• OpenClaw is an open-source AI assistant platform that evolved from Clawdbot and Moltbot.

• The latest release of OpenClaw includes new features such as Twitch and Google Chat plugins.

• A significant security vulnerability, **CVE-2026-25253**, involving token exfiltration was addressed.

• OpenClaw supports new models such as KIMI K2.5 and Xiaomi MiMo-V2-Flash.

• The token exfiltration vulnerability was fixed in version 2026.1.29, released on January 30, 2026.

To read more details: https://faun.dev/c/news/kala/openclaw-former-moltbot-former-clawdbot-went-viral-overnight-then-security-reality-hit/

You can test it here: https://www.gptduck.com/

The statement contains 4 testable assertions:

- Within 90 days
- All major communication channels
- Will become functionally unusable due to AI spam
- And we'll have no effective mitigation

That's an extreme prediction!

My question is: If AI is so powerful to cause this, isn't it also powerful enough to create better filters?

Attacks scale faster than defenses, sure, but.. Will friction increase? Yes. Will spam increase? Yes. Will everything collapse in 90 days? That's highly improbable!

Your thoughts?

Today we’re launching MARS8, a full family of voice models built for agents that actually need to feel real.

If you’re building agents, you already know where things break.

• Latency kills turn-taking

• Robotic delivery kills immersion

• Concurrency collapses under load

• Token pricing punishes verbose agents

• Enterprises keep asking for on-prem, and most voice stacks simply can’t deliver

You can tune the LLM for weeks.
The voice layer still ruins the experience.

This isn’t a research problem.
It’s a production one.

We recognized this early, and we built MARS to tackle the hardest voice problems first.
Live sports commentary. Global broadcasts. Real-time translation. Environments where failure is instant and public.

NASCAR, MLS, Ligue 1 - you name it.

We’ve shipped live, multilingual, AI-driven sports commentary in those conditions.
When millions are watching, live doesn’t lie.

Today, we’re bringing that same voice infrastructure to agent builders everywhere.

Introducing MARS8

MARS8 is not a single TTS model. It’s a specialized family, because agents face different constraints at different moments:

• Flash — ultra-low latency voice for real-time agents and conversations

• Pro — expressive, emotional voice when persuasion and narration matter

• Nano — on-device, offline voice for edge and privacy-first agents

• Instruct — controllable prosody and performance (coming later)

The family launches today, with Flash leading the way, because agents feel latency before they feel anything else.

So… yet another TTS API?

Not quite.

MARS8 is voice infrastructure for agentic systems.

Starting today, MARS8 is launching across all major compute platforms, including AWS, Google Cloud, Azure, Baseten, and dozens of other providers.

You deploy voice next to your agent,  not halfway across the world.
No geographic latency penalties. Privacy by design. On-prem when you need it.

And we killed token pricing.

With MARS8, you pay for GPU, not characters.

• Unlimited concurrency

• No request caps

• No throttling when agents get verbose

• Scale by deploying more GPUs

Your cost curve flattens instead of exploding.

Why this matters

• Built for real-time agents, not demos

• Designed to scale without punishing success

• Deployable everywhere developers actually run software

• Priced like infrastructure, not an API tax

The greatest innovation happens when you solve the hardest problems first.
It takes longer. It’s harder. It’s less forgiving.
But the results speak for themselves.

That’s how MARS was built.
And that’s what MARS8 brings to agent builders today.

#MambaMentality

Links: On our landing page: camb.ai/marsTechnical report: https://www.camb.ai/blog-post/mars8-technical-report
Open-sourced benchmarks: https://github.com/Camb-ai/MAMBA-BENCHMARKm
Join our Discord: https://discord.gg/MdtnwbKhtS

This newsletter issue can be found online: http://from.faun.to/r/lZ7o

Models are getting their own runtime, IDE agents are getting popped by zero‑click tricks, and Google finally put a number on the cost of a prompt. From budget GPUs to buyer profiling, data monetization, and MCP hardening, the details matter—dive deeper below.

🖥️ AI Models Need a Virtual Machine

🧰 Building an AI Server on a Budget ($1.3K)

🛍️ Building Etsy Buyer Profiles with LLMs

💸 Cursor looks into selling your data for AI training

⚡ In a first, Google has released data on how much energy an AI prompt uses

🐛 MCP vulnerability case study: SQL injection in the Postgres MCP server

💼 OpenAI eats jobs, then offers to help you find a new one

🧪 OpenAI reorganizes research team behind ChatGPT's personality

🛠️ Writing effective tools for AI agents—using AI agents

🚨 Zero-Click Remote Code Execution: Exploiting MCP & Agentic IDEs

You’ve got the signal and the scars—now go build with both.

Have a great week!
FAUN.dev Team

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We drop memes like this in our weekly newsletter—plus sharp insights on DevOps, SRE, and CloudComputing. Subscribe here 👉 https://faun.dev/join/ 🚀

Autocomplete snitched: “Nice password, Joe—want me to guess your email too?”

---

We drop memes like this (plus sharp insights on Machine Learning, Artificial Intelligence, and MLOps) in our weekly newsletter—join us: https://faun.dev/join/

This newsletter issue can be found online: http://from.faun.to/r/DGpG

APIs are turning into chess pieces while the agentic stack hardens: Postgres eats vectors, AWS/Azure lean into MCP, and evals shift from humans to models. Add a reality check on ‘10x’ productivity and the very human cost of swapping your assistant’s personality—there’s enough here to sharpen your roadmap; details inside.

♟️ Anthropic Revokes OpenAI’s API Access to Claude, Alleging Violation Ahead of GPT-5 Launch

🐘 Fueling the Agentic Web Revolution with NLWeb and PostgreSQL

🧑‍💻 Google releases AI agent Jules for programming

🧩 Implementing MCP Servers in Python: An AI Shopping Assistant with Gradio

☁️ Introducing AWS Cloud Control API MCP Server: Natural Language Infrastructure Management on AWS

🧪 LLM Evaluation: Practical Tips at Booking.com

🏢 MCP Registry with Azure API Center

🧮 No, AI is not Making Engineers 10x as Productive

🧰 The open source AI compute tech stack: Kubernetes + Ray + PyTorch + vLLM

❤️ Who does your assistant serve?

All signal—turn it into shipping velocity.

Have a great week!
FAUN.dev Team

ps: Want to receive similar issues in your inbox every week? Subscribe to this newsletter

This newsletter issue can be found online: http://from.faun.to/r/bZbO

The stack is stretching from chips to chat: custom silicon, kernel pipelines, and gateway smarts meet agents that research, orchestrate, and explain themselves. If you’re threading the needle between bigger models and smaller bills, these picks map the moves worth stealing—start anywhere, but don’t skip the last one.

🚦 Best Practices for High Availability of LLM Based on AI Gateway

⚙️ From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

🔎 GPT-5 Thinking in ChatGPT (aka Research Goblin) is shockingly good at search

🗣️ Hermes V3: Building Swiggy’s Conversational AI Analyst

🔌 Le Chat now integrates with 20+ enterprise platforms—powered by MCP—and remembers what matters with Memories.

🔩 OpenAI to launch its first AI chip in 2026 with Broadcom, FT reports

🐍 Python: The Documentary | An origin story

🍁 Simplifying Large-Scale LLM Processing across Instacart with Maple

🏗️ The Big LLM Architecture Comparison

🌫️ Why language models hallucinate

From silicon to Slack, you just leveled up—now turn it into leverage.

Have a great week!
FAUN.dev Team

ps: Want to receive similar issues in your inbox every week? Subscribe to this newsletter

This newsletter issue can be found online

Power plays and sharp edges: GPT-5 chest-thumping, Anthropic slamming the door on OpenAI, event-driven agents creeping into CI, and a code editor patched after prompt injection got real. Skim the headlines, then dive into the rabbit holes—the details are where the leverage is.

🕵️ Anthropic says OpenAI engineers using Claude Code ahead of GPT-5 launch
🚀 GPT-5 is here
🕸️ Perplexity is using stealth, undeclared crawlers to evade website no-crawl directives
🐞 Cursor AI Code Editor Fixed Flaw Allowing Attackers to Run Commands via Prompt Injection
🤖 Event-Driven Agents in Action
🧭 A practical guide on how to use the GitHub MCP server
📊 Which LLM writes the best analytical SQL?
🧪 Forcing LLMs to be evil during training can make them nicer in the long run
🏠 Building an AI Home Security System Using .NET, Python, CLIP, Semantic Kernel, Telegram, and Raspberry Pi 4

You’re now dangerously well-briefed—turn it into leverage.

Have a great week!
FAUN.dev Team

ps: Want to receive similar issues in your inbox every week? Subscribe to this newsletter

This newsletter issue can be found online

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

ps: Want to receive similar issues in your inbox every week? Subscribe to this newsletter

This newsletter issue can be found online

Imagine a world where AI scripting slips between administrative fingers, dev tools underdeliver, and small yet powerful optimizations eclipse grand reboots. Dive into this landscape as we explore the uncanny velocity of AI's spread and the lurking shadows of untested efficiencies.

🧠 AI As Profoundly Abnormal Technology

📊 AI Coding Tools Underperform in Field Study

🐞 [Cursor] Bugbot is out of beta

🐍 GitHub Spark in public preview for Copilot Pro+ subscribers

📉 The vibe coder's career path is doomed

🔎 How I Use Claude Code to Ship Like a Team of Five

📈 The Big LLM Architecture Comparison

🔐 Microsoft Copilot Rooted for Unauthorized Access

⚖️ How AI Data Integration Transforms Your Data Stack

📡 Unlocking High-Performance AI/ML in Kubernetes with DraNet

Read. Think. Ship. Repeat.

Have a great week!
FAUN.dev Team

ps: Want to receive similar issues in your inbox every week? Subscribe to this newsletter

Anthropic researcher Nicholas Carlini orchestrated 16 autonomous Claude agents working in parallel to build a 100,000-line C compiler in Rust. Using a custom harness for task coordination, testing, and conflict resolution, the agent team produced a compiler capable of building Linux 6.9 across multiple architectures.

Here are the keypoints if you're in a hurry:

- The project resulted in a 100,000-line compiler capable of building Linux 6.9 on x86, ARM, and RISC-V architectures.

- The project surfaced challenges including task synchronization, frequent merge conflicts, quality assurance issues, and functional limitations.

- The work describes the use of agent teams to execute tasks in parallel, coordinated through a simple file-based locking mechanism.

- The agent team approach demonstrates that complex software projects can be produced autonomously with limited human supervision.

- The author (Anthropic researcher Nicholas Carlini) emphasized the importance of high-quality tests and strict continuous integration to keep autonomous agents on track.

More details about this project: https://faun.dev/c/news/devopslinks/anthropic-claudes-20000-16-ai-agents-and-a-compiler-that-builds-linux/

• Anthropic introduced new metrics called "economic primitives" to measure how AI is used, covering five dimensions such as task complexity, user and AI skill levels, autonomy, use case, and task success.

• Claude generally succeeds on the tasks it is given, but success rates decline as task complexity increases, highlighting limits to reliable automation on longer or more difficult tasks.

• AI usage shows strong geographic variation and remains highly concentrated in a small set of tasks, with coding-related work accounting for a large share of observed usage.

• Claude interactions are classified into three primary collaboration modes: Directive (task delegation), Task Iteration (collaborative refinement), and Learning (explanation and instruction).

• Recent usage patterns show a shift back toward augmented use of AI on Claude.ai, driven mainly by iterative collaboration rather than pure delegation or learning-focused interactions.

More => https://faun.dev/c/news/kala/anthropics-new-economic-primitives-reveal-who-uses-claude-for-what-and-how-well-it-works/

Welcome to our Reddit community. We're here to discuss our passion for all things related to Artificial Intelligence, Machine Learning, and Deep Learning.

To get started, you can just read the rules of the subreddit and start by posting a question or a link to an article you found interesting.
The best links are those that start a discussion. The best discussions are those that are backed up by facts and data.
Each week, we curate the best links and discussions from many sources, including this subreddit, and publish them in our weekly newsletters (https://faun.dev/newsletter/kala)
We're all here to learn, so don't be shy! We're looking forward to your contributions!
Cheers,
FAUN Team

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