I spent few days mapping the context engineering landscape, here are the 5 main approaches

I've been building AI agents pipelines for a few months now, and honestly, the context engineering space is overwhelming. RAG, vector databases, MCP servers... everyone's using different tools for everything. So I spent some time organizing it all. Here are the 5 main categories I found, with the t

10 Active Open‑Source AI & LLM Projects Beginners Can Actually Contribute To (With GitHub Links)

Most “top AI projects” lists just dump big names like TensorFlow and PyTorch without telling you whether a beginner can realistically land a first PR. This list is different: all 10 projects are active, LLM‑centric or AI‑heavy, and have clear on‑ramps for new contributors (docs, examples, “good firs

LLM Automation: Top 7 Tools & 8 Case Studies in 2026 - AIMultiple

Source: AIMultiple

Can we go beyond retrieve-and-dump?

After working with a number of RAG systems I’m starting to wonder if these stacks are hitting a wall in terms of what they can actually deliver. In theory, RAG should be a great fit for knowledge-heavy workflows, but in practice I keep finding that outputs are just shallow and fragile, especially w

My Ultimate AI Stack!

Over the past year I’ve been experimenting with tons of AI tools, but these are the ones I keep coming back to: [Perplexity.ai](http://Perplexity.ai) – real-time research with cited answers from the web. [Cosine.sh](http://Cosine.sh) – in-terminal AI engineer for debugging & coding help. [