Same rule as the last nine parts: verified free tiers, not disguised trials. This is the last planned category — education, health/wellness (informational only), and a few misc picks that didn't fit anywhere else.

Education & Learning

1. Microsoft Copilot — free, genuinely solid as a study/research assistant in Edge.
2. Duolingo — free, AI-assisted language practice, no paywall on the core app.
3. Quizlet — free tier, AI-generated study sets and practice tests.
4. Khanmigo — free access varies by school/district partnership — worth checking your institution's status directly.
5. NotebookLM — worth repeating from Part 5 — genuinely one of the best free study tools if you upload your own material.

Health & Wellness (informational tools only, not medical advice)

1. Ada Health — free symptom checker, informational only, not diagnostic.
2. Woebot — free AI mental health support chatbot, not a replacement for professional care.

Misc / Didn't Fit Elsewhere

1. DeepL Translate — free, still the sharpest AI translation tool available.
2. Spline — free tier, AI-assisted 3D design in the browser.
3. Luma AI — free tier, turns photos/video into 3D captures.

Bonus: Fully Open-Source / Free Forever

1. Anki — not AI-branded, but free, open-source spaced repetition — genuinely one of the best free study tools period, AI or not.
2. LibreTranslate — free, open-source translation, self-hostable alternative to DeepL.

⚠️Honest catch: Khanmigo's free access specifically depends on your school/district having a partnership — it's not universally free for individuals, worth checking before recommending it to someone. Health tools listed are informational only — always a doctor for anything real, not a chatbot.

That's the last planned category — 10 parts, 100+ tools total, all verified free tiers or genuinely open-source. If people want more, happy to do a "hidden gems" round-up of underrated tools that didn't make any list, otherwise this wraps the series.

What's a tool you use constantly that never made it into any of these 10 parts?

Same rule as the last seven parts: verified free tiers, not disguised trials. This round: no-code & automation.

1. Zapier — free tier, AI-powered workflow automation, the standard everyone benchmarks against.
2. Make — free tier, more visual/flexible than Zapier for complex automations, decent free operation limit.
3. Bubble — free tier, AI-assisted no-code app building, genuinely capable for a real MVP.
4. Softr — free tier, builds apps/client portals on top of Airtable/Google Sheets with AI blocks.
5. n8n — free tier (self-hosted is unlimited), open workflow automation with strong AI node support.
6. Airtable — free tier, AI features for summarizing and organizing records built right in.
7. Glide — free tier, turns spreadsheets into AI-assisted apps, no code required.
8. Pipedream — free tier, developer-friendly automation with generous free execution limits.
9. IFTTT — free tier, simpler than Zapier, good for personal/small automations.
10. Relevance AI — free tier, build AI agents/workflows without code.

Bonus: Fully Open-Source / Free Forever

1. n8n (self-hosted) — worth repeating separately — fully free forever if you self-host instead of using their cloud tier.
2. Node-RED — free, open-source, flow-based automation tool, runs entirely on your own machine.

⚠️Honest catch: Zapier and Make cap free tiers at a small number of "tasks" or "operations" per month — fine for personal projects, tight if you're automating anything at real volume. n8n is the standout here since self-hosting removes the cap entirely, at the cost of needing to actually set up hosting yourself.

I've mostly used Zapier for quick personal automations — haven't gone deep on Bubble or n8n yet, so take those with slightly less firsthand weight than the rest.

Part 9 (presentations & voice/transcription tools) coming next.

What's your go-to for automating repetitive stuff?

A few weeks back I organized Microsoft's official ML training content into one structured, free roadmap — no paid course, no scattered blog posts, just the actual DP-100 (Azure Data Scientist Associate) material in the order it should be learned.

It ended up doing way better than I expected — 34K+ weekly visitors on the original post — so I figured I'd share it here too.

What it covers, in order:

1. Core ML concepts — what regression, classification, clustering actually are
2. Hands-on coding with Python + scikit-learn — regression, classification, clustering, deep learning
3. Training models at scale with Azure ML — workspaces, compute, MLflow tracking
4. MLOps — AutoML, hyperparameter tuning, pipelines, real production deployment
5. New: Responsible AI + intro to generative AI and agents
6. New: Building with Generative AI — hands-on agent development + a full 6-module path building actual GenAI apps

Most beginner resources stop right after step 2 — you train a model in a notebook and that's it. This goes all the way to deploying a model to a managed endpoint, which is the actual job.

Grown from a ~10 hour roadmap to a full ML + GenAI path (~25-26 hours), still 100% free, self-paced, official Microsoft content only.

Link: https://learn.microsoft.com/collections/86w0cztk0gjpm4?wt.mc_id=studentamb_513418

Open to feedback on what's still missing or worth reordering — that's basically how the last version got better.

Same rule as the last six parts: verified free tiers, not disguised trials. This round: data & analysis.

1. Google Colab — free, includes GPU access, the backbone of most free ML/data work.
2. Kaggle — free datasets, notebooks, and GPU/TPU hours, genuinely generous free tier.
3. Julius AI — free tier, analyze data by chatting with it, no code required.
4. Tableau Public — free, real AI-assisted visualization, one catch below.
5. Google Sheets — free, and its built-in AI (Explore/formula suggestions) is more capable than people give it credit for.
6. DataRobot (free trial tier) — limited free access, no-code automated ML.
7. Hex — free tier for collaborative notebooks with AI-assisted analysis.
8. Deepnote — free tier, real-time collaborative data notebooks with AI features.
9. Obviously AI — limited free tier, no-code predictive modeling from spreadsheets.
10. Polars — not AI itself, but free and open-source, worth knowing if you're tired of pandas choking on large datasets.

Bonus: Fully Open-Source / Free Forever

1. JupyterLab — free, open-source, the standard notebook environment, runs entirely locally.
2. Metabase — worth repeating from Part 6 — free, open-source, self-hosted BI/dashboarding.

⚠️Honest catch: Tableau Public's free tier requires all your workbooks to be publicly visible — fine for a portfolio piece, not for anything private. Julius, DataRobot, and Obviously AI cap free usage at a small number of queries/rows per month, genuinely useful for learning and small projects, not for production-scale analysis.

I lean on Colab and Kaggle the most — free GPU access alone makes them worth knowing even outside data analysis specifically.

Part 8 (no-code & automation tools) coming next.

What's your go-to for quick data work?

Same rule as the last five parts: verified free tiers, not disguised trials. This round: marketing & business.

1. HubSpot AI Tools — free tier includes AI content generation, email tools, and basic CRM features.
2. Canva — worth repeating here too — free tier's Magic Write and design AI cover a lot of marketing content needs.
3. MailChimp — free tier includes AI-assisted email subject lines and content suggestions.
4. Google Analytics — free, and its AI-driven insights are genuinely underused by most people who have it.
5. Notion AI — worth repeating — solid for drafting business docs, meeting notes, and content calendars.
6. Buffer — free tier for scheduling, includes AI caption/post suggestions.
7. Canva Magic Write — free AI copywriting built into your design workflow, useful for ad copy specifically.
8. Zoho CRM (free tier) — genuinely free for small teams, includes basic AI lead scoring.
9. Answer the Public — limited free searches, surfaces what people are actually asking about a topic — useful for content ideas.
10. Ubersuggest — limited free tier, keyword and SEO content suggestions.

Bonus: Fully Open-Source / Free Forever

1. Metabase — free, open-source business analytics/dashboarding, self-hosted.
2. Mautic — free, open-source marketing automation platform, self-hosted alternative to paid tools like HubSpot.

⚠️Honest catch, same pattern as every part so far: most of the CRM/analytics tools here (HubSpot, Zoho, Buffer) genuinely cap free tiers at a certain number of contacts, users, or scheduled posts — fine for a student project or small side hustle, not built for scaling a real business without eventually paying. Answer the Public and Ubersuggest specifically limit you to a small number of free searches per day, so budget your usage.

I mainly use Notion AI and Canva for anything business/content-adjacent — haven't gone deep on the CRM side personally, so take those recommendations with slightly less firsthand confidence than the writing/coding parts of this series.

Part 7 (data & analysis tools) coming next.

What's your go-to for marketing/business tools right now?

Same rule as the last three parts: verified free tiers, not disguised trials. This round: video.

1. CapCut — free AI editing, auto-captions, background removal, genuinely powerful free tier.
2. Runway — free tier for AI video generation/editing, good starting point for text-to-video experiments.
3. Descript — free tier, edit video by editing text — genuinely changes how you think about editing.
4. Kapwing — free tier covers auto-subtitles, resizing, basic AI editing tools.
5. Pictory — turns long-form text/blog content into short videos, limited free tier.
6. Clipchamp — free, built into Windows, decent AI-assisted editing without leaving your OS.
7. HeyGen — free tier for AI avatar/talking-head videos, useful for quick explainer content.
8. VEED.io — free tier, auto-subtitles, background removal, decent for quick social clips.
9. Fliki — free tier, turns text into narrated videos with AI voices.
10. Opus Clip — free tier, auto-extracts short clips from longer videos, good for repurposing content.

Bonus: Fully Open-Source (Free Forever, No Signup)

1. Shotcut — free, open-source video editor, no AI built in but genuinely powerful and permanently free.
2. OpenShot — free, open-source, beginner-friendly editor.
3. Stable Video Diffusion — open-weight, self-hosted AI video generation model, same "bring your own GPU" tradeoff as the image models from Part 3.

Honest catch, same as last time: most of the hosted tools above cap free usage at a certain number of minutes/exports per month, and several free tiers add a small watermark unless you go paid — CapCut and Clipchamp are the most generous free tiers here, watermark-free for standard exports. Worth checking each tool's current export limits before committing to a workflow.

I mostly use CapCut for quick edits and Descript when I actually need to script something out first.

Part 5 (research & academic tools) coming next.

What's your go-to for video editing right now?

Read Part 1 — Writing Toolkit . Same rule this time: verified, genuinely free tiers, not disguised trials.

This round: coding & development.

1. GitHub Copilot — free tier now available to everyone, not just students. Still the most integrated option across editors.
2. Claude — free tier, strong for debugging and explaining unfamiliar code, not just writing it.
3. ChatGPT — free tier, solid all-rounder for quick snippets and explaining errors.
4. Google Gemini — free tier, handles longer codebases/context well.
5. Replit — free in-browser IDE with built-in AI assistant, zero setup.
6. Codeium — free AI autocomplete across most major editors, genuinely no-catch free tier.
7. Tabnine — free tier, lightweight autocomplete, good if you want something less "chatty."
8. Google Colab — free notebooks with GPU access, essential if you're doing anything ML-adjacent.
9. CodeWhisperer / Amazon Q Developer — free tier, decent if you're already in the AWS ecosystem.
10. Sourcegraph Cody — free tier, good at understanding large/multi-file codebases specifically.

I lean on a mix of these depending on the project — Copilot for flow, Claude when something's actually broken and I need it explained, Colab for anything ML-related.

Part 3 (design & image tools) coming next if this one's useful.

What's your actual daily-driver combo?

Most "learn machine learning" advice is either a 40-hour paid course or a scattered pile of blog posts that assume you already know half the material.

I got tired of that, so I built a structured path using only official Microsoft Learn content — the same material behind Microsoft's actual DP-100 (Azure Data Scientist Associate) certification, just organized in the order it should be learned in.

What it actually covers, in order:

1. Core ML concepts (what regression, classification, clustering actually are)
2. Real hands-on coding with Python + scikit-learn — regression, classification, clustering, deep learning
3. Training models at scale with Azure ML — workspaces, compute, MLflow tracking
4. MLOps — AutoML, hyperparameter tuning, pipelines, actual production deployment

That last part is the piece most beginner resources skip entirely — they teach you to train a model in a notebook and just... stop. This goes all the way to "deploy a model to a managed endpoint," which is the actual job, not just the fun part.

Free, self-paced, no signup beyond a Microsoft account. I'm a Computer Engineering student who built this while learning it myself — not an instructor, just organized what I wish existed when I started.

Link: https://learn.microsoft.com/collections/86w0cztk0gjpm4?wt.mc_id=studentamb_523020

Happy to hear what's missing or what should be reordered.

Everyone's drowning in "50 AI tools you need!!" listicles that are 80% dead links and fake free tiers. So I'm doing the opposite — small, verified, category-by-category lists of tools that are actually free, not "free trial that dies in 7 days."

First up: writing & content.

1. ChatGPT — the default for a reason. Brainstorming, editing, drafting, all in one.
2. Grammarly — still the sharpest free grammar/tone checker out there.
3. QuillBot — paraphrasing and summarizing that doesn't sound robotic.
4. Copy.ai — free tier for marketing copy, good for quick hooks/headlines.
5. Notion AI — writing assistance built right into your notes/docs.
6. Google Gemini — free tier, strong for research-heavy writing.
7. Microsoft Copilot — free in Edge, genuinely useful for drafting and rewriting.
8. Rytr — free tier, good for short-form/social copy.
9. Wordtune — rewrites sentences to actually sound like you, not generic AI-speak.
10. DeepL Write — underrated, great for tightening tone and clarity.
11. Canva Magic Write — if you're already designing in Canva, this is free and built in.
12. Simplified — free tier AI copywriting + basic design in one place.

I use a chunk of these regularly for project write-ups and content. This is Part 1 of a series — coding, design, video, and research tools coming in follow-up posts if this one's useful to people.

What's in your writing stack that should've made this list?

Everyone talks about "learning AI."

Almost nobody tells you where to actually start.

Over the past few months, I've been learning AI, ML, Cloud, and Agentic AI. Instead of bookmarking hundreds of random links, I narrowed it down to the resources that genuinely helped me understand and build things.

If you're starting your AI journey, this list should save you a lot of time.

📌 Save this post—you'll probably come back to it later.

🤖 Generative AI

• Google AI Studio — https://aistudio.google.com/
• OpenAI Cookbook — https://cookbook.openai.com/
• Hugging Face Learn — https://huggingface.co/learn
• DeepLearning AI Short Courses — https://www.deeplearning.ai/short-courses/
• Anthropic Prompt Engineering Guide — https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview

☁️ Cloud

• Google Cloud Skills Boost — https://www.cloudskillsboost.google/
• Microsoft Learn — https://learn.microsoft.com/
• AWS Skill Builder — https://skillbuilder.aws/
• Oracle MyLearn — https://mylearn.oracle.com/

📊 Machine Learning

• Kaggle Learn — https://www.kaggle.com/learn
• Stanford CS229 — https://cs229.stanford.edu/
• Andrew Ng – Machine Learning Specialization — https://www.coursera.org/specializations/machine-learning-introduction

🧠 LLMs & AI Agents

• LangChain Documentation — https://python.langchain.com/
• LlamaIndex Documentation — https://docs.llamaindex.ai/
• Model Context Protocol (MCP) — https://modelcontextprotocol.io/
• Google Agent Development Kit (ADK) — https://google.github.io/adk-docs/
• OpenAI Agents SDK — https://openai.github.io/openai-agents-python/
• CrewAI — https://docs.crewai.com/
• Microsoft AutoGen — https://microsoft.github.io/autogen/

🐍 Python

• CS50's Introduction to Python — https://cs50.harvard.edu/python/
• Python for Everybody — https://www.py4e.com/

📈 Data Science

• NumPy Documentation — https://numpy.org/doc/
• Pandas Documentation — https://pandas.pydata.org/docs/
• scikit-learn Documentation — https://scikit-learn.org/stable/

🛠️ AI Development Tools

• Google Colab — https://colab.research.google.com/
• Kaggle Notebooks — https://www.kaggle.com/code
• Jupyter Notebook — https://jupyter.org/
• Weights & Biases — https://wandb.ai/
• Ollama — https://ollama.com/

💻 Coding Practice

• LeetCode — https://leetcode.com/
• NeetCode — https://neetcode.io/
• Exercism — https://exercism.org/
• Frontend Mentor — https://www.frontendmentor.io/

🏆 Competitions

• Kaggle Competitions — https://www.kaggle.com/competitions
• AIcrowd — https://www.aicrowd.com/

📚 Research Papers

• Papers with Code — https://paperswithcode.com/
• arXiv — https://arxiv.org/
• Semantic Scholar — https://www.semanticscholar.org/
• Distill — https://distill.pub/

⭐ Bonus (Open Source AI)

• Awesome Generative AI — https://github.com/steven2358/awesome-generative-ai
• Awesome LLM Apps — https://github.com/Shubhamsaboo/awesome-llm-apps
• Microsoft AI-For-Beginners — https://github.com/microsoft/AI-For-Beginners
• Microsoft Generative AI for Beginners — https://github.com/microsoft/generative-ai-for-beginners
• Microsoft Zero to Agentic AI (Free) — https://learn.microsoft.com/collections/6q08f7tjr3yg4m/?wt.mc_id=studentamb_523020

🔥 My honest advice

Don't try to learn everything at once. Pick one roadmap, build real projects with it, and stay consistent. Consistency beats hoarding tabs.

What free resource actually moved the needle for you? Drop it below — I'll add the best ones to the list with credit.

Learn. Build. Ship.

Welcome to r/MergedPRs — a community dedicated to helping students and developers become exceptional software engineers through practical learning, curated resources, and meaningful discussions.

Whether you're writing your first line of code or architecting production systems, this community exists to help you learn with intention—not noise.

What you'll find here

• 🚀 Curated learning roadmaps
• 🤖 Artificial Intelligence & Machine Learning
• 🧠 Agentic AI, LLMs & Generative AI
• 💻 Software Engineering & Programming
• ⚡ Open Source projects and code walkthroughs
• 📚 Free courses, certifications, and learning resources
• 🛠️ Project breakdowns & architecture discussions
• 💼 Career development and engineering best practices

Community Principles

We value signal over noise.

• Share knowledge generously.
• Back opinions with experience or evidence.
• Prioritize practical learning over hype.
• Respect beginners and experienced engineers alike.
• Keep discussions constructive and professional.
• Avoid spam, low-effort promotion, and misleading content.

Why "Merged PRs"?

Every merged pull request represents progress.

Every bug fixed, project completed, concept understood, and lesson learned moves you one step closer to becoming a better engineer.

This community celebrates that continuous improvement.

Contribute

Whether you're sharing:

• a useful roadmap,
• an open-source project,
• an engineering lesson,
• a certification,
• or simply asking a thoughtful question—

your contribution can help someone else grow.

Learn. Build. Ship.

— Prem Rajesh Sargara

Most tutorials on Agentic AI throw you straight into MCP, LangGraph, or agent frameworks without explaining why any of it exists or what problem it solves.

I hit the same wall learning this myself, so instead of jumping around randomly, I organized official Microsoft Learn modules into one structured progression:

1. AI & agent fundamentals
2. Build your first agent
3. Tool calling
4. MCP (Model Context Protocol)
5. Multi-agent systems
6. A2A (agent-to-agent collaboration)
7. Knowledge-grounded agents
8. Production monitoring & evaluation

I didn't create any of the content — it's all official Microsoft Learn modules. I just put them in the order I wish I'd had when starting out. Everything's free, self-paced, roughly 9-10 hours total.

I'm a Computer Engineering student, still learning this myself — not claiming expertise here. Sharing in case it saves someone else the time I spent figuring out the right order.

Link: https://learn.microsoft.com/collections/6q08f7tjr3yg4m/?wt.mc_id=studentamb_523020

Happy to hear feedback on anything that should be added, reordered, or cut.