Dive into Stack Overflow's 2025 insights, AI's role in engineering, and why vibe coding might be your next legacy headache, plus top links from the web.

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Welcome, Developers! 👋

Today, we feature Stack Overflow's 2025 survey insights, game performance debugging without source code, and practical vibe coding tips.


We're also covering embedding-powered RAG tools, AI as a learning companion, and some interesting tech pieces. All delivered in our new lighter, more informative design—let us know what you think!

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🔖 The Reading Room

Articles we have hand-picked for you:

Stack Overflow Developer Survey 2025

Results for the 2025 edition are out with nearly 50k developers participating. Some highlights include AI adoption increasing from 76% to 84%, OpenAI's GPT models capturing 82% of LLM users and Visual Studio Code maintaining dominant its position as go-to IDE for the fourth consecutive year despite AI-powered competition.

By Stack Overflow →

AI is a Floor Raiser, not a Ceiling Raiser

This article talks about two aspects; AI being used for more accessible learning and allowing engineers proficient in language A to build something in language B. This wasn't viable previously but now AI has opened up a lot of possibilities for engineers with strong fundamentals.

By Elroy →

Profiling Without Source Code – How I Diagnosed Trackmania Stuttering

A game dev tired of random stuttering in Trackmania decided to take things in his own hands. Despite no access to game's source code, Lars used the Superluminal profiler to analyze memory addresses, library calls to find an issue in game's scripting platform.

By Lars Thießen →

Gemini Embedding: Powering RAG and context engineering

Embedding converts raw text into vectors that LLMs can understand and process, this enables all the cool things that we see AI doing. This blog by Google showcases some successful tools being powered by Gemini Embedding models.

By Vishal Dharmadhikari and Janie Zhang →

I Know When You're Vibe Coding

How code gets written matters less than ensuring it fits the codebase's established patterns and conventions. AI-generated code often works perfectly but ignores existing libraries, duplicates functionality, and breaks team standards. Developers need to take responsibility for what gets merged, regardless of whether they or an AI wrote the initial draft.

By Alex Kondov→

⏳ Back in Time

Most clicks from last newsletter:

🔗 The Link Lounge 

Unordered finds from around the web:

Find something cool? You can send us links to feature here via email.

🧰 The Toolbox

Tools and products we're excited about today:

LLM Leaderboard

Artificial Analysis releases a leaderboard ranking over 100 AI language models, evaluating performance across intelligence, speed, price, and context metrics for easier comparisons.

Learn more →

Sourcebot

Sourcebot is a self-hosted tool that enables developers to search, navigate, and ask natural language questions about their codebase with AI-powered answers and inline citations.

Learn more →

Mistral Codestral

Mistral AI announced Codestral 25.08, their updated coding-focused model with enhanced instruction following and better acceptance rate. The API is ridiculously cheap but there's no IDE plugin yet for VSCode.

Learn more →

ElysiaJS 

ElysiaJS is a trending TypeScript web framework built for Bun that provides end-to-end type safety, automatic API documentation generation, and exceptional performance (21x faster than Express) with an ergonomic developer experience.

Learn more →

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