Federal Preemption Effort

The proposed executive order would establish an "AI Litigation Task Force" under the Attorney General, tasked with suing states whose AI laws are seen as obstructing national AI policy.

Targeted State Regulations

Key targets include recent laws in California (AI safety and catastrophic risk) and Colorado (algorithmic discrimination), which the order argues hamper innovation.

Funding as Leverage

The order could withhold Broadband Equity Access and Deployment (BEAD) funding from states that don’t align with the federal AI standard, putting financial pressure on state governments.

National Standard Push
Trump is arguing for a single federal standard for AI, instead of a decentralized patchwork of 50 state laws.

Legal Challenges Expected
Observers warn that the order may face strong constitutional challenges, particularly around state sovereignty and the limits of presidential power. 

Data-Center Revenue Breaks All Records

Nvidia’s data-centre business pulled in $51.2 billion this quarter, a 66% year-over-year increase.

Quarterly Revenue Surges by Another $10 Billion

Overall revenue reached about $57 billion, with the data-centre segment alone growing by $10 billion in three months.

AI Server Chips Drive Massive Demand

CEO Jensen Huang said AI server chips (particularly the “Blackwell” architecture) are “off the charts” and cloud GPUs are effectively sold out.

Nvidia Projects an Even Bigger Q4

The company’s outlook for Q4 is even more aggressive: Nvidia expects roughly $65 billion in revenue, implying another $8 billion growth in just three months.

Company Pushes Back Against “AI Bubble” Fears

Despite concerns of an AI bubble, Nvidia remains confident, positioning this surge as a tipping point in AI hardware demand rather than a peak.

Define the Problem You Want the Model to Solve

Start by choosing a simple task like sentiment detection, image classification, or spam filtering. Keep the scope small so you can understand every step without getting overwhelmed.

Collect and Prepare a Small Dataset

Gather sample data from open datasets or create your own. Clean it, label it, remove duplicates, and split it into training and testing sets. Good data matters more than fancy algorithms.

Choose a Lightweight Model Architecture

Pick something simple: logistic regression, a small neural network, or a decision tree. These are easy to train, quick to test, and perfect for learning the basics before jumping into advanced models.

Train and Fine-Tune the Model

Feed your dataset into the model, adjust parameters like learning rate or number of epochs, and watch how performance changes. This is where you learn what actually affects accuracy.

Test, Evaluate, and Improve

Run the model on your test set, check accuracy, precision, or loss, and look at where it fails. Improve the dataset, tweak parameters, or try a slightly bigger model, this cycle is what makes the model better.

Regie AI – Sales Enhancement

Regie AI is built for teams that want to stop wasting hours crafting sales emails and sequences. It studies your audience, your brand voice, and your past conversions to create messaging that feels natural, not templated. What you get is faster outreach, higher reply rates, and a smoother workflow for your sales team. It’s basically a writing partner that never runs out of ideas.

WolframAlpha – Computational Knowledge Engine

Wolfram Alpha isn’t your typical search tool. It doesn’t give you links; it gives you answers backed by real computation. Whether you’re solving math, decoding science queries, or analyzing data, it turns complex problems into neat, verified results. It’s the kind of tool that makes you feel smarter just by using it.

Disco AI – Legal Technology

Disco AI helps legal teams cut through mountains of documents without losing their sanity. It automatically reviews, tags, and prioritizes case materials so lawyers can focus on strategy instead of sorting files. The tool shines in high-volume litigation by speeding up discovery and reducing human error. It’s legal tech without the usual complexity.

Aimi FM – Generative Music Revolution

Aimi FM blends human creativity with generative AI to create music that evolves in real time. Instead of listening to a fixed track, you get endless compositions that shift with mood and style. It’s perfect for creators, chill listeners, or anyone who wants music that feels alive. Think of it as a playlist that never repeats itself.

Nmodes

Nmodes builds conversational AI that understands context instead of reacting with canned responses. Their tech helps businesses automate customer support and lead interactions without losing the “human feel.” It’s built to learn from real conversations, so responses get sharper over time. Great for companies that want smarter chatbots, not just faster ones.

Chattermill

Chattermill turns scattered customer feedback into clear insights. It reads surveys, reviews, support tickets, everything, then maps patterns behind customer sentiment. Teams use it to understand why users churn, what they love, and where product gaps sit. It’s basically the brain you wish every CX tool had.

Lirvana Labs

Lirvana Labs works on AI models that understand real-world context at scale. Their focus is on building decision-making systems that help companies automate workflows and reduce operational friction. The goal is simple: make complex processes easier and smarter without adding extra tools to the stack. It’s a thoughtful, research-heavy startup with strong technical depth.

Entefy

Entefy develops AI systems that unify communication, data, and automation into a single platform. Instead of juggling multiple apps, users get an intelligent layer that handles search, privacy, and collaboration. It’s built for teams that want efficiency without losing control of their data. Think of it as a digital workspace powered by AI instincts.