Kala

At FAUN.dev, our goal has always been to help developers stay informed - not by chasing headlines, but by understanding the stories that truly matter.

Today, we're introducing a new approach to developer news.

Using advanced retrieval and analysis systems, FAUN.dev News now connects facts, people, and context into coherent, data-driven narratives. Each story is crafted to reveal how ideas, technologies, and organizations intersect.

Every article now includes:
- Visual maps of key entities and connections
- Contextual relationships between people, tools, and events
- Concise insights and clear takeaways to guide your understanding
- And more!

Our aim is simple: make technical news more structured, transparent, and meaningful - so you can grasp complex developments faster and with confidence.

Explore the new FAUN.dev News at faun.dev/news

P.S: We're still experimenting with new features and sections - we only published a few entries but we'd like to know your feedback, it will be really helpful! Please reply to this email and share your thoughts!

Big swings collide this week: agents that write clean Pinot SQL, GPUs wrung to the metal, and a brutal study saying AI editors can make seniors slower—even as hyperscalers roll their own chips. If that mix sparks something, dive into the details and steal what ships.

🎓 5 Free AI Courses from Hugging Face
🎯 Becoming a Research Engineer at a Big LLM Lab - 18 Months of Strategic Career Development
🤖 Building a Natural Language Interface for Apache Pinot with LLM Agents
🔎 Implementing Vector Search from Scratch: A Step-by-Step Tutorial
🧮 Inside NVIDIA GPUs: Anatomy of high performance matmul kernels
📓 Jupyter Agents: training LLMs to reason with notebooks
🏭 Microsoft Shifts to In-House AI Chips, Reducing Nvidia, AMD Reliance
📉 The productivity paradox of AI coding assistants

Less noise, more leverage—ship something smarter today.

Have a great week!
FAUN.dev Team

Microsoft’s diving deep into its own silicon with the Azure Maia AI Accelerator and Cobalt CPU. Not just chips—full-stack muscle. Custom hardware. Tuned networking. Wild thermal tricks like in-chip microfluidic cooling to keep it all from melting down.

This is not about performance. It’s a system rethink. Vertical integration from silicon to server room—built for the scale AI actually needs.

NVIDIA Hopper packs serious architectural tricks. At the core: Tensor Memory Accelerator (TMA), tensor cores, and swizzling—the trio behind async, cache-friendly matmul kernels that flirt with peak throughput.

But folks aren't stopping at cuBLAS. They're stacking new tactics: warp-group MMA, SMEM pipelining, Hilbert curve scheduling, and cluster-wide data reuse. All in plain CUDA C++ with a dusting of inline PTX. No magic libraries, just smart scheduling and brutal efficiency.

A July 2025 METR trial dropped a twist: seasoned devs using Cursor with Claude 3.5/3.7 moved 19% slower - while thinking they were 20% faster. Chalk it up to AI-induced confidence inflation.

Faros AI tracked over 10,000 developers. More AI didn’t mean more done. It meant more juggling, more PRs, same velocity.

Apiiro's 2024 report? Not subtle. Privilege escalation paths up 322%. Critical CVEs in AI-generated code? 2.5x higher.

MiQ plugged Google’s Agent Development Kit into their stack to spin up LLM agents that turn plain English into clean, validated SQL. These agents speak directly to Apache Pinot, firing off real-time queries without the usual parsing pain.

Behind the scenes, it’s a slick handoff: NL2SQL tools map intent, validators check for query gaffes, and conversation memory keeps the chat flowing. No need to repeat yourself like it’s 2019.

To land a big career role like Mistral, mix efficient tactical moves (like LeetCode practice) with strategic ups, like building a powerful portfolio and a solid network. Balance is key; aim to impress and prepare well without overlooking the power of strategy in shaping a successful career.

Hugging Face dropped an open pipeline and dataset for training small models—think Qwen3-4B—into sharp Jupyter-native data science agents. They pulled curated Kaggle notebooks, whipped up synthetic QA pairs, added lightweight scaffolding, and went full fine-tune. Net result? A 36% jump on DABStep’s easy benchmark.

There’s no doubt that search is one of the most fundamental problems in computing. Vector search matches meanings by converting text into numerical vectors and finding the closest matches in a high-dimensional space. By following this step-by-step guide, you can build a vector search system from scratch in Python using cosine similarity and visualize how it works.

Hugging Face just rolled out a sharp set of free AI courses. Real topics, real tools—think AI agents, LLMs, diffusion models, deep RL, and more. It’s hands-on from the jump, packed with frameworks like LangGraph, Diffusers, and Stable Baselines3.

You don’t just read about models—you build ‘em in Hugging Face Spaces, show them off on the Hub, and battle it out on public leaderboards. Challenge-based learning done right.

Language-agnostic AI auditor that autonomously builds and refines adaptive knowledge graphs for deep, iterative code reasoning.

Build reliable AI and agentic applications with DataFrames

 Real-time & local speech-to-text, translation, and speaker diarization. With server & web UI.

Git Based Memory Storage for Conversational AI Agent

Did you know that bitsandbytes’ Adam8bit can reduce Adam’s optimizer memory from about 2× the model size down to ~0.5× by quantizing the m and v moments using blockwise dynamic scaling? In float16 training, those optimizer buffers (and gradients) often dominate memory - so this frees up tens of gigabytes. It’s a drop-in optimizer that closely tracks FP32 behavior, letting you use that saved RAM for longer context windows or larger effective batch sizes without touching your model code.

"We tune models because it's measurable; production fails because we ignore data contracts, monitoring, and rollback. If you can’t diff data, trace decisions, and revert in minutes, your accuracy is just a slide number."
— SenseiOne

This week, we’re highlighting Shrey Shah, an AI agent developer and Cursor Ambassador with five years of building with AI—from early GitHub Copilot to running workshops on prompting, evals, and agent workflows. At Vivun, he’s a Senior Software Engineer | AI, shipping knowledge‑graph recommendations, vector search, and scalable agents grounded in tight test harnesses and iterative evals.