r/letscodecommunity

I built a 100% invisible AI overlay that runs undetected over DoSelect, SEB, Mettl, and Lockdown Browsers.

How does it give you an unfair advantage? ⚡ Built-in Stealth Browser: Open ChatGPT or any AI directly inside the invisible overlay without getting caught. ⚡ Lightning Speed: Integrated with Groq API and Gemini 2.5 Flash for instant answers. ⚡ Zero cursor movement.

It’s a 100% invisible hardware overlay that reads your screen and solves aptitude/coding exams in milliseconds.

Watch it completely bypass a locked-down DoSelect browser: 👇https://youtu.be/7ENiBirqFQE?si=Lpo\_hNk5czkOBBwF

Website: https://shadus.vercel.app/

I just discovered this amazing community and site of let's code. It's truly a treasure.

I am currently exploring different career roadmaps. While I am highly interested in AI and ml engineering roadmaps, I had a doubt that whether should I follow every resource mentioned under a topic or just choose one?

For example, in the given image, while learning deep learning frameworks, should I cover all 3 resources or just choose??

I've spent the last few months building something and I'm finally at the point where I want to share it properly rather than just quietly hoping people find it.

The idea came from a frustration I kept seeing (and feeling myself): SQL tutorials teach the syntax fine but there's never a reason to care about the answer. You filter a table called employees, get a result, and nothing happens. Your brain doesn't bother keeping it.

I wanted to try a different approach. QueryCase teaches SQL through detective investigations. You get a briefing from Chief Fox (our mascot), a real database to query, and a mystery to crack. The JOIN matters when a suspect has an alibi. The WHERE clause matters when you're trying to find who entered the building at 22:13. The SQL is the tool for solving something, not the point in itself.

Here's what's actually in it:

• A structured learning path across 54 cases, going from Recruit through Rookie, Detective, Senior Detective, and Chief Detective. Each rank has drills and a level exam to pass before you progress.
• Sandbox mode where you can explore real datasets (IMDB movies, Spotify, sports stats, Steam games) and run whatever you want with no pressure and no mystery attached. Just free exploration against actual data.
• Everything runs in the browser using DuckDB WASM so there's nothing to install.

I'm a solo developer and this is genuinely early days. I'm sharing here because this community is exactly the kind of people I built it for, and I'd rather get honest feedback now than find out later I've built the wrong thing.

What's missing? What would make you actually stick with something like this versus what you've used before?

querycase.com if you want to take a look.

Any feedback appreciated!

For context, I spent a good chunk of my career as a senior engineer at Amazon, and one habit that stuck with me is turning anything I do more than twice into something repeatable. When I started using Claude Code/Codex heavily I wrote skills by hand for the workflows it kept fumbling.

But as agents get more autonomous, I'm not babysitting each step anymore, I just hand off a task and come back to a result. The painful patterns still happen, and AI still rediscovers them session after session, but I'm no longer sitting there to catch them and turn them into a skill.

So I built a self-learning skill. Instead of me noticing the pattern and writing the skill, the agent does it itself: when a session involves a hard debugging path, a rediscovered procedure, or a multi-step process it had to figure out, it captures the working approach and the dead-ends it ruled out into a new skill.

Repo (MIT): https://github.com/kulaxyz/self-learning-skills

I'd genuinely value feedback on the approach, especially from people running agents more autonomously: once you've stopped watching every step, how are you capturing what your agent learns mid-session?

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.