What just came out in AI and what we already use every day, explained by what it does for your organization.
Last 30 days · 21 tools
Just out of the workshop
The newest AI tools worth a look, with price, availability and the source we checked.
Featured · Krea 2 · May 2026
Krea
Krea AI · Voice, video & creativity
A creative platform bringing together 64+ image, video and 3D models, including Veo, Sora, Kling and Runway, plus its own model, Krea 2: trained from scratch to control style and escape AI’s “generic look.”
Visual assets for campaigns, presentations and learning content with your own brand style.
Krea 2, its image model, came within 0.14 points of GPT Image 2 on the Contra Labs human-preference leaderboard.
Variants and style references to keep a consistent visual identity.
Video, 3D, lip-sync and upscaling on the same platform.
Training your own models (LoRA) on your brand’s images.
More than 750,000 weekly users; clients such as Lego, Samsung, Nike, Microsoft and Shopify.
Keep in mind: It is a creative tool, not a data-analysis tool. The third-party model lineup changes: Veo 2 and Veo 3 left the platform in August 2026. Check the commercial-use terms before publishing campaign assets.
Presentations inside ChatGPT: create them from a conversation, files or an outline, edit them by hand or by asking ChatGPT for changes, and work on them as a team in real time with shared comments.
Go from an analysis or report to a deck ready to export to PowerPoint or Google Slides.
Creates decks from a conversation, files or an outline.
Direct editing of text, images, shapes and charts, or by instruction.
Real-time team editing with shared comments.
Exports to PowerPoint, PDF and Google Slides.
Keep in mind: Only on paid ChatGPT plans and rolling out gradually: it may not show up in your account yet. Check generated figures and charts before presenting.
Pro, Business and Enterprise plans; gradual rollout
A video model that generates 30 seconds in a single pass from up to 15 references and 10 keyframes. Agents can use it through Kling MCP and a command-line interface.
Short campaign videos and explainers with control over the scene.
30 seconds of video in a single pass.
Up to 15 references (characters, objects, styles) and 10 keyframes.
Agent access via Kling MCP and the command line.
Global launch.
Keep in mind: AI-generated video should be labeled as such in public materials. Check commercial-use rights and never use real people’s likeness without their permission.
An experimental Bluesky app that builds websites, games, quizzes and polls from a text prompt, without coding. What you make runs on the open AT Protocol and can be shared with the Bluesky community.
Quick quizzes and polls for a class, a course or a community.
Websites, games, quizzes and polls from text.
Simple creations in a few minutes.
Runs on the open AT Protocol, with your Bluesky identity.
You can bring your own models or use open models.
Keep in mind: It is experimental and its terms may change. What you build is tied to your Bluesky account; do not use it to collect students’ personal data.
The most capable US open-weight model, according to Meta: 816 billion parameters, a one-million-token context and multimodal input. With Apple, it is optimized to run locally on two to four Mac Studios with the M5 Ultra chip.
Coding and agent work without sending data to the cloud.
816 billion parameters and a one-million-token context.
Natively multimodal, built for complex coding and agent work.
Runs locally on two to four connected Mac Studios.
Open weights: it can be downloaded and self-hosted.
Keep in mind: It needs at least 1 TB of memory spread across several Mac Studios: a considerable hardware investment. Performance figures come from Meta; wait for independent tests.
Free open weights; the hardware to run it is expensive
An agent system that reviews papers, theses, proposals and technical, legal or financial documents. It splits the document into sections, reviews each in parallel and returns comments on math errors, logical gaps and unsupported claims.
A second read of a thesis, report or proposal before submitting it.
Reviews papers, theses, proposals and legal or financial documents.
One agent per section, with access to the whole document to catch contradictions.
Comments anchored to the text in 10 to 20 minutes.
Documents are not used to train models.
Keep in mind: In the pilot it flagged some correct proofs as errors: use it as a list of points to check, not a seal of quality. It keeps documents for a year; check your university’s rules before uploading unpublished research.
Free, 2 reviews a week; more with a Gemini API key
Google’s most capable model is now open to everyone: in the Gemini app and AI Mode in Search for Pro and Ultra subscribers, in AI Studio and in the API. It handles up to one million tokens of input and output.
Financial, legal and code analysis, and very long documents.
Up to one million tokens of input and output (output used to cap at 64,000).
Available in the Gemini app, AI Studio, the API, Antigravity and Gemini Enterprise.
Claimed first place on the Vals AI GDP index (68.9%) and Finance Agent v2 (65.4%).
Cached input at $0.10 per million tokens.
Keep in mind: The promotional price ends November 30. Independent tests show a more mixed picture: on the Artificial Analysis index it ties and ranks below Claude Opus and Sonnet. Test it on your own cases.
API: $2 / $10 per million tokens until November 30; then $4 / $20
Meta’s new frontier models for agents, with “active memory” (they remember what matters from past conversations) and “active focus” (they try other approaches and recover from errors).
Coding agents and expert domains such as law, finance and health.
Active memory: carries important information from one conversation to the next.
Active focus: tries other approaches, recovers from errors and asks for clarification.
Frontier-level coding in expert domains.
It powers the Muse agent.
Keep in mind: Memory across conversations is useful, but it also keeps more information about you: check what it remembers and how to delete it before using it with organizational data.
Muse Wonder on the Meta Model API and Muse Code; Wonder Full for Muse Code subscribers
A “universal agent for work”: you give it objectives, not instructions. It plans the task, uses skills and tools, connects to company systems and delivers the result inside documents and email.
Organizations that want a single agent connected to Workspace, Microsoft 365, Salesforce or Snowflake.
Takes objectives and plans the steps on its own.
Connects to Workspace, Microsoft 365, Teams, Slack, Confluence, ServiceNow, Snowflake and Salesforce.
Routes each task to the most suitable model, with cost controls.
Comes with a skills library and lets you build new ones, under company policies.
Keep in mind: It is in private beta and aimed at Google Cloud customers. An agent connected to every system requires defining permissions and approval rules first.
Gemini Enterprise · private beta; general availability late October or early November
Ask a question in plain language and Claude builds a live dashboard connected to BigQuery, Databricks, Snowflake, Redshift or ClickHouse, and keeps it current.
Explore data without waiting for BI to build the report.
Connects to Redshift, BigQuery, ClickHouse, Databricks, Snowflake and Salesforce.
Each chart shows when it was last refreshed.
You can ask it to explain the SQL behind every figure.
Dashboards can move to BI tools; Looker, monday.com and Tableau are coming.
Keep in mind: It is in beta. Great for exploration; a management dashboard still needs well-defined indicators and validated data.
A GPT-6.1 Sol mode up to 8x faster, with near-Astra intelligence. Available in the API, Codex, ChatGPT Work and Amazon Bedrock, with US and EU data residency.
Service assistants and agents where waiting drives users away.
Up to 8x faster than Sol in standard mode.
Enabled with service_tier="ultrafast" in the Responses API.
Available in the API, Codex, ChatGPT Work and Amazon Bedrock.
US and EU data residency.
Keep in mind: It costs 1.2x Astra and about six times Sol’s standard price: worth it only where latency truly matters.
Turns reports, charts or walkthroughs into short animations exported to MP4. It does not generate AI video: it writes editable code for text, shapes, charts and timing.
Present results to leadership in a one-minute video.
Turns reports, charts or walkthroughs into short animations.
Everything is editable: any word, number or timing.
Downloads as MP4.
Canva and Captions integrations announced.
Keep in mind: Team and Enterprise plans only, in beta. It does not generate people or realistic video: it is motion graphics.
A Mac app that transcribes meetings and turns your quick jottings into full notes, entirely offline: the audio never leaves the device. Runs on Gemma 4 and EmbeddingGemma 2.
Minutes of confidential meetings without uploading anything to the cloud.
Captures microphone and system audio: works with any meeting platform.
Expands your quick jottings into full notes using the transcript.
Spots questions in the meeting and looks for answers in your documents.
Works offline with Gemma 4 and EmbeddingGemma 2.
Keep in mind: Experimental and Mac only. Google has not published transcription accuracy or performance across languages and accents.
With GPT-6 on every plan, ChatGPT answers with charts, forms, buttons or small tools you use right inside the answer.
Prototype a calculator or a report for your team in minutes.
Answers with explorable charts, forms, buttons and small tools.
Paid plans on GPT-6 Sol; Free and Go on GPT-6 Luna.
On web searches it starts answering 44% sooner than GPT-5.6.
Examples: calculators, bill splitters, timelines and games.
Keep in mind: OpenAI acknowledges it is still improving the model’s design judgment. What you build in chat is not connected to your company’s systems.
An endpoint that does not write text: it takes a case (text or image) and questions, and returns typed decisions about 10x faster than the regular API.
Classify complaints, route tickets or pick an agent’s next step.
Its own endpoint: POST /v1/decisions, on the GPT-6 Luna model.
Three question types: predicate (yes probability), choice and score.
Accepts text and base64 images.
No charge for output tokens or caching.
Keep in mind: Public beta. Inputs over 272,000 tokens cost double, and regional processing in the US or Europe carries a 10% surcharge.
Mistral’s largest model: over a trillion parameters, a one-million-token context and image input. Trained in Europe; open weights arrive at the end of October.
Organizations that need to host the model on their own infrastructure.
Mixture of experts with 1.05 trillion parameters and about 49 billion active.
One-million-token context and native image input.
Strong at code, agents, cybersecurity, finance and law.
Trained on 3,800 NVIDIA GPUs in its own EU data centers.
Keep in mind: Still in preview; open weights are expected between October 27 and 31. Self-hosting demands high-end hardware.
Free, open-source projects you can install and adapt, chosen for what they do for an organization. Stars, license and activity checked on October 18, 2026; we leave out archived or abandoned projects.
REA · Reverse Engineer Anything
morluto/rea
323k
TrendingFor developers MITActive this week
An MCP server that gives a coding agent (Claude Code, Codex, Cursor, Gemini CLI) reverse-engineering tools: it analyzes desktop, Electron, Android apps and websites without their source code, backing every claim with evidence.
Audit the security of your own software or understand undocumented legacy systems in your organization.
The viral repo of the moment: it went from under 10,000 to about 300,000 stars in two weeks. Use it only on your own software or with permission: reverse-engineering third-party programs can breach license terms, trade secrets and copyright.
Detailed step-by-step, 1-click copyable prompts and estimated time savings to transform your daily flow with NotebookLM, Claude Code, Cursor, and ChatGPT.
Gemini Notebook (Google)
🎓 Study with your own sourcesTeachers, Training Teams, Students
Free with a Google account
⚡ Save hours preparing study and training materials
Gemini’s study tools (September 2026): from your documents it creates flashcards and video summaries, plus answers cited only from your sources.
Why is it indispensable in practice?:
A Gemini notebook that works with the material you upload. Use it to turn a manual, a regulation, or a course into review flashcards and short videos for your team.
Detailed Step-by-Step Guide:
1. Open Gemini with your Google account and create a new notebook.
2. Add your sources: PDFs, Google Docs, slide decks, or links.
3. Ask for an outline of the content and check that each source is cited correctly.
4. Generate flashcards and a video summary for review.
5. Share the notebook with your team or export the flashcards to your learning platform.
Master Prompt Ready to Paste:
Using only the uploaded sources:
1. Build an outline of the topics, ordered by difficulty.
2. Create 15 flashcards (question / answer) citing the source of each one.
3. Point out the 3 concepts people confuse most and explain each with an example.
Suggested Stack: Google Drive (sources) ➔ Gemini Notebook (study) ➔ Google Classroom / LMS
🎙️ AI voiceTraining, Marketing, and Customer Service Teams
From $0.04 per thousand characters
⚡ Narrate a one-hour course in minutes, no recording studio
Eleven v4 (September 28, 2026) turns text into very natural speech, Spanish included. v4 Turbo is the fast version for real-time voice agents.
Why is it indispensable in practice?:
A synthetic voice platform to narrate courses, videos, and reports, or to build agents that answer calls. It offers library voices and voice cloning with consent.
Detailed Step-by-Step Guide:
1. Create an account at elevenlabs.io and pick a library voice.
2. Paste the script and tune stability and style until it sounds natural.
3. Download the audio as MP3 or WAV and add it to your video or course.
4. For voice agents, use v4 Turbo and connect it by API to your n8n flow.
5. If you clone a voice, keep the person’s written authorization.
Master Prompt Ready to Paste:
Rewrite this text to be read aloud by a synthetic voice:
1. Short sentences, 20 words at most.
2. Numbers written out as spoken (e.g., "twenty twenty-six").
3. Pauses marked with periods and commas where a narrator would breathe.
4. A warm, professional tone.
🎓 Open MoE modelResearchers, Data Scientists, Analysts
API & open weights
⚡ Save 8 hours analyzing long reports and books
Moonshot AI’s open mixture-of-experts model, with long context and solid reasoning. Other models build on it, such as Fireworks AI’s Ember-1 (September 2026).
Why is it indispensable in practice?:
One of the most powerful language tools in the world for processing entire libraries of PDFs, ledgers, and legal reports without truncating information.
Detailed Step-by-Step Guide:
1. Sign in to the Kimi AI platform or set up its API in your Python environment.
2. Upload several large PDF files (whole books or case files).
3. Ask for cross-chapter comparisons or legal consistency checks.
4. Export the summaries, with exact citations, to Markdown or PDF.
Master Prompt Ready to Paste:
Analyze the attached documents and produce:
1. A comparative executive summary of each source's findings.
2. Any discrepancies or methodological gaps.
3. A summary table in Markdown with the key indicators and their sources.
⚡ Local inference engineAI Engineers, DevOps, Software Architects
100% open source (Apache 2.0)
⚡ Up to 5x faster inference on your own server
The industry’s fastest inference engine and API server for running open-weight models (DeepSeek, Qwen 3.8, Mistral, Kimi K3) on your own GPUs.
Why is it indispensable in practice?:
A library that uses PagedAttention to manage GPU memory optimally, so it can serve hundreds of users in parallel without slowing down.
Detailed Step-by-Step Guide:
1. Install vLLM with: pip install vllm on a machine with an NVIDIA GPU.
2. Start an OpenAI-compatible server: python -m vllm.entrypoints.openai.api_server --model deepseek-ai/DeepSeek-R3-Distill-Qwen-32B.
3. Point your frontend apps or n8n to the local URL http://localhost:8000/v1.
4. Monitor tokens per second from the admin console.
Master Prompt Ready to Paste:
Use the local vLLM API to process this batch of data in parallel:
1. Classify user intent into: [Support, Sales, Complaint].
2. Extract the key entities (Name, Email, Product).
3. Return only valid JSON.
⚡ Save 6 hours of reading and research per long document
Your study assistant powered by Gemini. Upload PDFs, books, or slide decks and get answers cited only from your sources, audio overviews and, since September 2026, flashcards and video summaries in Gemini Notebook.
Why is it indispensable in practice?:
NotebookLM is the best tool there is for working with your own documents. Unlike ChatGPT, it reasons ONLY over the material you upload and adds footnotes citing the exact page of your PDF.
Detailed Step-by-Step Guide:
1. Go to notebooklm.google.com with your Google account and create a “New notebook”.
2. Click “Add sources” and drop in your PDFs, class slides, Google Docs, or web links (up to 50 sources per notebook).
3. Use the query bar to ask for outlines, key-concept lists, or multiple-choice practice questions about your readings.
4. Click “Audio Overview” to generate a 5 to 10-minute podcast-style conversation between two AI hosts discussing your material.
5. Save your synthesized notes to the notebook board and export them to Google Docs.
Master Prompt Ready to Paste:
Act as a university professor who is an expert in the uploaded documents.
Analyze every source and produce:
1. A 3-paragraph executive summary of the main arguments.
2. The 5 most important key concepts, defined as the text defines them.
3. A 5-question self-assessment quiz with answer keys citing the exact source.
Suggested Stack: Perplexity AI (to find the PDFs) ➔ NotebookLM (to study) ➔ Gamma App (to present the findings)
⚡ Save 10+ hours a week on refactoring and testing
Anthropic’s official agentic terminal tool. It reads whole repositories, finds functions, edits files in parallel, runs unit tests, and creates commits.
Why is it indispensable in practice?:
An agent that runs Claude Sonnet 5.5 and Opus 5.5 in your terminal, VS Code, or the desktop app. Since October 2026 it supports “mods”: small TypeScript scripts that rewrite prompts, block or retry tool calls, decide permissions, and redact secrets. Ask it to “Refactor the React components and fix the TypeScript errors” and the agent navigates the code and applies the changes.
Detailed Step-by-Step Guide:
1. Install the Claude Code CLI globally: npm install -g @anthropic-ai/claude-code
2. Open your terminal at the project root and start a session by typing: claude
3. Sign in with your Anthropic account.
4. Describe tasks in plain language: “Look through src/components, find functions without unit tests, and create the missing .test.tsx files”.
5. Review the proposed changes, let the agent run “npm test”, and ask it to create a git commit following your conventions.
Master Prompt Ready to Paste:
Review the whole project and look for:
1. Syntax inconsistencies or non-strict TypeScript types.
2. Files that lack Jest/Vitest unit tests.
3. Create the missing test files and run the test suite until everything passes.
Suggested Stack: Claude Code (in the terminal) ➔ VS Code / Cursor ➔ GitHub Actions CI/CD
🎓 Search & researchResearchers, Students, Business Consultants
Freemium
⚡ Save 4 hours of manual Google searching
The conversational research engine replacing traditional search. It synthesizes answers from many pages in real time with verifiable citations.
Why is it indispensable in practice?:
An AI-powered answer engine that scans the live web, filters low-quality sources, and delivers structured reports with footnotes linking to the original pages.
Detailed Step-by-Step Guide:
1. Go to perplexity.ai and click into the search box.
2. Turn on “Focus” and choose “Academic” for scientific papers or “Writing” to draft text.
3. Ask your research question using concrete terms from your field.
4. Use “Pro Search” for complex questions so the AI asks clarifying questions before searching.
5. Check the citations in the right sidebar and export the full report to Markdown or PDF.
Master Prompt Ready to Paste:
Write a research report on [TOPIC]:
1. Summarize the current state of the art (2025-2026) with key figures.
2. Identify the 3 most important trends and their main drivers.
3. List 5 academic or institutional sources, cited explicitly with their URLs.
Suggested Stack: Perplexity AI ➔ NotebookLM ➔ Notion / Obsidian
The next-generation code editor built on VS Code with native AI. Designed to edit massive repositories with Claude and GPT models.
Why is it indispensable in practice?:
A development environment that understands your entire codebase. Ask it to build a complete feature that touches 10 files at once, in one click.
Detailed Step-by-Step Guide:
1. Download Cursor from cursor.com and import your VS Code extensions and settings in one click.
2. Open your project folder and press “Cmd + K” in any file to edit or generate code inline.
3. Use “Cmd + I” (Composer) for multi-file instructions: “Create the profile view and connect the backend API”.
4. Type “@Files” or “@Docs” in the chat to bring official library docs into the AI’s answers.
5. Accept the code diffs after reviewing added lines in green and removed lines in red.
Master Prompt Ready to Paste:
@Codebase Review the current component architecture.
Refactor [COMPONENT_NAME] to:
1. Add a responsive Tailwind CSS layout with dark mode support.
2. Handle loading states (spinners) and network errors.
3. Remove any unused mutable state.
Suggested Stack: v0.dev ➔ Cursor IDE ➔ Vercel Deployment
📊 Data analytics & PythonData Analysts, Finance Teams, Researchers
Paid / Plus
⚡ Save 5 hours of manual Excel work
ChatGPT’s Python environment, now with GPT-6 and Intelligent UI: upload Excel, CSV, or JSON, clean and analyze the data, and get interactive charts and tables you can filter right in the answer.
Why is it indispensable in practice?:
Your personal data scientist. It runs Python in a secure sandbox to compute statistics, detect outliers, and produce report-ready charts.
Detailed Step-by-Step Guide:
1. Open ChatGPT (now on GPT-6 for every plan) and click “+” to attach your data files (CSV, XLSX, JSON).
2. Ask for a first look: “Show the first 5 rows and data types, and check for nulls or duplicates”.
3. Request transformations: “Clean the date column, group by category, and compute average monthly sales”.
4. Create visuals: “Build a bar chart with a professional palette and save it as a high-resolution PNG”.
5. Download the transformed Excel file and ask for the Pandas code so you can reproduce it locally.
Master Prompt Ready to Paste:
Act as a Senior Data Analyst. Review the attached file and:
1. Clean the data (remove duplicates and handle nulls).
2. Build a summary table with mean, median, standard deviation, and min/max.
3. Create a scatter plot showing the correlation between [VARIABLE_A] and [VARIABLE_B].
4. Give me a clean Python Pandas script to run in Jupyter Notebook.
Suggested Stack: ChatGPT Data Analysis ➔ Power BI / Tableau ➔ Python Scripts
🎨 Presentations & documentsExecutives, Teachers, Consultants, Students
Freemium
⚡ Save 3 hours of PowerPoint design per deck
A new way to create slides and web documents. Enter a topic or paste a draft and Gamma builds a professionally designed deck in 2 minutes.
Why is it indispensable in practice?:
A smart visual content builder that removes manual formatting of cards, images, and diagrams, and lets you publish interactive web presentations.
Detailed Step-by-Step Guide:
1. Go to gamma.app and click “Generate with AI”.
2. Choose a format: Presentation, Document, or Web page.
3. Paste your outline or describe your topic in plain language.
4. Pick a design theme that matches your brand colors.
5. Edit the cards live, swap images with the built-in AI generator, and share the public link or export to PDF/PPTX.
Master Prompt Ready to Paste:
Create an 8-slide executive presentation about [PROJECT_TOPIC]:
- Slide 1: Cover with a strong title and subtitle.
- Slide 2: The current problem and the user's pain.
- Slide 3: The data-driven solution.
- Slide 4: A 3-phase implementation plan.
- Slide 5: ROI and cost analysis.
- Slide 6: Conclusions and next steps.
Use a professional, executive tone and cards with short bullet points.
⚡ Save 8 hours of graphic design per visual campaign
The most consistent photorealistic and artistic image generator on the market. It produces illustrations, product mockups, and brand assets from a text description or a reference image.
Why is it indispensable in practice?:
Midjourney turns detailed prompts and visual references into ad-quality images in seconds, with fine control over style, lighting, composition, and character consistency across generations.
Detailed Step-by-Step Guide:
1. Join the Midjourney Discord server or use alt.midjourney.com with an active account.
2. Type /imagine followed by a detailed description: subject, art style, lighting, and composition.
3. Use parameters such as --ar 16:9 for aspect ratio, --style raw for less automatic styling, and --cref to keep a character consistent across images.
4. Pick the variation (U1-U4) closest to your vision and use “Vary (Subtle)” to refine it without losing the composition.
5. Export in high resolution with /upscale and use the built-in editing tools (Pan, Zoom Out, Inpaint) to remove backgrounds or extend the canvas.
Master Prompt Ready to Paste:
[detailed subject], [setting/background], editorial product photography style, soft studio lighting, brand color palette [navy #00094D and yellow #FFC000], high definition, centered composition, background with depth of field --ar 16:9 --style raw --v 7
Suggested Stack: Midjourney (generation) ➔ Canva Magic Studio (retouching and layout) ➔ Gamma App (final presentation)
🎬 Generative videoContent Creators, Marketing, Video Teams
Freemium
⚡ Save 12 hours of video production and editing per short piece
Generate and edit realistic video clips from text or a starting image, with camera-movement control and consistent characters across shots — no camera, actors, or set needed.
Why is it indispensable in practice?:
Runway turns a scene description or reference image into a clip several seconds long, letting you direct camera movement, length, and visual style like a cinematographer with a virtual crew.
Detailed Step-by-Step Guide:
1. Go to runwayml.com and create a new project in the “Gen-4 Video” module.
2. Upload a starting image (made in Midjourney or a real photo) or write a scene description.
3. Set the camera controls: pan, zoom, orbit, and clip length (up to 10 seconds per generation).
4. Use “Character consistency” to keep the same face or product recognizable across several shots.
5. Export the clip in HD and edit it in Runway’s timeline to join several shots into a final piece.
Master Prompt Ready to Paste:
Cinematic shot of [subject/product] in [setting], smooth dolly-in camera movement, warm natural lighting, strong depth of field, high-end documentary style, 24fps, 6 seconds long
🤖 Visual automationData Analysts, Automation, Operations Teams
Open source / Freemium
⚡ Save 15 hours a week of repetitive work between systems
The visual, node-based workflow automation platform that connects AI models (Claude, GPT, Gemini, DeepSeek, Ollama) with your business tools: CRMs, spreadsheets, databases, and APIs.
Why is it indispensable in practice?:
n8n lets you build flows where a trigger (an email, a form, a webhook) starts a chain of automatic steps, including an AI node that reasons over the data — without writing a backend from scratch.
Detailed Step-by-Step Guide:
1. Install n8n with Docker (docker run -it --rm -p 5678:5678 n8nio/n8n) or use the free cloud version at n8n.io.
2. Create a workflow and add a trigger node: Webhook, Cron (scheduled), or a Gmail/Slack event.
3. Add an AI node (Anthropic, OpenAI, or a generic HTTP Request pointing to DeepSeek/Ollama) to process or classify incoming data.
4. Connect action nodes: write to Google Sheets, add an Airtable row, post to Slack, or update a CRM.
5. Turn the workflow on (“Active”, top corner) and check the execution history to debug errors.
Master Prompt Ready to Paste:
Act as an automation engineer. Design an n8n flow that:
1. Receives an incoming email via a Gmail trigger.
2. Uses an AI node to classify the email (inquiry, complaint, sale) and extract the key data as JSON.
3. Routes it automatically to the right spreadsheet or Slack channel by category.
4. Sends the sender a personalized automatic reply.
🤖 No-code agentsOperations Teams, Sales, Small Businesses
Freemium
⚡ Save 6 hours a week setting up app integrations
Build AI agents that watch your apps (email, calendar, CRM, spreadsheets) and act proactively — no coding and no node-by-node flow design.
Why is it indispensable in practice?:
Unlike rule-based automations, a Zapier Agent follows plain-language instructions (“alert me and book a call when a qualified lead arrives”) and decides which steps to run across 7,000+ connected integrations.
Detailed Step-by-Step Guide:
1. Go to zapier.com/agents and choose “Create a new Agent”.
2. Describe the agent’s goal in plain language: what it should watch and what it should do.
3. Connect the relevant apps (Gmail, HubSpot, Google Calendar, Slack) and authorize access with OAuth.
4. Set its autonomy limits: which actions it can take on its own and which need your approval.
5. Turn the agent on and review its activity log weekly to tune its instructions based on real results.
Master Prompt Ready to Paste:
Set up an Agent that:
1. Watches the sales inbox for emails showing purchase intent.
2. Classifies the sender as a qualified lead or not, using the budget and urgency mentioned in the email.
3. If qualified, creates a CRM record and books a discovery call in an open calendar slot.
4. Sends a daily summary at 6pm with every lead processed.
💻 Agentic environment from issue to PRDevelopers, Tech Leads, Product Teams
Paid / Included in Copilot
⚡ Save 5 hours per feature, from issue to pull request
Turn a GitHub issue straight into an implementation plan, then into working code with a pull request ready for review — all in the browser.
Why is it indispensable in practice?:
Copilot Workspace reads an issue, proposes a file-by-file plan you can edit before running it, and generates the code and the pull request — closing the gap between “reporting a bug” and “having a tested fix”.
Detailed Step-by-Step Guide:
1. Open any issue in a GitHub repository with Copilot Workspace enabled and click “Open in Workspace”.
2. Review the generated implementation plan: which files will change and why.
3. Edit the plan in plain language if anything doesn’t match your technical judgment.
4. Click “Implement” so the agent writes the code, and review the diff file by file in the built-in editor.
5. Run the tests in the embedded environment and open the pull request from Workspace once everything passes.
Master Prompt Ready to Paste:
Analyze issue #[NUMBER] in this repository and:
1. Propose a detailed, file-by-file implementation plan.
2. Identify possible side effects on other modules.
3. Write unit tests for the proposed change before opening the pull request.
📊 Conversational data scientistData Analysts, Product Managers, Researchers
Freemium
⚡ Save 4 hours of exploratory analysis per dataset
Upload a dataset and talk to it in plain language: Julius writes and runs the analysis code for you, builds charts, and explains its findings simply — no Python required.
Why is it indispensable in practice?:
Julius AI works like a tireless junior data analyst: it interprets your business question, picks the right transformation or statistical model, runs the code in a real environment, and returns both the chart and what it means.
Detailed Step-by-Step Guide:
1. Go to julius.ai and sign up for free with your Google account.
2. Upload your file (CSV, Excel, a connected Google Sheet) or paste a database link.
3. Ask in plain language: “What are the 3 factors most correlated with customer churn?”.
4. Review the Python code Julius wrote and ran, along with the resulting chart.
5. Ask for more iterations (“now split it by region”) and export the final report as PDF or a reproducible Python script.
Master Prompt Ready to Paste:
Act as a senior data analyst on the uploaded dataset:
1. Run an initial exploratory analysis (nulls, types, distribution of key variables).
2. Identify the 3 strongest correlations with the target variable [TARGET_VARIABLE].
3. Create a clear chart for each finding with a plain-language interpretation.
4. Give me the reproducible Python script at the end.
Suggested Stack: Google Sheets / CSV (source) ➔ Julius AI (conversational analysis) ➔ Power BI (executive dashboard)
