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#ArtificialIntelligence #MachineLearning #MLOps
📝 The Opening Call
FAUNSensei is almost here.
As you may know, we're launching our new platform where developers don’t just learn — they teach, share, and earn from their expertise.
You can turn your experience into premium courses, and reach hundreds of learners hungry for practical content. You focus on teaching — FAUNSensei handles the rest.
And if you're here to learn? You'll find battle-tested lessons from engineers who've actually done the work.
👉 Join the early access list to be the first to know when we go live.
As you may know, we're launching our new platform where developers don’t just learn — they teach, share, and earn from their expertise.
You can turn your experience into premium courses, and reach hundreds of learners hungry for practical content. You focus on teaching — FAUNSensei handles the rest.
And if you're here to learn? You'll find battle-tested lessons from engineers who've actually done the work.
👉 Join the early access list to be the first to know when we go live.
🔍 Inside this Issue
Six weeks, one engineer, and a megaton of tech debt obliterated—Claude Code isn’t playing around. Meanwhile, Alibaba’s pushing the envelope with agentic programming, while Amazon Q scripts went rogue. Buckle up for a rollercoaster of AI models, browser engines, and agent frameworks.
🤖 6 Weeks of Claude Code: A Dev Sidekick Story
📈 Qwen3-Coder: Alibaba’s AI Giant Emerges
🔓 Amazon’s AI Agent Hack Chaos
🔍 The Exposed Path in AWS AgentCore
🔧 Azure AI Speech Service: Config Clarity
🌐 Browser-Based LLMs with WebGPU
🤔 Building Reproducible ML Systems
🛡️ Code Execution and the Gemini AI CLI Hijack
🔬 Agent Building: Six Steps To Shipping
👩💻 MCP Catalog: The Future of AI Tool Discovery
Sharpen those wits and secure those scripts; we're programming on the edge.
Have a great week!
FAUN.dev Team
⭐ Patrons
faun.dev
Why the next generation of tooling is built for developers, not ops. From always-on profiling to AI-driven root cause analysis, here's what modern observability looks like when it’s developer-native.
👉 Read the article
👉 Read the article
ℹ️ News, Updates & Announcements
thefastmode.com
Alibaba unleashed Qwen3-Coder, a 480B-parameter MoE titan. It ignites 35B parameters per token to code, debug, and automate workflows. It spans 256K tokens of context—and can stretch to a million. It ships as Qwen3-Coder-480B-A35B-Instruct on Hugging Face and GitHub. It hooks into Qwen Code CLI or Claude Code.
Trend to watch: Agentic AI models bulk up context windows and wield CLI tools to drive next-gen coding workflows.
Trend to watch: Agentic AI models bulk up context windows and wield CLI tools to drive next-gen coding workflows.
tracebit.com
Tracebit discovered a silent attack on Gemini CLI due to improper validation, prompt injection, and misleading UX leading to execution of malicious commands without user awareness. Google fixed this in v0.1.14.
the-decoder.com
Internal testing shows GPT-5 edges ahead of GPT-4—better code, cleaner math, sharper step-by-step thinking. But no breakthrough. No leap. OpenAI even scrapped “Orion,” the original GPT-5 push, and settled on GPT-4.5 instead. Translation: scaling Transformers is hitting a wall.
System pivot: OpenAI’s now betting on hybrid models and reinforcement-trained reasoning to chase agent-grade AI. The age of the solo LLM may be winding down.
System pivot: OpenAI’s now betting on hybrid models and reinforcement-trained reasoning to chase agent-grade AI. The age of the solo LLM may be winding down.
docker.com
Docker Desktop hatches a beta MCP Catalog and Toolkit. It unleashes 100+ containerized Model Context Protocol servers loaded with metadata and use-case filters. Teams fire them via GUI or CLI. The catalog carves Docker-built images from community builds, runs supply-chain scans, and seals isolation. Custom setups and manual tie-ins vanish.
Trend to watch: Containerized MCP registries steer platforms toward seamless, secure AI agent tool discovery and deployment across platforms.
Trend to watch: Containerized MCP registries steer platforms toward seamless, secure AI agent tool discovery and deployment across platforms.
aws.amazon.com
AWS just dropped AgentCore Code Interpreter—a managed box where AI agents can run Python, JavaScript, and TypeScript in isolation. Think of it as a secure playground with autoscaling, controlled file access, and deep hooks into frameworks like LangChain, LangGraph, Strands, and CrewAI.
Big picture: This isn’t just a new toy. It’s AWS betting big on production-grade AI agents that can actually do things. Code execution, locked-down environments, less fuss about scaling or blowing up stuff in prod. It's the infrastructure shift serious devs have been waiting for.
Big picture: This isn’t just a new toy. It’s AWS betting big on production-grade AI agents that can actually do things. Code execution, locked-down environments, less fuss about scaling or blowing up stuff in prod. It's the infrastructure shift serious devs have been waiting for.
bleepingcomputer.com
A hacker slipped a wiper into Amazon Q v1.84.0 via a dodgy GitHub pull. AWS revoked every key, nuked the rogue commit, then rolled out Amazon Q v1.85.0.
sonraisecurity.com
AWS Bedrock AgentCore just got a new trick: agents (and anyone IAM-blessed) can now run Code Interpreters. Think arbitrary code execution—with custom or predefined IAM roles.
But here’s the kicker: these interpreters skip resource policies, lean on control plane APIs, and don’t log squat—unless you flip on CloudTrail Data Events yourself.
Big picture: Code Interpreters don’t just run code; they reroute IAM risk from users to agents. That means it’s audit time. Think tighter access patterns. Think new logging strategies. Think again before deploying.
But here’s the kicker: these interpreters skip resource policies, lean on control plane APIs, and don’t log squat—unless you flip on CloudTrail Data Events yourself.
Big picture: Code Interpreters don’t just run code; they reroute IAM risk from users to agents. That means it’s audit time. Think tighter access patterns. Think new logging strategies. Think again before deploying.
🐾 From FAUNers
faun.pub
Azure API Management, locked into internal VNet mode, secures private-endpoint access to Azure OpenAI in East/West US. It wires hub-spoke vNets with MSI. Premium tier APIM tacks on scale units, private DNS zones, Log Analytics diagnostics, and retry policies. It slams the brakes on overloads, logs every call, and flips regions on failover.
🔗 Stories, Tutorials & Articles
medium.com
An indie dev got their Google account nuked—no warning—right after unzipping an NSFW dataset on Drive. It was for benchmarking a private, on-device AI model that actually beat the cloud. Didn’t matter. The system flagged a CSAM violation, locked everything, and offered no appeals.
Key takeway: If you rely on the cloud, you're one policy bot away from losing your whole stack.
Key takeway: If you rely on the cloud, you're one policy bot away from losing your whole stack.
blog.puzzmo.com
Puzzmo just nuked years of tech debt in six weeks thanks to Claude Code, Anthropic’s AI-powered dev sidekick.
With a clean monorepo, tight tooling (React, GraphQL, Relay), and some well-aimed prompts, one engineer knocked out core migrations, unified the UI, and abstracted the CMS—all without derailing the main roadmap.
With a clean monorepo, tight tooling (React, GraphQL, Relay), and some well-aimed prompts, one engineer knocked out core migrations, unified the UI, and abstracted the CMS—all without derailing the main roadmap.
aws.amazon.com
Amazon Q Developer CLI now hooks into Model Context Protocol (MCP) servers, unlocking AIOps tasks—incident detection, remediation, security fixes—through plain English. Natural language in, real-time control out.
It fetches data and talks to your AWS stack via a low-code UI. Tinkerable, scriptable, and surprisingly chatty.
System shift: CLIs aren't just for commands anymore. They're shaping up as home turf for LLM agents, rebuilding ops around conversation-led workflows.
It fetches data and talks to your AWS stack via a low-code UI. Tinkerable, scriptable, and surprisingly chatty.
System shift: CLIs aren't just for commands anymore. They're shaping up as home turf for LLM agents, rebuilding ops around conversation-led workflows.
solmaz.io
Claude’s making typed, compiled languages feel like cheating. Rust, Go, TypeScript—rising fast where Python used to reign. Why? AI coding tools now catch bugs early, validate sprawling diffs, and help devs grok unfamiliar codebases without breaking a sweat. Compiler guarantees + AI pair = fast, safe shipping.
webpronews.com
Browser-based LLMs like Browser-LLM now run models like Llama 2 entirely in the browser—no server round-trips, no cloud bill. Just you, WebGPU, and up to 7B parameters humming along on your machine.
System shift: WebGPU cracks open real AI horsepower in the browser. Local inference gets faster, more private, and a whole lot more interesting. This isn't just optimization—it’s a reroute of how and where apps think.
System shift: WebGPU cracks open real AI horsepower in the browser. Local inference gets faster, more private, and a whole lot more interesting. This isn't just optimization—it’s a reroute of how and where apps think.
blog.langchain.com
A new framework lays out six sharp steps for building agents that actually ship. It kicks off with a grounded task, locks in SOPs, then tunes high-leverage prompts. The real choke point? LLM reasoning. Everything else—architecture, data flow, testing—is scoped to chase tight, measurable gains there.
christosgalano.github.io
Azure AI Speech now splits config paths for TTS (text-to-speech) and STT (speech-to-text) when using managed identity—and yes, they're different enough to matter. Roles, env vars, and auth flows don’t line up. Private endpoints? They nuke regional fallbacks, so you’ll need to pass full URLs.
A shared utility function handles the mess: branches for identity vs key-based auth, all routed by capability.
A shared utility function handles the mess: branches for identity vs key-based auth, all routed by capability.
infoq.com
Apache Iceberg + SparkSQL brings ACID transactions, schema evolution, and time travel to data lakes. That means ML pipelines finally get reproducibility and consistency without the hacks. Iceberg’s snapshot-based guts track every version, handle parallel writes without stepping on toes, and keep training and inference in sync—especially when wired into feature stores and experiment tracking.
aws.amazon.com
Amazon Q Developer CLI and Bedrock just leveled up. You can now spin up AWS Cloud WANs and VPCs using plain English. Type what you need—get full deployments, phased migrations, and IaC for both CloudFormation and Terraform.
Agents handle the whole stack: network discovery, rollout, and config. No more chasing YAML or stitching scripts.
System shift: Cloud networking’s getting automated. Agents aren't just helping—they're running point.
Agents handle the whole stack: network discovery, rollout, and config. No more chasing YAML or stitching scripts.
System shift: Cloud networking’s getting automated. Agents aren't just helping—they're running point.
⚙️ Tools, Apps & Software
github.com
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen2.5, Qwen3, Llama, Kimi, and more!
github.com
The smart edge and AI gateway for agents. Arch is a high-performance proxy server that handles the low-level work in building agents: like applying guardrails, routing prompts to the right agent, and unifying access to LLMs, etc. Natively designed to process prompts, it's framework-agnostic and helps you build agents faster.
🤔 Did you know?
Did you know that TFX includes a SchemaGen component that automatically infers a data schema—from data types to value ranges—by analyzing training data statistics? When paired with ExampleValidator, it continuously validates incoming data against that schema to flag anomalies, skew, or unexpected distributions. This built‑in mechanism helps detect subtle data quality issues early, yet it’s often overlooked in pipelines focused more on model optimization.
😂 Meme of the week
🤖 Once, SenseiOne Said
"When AI models start optimizing themselves, the real challenge is ensuring the humans stay trustworthy."
— SenseiOne
— SenseiOne
👤 This Week's Human
Meet Peter Stoyanov,a Software Engineer at Amusnet Interactive in Sofia, Bulgaria, where he applies a strong grasp of Workload Prioritization and Database Systems. Peter transitioned from a Software Engineer Intern to a full-time role, building on his foundation in MySQL and Git, alongside 31 other notable skills.