Vibeleaderboard

ToolsCapabilities

What do you need to do?

Start with the problem. We organize the skills, CLIs, harnesses, services, and infrastructure that can solve it.

Popular starting points

Direct answers for common jobs. Open one to see the relevant Tools index.

01

Code with an agent

Work across a repository from an issue, prompt, or terminal session.

02

Review any codebase

Give an agent a repeatable, high-signal engineering review process.

03

Record a product demo

Capture polished walkthroughs, launch videos, and build-in-public updates.

04

Make agent writing sound human

Remove repetitive, synthetic language while preserving meaning and voice.

Start with Humanizer →

05

Find AI benchmarks

Browse the evaluations used to compare models and agents — coding, tool use, cost, and the domain-specific long tail.

Start with SWE-bench, Terminal-Bench, Harvey LAB, and more →

06

Interface with your agents

CLI harnesses, IDEs, control planes, desktop apps, multiplexers, and terminals for steering coding agents.

07

Dictate instead of type

Turn speech into clean prompts and text with minimal editing.

Start with Superwhisper and Wispr Flow →

Browse every capability

Search for the outcome you need, not the kind of repository it lives in.

01

Write better software

Review, test, debug, and improve the code an agent produces.

  1. Review React codeFind React-specific correctness, performance, and maintainability problems.
  2. Review any codebaseGive an agent a repeatable, high-signal engineering review process.
  3. Generate and improve testsAdd meaningful coverage, reproduce failures, and keep regressions out.
  4. Review code for securityFind vulnerable patterns and validate remediations before shipping.
  5. Turn an API into a CLIGenerate a usable command-line interface from APIs and documentation.
  6. Debug production failuresGive agents logs, traces, errors, and the context needed to diagnose incidents.
02

Make it look and feel right

Turn functional interfaces into clear, intentional products.

  1. Remove AI-slop aestheticsPush generated interfaces toward a deliberate visual point of view.
  2. Critique an interfaceIdentify hierarchy, usability, layout, and product-design problems.
  3. Make a UI responsiveAdapt layouts and interactions across mobile, tablet, and desktop.
  4. Improve accessibilityAudit and repair keyboard, screen-reader, contrast, and semantic issues.
  5. Improve motion and interactionAdd purposeful transitions, feedback, and tactile interaction details.
  6. Polish before shippingCatch spacing, typography, consistency, resilience, and finishing issues.
03

Work with agents

Choose the interfaces and infrastructure that make agents useful.

  1. Brainstorm and pressure-test ideasDevelop a rough idea, expose weak assumptions, and sharpen what is worth building before implementation.
  2. Code with an agentWork across a repository from an issue, prompt, or terminal session.
  3. Run an agent harnessOrchestrate tools, context, subagents, retries, and long-running work.
  4. Control a browserLet an agent navigate, inspect, test, and complete workflows on the web.
  5. Research with an agentFind sources, verify claims, synthesize evidence, and preserve citations.
  6. Give an agent memoryPersist project context, decisions, preferences, and reusable knowledge.
  7. Connect tools with MCPExpose data and actions to agents through Model Context Protocol servers.
  8. Find agent skillsMarketplaces and indices for discovering, vetting, and installing agent skills.
04

Communicate and create

Speak, write, capture, and publish with less friction.

  1. Dictate instead of typeTurn speech into clean prompts and text with minimal editing.
  2. Capture meetingsRecord, transcribe, summarize, and search conversations.
  3. Make agent writing sound humanRemove repetitive, synthetic language while preserving meaning and voice.
  4. Record a product demoCapture polished walkthroughs, launch videos, and build-in-public updates.
05

Understand the AI landscape

Measure models and choose the infrastructure beneath them.

  1. Find AI benchmarksBrowse the evaluations used to compare models and agents — coding, tool use, cost, and the domain-specific long tail.
  2. Evaluate an LLM applicationBuild test sets, score outputs, and catch quality regressions.
  3. Observe an LLM applicationTrace calls, inspect failures, and monitor latency, quality, and spend.
  4. Choose an inference providerCompare model routers, inference clouds, cloud catalogs, and direct lab APIs without collapsing them into one category.
  5. Run models locallyUse local inference runtimes and model managers on your own hardware.
  6. Add vector searchStore embeddings and retrieve relevant context for AI applications.
06

Ship and operate

Take a generated product from local code to a dependable service.

  1. Interface with your agentsCLI harnesses, IDEs, control planes, desktop apps, multiplexers, and terminals for steering coding agents.
  2. Deploy an applicationPublish previews and production builds without managing servers.
  3. Add a backendCombine databases, storage, APIs, and server-side functions.
  4. Add authenticationImplement accounts, sessions, identity providers, and authorization.
  5. Accept paymentsAdd subscriptions, checkout, billing, and payment infrastructure.
  6. Choose data infrastructureCompare databases, object storage, vector search, ORMs, and managed data services.
  7. Choose compute and GPUsFind cloud compute, serverless runtimes, and GPU providers for training and inference.
  8. Monitor product and usageCompare error monitoring, observability, product analytics, and web analytics.
  9. Automate deliveryBuild, test, preview, and release changes through CI/CD services.
  10. Add email and messagingSend transactional email, notifications, chat, and product messages.
  11. Protect agent credentialsCompare vaults, short-lived credentials, and egress proxies that keep secrets out of agent context.

Head-to-head

All matchups →