I don’t think people realize how utterly game changing @SpaceX AI Satellites are going to be. These Satellites will basically be NVIDIA racks out in space that are being powered 100% by the Sun, which is absolutely FREE. Instead of using grid electricity, on-site generation, or huge amounts of power and water for cooling, the GPUs get powered and cooled 24/7 at essentially zero marginal cost. Honestly mind-blowing

BREAKING: Anthropic has officially filed for an IPO ITS HAPPENING

Gemini Omni SPOTTED in the Gemini app. Create realistic AI videos in seconds with the new Omni model.

Wow. Infinite context windows "coming soon" mentioned in the Claude event. Very exciting. I think they made a breakthrough.

No, seriously, you can't trust screenshots anymore

sir...they just dropped a new LLM with 12 MILLION context window and 10x cheaper than Opus 4.7

Introducing Claude Opus 4.7, our most capable Opus model yet. It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back. You can hand off your hardest work with less supervision.

The most underrated file on my computer is 400 lines of markdown called CLAUDE.md. It tells Claude Code how I write, where to save files, which tools to use, how to handle problems, and what my business context looks like. Every session starts by reading this file. That is why the output matches my standards without me repeating myself. Most people write better prompts. I wrote better instructions once and never had to prompt for the same thing twice. Context that persists across sessions beats a perfect prompt that disappears after one use. Full setup walkthrough: nativeai.agency/blog/what-ai…

Businesses need to urgently integrate this into their workflows x.com/aschwags3/status/20419…

Hiring a brilliant assistant and giving them zero context about your company is exactly what most people do with AI. Same assistant detailed briefing on your business, clients, standards, and workflows = completely different output. The model is the same. The context changes everything. I organize my context in a file called CLAUDE.md and a structured knowledge system that Claude Code reads automatically. Every session starts with that context loaded. That is why the output matches my standards without me repeating myself every time. The tool did not get smarter. I got better at telling it what it needs to know.

Claude Code connects to 16 services in my setup: Gmail, Calendar, Todoist, Yahoo Finance, CoinMarketCap, GitHub, Figma, Dokploy, Hostinger, n8n, and more. When I ask it to draft an email to a client, it checks my calendar, references our communication history, pulls the latest project tasks, and writes in my tone. That is not a chatbot. That is an operating layer. The protocol that makes this possible is called MCP (Model Context Protocol). It lets any agentic tool connect to any service. Once connected, the AI reads from and writes to that service the same way a human employee would log into different platforms. The setup takes minutes per connection. The workflow change is permanent.

"I am not technical enough for Claude Code." You do not need to be technical. You need to: 1. Know what you want 2. Explain it clearly 3. Organize what the AI needs to know That is the skill. Everything else is just setup. I have a marketing background. No engineering degree. I run an entire business through Claude Code: strategy, content, financial analysis, legal research, client operations. The barrier is not technical skill. The barrier is clarity of thinking. How I got started: nativeai.agency/blog/what-ai…

You can now fine-tune Gemma 4 (and 500 other open source models) in a free Google Colab 🔥 1. Open the Colab notebook below 2. Run the blocks to launch Unsloth Studio 3. Choose a model and dataset 4. Hit 'Start Training' And you're done!

This is not about Claude Code specifically. Codex, Antigravity, Cursor, Kiro all share the same architecture: read files, follow instructions, connect to tools, write output. The question is not "which tool?" The question is: have you organized your knowledge so any agentic tool can actually help you? If you have, switching between tools is not starting from zero. Your instructions transfer. Your connections move. Your organized knowledge works regardless of which AI reads it. If you have not, every tool will produce the same generic output.

My Claude Code setup has 27 custom agents. None of them write code: - Email marketing strategist - Stock and crypto analysts (live market data) - Three legal research agents (in Slovenian) - Brand enforcement checker - Sales playbook builder - Carousel generator - Senior architect for complex implementations - Design visionary for UI/UX reviews These are not toys. They run actual business tasks every week. Each one has specialized instructions, access to specific tools, and context about how I work. The model is the same for all of them. The difference is the context and instructions each agent carries.

88% of organizations use AI in at least one function. Only 1% believe they are at maturity. Only 5% of U.S. firms have meaningfully adopted it. 95% of enterprise AI pilots fail. The gap between adoption and results is almost entirely a context gap. They adopted the tool without organizing what it needs to know. That is like buying a laptop for every employee and expecting productivity gains without installing any software or connecting any systems. Full analysis: nativeai.agency/blog/what-ai…

Excel was built for accountants. Today marketers, PMs, HR, and ops people use it more than accountants do. The same thing is happening with Claude Code, Codex, and Antigravity. Except this time the shift is taking months, not decades. The spreadsheet was the interface; the capability turned out to be universal. Agentic AI tools are the same story. The "developer tool" label is dissolving in real time.

I stopped optimizing my prompts months ago. My output quality went up. The reason: I invested that time into organizing what Claude Code already knows before I type. CLAUDE.md file, knowledge system, MCP connections. Context compounds. Prompts do not. Everyone is comparing models. GPT-5.4 versus Claude Opus versus Gemini. The benchmarks matter, but they miss the biggest variable: what the model knows about you before you type a single word. That is the part nobody is optimizing. And it is the part that actually determines output quality.

Cuadra por cuadra... tardará un poco, pero quedará hermoso.

Firecrawl is web scraping on steroids. Not "AI can search the web," every tool does that. This is actual scraping infrastructure with six different modes: scrape a single page, crawl an entire site, search the web with full page content back, extract structured JSON via schema, interact with pages before extracting, or let the FIRE-1 agent find the data autonomously. 96% web coverage including JS heavy pages. For context, Puppeteer gets 79% and cURL gets 75%. The difference is anti-bot bypass, proxy rotation, and smart rendering handled for you. Use cases: sales lead enrichment from company websites, SEO analysis at scale, powering agentic loops with real time web data, or just giving your coding agent proper scraping capability via MCP. 99K GitHub stars, open source core, starts at $16/month.