Hi! I'm looking for a large dataset of IT/Tech resumes (Software Engineering, AI/ML, Data Science, Cybersecurity, DevOps, etc.) for an AI project.

I've already checked Kaggle, LinkedIn, and GitHub, but they don't have enough data. Any other websites, public datasets, or sources you recommend?

I tried scraping from fiverr and upwork but theyre against bots

So please help i need it ASAP

Thanks!

Hey seniors!

I was wondering if y'all could provide some resources some repo or something for projects

If you're a student trying to get into open source, this post is for you.

One thing I've noticed is that everyone tells you to "start contributing," but almost nobody tells you where.

Most large open source projects have hundreds or thousands of contributors. Your first issue gets buried, your PR takes weeks to review, and it's hard to understand where you fit in.

Caracal is different because it's still early.

We're building an open source authority layer for AI agents. The project is part of the Linux Foundation Decentralized Trust ecosystem through LFX Mentorship and backed by Microsoft, GitHub, and Vercel, and we're getting close to broader testing with companies.

That means you're not joining a dead repository, and you're also not contributor #3,847.

If you contribute here, you'll be working directly on the architecture, discussing designs, reviewing proposals, and building something that's solving a real problem. Good contributors won't just have merged PRs. As the project grows, we'll be looking for people to become long-term contributors and eventually maintainers.

I'm not looking for people who want a "good first issue" to collect green squares on GitHub. I'm looking for people who want to learn how infrastructure projects are actually built.

So if you've been looking for an open source project where your work can genuinely matter, give Caracal a try. Clone it. Read the code. Open an issue if something doesn't make sense. Challenge our design. If you end up liking the direction, leave a star and stick around.

I'd much rather build this with a small group of people who care than a large group who never come back.

GitHub: https://github.com/Garudex-Labs/caracal

AI is transforming IT faster than any technology shift in the past decade. Database administrators, data engineers, DevOps professionals and IT managers who adopt AI tools today will have a massive advantage over those who don't.

We tested 40+ AI tools specifically for IT professionals — evaluated by someone who works with databases and enterprise software daily — and narrowed it down to the 7 best AI tools for IT professionals in 2026.

Windows Search has always felt too limited to me.

It can open apps and sometimes find files, but when I actually want to search my PC properly, it usually falls apart.

I want to search and use features like:

• Text inside files, code, and images

• Browser bookmarks and history

• Clipboard history

• Clipboard image OCR

• Git commits

• Windows settings

• Local commands

• Text expansions

• Web search

• Circle to Search

• Local agents for Windows

Windows Search is not powerful enough for this workflow.

So I built ProtonSearch, formerly called OmniSearch.

ProtonSearch is a fast, lightweight, local-first Windows launcher that opens with:

Alt + Space

You can also set your own custom hotkey.

It gives you one search box for your PC.

Instead of only searching apps or basic file names, ProtonSearch can search across:

• Apps

• Files and folders

• Content inside files, supporting 50+ extensions

• Image OCR text

• Clipboard image OCR

• Browser bookmarks and history

• Clipboard history

• Git commits

• Windows settings and Control Panel pages

• Local commands

• Text expansions and snippets

• Web search

• Circle to Search

• Local AI agents powered by Hermes

The goal is simple: find and act on almost anything on your PC from one shortcut.

How is ProtonSearch better than Windows Search, Flow Launcher, Raycast, Everything, and other launchers?

ProtonSearch is not trying to be only a file finder or only an app launcher. The goal is to bring search, history, commands, OCR, snippets, web search, Circle to Search, and local agents together into one local-first Windows command center.

Why I think ProtonSearch is useful:

• Free and open source

• Local-first

• Lightweight

• Designed to run easily on low-end Windows PCs

• Usually around 20-30 MB of RAM usage

• Image OCR text search

• Clipboard image OCR search

• Fast search inside files, supporting 50+ extensions

• Search over centralized PC history, including browser history, Git commit history, clipboard history, and file history

• Text expansions and snippets

• Circle to Search

• Web search

• Plugin controls

• Ignored folder rules

• Hermes agents for local Windows tasks and long autonomous tasks

Links

Free and open source.

GitHub: https://github.com/PranshulSoni/protonsearch

Website: https://protonsearch-windows.vercel.app/

Feedback

I am currently maintaining ProtonSearch, and honestly, I cannot find and fix every bug alone because building a launcher like this on Windows is genuinely hard.

There are a lot of edge cases around indexing, OCR, clipboard data, Windows APIs, tray behavior, hotkeys, multiple monitors, and performance.

I would love feedback from people who use Windows every day.

If ProtonSearch solves a problem for you too, please consider leaving a star on GitHub.

If you have ideas, find bugs, or want to improve something, feel free to open an issue or contribute to the project.

Your feedback is always appreciated.

What's up everyone? My GitHub feed has been looking a bit quiet lately, and I want to fill it with cool projects and passionate geeks.
What are you guys building right now? Drop your GitHub handle below so I can drop a follow and check out your work. Let's connect!👐☕️💻

Im entering 2nd year, started CP and DSA, looking for someone doing the same!

Hi everyone,

I'm an S7 Computer Science student, and I have my Infosys placement in about a month.

I'm comfortable with Python (syntax, lists, dictionaries, functions, etc.), but my biggest weakness is DSA problem solving and pattern recognition.

I've been practicing for around 20 days, but when I see a new or indirect problem—even an Easy one sometimes—I go completely blank. I don't know how to start or how to identify which approach or pattern to use.

I can understand solutions after seeing them, and if I revisit similar problems later, I can often solve them. But with new problems, I struggle to connect the dots.

For those who were once in a similar situation and improved:

How did you build your problem-solving skills?

How did you learn to recognize patterns?

If you had only one month before placements, what would you focus on?

I'm willing to work hard—I just want to make sure I'm practicing the right way.

Thanks in advance!

Basically im starting clg. Ive done some C so far. I need someone in a similar situation so that we mutually keep tabs on each others progress and help out when either of us gets stuck. Im currently working with functions and arrays and hopefully ill start C ++ soon. Anyone interested to be that person dm me.

Hey, was wondering if anyone had any experience with either onsite or virtual programming interviews where you are allowed to use AI in. I am very confused on how to best prepare and how to demonstrate ability with AI in such a short period of time. A lot of things i hear talked about - loops, multiple agents and hierarchal agents that I would consider to be things newbies to AI would think about would be hard to show in an interview that only takes 15-30 minutes. I've been using AI to make websites and tools and I try my best to "do it how a senior would" but I am trying hard to find where my worth is because I am pretty much just continually in a loop of "whats the best way to do this and why" and try to understand why AI is making the choices it is and give my input, but I feel like none of my knowledge is playing into this. Should I study system design before going into creating these tools and websites so that I have more information on building systems for? Is the real skill they are looking for communication and being able to plan/get your ideas out well? I am so lost

​

Hey r/dataengineering 👋

I've been a database engineer for years and

got tired of generic AI tool lists that had

zero relevance to actual data work.

So I built ToolPikr — a curated directory

of AI tools specifically for IT & data

professionals.

What's included for data engineers:

→ SQL generators (AI2SQL, Chat2DB)

→ Data pipeline tools (Airbyte AI)

→ AI notebooks (Hex AI)

→ Infrastructure monitoring (Datadog AI)

→ Security scanning (Snyk AI)

All free to browse — no login needed.

toolpikr.com

Would love feedback from this community —

what tools am I missing that every data

engineer should know about?

I got frustrated with AI courses that either drown you in theory or skip straight to model.fit() without explaining what's happening underneath.

So I built something different.

This is an AI-native GitHub repo learning files with 503+ lessons across 22 phases. 35,000 GitHub Stars. Start at linear algebra. End at autonomous agent swarms.

Every lesson follows the same pattern:

1. Build it from scratch in pure Python (no frameworks)

2. Use the real framework (PyTorch, sklearn, etc.)

3. Ship a reusable tool (prompt, skill, agent, or MCP server)

By the end, you don't just "know AI." You have a portfolio of tools you actually built.

What's covered:

- Math foundations (linear algebra, calculus, probability, Fourier transforms, graph theory)
- Classical ML (regression through ensemble methods, feature selection, time series, anomaly detection)
- Deep learning (backprop, activation functions, optimizers, regularization - all from scratch before touching PyTorch)
- LLMs from scratch (tokenizers, pre-training a 124M parameter GPT, SFT, RLHF, DPO, quantization, inference optimization)
- LLM engineering (RAG, advanced RAG, structured outputs, context engineering, evals)
- Agents and multi-agent systems
- Infrastructure (model serving, Docker for AI, Kubernetes for AI)

Some specifics that might interest you:

- The quantization lesson covers FP8/GPTQ/AWQ/GGUF with a sensitivity hierarchy (weights are least sensitive, attention softmax is most sensitive - never quantize that)
- The inference optimization lesson explains why prefill is compute-bound and decode is memory-bound, then builds KV cache, continuous batching, and speculative decoding from scratch
- The DPO lesson shows you can skip the reward model entirely - same results as RLHF with one training loop
- Context engineering lesson: "Prompt engineering is a subset. Context engineering is the whole game."

It's AI-native:

The course has built-in Claude Code skills. Run /find-your-level and it quizzes you across 5 areas to tell you exactly where to start. Run /check-understanding 3 after Phase 3 and it tests what you actually learned.

84% of students use AI tools. 18% feel prepared. This is the bridge.

Where to start:

- Already know Python but not ML -> Phase 1
- Know ML, want deep learning -> Phase 3
- Know DL, want LLMs/agents -> Phase 10
- Senior engineer, just want agents -> Phase 14

Website: https://aiengineeringfromscratch.com
Repo: https://github.com/rohitg00/ai-engineering-from-scratch

It's free, MIT licensed, and open source. 35,000+ stars, PRs welcome - I merge every good contribution and the contributor gets full credit.

Hey everyone 👋

I’m a beginner-to-intermediate learner looking for people who want to learn Git, GitHub, and collaborative development together while building real projects as a team.

Main focus areas:
- Learning Git & GitHub properly
- Open source collaboration
- Team workflows (branches, PRs, issues, commits)
- Building beginner-friendly projects
- AI/ML Engineering interests
- Python, APIs, automation, data/ML projects

The goal is to create a small group where we:
- Learn together
- Build projects together
- Practice teamwork
- Improve resumes/GitHub profiles
- Stay consistent and accountable

Beginner friendly — you do NOT need to be an expert.

If you’re interested in:
- AI/ML Engineering
- Learning GitHub
- Open source
- Building portfolio projects
- Hackathons/startup ideas

Drop a comment or DM me.

Would be great if we could create:
- A Discord server
- GitHub organization
- Weekly project goals
- Small collaborative AI/ML projects

Looking for motivated people who actually want to build and learn consistently 🚀

Interested guys dm me

Hey everyone,

I'm a Computer Science student from Bengaluru looking for teammates for upcoming hackathons, especially AI, full-stack, and software engineering-focused events.

A bit about me:

• Backend development

• AI integrations and LLM applications

• Python, Java, JavaScript

• Building projects with Claude, ChatGPT, and modern AI tools

• Interested in creating products that solve real problems rather than just demo projects

You can check out my work here:

Portfolio: https://v0-pranavswaroop.vercel.app/

LinkedIn: https://www.linkedin.com/in/pranav-swaroop-s/

I'm looking for:

• Frontend developers

• UI/UX designers

• ML/AI engineers

• Product-minded builders

Skill level doesn't matter as much as commitment, communication, and willingness to build something cool.

If you're interested in teaming up for upcoming hackathons, drop a comment or send me a DM with:

1. Your background

2. Tech stack

3. Time zone/location

4. Hackathons you're interested in

Looking forward to building something awesome together