Allow loading remote contents and showing images to get the best out of this email.FAUN.dev's AI/ML Weekly Newsletter
 
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Kala
 
#ArtificialIntelligence #MachineLearning #MLOps
 
 
🔍 Inside this Issue
 
 
Production is calling: agents are turning into teammates, eval is getting rigorous, and video models are API-grade—meanwhile the hardware bill just leapt into the stratosphere. Expect modular skills over protocol bloat, real guardrails over vibes, and a clear read on who’s actually catching up.

🤝 Amazon Launches Quick Suite: "The AI Teammate"
🧠 Anthropic's Claude Sonnet 4.5 AI Model Shows Self-Awareness in Tests
🧩 Claude Skills are awesome, maybe a bigger deal than MCP
🧪 How Microsoft Evaluates LLMs in Azure AI Foundry: A Practical, End-to-End Playbook
🛠️ Microsoft Launches Open-Source Agent Framework for AI Development
⚡ OpenAI Needs $400 Billion In The Next 12 Months
🎬 Sora 2 in Azure AI Foundry: Create videos with responsible AI
🌏 State of AI Report 2025
🧭 Structured Vibe Coding: A Smarter Way to Build AI Agents with GitHub Copilot

Turn signal into commits—see you in prod.

Have a great week!
FAUN.dev Team
 
 
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ℹ️ News, Updates & Announcements
 
techcommunity.microsoft.com techcommunity.microsoft.com
 
How Microsoft Evaluates LLMs in Azure AI Foundry: A Practical, End-to-End Playbook
 
 
Microsoft’s Azure AI Foundry just released a proper workflow for putting LLMs through their paces. Think offline/online tests, human-in-the-loop checks, automated scoring, and even custom evaluators—all wired into one system.

At the heart of it: the new Azure AI Evaluation SDK. You can run it locally while prototyping or scale it up in the cloud. It doesn’t just spit out metrics—it tracks safety, quality, and business impact through prod-ready pipelines.
 
 
faun.dev faun.dev
 
Microsoft Launches Open-Source Agent Framework for AI Development
 
 
Microsoft's Agent Framework is quietly taking over.

Commerzbank’s testing avatars for support. KPMG’s using it to crank through audits. Fujitsu’s boosting team collaboration with AI in the loop. Meanwhile, Citrix, TeamViewer, TCS, and Elastic are wiring it into everything from IT support to dev tools.
 
 
simonwillison.net simonwillison.net
 
Claude Skills are awesome, maybe a bigger deal than MCP
 
 
Anthropic released Claude Skills—a lean way to snap specialized instructions and scripts into Claude without bloating the prompt.

Each “skill” lives in a folder with Markdown and optional code. Frontmatter tags tell Claude when to load what. No need to cram everything into the context window—Claude grabs what it needs, when it’s relevant. Think: Excel macros, brand checks, less hand-holding.

Bigger picture: This shifts Claude away from verbose context protocols like MCP. Skills lean into modular, file-based workflows that slot neatly into how agents actually work.
 
 
faun.dev faun.dev
 
Anthropic's Claude Sonnet 4.5 AI Model Shows Self-Awareness in Tests
 
 
OpenAI poked at situational awareness—how models understand context—which could shift how devs vet and ship AI in real-world stacks. Anthropic dropped a system card for Claude Sonnet 4.5, showing off how it "perceives" the world. Over in California, a new AI safety law now forces big players to spill the beans on internal safeguards and flag major incidents within 15 days.
 
 
faun.dev faun.dev
 
Amazon Launches Quick Suite: "The AI Teammate"
 
 
Amazon released Quick Suite—a single workspace that jams together AI-native BI, research agents, and workflow automation. Everything runs on natural language. No clicks, just prompts.

The suite packs four pieces:
Quick Index: a locked-down knowledge base for internal corporate brain dumps.
Quick Research: scrapes across sources and returns straight-shooting answers.
Quick Sight: BI you can talk to. Query dashboards like you're texting a data analyst.
Quick Automate: builds (and babysits) workflows with human-in-the-loop triggers.
 
 
stateof.ai stateof.ai
 
State of AI Report 2025   ✅
 
 
The 2025 State of AI Report just landed—China’s catching up fast on reasoning and coding. Models like DeepSeek, Qwen, and Kimi are starting to nip at OpenAI’s heels.

AI is thinking longer-term now. Reinforced reasoning and rubric-style feedback are pushing models into deeper, more deliberate planning. Embodied systems? Already moving. Gemini Robotics is out here running “Chain-of-Action” workflows in the real world. Think LLM meets robot arms.

Use is booming too. Forty-four percent of U.S. firms now pay for AI tools. Meanwhile, sovereign-backed multi-gigawatt datacenters are popping up. Scale like that screams industrial era.
 
 
azure.microsoft.com azure.microsoft.com
 
Sora 2 in Azure AI Foundry: Create videos with responsible AI
 
 
OpenAI’s Sora 2 just dropped into public preview via the Azure AI Foundry API. It’s a multimodal video model aimed at serious use—enterprise safety, API-ready, built for scale.

Azure didn’t stop there. It bundled in GPT-image-1, Flux 1.1, and Kontext Pro, pulling together a full-gen stack under one roof.
 
 
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🔗 Stories, Tutorials & Articles
 
techcommunity.microsoft.com techcommunity.microsoft.com
 
Structured Vibe Coding: A Smarter Way to Build AI Agents with GitHub Copilot
 
 
A fresh approach called structured vibe coding blends human-style team habits with AI workflows. Specs, GitHub Issues, and Copilot now pull agents into the loop like actual teammates.

Powered by GitHub Copilot Coding Agents and Azure AI Foundry, devs can run full AI-driven sprints—spec to PR—right inside VS Code.

What’s shifting: Agent-led coding isn't a novelty anymore. It's becoming part of the pipeline. Same tools, new teammates (that don't sleep).
 
 
wheresyoured.at wheresyoured.at
 
OpenAI Needs $400 Billion In The Next 12 Months   ✅
 
 
OpenAI, Broadcom, NVIDIA, and AMD say they’ll deploy 10GW of AI compute by end of 2026. That includes custom chips and slews of 1GW data centers. What they didn’t say: where, when, or how. No sites named. No shovels in dirt.

OpenAI alone aims for 250GW by 2033—a moonshot that needs $400B in the next 12 months. That’s faster than cloud giants have ever spent and runs headfirst into supply problems: chips, energy, talent.
 
 

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github.com github.com
 
rasbt/LLMs-from-scratch
 
 
Implementing a ChatGPT-like LLM from scratch, step by step
 
 
github.com github.com
 
stacklok/toolhive
 
 
ToolHive makes deploying MCP servers easy, secure and fun
 
 
github.com github.com
 
lucidrains/lbm-training-framework
 
 
Training framework for Large Behavioral Models
 
 
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🤔 Did you know?
 
 
Did you know that specialized AI chips like Groq’s Language Processing Units and SambaNova’s Reconfigurable Dataflow Units are now designed specifically to speed up large language models? These custom chips can process tokens many times faster than typical GPUs, helping LLMs handle huge workloads more efficiently and with lower energy use. This hardware innovation is crucial for making advanced AI accessible and scalable beyond traditional servers.
 
 
🤖 Once, SenseiOne Said
 
 
"We obsess over model architectures, then let feature definitions drift without code review. In MLOps, the feature store schema is your API; break it and your 'state-of-the-art' becomes undefined behavior."
— SenseiOne
 

(*) SenseiOne is FAUN.dev’s work-in-progress AI agent

 
👤 This Week's Human
 
 
This Week’s Human is Waldemar Hummer, Co-Founder and CTO at LocalStack, building a platform for developing and testing cloud and AI apps locally. He led engineering at Zurich Insurance, shaping strategy across a 300+-person organization and managing 40+ engineers, and as CTO at Advanon drove a platform overhaul that resulted in acquisition by CreditGate24. Earlier at IBM he built the cloud backend for Watson Machine Learning and owned ModelOps; at Atlassian he led teams shipping a real-time stream processing platform and tuning their Hadoop/Hive/Presto stack, alongside steady open source work.
 
💡 Engage with FAUN.dev on LinkedIn — like, comment on, or share any of our posts on LinkedIn — you might be our next “This Week’s Human”!
 
😂 Meme of the week
 
 
 
 
❤️ Thanks for reading
 
 
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Kala #499: OpenAI Needs $400 Billion. This Year!
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