Popular AI tools
View all →NotebookLM
NotebookLM ingests PDFs, docs, and slides you provide, answers with inline references, and can turn dense material into spoken audio overviews—aimed at students, researchers, and analysts who need traceability more than generic chat.
Bolt
Bolt targets rapid UI shells, marketing sites, and mobile prototypes with hosting and GitHub sync—use it when speed matters more than bespoke backend complexity.
Gemini
Gemini pairs conversational answers with strong Google Workspace and Search adjacency—useful when your prompts mix text, images, and quick research across languages.
GitHub Copilot
Copilot meets developers where PRs and issues already live—inline suggestions, workspace-aware chat, and agent flows that can touch multiple files inside VS Code or JetBrains.
Replit Agent
Describe a product, iterate in the design canvas, and let Replit Agent scaffold code, dependencies, and deploys—popular with students and indie hackers validating ideas in hours.
ChatGPT
ChatGPT spans everyday Q&A, long-form writing, code explanation, and image or file inputs—plus GPTs and connectors when you need repeatable workflows instead of one-off chats.
Claude
Claude shines when you need to load large PDFs, compare versions, or iterate on nuanced prose—team features and projects help keep institutional knowledge in one thread.
DeepSeek
DeepSeek is a go-to when you want chain-of-thought style answers, math-heavy prompts, or repository-scale coding help without burning premium credits.
MCP picks
More →Docker MCP
Lets agents inspect containers, images, compose projects, and logs through Docker’s MCP integration—so debugging local stacks does not require copy-pasting docker ps output into chat.
MongoDB MCP
Lets assistants run explain plans, inspect collections, and draft aggregation pipelines against MongoDB clusters you authorize—useful when debugging document models and slow queries without pasting dumps by hand.
Redis MCP
Exposes scoped Redis commands and key introspection so agents can debug caches, feature flags, and session stores without dumping entire databases into the prompt.
Linear MCP
Keeps engineering context in sync by letting agents read and mutate Linear issues while staying inside the editor or chat surface.
Atlassian Rovo MCP
Exposes Jira, Confluence, and Compass knowledge through Atlassian’s hosted MCP so roadmap and documentation stay authoritative for enterprise assistants.
Tavily Search MCP
Brings Tavily’s real-time search and structured snippets into MCP clients so agents can cite fresh web evidence instead of relying on stale training cutoffs.
Skills to try
More →Incident response
Structures on-call work: timeline, blast radius, mitigations, and customer comms—so fixes stay coordinated instead of chaotic thread hopping.
Humanizer
Removes Wikipedia-documented “AI writing” tells—significance inflation, -ing fluff, em-dash stacks, chatbot closers—and runs a final “still obviously AI?” pass before shipping prose.
Performance profiling
Finds real bottlenecks using traces, flame graphs, and system metrics before rewriting code—so optimizations target measured latency, not guesses.
API design and versioning
Shapes REST or RPC surfaces with predictable errors, pagination, and deprecation rules before implementation locks you into brittle clients.
Requesting code review
Frames review requests around risk, test gaps, and rollout impact—so reviewers know where to spend their limited attention.
Executing implementation plans
Executes a written plan in order, stopping at checkpoints to reconcile assumptions—useful for spec-driven builds and multi-step refactors.