Semantic search finds content by meaning rather than exact keywords. An embedding model converts text into high-dimensional vectors, where similar meanings map to nearby points. pgvector stores these vectors in PostgreSQL and uses approximate nearest neighbor (ANN) indexes to find the closest matches quickly—scaling to millions of rows without leaving the database. Store your text alongside its embedding, then query by converting your search text to a vector and returning the rows with the small