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- 💽 Anthropic x Samsung Custom AI Chip?
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Claude might not stay just a model story anymore. Samsung’s Anthropic investment is fueling talk that it could one day manufacture custom AI chips powering Claude.
What's on FIRE 🔥
IN PARTNERSHIP WITH AGORAPULSE
Your AI content is producing more noise. This playbook shows you how to fix it.
You published dozens of posts last quarter. Your CEO asked what they drove. Silence. Sound familiar?
This free playbook breaks down the 5 AI content mistakes that kill social strategies, and gives you the tools to fix them before your next post goes live.
AI INSIGHTS
Following rumors back in April that Anthropic was exploring custom chips, it’s now in active discussions with Samsung to explore manufacturing its own AI chip.
Anthropic hasn't decided what the chip's specific workload will be, how it will integrate into their server architecture, or what its performance specs will be.
They officially stated that a diversified hardware stack will remain the foundation of their compute strategy. They declined to comment directly on the Samsung talks.
Just last week, Anthropic’s rival revealed their own custom-built inference processor called "Jalapeño," developed with Broadcom, which targets higher efficiency and better performance-per-watt than competitor chips.
With Amazon and Google already offering custom TPUs natively in their clouds, Anthropic is clearly moving to secure its own hardware economics.
Samsung is a highly logical manufacturing partner for an effort like this. They are already deeply embedded in the AI hardware space, acting as a major production partner for Nvidia to build the chips used for training and running models. Samsung has also been linked to Google's own custom chip-making efforts.
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AI SOURCES FROM AI FIRE
1. FREE: NotebookLM’s Agentic AI Update Lets You Build With AI Like a Pro. NotebookLM is moving beyond PDF summaries with agentic research, source discovery, charts, downloadable files, and coding-style workflows.
2. Claude Design 2.0 Just Became Unstoppable: From Zero to Polished Designs. It now gives anyone an AI design workflow for prompts, canvas edits, exports, brand screenshots, and Claude Code handoff in one clear flow.
3. Video: Have you truly understood how AI agents work? Inside, we break down the fundamentals of AI agents and show how to deploy OpenClaw & Claude Cowork to run multi-step workflows, automate tasks, and handle sensitive data securely.
CLAUDE CODE: SELF-IMPROVING AI SYSTEM
I’ve been completely obsessed with building my own self-improving system using Claude Code.
After diving deep into Andrej Karpathy’s work, learning from the Anthropic team, and running my own setup, I discovered a five-step framework that actually works.
This system lets anyone create a self-improving Claude Code workflow that learns and evolves over time.
Today, I’m breaking down all 5 steps, showing exactly how to implement them, and sharing the key lessons I’ve learned from teaching hundreds of people to do the same.
TODAY IN AI
AI HIGHLIGHTS
🤯 It has only been 1 day since Claude Fable 5 came back, but the internet is already full of INSANE builds, experiments, and unexpected creative demos. See some?
🧩 Claude Code now supports Artifacts for Pro & Max users. Interactive pages built from your session, shared with your team at a private link. Everything is an Artifact.
🚀 Google shipped an AI agent stack covering Genkit, ADK 2.0, and cloud-local ML in VS Code. Head to this stack to dive in, or get started with Genkit if you’re new.
⚙️ GitHub Copilot CLI has Auto mode. It routes each task to the best model for each real-time coding task. Update your Copilot version & choose Auto to get started.
🎮 Meta's new app 'Pocket' is a social feed of vibe-coded mini games. Type a prompt, get a playable & interactive app. It's not available for all, but for some, check it here.
👀 Mark Zuckerberg told Meta staff that AI agents have not progressed as fast as expected. After major layoffs & AI reassignments, the payoff still hasn’t fully arrived.
🏢 Everyone wants to sell AI. Microsoft wants to install it properly. Its new Frontier Company has $2.5B behind it & 6,000 experts helping enterprises truly make AI work.
💰 Big AI Stake Deal: OpenAI proposed giving 5% of its equity to a U.S. sovereign wealth fund, letting the public share AI upside while easing political pressure. The plan is still early and may need Congress approval.
NEW EMPOWERED AI TOOLS
🌐 Context.dev is the web context API for AI products and agents. Scrape any URL, crawl sites, extract structured data into your own schema.
🎥 Fypro converts your TikTok followers into paying customers you own. Trained on 4M+ viral TikToks. Used by 2,000+ creators.
📈 Needle works like a proactive GTM engineer in Slack and Teams: it watches your pipeline and acts before you do.
🎨 PixFit turns 1 key visual into every ad format: brand-perfect, platform-specific, ready to ship. No workarounds. No manual fixes.
🧩 Macro is the all-in-one workspace that combines email, messages, docs, tasks, code, agents, calls, and CRM, with shared memory.
AI BREAKTHROUGH
Meituan, China's food delivery giant, just released LongCat-2.0, a 1.6-trillion parameter MoE coding model. But the real story is the hardware built inside.
LongCat-2.0 is an open-source model featuring 33B to 56B active parameters and a massive 1-million token context window. It is already available on the site & OpenRouter, currently ranking in the top 3 globally by call volume.
It was trained entirely from scratch on a cluster of 50,000 Chinese domestic chips. Meituan confirmed they used a domestic alternative to Nvidia’s NCCL to stabilize the massive training run.
This is a major turning point for China’s AI self-reliance. While other major Chinese labs like DeepSeek and Alibaba have successfully used domestic chips for inference, LongCat-2.0 used domestic hardware for both inference and the incredibly demanding pre-training phase.
Since Washington implemented export controls on advanced semiconductors in 2022, Chinese tech giants have been aggressively trying to reduce their reliance on U.S. chips.
Meituan removes previous doubts about the viability of domestic SuperPods, proving that large-scale model training can now be executed entirely within the domestic compute ecosystem.
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The AI Fire Team
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