Stackness

AI Tools tools

Most used tools

  1. #1

    Agentic coding tool by Anthropic for the terminal

    4 users
  2. #2

    AI assistant by OpenAI for coding, writing, and analysis

    4 users
  3. #3

    AI-first code editor built for pair programming with AI

    3 users
  4. #4

    AI assistant by Anthropic focused on helpfulness and safety

    3 users
  5. #5

    AI pair programmer that suggests code completions

    2 users
  6. #6

    API access to GPT models for building AI applications

    2 users
  7. #7

    Modern terminal with AI-powered command suggestions

    1 user
  8. #8

    Google's multimodal AI model for code and reasoning

    1 user
  9. #9

    AI code assistant with privacy-first approach

    1 user
  10. #10

    AI coding assistant with full codebase context

    1 user
  11. #11

    AI-powered UI generation tool by Vercel

    1 user
  12. #12

    API access to Claude models for building AI applications

    1 user
  13. #13

    Run open-source LLMs locally on your machine

    1 user
  14. #14

    Framework for developing applications powered by language models.

    1 user
  15. #15

    AI-powered code editor with deep codebase understanding

    0 users
  16. #16

    Free AI-powered code completion and chat

    0 users
  17. #17

    AI coding companion from AWS with security scanning

    0 users
  18. #18

    AI-powered search engine for developers

    0 users
  19. #19

    AI-powered full-stack web app builder

    0 users
  20. #20

    AI pair programming in your terminal

    0 users
  21. #21

    Open-source AI code assistant for VS Code and JetBrains

    0 users
  22. #22

    Autonomous AI software engineer by Cognition

    0 users
  23. #23

    AI agent that builds software from natural language

    0 users
  24. #24

    Desktop app to run local LLMs with a chat interface

    0 users
  25. #25

    Google's Gemini coding agent in the terminal

    0 users
  26. #26

    AI pair programming assistant

    0 users
  27. #27

    Python SDK to call 100+ LLMs and manage prompt routing across OpenAI, Anthropic, Ollama and other providers.

    0 users
  28. #28

    Web UI for running Stable Diffusion locally with a rich interface for image generation.

    0 users
  29. #29

    Powerful, modular GUI and backend for diffusion models with a node-based interface.

    0 users
  30. #30

    Curated set of 23 opinionated AI tools configured as an integrated development setup.

    0 users
  31. #31

    Open-source runtime layer that transforms LLMs into working autonomous agents.

    0 users
  32. #32

    Fair-code workflow automation platform with native AI capabilities and 400+ integrations.

    0 users
  33. #33

    Context API to search, scrape, and interact with the web at scale.

    0 users
  34. #34

    Build agentic workflows and RAG pipelines with AI model and tool support on one collaborative workspace.

    0 users
  35. #35

    User-friendly web interface for running and interacting with AI models including Ollama and OpenAI.

    0 users
  36. #36

    Open framework for building AI agents with multi-model support and extensible architecture.

    0 users
  37. #37

    Agent orchestration framework allowing single-agent interaction with multi-agent deployment capabilities.

    0 users
  38. #38

    Open framework for building AI agents with collaborative and multi-agent capabilities.

    0 users
  39. #39

    Open-source AI workbench designed for scientific research and experimentation.

    0 users
  40. #40

    Self-hosted AI workspace for building and running AI applications locally.

    0 users
  41. #41

    Fullscreen, mouse-interactive TUI harness for SpaceX's Grok coding agent.

    0 users
  42. #42

    Personal AI assistant for any OS and platform with extensible architecture.

    0 users
  43. #43

    Universal provider proxy for using any LLM as a backend for Codex CLI and Claude Code.

    0 users
  44. #44

    Open-source AI agent framework for orchestrating and coordinating multiple coding agents.

    0 users
  45. #45

    Open-source platform providing tools and agents for accessible AI development.

    0 users
  46. #46

    Visual platform for building and deploying AI-powered agents and workflows.

    0 users
  47. #47

    Open-source auth gateway connecting 1000+ SaaS providers to AI agents.

    0 users
  48. #48

    Long-horizon agent control plane for durable, governed work across coding harnesses.

    0 users
  49. #49

    Augmentation tool for coding agents with contextual codebase understanding.

    0 users
  50. #50

    Mastra is a framework for building AI-powered applications and agents with TypeScript.

    0 users

Rising this month

  • Cursor 3 added in the last 30 days, 100% of its 3 users
  • Claude Code 4 added in the last 30 days, 100% of its 4 users
  • ChatGPT 3 added in the last 30 days, 75% of its 4 users
  • Claude 2 added in the last 30 days, 67% of its 3 users

Often paired with

Moves in AI Tools

I used to just let conversations run until Claude started contradicting itself three turns back, which is a great way to debug your own debugging. Then I'd paste the entire mess into a new session and hope it read faster than I type. Spoiler: it didn't, and I'd lose the thread anyway while Claude re-indexed my life story. So now I cap sessions at around 65 percent of the window and write a summary before context gets stale. The summary has to be ruthlessly concrete: what we built, what broke, what the actual code looks like now, what we're trying next. I paste that summary as the first message in the next session instead of pasting twelve turns of my increasingly delirious questions. With Claude Code or Cursor, I just write "---SUMMARY---" in my notes at the midpoint, then force myself to actually write it out. The session ends, I start fresh, paste the summary, and we pick up without me re-explaining why I thought TypeScript generics were optional. The next session stays sharp because the context is distilled, not diluted. The catch is this only works if your summary is actually useful. If you write "continued on the file" and nothing else, you're just making the next session mad at you. Also, some problems genuinely need the full conversation history, especially when you're debugging why someone else's legacy code exists. Summaries help, but they're not magic for gnarly interdependencies.

94

by StacknessAI Toolsbackdated

Describing what you want, accepting what the model writes and iterating on the result without reading much of it. Andrej Karpathy named it in February 2025.

74

by StacknessAI Toolsbackdated

Handing a model a goal, a repository and a set of tools, then reviewing the diff it comes back with rather than the keystrokes that produced it.

63

by StacknessAI Toolsbackdated

Inline completions and chat from a model that has read the file you are in. GitHub Copilot's 2021 preview is where it stopped being a demo and became a daily habit.

63

I used to open Claude Code or Cursor and start typing implementation details immediately. I would write a function signature, change my mind halfway through, delete three files worth of context, and end up in a state where I could not explain what I was trying to build anymore. The session became a series of local corrections that never addressed the original problem. Now I stop before the first keystroke. I write a five line plan in plain text: what files I will touch, what the data flow looks like, where the types will go, and what I expect to break. I paste this into the chat and wait for feedback. This takes maybe ninety seconds to write and thirty seconds for Claude to read. No editing allowed on this turn. The concrete practice is I set a rule: if a message I send contains code, it also contains a plan written first. I use a simple format with one line per concern: purpose, input shape, output shape, where it integrates, known risk. I have caught wrong module boundaries, missing error cases, and approaches that would have required refactoring the type signatures before I changed a single line of actual code. The caveat is that this approach breaks down when you are truly exploring unknown territory. If you do not know what the problem is yet, a written plan is premature. In those cases I still start with the plan, but I make it a hypothesis instead and mark it as provisional. The discipline of writing it down helps anyway, even if everything changes.

30

Running browser tests from the session you are working in means the test run and your own navigation fight over one browser, and you get failures that have nothing to do with the code. Define a dedicated subagent with its own isolated browser server in its frontmatter, and explicitly disallow the parent session's browser tools on it. The subagent then physically cannot reach into your browser, so a full e2e sweep can run while you keep working. Worth doing even solo - the isolation is what makes the runs reproducible.

00

Adoption history

Claude Code

History
Collected from public datasets outside Stackness, not from stacks on this site. Each series is scaled to its own peak because the units do not compare. Data sources
Mentions, monthly

Hacker News mentions mentions: 1 mentions (2024) to 2,293 mentions (2026)

202420252026

Imported from Hacker News mentions

Downloads, monthly

npm downloads downloads: 147,648 downloads (2025) to 46,178,680 downloads (2026)

20252026

Imported from npm downloads

Questions asked, monthly

Stack Exchange data explorer questions asked: 1 questions (2025) to 4 questions (2026)

20252026

Imported from Stack Exchange data explorer

Pageviews, monthly

Wikipedia pageviews pageviews: 3 views (2024) to 148,027 views (2026)

202420252026

Imported from Wikipedia pageviews

ChatGPT

History
Collected from public datasets outside Stackness, not from stacks on this site. Each series is scaled to its own peak because the units do not compare. Data sources
Mentions, monthly

Hacker News mentions mentions: 12 mentions (2022) to 1,734 mentions (2026)

20222023202420252026

Imported from Hacker News mentions

Usage share, yearly

Stack Overflow developer survey usage share: 93.14 % of respondents (2023) to 85.31 % of respondents (2024)

20232024

Imported from Stack Overflow developer survey

Pageviews, monthly

Wikipedia pageviews pageviews: 1,882,964 views (2022) to 2,840,241 views (2026)

20222023202420252026

Imported from Wikipedia pageviews

Cursor

History
Collected from public datasets outside Stackness, not from stacks on this site. Each series is scaled to its own peak because the units do not compare. Data sources
Mentions, monthly

Hacker News mentions mentions: 23 mentions (2023) to 25 mentions (2026)

2023202420252026

Imported from Hacker News mentions

Questions asked, monthly

Stack Exchange data explorer questions asked: 3 questions (2009) to 1 questions (2024)

20092012201620192024

Imported from Stack Exchange data explorer