Law requires AI to tell you it’s AI + Samsung’s tiny AI model beats giant reasoning LLMs

Welcome to The Prompt Innovator Newsletter

Hello, TPI Trailblazers! ⚡️
This week: ruthless focus, smoother flow. We’re cutting work-in-progress, automating the busywork, and making every sprint tell a clear story from intent → impact.

What you get in this FREE Newsletter

In Today’s 5-Minute AI Digest. You will get:

1. The MOST important AI News & research
2. AI Prompt of the week
3. AI Tool of the week
4. AI Tip of the week

…all in a FREE Weekly newsletter. 

Let’s spark innovation together!

1. The $230 Million Question: Can AI Hardware Be Saved From Itself?

After a year of spectacular hardware failures, the legendary Apple designer is betting that AI devices don't need to be faster—they need a soul.

The article asks whether AI hardware can be saved after several high-profile failures like the Humane AI Pin and Rabbit R1. It points out that these devices tried to replace smartphones but didn’t actually solve real problems, often doing everyday tasks worse and costing too much.

Now, Jony Ive (the designer behind many Apple products) and Sam Altman (CEO of OpenAI) are exploring a new type of AI device that might not even have a screen. Their goal is to create technology that supports people’s wellbeing—helping them feel calmer and less stressed—instead of simply being faster or more powerful.

The article argues that the next successful AI hardware will need to fit naturally into habits people already have. It highlights Meta’s smart glasses as an example that works because they add to normal behavior rather than forcing new ones.

Ive and Altman’s concept is still in the early stages, possibly targeting a 2026 release, and there have been some technical hurdles. But the author suggests the real breakthrough might come from designing technology with a clear human purpose, not just better specs.

[Read the full story]

2. New California law requires AI to tell you it’s AI

Bots, Be Honest: California’s “Tell-Me-You’re-AI” Mandate

California just said, “bots gotta introduce themselves.” Governor Gavin Newsom signed SB 243, a law that forces “companion” AI chatbots to clearly tell users they’re AI—not people. The rules aim to curb deception and add safety rails: starting in 2026, qualifying chatbot operators must file annual public reports to the state’s Office of Suicide Prevention on how they detect and respond to users showing signs of self-harm. The law lands alongside a broader kids’ online safety push (think age checks and usage nudges), signaling that disclosure and duty-of-care are going mainstream in AI product design. Expect visible “I’m an AI” labels, safer escalation flows, and fewer ambiguous bot-human chats for Californians—and likely beyond, as platform policies ripple out.

[Read the full story]

3. Samsung’s tiny AI model beats giant reasoning LLMs

Samsung (SAIL Montréal) reporting a “tiny” model called TRM (Tiny Recursive Model) that reportedly outperforms much larger large language models (LLMs) on challenging reasoning benchmarks.

Here are the key points:

• TRM has only 7 million parameters, which is vastly smaller (less than 0.01 %) compared to typical large models.

• The model uses a recursive refinement process. Given a question, an initial guess, and a latent reasoning feature, it cycles through a number of refinement steps to improve its internal reasoning and its prediction.

• They experimented with repeating this process up to 16 times, letting the model iteratively correct mistakes.

• Through architectural choices (e.g. using fewer layers to avoid overfitting) and simplifications (removing reliance on complicated fixed-point theory, backpropagating through the recursion), TRM shows strong performance.

• On benchmarks:

  • Sudoku-Extreme: accuracy jumps from ~56.5 % (with prior HRM model) to 87.4 % with TRM.

  • Maze-Hard: TRM 85.3 % vs HRM 74.5 %.

  • ARC-AGI (Abstraction & Reasoning Corpus): TRM achieves 44.6 % on ARC-AGI-1, and 7.8 % on ARC-AGI-2, surpassing HRM (27 M parameters) and some large LLMs (for example, Gemini 2.5 Pro has 4.9 % on ARC-AGI-2).

• They also introduced an adaptive mechanism (ACT) to decide when to stop refining and move on, simplified to reduce training cost.

The core claim is that TRM demonstrates a path to achieving strong reasoning performance with far fewer resources, challenging the notion that “bigger is always better” in AI.

[Read the full story]

4. How AI is changing the way we travel

From Reels to Real-Time Rebooking: How AI Plans (and Saves) Your Holiday

AI is quietly reshaping every stage of travel—from the first spark on Instagram Reels to an itinerary that builds (and fixes) itself. In this interview-driven piece, Saudi Tourism Authority chief Fahd Hamidaddin says we’re moving into “agentic AI,” where systems don’t just recommend; they act—rebooking weather-hit flights, reshuffling reservations, and smoothing the messy bits in real time. The upside: hyper-personalised trips and pressure relief on overcrowded hotspots as algorithms surface lesser-known gems. The catch: subtle algorithmic nudges can narrow our choices, so transparency, consent, and traveler control have to be non-negotiable.

[Read the full story]

AI Prompt of the Week:
The Problem Breakdown Engine

The Challenge
You know something needs fixing, but the problem feels like a tangled mess. Where do you start? Who should own it? What are the actual steps? Vague assignments create confusion, missed deadlines, and work that doesn't solve the real issue.

The Solution
Use the RASCE framework to turn any complex problem into a clear action plan. RASCE stands for Role-Action-Steps-Constraints-Examples. Think of it as a systematic way to answer: Who does what? How do they do it? What's in the way? And what does success look like?

The Prompt

Copy this template and fill in the bracketed sections:

I need help solving: [describe your problem in 1-2 sentences]

Act as my problem breakdown specialist. Here's what I need:

Background:

* The problem: [what's broken or needs to happen]

* Why it matters: [impact on business/team/customers]

* Current state: [what's happening now]

* Timeline: [when this needs to be done]

Break it down using RASCE:

1. Role – Who should own this? (specific person, team, or function)

2. Action – What's the main thing that needs to happen? (one clear sentence)

3. Steps – What are the 5-7 actions needed to make this happen? (in order)

4. Constraints – What limits exist?

   - Budget: [amount or "none specified"]

   - Time: [deadline or "flexible"]

   - Resources: [team capacity, tools available]

   - Rules: [compliance, policies, technical limitations]

5. Examples – Show me 2-3 examples of similar work done well

   (either from our company, competitors, or made-up scenarios)

Also include:

* Which steps depend on each other?

* Where do we need expert help or approval?

* Any quick wins we can knock out in under 2 hours?

* The top 2 risks that could derail this

* A simple "definition of done" checklist

Format the answer as:

* Clear RASCE sections

* A simple dependency map (if steps rely on each other)

* Risk + how to avoid it (2 points per risk)

* The single best action to take in the next 60 minutes

Why This Works

• No more confusion: Transforms "someone should probably look into this" into "Sarah will do X by following these 5 steps."

• Spots problems early: Budget limits, missing skills, and timeline conflicts show up before you start work.

• Examples make it real: Seeing how others tackled similar problems helps your team picture what success looks like.

• Keeps scope tight: The constraints section forces honest conversations about what's actually doable.

• Works everywhere: Use it for product launches, fixing broken processes, hiring plans, tech projects, or strategy work.

Real Examples

Example A: Reducing Customer Support Backlog

The Situation
Your support response time has doubled to 48 hours. The team is drowning in tickets, and customers are getting frustrated.

How to use RASCE:

• Problem: 1,200 open tickets, response time at 48 hours (was 24 hours)

• Role: Support Team Lead

• Action: Implement a triage system to cut response time to 12 hours

• Constraints: Can't hire anyone new, customer satisfaction must stay above 4.2, can't automate sensitive queries

• Examples: "Company X created template responses for their 60% most common questions" or "Team Y trained junior agents to handle the top 10 issues independently"

What you'd learn: AI breaks this into steps like "Categorize tickets by urgency," "Create response templates," "Train team on triage process"—plus flags the risk that rushed responses might hurt quality.

Example B: Launching a New Product Feature

The Situation
Your dev team built a feature, but marketing and sales don't know how to talk about it. Launch is in 4 weeks.

How to use RASCE:

• Problem: Feature is 70% done but no go-to-market plan exists

• Role: Product Marketing Manager

• Action: Create messaging, sales enablement materials, and launch sequence

• Constraints: 4-week deadline, €8k content budget, sales team is already booked solid

• Examples: "Last feature used a beta waitlist to build early buzz" or "Competitor Y soft-launched to existing customers first"

What you'd learn: AI maps out creating positioning docs, training sales, coordinating with customer success—and warns that the tight sales calendar might delay training sessions.

Tips to Get More from This Prompt

Make it a delegation tool
The RASCE output becomes a perfect briefing document. Share it with your team so everyone's aligned on who's doing what and why.

Pair it with last week's Decision Filter
Use the Decision Filter to choose the right option, then use RASCE to figure out how to execute on that choice.

Ask the "unlimited resources" question
Add: "If I had unlimited budget and time, what would you do differently?" This reveals what you're really sacrificing due to your constraints.

Get a pre-flight checklist
Request: "Give me a 5-point checklist to verify before I start Step 1." Catches missing approvals or forgotten prep work.

Create templates for repeat problems
If you solve similar problems often (vendor evaluations, sprint planning, customer onboarding), ask AI to turn the RASCE output into a reusable template.

Quick Version (for when you're in a hurry)

I need to solve [describe problem]. Break it down using RASCE: 

- Who should own it? (Role)

- What's the main thing to do? (Action)

- What are the 5-7 steps? (Steps)

- What's limiting me? Budget, time, resources, rules (Constraints)

- Show me 2-3 examples of this done well (Examples)

Also: show dependencies, flag risks with fixes, and tell me what to do in the next hour.

Bottom Line

Turn "this is too complicated" into a battle plan with clear ownership, sequenced steps, and guardrails—so your team ships instead of spinning.

AI Tool of the Week
Heyy — AI-Powered Messaging Agents for Omni-Channel Customer Conversations

The Pitch

Heyy enables businesses to spin up “AI Employees” that manage customer messaging across WhatsApp, Instagram, Messenger, and website chat widgets. These agents can answer FAQs, qualify leads, resolve common issues, escalate when needed, and tie into your CRM or e-commerce systems.

Because it runs 24/7, Heyy offers instant responses to customers, which can help improve conversion rates and reduce response latency — freeing your human team to focus on more complex or high-value tasks. heyy.io

Why It’s Worth Your Time

• Omnichannel automation: You manage all customer chat channels in one interface. 
  

• Smart escalation logic: When queries are complex or sentiment indicates frustration, the AI routes them to humans with full context. 
  

• Deep integrations: Heyy connects with CRMs, e-commerce platforms, internal tools, and supports custom actions via API/webhooks.
  

• Revenue enablement: Beyond support, you can use the AI agents to upsell, cross-sell, or recommend alternatives if stock is unavailable.
  

• Scalable & cost-efficient: For businesses scaling customer interactions, Heyy helps expand capacity without adding many human agents.
  

Where It Struggles (Reality Check)

• Response accuracy: In ambiguous or poorly defined queries, the AI may produce incorrect or generic answers.
  

• Customization limits in low tiers: Some advanced features or fine-tuning might only be available in higher or enterprise pricing tiers. (Reported in reviews) aidealise.com
  

• Dependence on data & training: The quality of responses depends heavily on your input data, conversation history, FAQs, and how well the AI is trained.
  

• Privacy & compliance caution: Because the tool handles customer data across multiple channels, ensuring GDPR, CCPA, or industry-specific compliance is essential.
  

• Over-automation risk: If overused, the system may feel impersonal—so balancing AI + human handoff is key.
  

Pricing Snapshot (2025 Estimate)

Public pricing is less clearly disclosed, and many reviews suggest that full features require a paid/enterprise tier. 
Heyy offers a free or trial entry point (with limited features) to test basic automation. aidealise.com
For full functionality — omnichannel, integrations, escalation logic — expect to upgrade. Some reviews mention ~$19/month for advanced features (depending on business size). aidealise.com

Speed-Run: How To Get Started in Minutes

1. Connect channels (WhatsApp, Instagram, Messenger, website chat)
   

2. Import or build your FAQ / knowledge base
   

3. Define escalation rules and triggers
   

4. Train/adjust sample dialogues
   

5. Launch a pilot, monitor outputs, and refine based on real interactions
   

Micro-Workflows That Punch

• Lead qualification flow: The AI asks qualifying questions up front, then passes qualified leads to sales with context.
  

• Order status & tracking: The agent fetches real-time order info from your backend and shares with customers.
  

• Return & refund initiation: Customers can start returns via chat; your system triggers the return workflow automatically.
  

• Post-chat surveys or cross-sell prompts: After resolution, the agent asks for feedback or recommends complementary products.
  

Alternatives to Keep on Radar

If you’re evaluating multiple messaging/automation platforms, compare Heyy against Intercom, Drift, ManyChat, Zendesk’s Answer Bot, or Tidio. These tools differ in sophistication, integrations, pricing, and ecosystem.

Bottom Line (Our Take)

Heyy is strong for businesses seeking to scale customer communications across multiple messaging channels without ballooning headcount. Its smart routing, integration capabilities, and omnichannel support make it a compelling “80/20” choice for many. However, to hit its full potential, you’ll need clean training data and careful configuration to avoid weak responses or customer frustration. If you're building a lean CX stack and want AI-powered “always-on” coverage, Heyy is absolutely worth a pilot.

AI Tip of the Week
Stop Cleaning AI Output — Force Structured JSON Across OpenAI, Claude & Gemini

Tired of messy paragraphs when you needed clean fields? Most leading models now support Structured Outputs (JSON that must match your schema). It’s the fastest way to cut hallucinated fields, flaky parsing, and brittle regex. platform.openai.com

What’s new (and worth it)

• Schema-locked replies: Tell the model your JSON Schema → it must return exactly that shape (keys, types, enums). 
  

• Works across stacks: OpenAI (Structured Outputs), Google Gemini (responseSchema), and Claude (via SDK patterns/tools) all support it. 
  

90-Second Setup (copy/paste flow)

1. Define your schema (required fields, enums, descriptions).
   

2. Enable structured output in your model call (e.g., OpenAI Structured Outputs / Gemini responseSchema).
   

3. Validate the response with a JSON Schema validator before writing to your DB/CRM. 
   

Prompt/Config Template (drop-in)

• System/Instruction: “Return only JSON that matches this schema. No extra keys or commentary.”
  

• Schema snippet (example):
  

{

  "type": "object",

  "additionalProperties": false,

  "properties": {

    "intent": { "type": "string", "enum": ["lead","support","feedback"] },

    "priority": { "type": "integer", "minimum": 1, "maximum": 5 },

    "customer_email": { "type": "string" },

    "summary": { "type": "string" }

  },

  "required": ["intent","summary"]

}

• Gemini config hint: set responseSchema + responseMimeType: application/json in the request. 
  

• OpenAI hint: prefer Structured Outputs over old “JSON mode” for strict schema adherence. platform.openai.com
  

• Azure OpenAI note: same concept; schema enforcement is recommended for function calling & data extraction. 

Where this shines

• Sales ops & lead forms: Guaranteed fields for CRM create/update.
  

• Support triage: Clean intents + priorities → reliable routing.
  

• Research extraction: Citations, dates, tickers, verdicts—no post-hoc parsing.
  

Pitfalls (and fixes)

• Model sneaks extra text: Set “return JSON only” and enforce additionalProperties: false; reject/reprompt if validation fails. learn.microsoft.com
  

• Enum drift: Use enums in schema; models then can’t invent categories. Google AI for Developers
  

• Claude specifics: If you’re not using an SDK helper, pair schema instructions with a post-validator (libraries like Instructor help). python.useinstructor.com
  

Bonus power-up: add a self-check pass

Ask the model to critique its own JSON (“verify all required keys are present; if not, fix and re-emit”). This “self-reflection” layer measurably boosts accuracy on complex tasks. promptingguide.ai

TL;DR: Flip on Structured Outputs and you’ll spend time shipping features, not scrubbing blobs. Start with OpenAI/Gemini schema configs, validate responses, and add a quick self-check loop for rock-solid pipelines.

Your Next Spark Awaits
Headlines Worth Your Time (as of Oct 14, 2025)

• Google commits $15 billion to build its largest AI data centre hub in India’s Andhra Pradesh, marking its biggest non-US investment to date. Reuters
  

• Salesforce announces $15 billion investment in San Francisco over five years to expand AI infrastructure and incubator capacity. Reuters
  

• California passes a pioneering law requiring AI chatbots to disclose they are AI, including safeguards when interacting with minors. The Verge
  

• Senator Josh Hawley circulates the GUARD Act, seeking to regulate AI companions used by minors and enforce disclosure requirements. Axios
  

• JPMorgan issues a report claiming the global AI race will reshape geopolitics, realigning alliances and creating new power dynamics. Axios
  

Why It Matters

• AI infrastructure arms race intensifies: Google and Salesforce’s massive bets underscore that AI isn’t just software anymore — compute, data, and real estate matter.
  

• Transparency is becoming law: The California provision is likely to ripple into global norms and regulation debates around chatbot design.
  

• Geopolitics and AI are merging: The JPMorgan framing shows that AI investment is now as much about strategic influence and national position as about tech advantage.

What to Do This Week

• Reassess your infrastructure risks: If your product depends heavily on AI backends, map alternate regions or cloud providers given rising demand.
  

• Update compliance & UX flows: For any chatbot or conversational AI in your stack, check you visibly flag “I’m AI” — even in markets without regulation yet.
  

• Review your partnerships: As competition escalates, contracts with compute providers, data centres, or AI services must factor in scaling terms, exit clauses, and geopolitical risk.
  

Monitor regulation pipelines: The GUARD Act and similar efforts in other states/countries may become benchmarks. Consider embedding transparency and safety options proactively.