We can't get the most out of our Anthropic Claude Licenses - $20/month or $100/month, unless we start creating "thinking" and "self Improving" systems. The real power of tools like Claude Code lies in building structured loops: Input → Reasoning → Execution → Feedback → Memory that turn AI from a tool into a self-improving engine. The future of vibe coding isn’t faster software generation, it’s orchestrating intelligence at scale. #AI #AIArchitecture #AIDevelopment #Claude #ClaudeCode #SystemDesign #FutureOfWork #AIEngineering #Innovation

2026 is the year of Vibe Coding describing what you want in plain language and letting AI generate most of the code. 92% of US developers now use AI tools daily, and nearly half of new code is AI-generated. The shift is real: less manual typing, more focus on architecture, user experience, and reviewing output. But with great power comes responsibility — security, debugging, and maintainability are the new challenges. How are you incorporating AI (vibe coding, agents, or tools like Sidekick) into your workflow this year? Wins or warnings welcome! #WebDevelopment #AICoding #VibeCoding #TechTrends2026

Do AI agents have bad memory, or are we just using the wrong tools? 🤔 At our latest Tech Council, Francisco Donadio introduced us to Engram, a memory persistence tool for agents that is changing how we handle long-term projects. We’ve all been there: agents lose the thread, context disappears, and you end up burning thousands of tokens scanning code over and over again. ➡️ Key takeaways from the session: -Instead of having the agent read millions of lines of code, Engram tells it exactly where to look. This leads to a massive reduction in token consumption. -Since it uses a local database (.sqlite) within the repo, the entire team can access the history of previous decisions. If someone asks why a specific design choice was made months ago, the agent has the answer. -Unlike solutions that dump everything into an unformatted .md file, Engram organizes information by "what, why, where, and what was learned." It’s about moving from volatile memory to a system that actually understands the project's history. Thanks, Fran, for the demo and for showing us how to further optimize our AI-integrated workflows 👏 At LoopStudio, we specialize in building secure, scalable software by integrating the latest AI efficiencies into our development process. Explore how we work: www.loopstudio.dev #SoftwareDevelopment #AI #Engram #TechCulture #LoopStudio #SecureCode

I benchmarked tokensave ( https://tokensave.dev ) against every comparable tool I came across: Dual-Graph, CodeGraph, code-review-graph, OpenWolf and more. The highlights: 37 MCP tools vs 5-22 in alternatives. 31 languages. Full call graphs, impact analysis, complexity metrics, dead code detection, type hierarchies, support for code porting. Single 25MB Rust binary, zero runtime deps, indexes thousands of files in millisecs (thank you Andrea Balducci for the challenge!). MIT licensed, every line auditable unlike some alternatives shipping proprietary cores. Almost 100M tokens saved globally with only a handful of installs. Pair it with RTK (https://lnkd.in/dpwhbw_2) as recommended by Zach Smith, a Rust CLI proxy that compresses dev tool output before it hits your context window, and the savings compounds: tokensave cuts what the AI needs to read, RTK cuts what it actually sees. Different layers, same goal.

I really wanted to share this from Enzo Lombardi I’ve been using code-review-graph and it has helped a lot, although it has been freezing often, which hurts productivity. I also experimented a bit with Caveman. Every small improvement helps I guess? Then as I was looking into OpenWolf, I came across TokenSave through Enzo's post. I also installed RTK! Another great suggestion from Enzo! Of course, proper planning and proper prompting are still the most important pieces. But Claude has become heavy on token costs. I’ve spent roughly $650 CAD this month developing my platform. It has been a major learning experience. Anthropic is seriously getting greedy. I've also used GLP5.1, Kimi2.6 and a few other open source models, I am seriously leaning away from Claude other than using Claude Code as a harness. My project, which I haven’t revealed yet, started at the end of March right as I got laid off, as an attempt to solve a personal problem. My hope is that it eventually becomes something others can benefit from as well. The biggest lesson so far: building something for personal use is one thing. Building something at a multi-user platform scale is completely different. A scaleable platform and use has so many layers. One of my goals is to minimize token and LLM usage wherever possible by using scripted APIs, automation, and deterministic logic instead of relying on prompts for everything. Operational cost of such a thing has to be accounted for. LLMs have their place. They are powerful when used properly. But some things should not be handled through an LLM or prompt at all. Nonetheless, I hope this helps everyone. #AI #AIDevelopment #LLM #Startup #FounderJourney

Margaret-Anne Storey's cognitive debt concept, anchored in Naur's 1985 "Programming as Theory Building," names what velocity metrics miss: a program is a theory held across developers' minds … source code is only the artifact. AI generates 140-200 lines/min while comprehension drops below 40% when developers delegate generation. Confidence in AI tools fell from 43% to 29% … usage climbed to 84%. The code compiles. The people behind it lost the plot. #cognitiveDebt #theoryBuilding #AIcoding #softwareEngineering

developers should at a minimum be in the loop at every step to see what AI is generating and ask for walkthroughs. At least be a peer reviewer of your own code.

Yes, I love the analogy! 🤓 As we refactor to solve Technical Debt, we should do learn and practise to solve for Cognitive Debt. ♻️🧠📈 For starters, read the whole damn conversation you had with your AI to solve your challenge and take it from there. 😅

cognitive debt is the very thing lots of my recent discussions focused on: many senior experts from different fields, who use the technology themselves daily, report this issue. and all report brain fry as well. so, sustainable process integration is still not on the horizon. especially since some start to do their own handwritten notebook on the side to not get lost. * brain fry concept: https://lnkd.in/eVURuFdG

The power in tech is shifting. For the last 30 years, the people with power were the ones who could write code. The best programmers built the best products. But AI is changing that equation. Today, the real leverage is shifting to builders who can orchestrate AI. People who know: • which AI to use • how to combine them • how to design workflows • how to turn ideas into systems The new advantage isn’t just coding. It’s orchestration. The next generation of builders won’t just write software. They’ll compose intelligence. And that’s a very different skill.