Welcome to GigaElixir Gazette, your 5-minute digest of Elixir ecosystem news that actually matters 👋.

. WEEKLY PICKS .

🤖 José Valim Launches Tidewave Web for Phoenix: Dashbit dropped a coding agent that runs directly in your browser alongside Rails and Phoenix apps, eliminating the constant back-and-forth by mapping UI elements directly to controllers, views, and templates.

Stop the translation hell

🔗 Official Interoperability Guide Exposes BEAM's Hidden Power: The Elixir team published comprehensive interoperability coverage across C++, Rust, Zig, Python, and Swift, while AtomVM enables Elixir in browsers via WebAssembly.

Break the isolation myth

🗄️ AI-Powered PostgreSQL Index Tuning Eliminates Manual Expertise Tax: Developer shared a comprehensive prompt for using LLMs to analyze Ecto codebases and automatically suggest database index optimizations, eliminating expensive PostgreSQL consultant fees.

Skip the consultant fees

⚡ Ruby vs Elixir Concurrency Comparison Confirms BEAM Superiority: Technical analysis proves Elixir's Task module dominates Ruby's Async gem with true preemptive multitasking and process isolation, while Ruby's cooperative fibers create single points of failure.

End the concurrency debate

Stop Believing the AGI Hype - Real Development Exposes LLM Limitations

A veteran developer's journey building an Elixir NIF using LLMs demolishes the "precursor to AGI" narrative with brutal honesty about what actually happens when you try to ship real code with AI assistance. After 7 rounds of LLM ping-pong between Grok 3, GPT-5, and Gemini 2.5 Flash—hours of copy-pasting that a C expert would finish in 20 minutes—the reality emerged: "Is this really supposed to be the precursor to AGI? Are you kidding me?"

The confidence failures expose the fraud behind vendor valuations. Gemini delivered "exemplary for production-quality" verdicts immediately followed by "subtle memory safety issues and unnecessary complexities" for identical code. Models repeatedly forgot previous improvements, suggested the same broken solutions, and led the developer down pointless rabbit holes about macOS requiring .dylib files. All three LLMs failed to solve basic OTP version compatibility issues despite their confident assessments.

Here's what the "fairy-tale claims aiming to inflate the valuations of LLM vendors and compute providers" don't mention: these tools generate starting points, not solutions. The developer got a working NIF published to Hex.pm through massive human intervention, debugging, and iteration that exposed AI's actual limitations versus the "breathless hyperbole peddled by AI-peddling con-sultants."

The breakthrough insight: LLMs excel at exploration but catastrophically fail at consistent reasoning and iterative refinement. They're bootstrapping tools for unfamiliar domains, not autonomous programming partners approaching general intelligence. Anyone claiming otherwise is selling compute and consulting contracts, not software solutions.

Remember, for realistic AI expectations:

1. Use LLMs for exploration, not execution – they excel at generating options but fail at consistent refinement

2. Expect confident wrongness – "production-ready" verdicts often precede contradictory feedback

3. Plan for extensive human debugging – the generated code is a starting point, not a solution

4. Choose models by domain performance – Grok 3 outperformed GPT-5 for actual C coding despite benchmark rankings

. TIRED OF DEVOPS HEADACHES? .

Deploy your next Elixir app hassle-free with Gigalixir and focus more on coding, less on ops.

We're specifically designed to support all the features that make Elixir special, so you can keep building amazing things without becoming a DevOps expert.

See you next week,

Michael

P.S. Forward this to a friend who loves Elixir as much as you do 💜