GPT tools with search history have so much value as a developer... We've never easily been able to "ctrl f" to find 1 of the hundreds of paths we've worked through in the past which may have not worked out but you suddenly need back a week later... Github repos are cool but they only capture the moment in time when a developer deems 'good' and not the iterations that went into that snapshot of 'good'...(unless you are weird about your commits) This little subtle nuance of these tools tracking your work-in-progress in a quick and easy searchable manner is invaluable!

Want to avoid the #1 no-code trap? 👇 Look for platforms offering: ✅ Full code export and ownership ✅ Clean, readable source files ✅ Standard framework output ✅ No strings attached Don't build your foundation on quicksand.

Tried Building URL shortener in Golang from the codingchallenges of John Crickett https://lnkd.in/g-JN3Vaw 1. Built the MVP in Go (TDD + clean architecture) 2. Dockerized it for reproducible local runs 3. Scaled it step-by-step. I ended up writing a complete guide on how a simple weekend project can evolve into a system that serves millions of requests per second — with trade-offs, diagrams, and metrics at every stage. Full breakdown (with architecture diagrams): https://lnkd.in/gdz5guc2 Golang solution: https://lnkd.in/gic64Wd5 Sometimes, the best system design lessons come from actually building the thing. #SystemDesign #Golang #Architecture #BackendDevelopment #Scalability #Engineering

Every day I'm more and more surprised of what's possible with Claude Code. Every time I come across a problem, I just throw it at Claude Code to see what happens. Today's problem: I have way too many URLs in an Obsidian page dedicated to Claude Skills and I'm too lazy to read and consume them all. Solution: I created a .md file where I described the steps that I wanted Claude Code and its sub-agents to execute: visit the url, fetch the content, summarize it, etc. Turned it into a custom slash command /2nd-brain-clean Results: 4 .md summary of what each URL is about. Next step: - create a Claude Skills map with links to each .md file, so that next time I need to use the Claude Skills Page, Claude knows where to find each resource. - Scale this approach to all the other URLs in that file - Iterate the approach for more pages

3️⃣ Clean Code Series: Variables📒 🧑💻 ☑️ Use searchable names (part 1) 💡We will read more code than we will ever write. By not naming variables that end up being meaningful for understanding our program, we hurt our readers. Make your names searchable.

3️⃣ Clean Code Series: Variables📒 🧑💻 ☑️ Use searchable names (part 1) 💡We will read more code than we will ever write. By not naming variables that end up being meaningful for understanding our program, we hurt our readers. Make your names searchable.

I've just published a new #rust crate `u8pool`: - Is a stack for u8 slices in a client-provided buffer - Uses preallocated memory to store byte slices - Optionally with a companion `Sized` object - The interface is stack-based, with forward and reverse iterators - The code is `no_std`, without dependencies Links - u8pool on crates.io: https://lnkd.in/eg8CvZTY - subproject on github: https://lnkd.in/e_J9urv8 I use `u8pool` in a json processing library, to store the path from the top to the current nested json element, together with the parser state on each level. I suppose all recursive parsers need something like it. Please try, review, tell about your use cases! If you liked `u8pool`, please star the github repo: https://lnkd.in/eFhGY2vY

Continuing my work on hybrid retrieval methods for RAG, I stumbled upon a framework called LightRAG - which uses the same idea I discussed earlier: combining vector and graph databases for smarter retrieval. LightRAG builds a knowledge graph of entities and relationships while keeping vector embeddings, letting the system retrieve information that’s both semantically similar and contextually connected. After exploring it, I decided to build my own prototype inspired by the same approach: https://lnkd.in/dhPEuyQJ It abstracts away most of the technical complexity and directly connects Qdrant (Vector DB) and Neo4j (Graph DB) to form a persistent, incremental knowledge base - one that can be appended over time instead of being re-indexed from scratch. Here’s what stood out: - Great for long-term or evolving corpora - Incremental updates keep costs low. - Hybrid retrieval gives better and mostly accurate chunks Not perfect for short or temporary RAG setups, but for living knowledge systems, this direction feels incredibly promising. Would love your feedback on the repo or ideas for improvement! DeepMask #RAG #VectorDatabases #GraphDatabases #LightRAG #LLM #AIEngineering

Building with RAG involves more than just selecting a language model; it requires the design of a full-stack system that is modular, scalable, and prepared for the future. Considerations range from open-source LLMs like LLaMA 3 to frameworks such as LangChain and vector databases like Qdrant. Each layer plays a crucial role, affecting latency, cost, and explainability. What tools are currently in your RAG stack? Share your insights, tips, or questions below—we're eager to hear from you! Visit our website for more information. Call us at 239-221-YAXS (9297).

🏄♀️ strx now live in PyPI at version 0.1.2 strx is built to bring consistent and simple APIs for string workloads, following the str_* function naming style. It’s inspired by R’s stringr package and includes a set of handy utilities to make text manipulation in Python more intuitive and enjoyable. If you work with text or data cleaning, give it a try — I’d love to hear your thoughts or ideas for new features! Check it out on PyPI: https://lnkd.in/gaCRnS5Y Available on GitHub: https://lnkd.in/gz_mwQ4E