Date: {{current_date_full_with_day}}

Hey {{first_name | AI Visionaries}},

We have a bunch of useful articles this edition:

As you can see, the focus has been on increasing the value to you- our subscribers!

-Renjit

PS: If you want to unleash the power OpenClaw AI agents to grow your business, setup time speak to me, here»

The most striking takeaway from the Gemma 4 release isn’t just the performance—it’s the efficiency: the 31B model is currently ranked #3 globally among open models on the Arena AI leaderboard, effectively outperforming giants twenty times its size.

Google DeepMind’s latest offering lands with a commercially permissive Apache 2.0 license, finally giving developers the freedom to build advanced reasoning tools and agentic workflows without the typical proprietary strings attached.

The model family is strategically tiered, ranging from highly optimized 2B and 4B Effective models for mobile and IoT devices to a 26B Mixture of Experts and a 31B dense model that can run on a single H100 or even a high-end consumer GPU.

These models arrive as native multimodal powerhouses with up to 256K context windows, built-in function calling, and support for over 140 languages right out of the box.

For the modern developer, this matters because the intelligence-per-parameter gap has officially closed, allowing you to deploy frontier-level, agent-ready AI directly on your own hardware while maintaining total digital sovereignty over your data and infrastructure. I am looking forward to installing the 4B model on my Mac Mini and testing it out with my Openclaw and Hermes Agentic harness.

The DTC beauty category is crowded. To break through, Jennifer Aniston’s brand LolaVie, worked with Roku Ads Manager to easily set up, test, and optimize CTV ad creatives. The campaign helped drive a big lift in sales and customer growth, helping LolaVie break through in the crowded beauty category.

Learn more

Nobody tells you there's a toolkit. They just hand you a list of tools.

A few weeks ago, someone in my network asked a question that stopped me: "Do I need an IDE? What even is an IDE?"

This wasn't a beginner. This was a professional trying to build with AI, staring at a wall of tool names and acronyms and feeling like everyone else got a manual they didn't.

I recognized the feeling. I had it a year ago. And the thing nobody told me then, the thing I wish someone had just said plainly, is that there's no single right tool. There's a toolkit. You develop it by building, and you keep reaching for different tools depending on the job.

Here's what that looks like after ten columns and a lot of shipped projects.

Chat is where thinking happens.

Leo, my AI Chief of Staff, lives in a Claude Project. I talk to Leo on my phone during my commute, at my desk, and late at night during weekly reviews. Chat is where I break down problems, plan builds, draft content, and think out loud. It's not a beginner tool I graduated from. It is still the tool I use most.

Chat is where you learn to direct AI — how to describe what you want, how to evaluate what comes back, how to iterate. Every other tool in the kit assumes you already know how to do this.

Cowork is where delegation lives.

Once you catch yourself running the same workflow repeatedly — same prompt, same format, same output — that's your signal. Move it to something that runs without you.

My meal-prep assistant kicks off every Friday morning, reads my calendar, and calculates portions for the week. I spec'd it on a train. An agent built it while I was at work. I didn't touch it until I reviewed the output. That shift from "I do this with AI" to "AI does this for me" is a different kind of building.

Claude Code is where serious builds happen.

Terminal-first, reasoning-heavy, the most powerful tool in the stack. My Daily Brief pipeline, the fluency assessment skill, my personal website were all built here. There is a learning curve. You're directing an agent through conversation in a terminal, and you need to be comfortable with that. But for the kind of building I want to do — systems that connect to APIs, run on schedules, manage state across services — nothing else matches it.

The supporting cast layers on, not in sequence.

Lovable is where I go occasionally for design. Fast prototyping, beautiful output out of the box. I used it at a hackathon to build two apps in a day. When the build gets serious, I sync to GitHub and continue in Claude Code. But I'll use Lovable again the next time I need a quick prototype. It's not a stage. It's a tool.

Antigravity surprised me. You describe what you want, it generates a detailed implementation plan you review and edit before anything gets built. Once the plan is right, you approve it, and the agent builds to your spec. For someone who thinks in specs rather than code, that workflow is the closest thing to managing a junior developer I've found.

Cursor is an engineer's tool. If you already write code, it's excellent. If you're the person asking "what's an IDE," it's not where you start.

The zoom-out

Well over a year in, I've shipped an AI Chief of Staff, a daily news brief, a meal-prep automation, a quiz generator, a fluency assessment skill, and more. Every one of them used a different combination of tools. Very few of them were built using just one.

The pattern that keeps repeating is that tooling literacy matters more than any individual tool. Knowing what to reach for. Knowing when to stay in chat and when to move to an agent. Knowing that the prototype tool and the production tool can be different tools and that's fine. Organizations that standardize on one AI build platform because it's "the most capable" miss this entirely. The team that can fluidly move between tools based on what the task requires will out-ship the team locked into a single platform every time.

Nobody hands you that map. Start where you are. Build your way there.

I built a tool that can help you along the way.

The fluency assessment skill combines Anthropic's 4D framework — Delegation, Description, Discernment, Diligence — with Steve Yegge's 8-stage Dev Evolution model into a single quarterly self-assessment. Run it in Claude Code, get scored on both axes, track your progress over time.

GitHub: github.com/n-pillai/ai-fluency-assessment-skill 

Nisha Pillai transforms complexity into clarity for organizations from Silicon Valley startups to Fortune 10 enterprises. A patent-holding engineer turned MBA strategist, she bridges technical innovation with business execution—driving transformations that deliver measurable impact at scale. Known for her analytical rigor and grounded approach to emerging technologies, Nisha leads with curiosity, discipline, and a bias for results. Here, she is testing AI with healthy skepticism and real constraints—including limited time, privacy concerns, and an allergy to hype. Some experiments work. Most don't. All get documented here.

OpenAI just reset the scale of the tech industry by raising a staggering $122 billion at an $852 billion valuation, confirming that the company is currently outgrowing the early trajectories of both Google and Meta by a factor of four.

With monthly revenue already hitting $2 billion, the team is aggressively pivoting toward a unified AI superapp that integrates ChatGPT, Codex, and agentic workflows into a single interface designed to replace fragmented tools with a system that understands intent and operates across complex data silos.

They had a bit of a mis-step with Sora, but their weekly active usage continues to impress. And they are making multiple acquisitions like OpenClaw to continue their dominance in the space of personal AI.

This expansion is supported by a massive diversification of their compute stack, moving beyond a single-provider model to a multi-cloud and custom silicon strategy aimed at structurally lowering the cost of intelligence while scaling toward one billion weekly active users.

So what?

This capital surge signals that the transition from experimental AI to core global infrastructure is effectively complete (or atleast that will be the story that the investment bankers spin).

For financial advisors, bad delegation creates delays, inconsistency and risk. That’s why the issue isn’t whether to delegate. It’s whether you’re doing it with structure. BELAY created the free Financial Advisor’s Delegation Guide to help advisors delegate with clarity, tighter workflows and better visibility into what gets done and when.

Get the Guide!

Imagine being locked inside a driverless car for two hours while it sits motionless in the fast lane of a busy highway—that’s exactly what happened to passengers in Wuhan this week after a massive system failure paralyzed Baidu’s Apollo Go robotaxi fleet.

This widespread outage affected at least 100 autonomous vehicles, causing them to freeze simultaneously across the city and creating dangerous obstacles in high-speed traffic zones.

While Baidu has yet to explain the technical glitch, this incident echoes similar recent failures in the West, such as Waymo’s fleet stalling in San Francisco during local power disruptions. The collapse of such a large-scale deployment serves as a stark reminder that even the most advanced AI drivers are still tethered to centralized systems that can fail catastrophically.

So what? It highlights a major hurdle for the industry: as we move from testing to mainstream adoption, the biggest threat to passenger safety might not be a lack of road awareness, but the vulnerability of the invisible network managing the entire fleet.

A potential client of ours wanted to check if Tiiny (a new device that is currently on kickstarter), would be good to host a local LLM to power their OpenClaw. That is when I got researching into this power-packed box.

Tiiny AI’s Pocket Lab is a compact local-LLM device with ~190 TOPS, 80 GB RAM, and 1 TB storage, capable of running large models on-device.

It uses TurboSparse and PowerInfer to boost efficiency, delivering ~10–12 tokens per second. At ~$1,400, it undercuts GPU rigs, but only wins economically if used continuously and for latency-sensitive, on-prem workloads.

For OpenClaw-style agents, it offers clear benefits:

Performance is sufficient for applicatons in low-frequency trading and reasoning tasks.

However, constraints matter:

Cloud GPUs or local 3090/4090 setups remain more flexible for most users.

Bottom line: Pocket Lab is a niche optimization. Best for always-on, single-node deployments.

Not a replacement for scalable or production-grade infrastructure. So we recommended against it for our client as he wanted to use it for crypto trading.

Attention is the hardest thing to buy. And everyone else is bidding too.

When people are scrolling, skipping, swiping, and split-screening their way through the day, finding uninterrupted moments where your audience is truly paying attention is the priority.

That’s where Performance TV stands out.

Check out the data from 600+ marketers on the most effective channels to capture audience attention in 2026.

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I get surprised when some people tell me that Claude doesn't "work" for them and ChatGPT is producing "fluff".

In 2023? Maybe true, but in 2026, you have to learn to wrangle the best out of these advanced models by using a combination of system prompts and context engineering. There is no excuse!

I am sharing my system prompt that I have been using on Claude (and I use a version of this in ChatGPT). Works well for me to extract the best out of these intelligent models.

How to use this?

1. Claude: Go to settings>general>personal preferences in Claude to put this in.
2. ChatGPT: Go to "Personal Preferences" (click on your user name at the bottom left and a menu will open up)

Use it as a starting point and evolve it for your own specific needs - that is where the magic happens.