Explore five AI updates and a 25‑minute Kyoto mini‑app walkthrough. Learn prompt templates, iterative design tips, and how to apply mini‑apps to recruiting workflows.

My site: https://natebjones.com
My substack: https://natesnewsletter.substack.com/

Takeaways:
 1. Claude’s New Memory Feature: Anthropic’s Claude now has retrieval-based memory, letting users steer what’s remembered—but it’s less predictable than ChatGPT’s editable memory.
 2. 1M Token Context Window: Claude Sonnet’s context limit expanded 5x to 1M tokens, enabling analysis of massive codebases and document sets in a single run.
 3. Meta’s Brain Modeling Push: Meta is modeling brain responses to video, likely to make algorithms more engaging—and addictive—by tuning content to neural patterns.
 4. Sam Altman’s Merge Labs: A new brain-computer interface startup backed by OpenAI and co-founded by Sam Altman, signaling growing competition with Neuralink.
 5. Gemini’s “Depressed Robot” Bug: Google’s Gemini has a self-deprecation loop that can cause infinite failure cycles on hard tasks; a reminder of AI quirks at scale.
 6. 25-Minute Kyoto App Build: Using ChatGPT-5, a functional, editable travel itinerary app was built in under 10 minutes and improved to a V2 in 15 more—without touching the code.
 7. Prompt Evolution Matters: Iterative refinement turned a casual 3-line prompt into a detailed, reusable template for building similar mini-apps.

Quotes:
“We’ve hit the point where making a functional mini-app takes less time than brewing a pot of coffee.”
“AI models now have personalities and quirks—just like new colleagues you need to learn before you trust them.”
“Claude’s memory isn’t surgical retrieval; it’s more like rummaging through a messy desk and pulling out something useful most of the time.”

Summary:
In this breakdown, I cover five major AI updates—Claude’s retrieval-based memory, its 1M token context boost, Meta’s brain modeling efforts, Sam Altman’s new brain-computer interface startup, and Gemini’s looping “depressed robot” bug—before walking through how I built a 14-day Kyoto travel itinerary app with ChatGPT-5. The app went from a three-line prompt to a functional version in under 10 minutes and a polished V2 in 15 more, all without editing code. Along the way, I show how iterative prompting and clear intent can yield surprisingly robust, reusable results.

Keywords:
Claude, Anthropic, ChatGPT-5, Kyoto app, memory feature, retrieval-based memory, 1M token context, Meta, brain modeling, Sam Altman, Merge Labs, Neuralink, Google Gemini, infinite loop bug, travel itinerary, prompt engineering, AI quirks, AI development, app building, iterative prompting

5 Big AI Updates + How I Built a $10K-Looking Travel App in 25 Minutes

Discover the Master Prompt Method — a step-by-step framework to make AI a persistent, company-wide OS. Real recruiting examples, Claude tips, and how to build your own master prompt.

🚀 Join the waitlist for Cohort 3 of Second Brain Enterprise: https://bit.ly/45bYHWp
🙋 Get the free Master Prompt Template & GPT: https://bit.ly/3G0dWZ1

What if AI could become your smartest, most efficient employee? I chatted with serial entrepreneur Hayden Miyamoto to introduce the Master Prompt Method —a powerful way to train your AI with full context about you and your business. We break down how it works and share practical tips so you can start building your own master prompt today.

🔥The Master Prompt Series:
1️⃣ The Master Prompt that Changed Everything: https://youtu.be/_K_F_icxtrI?si=TS_1uIRZx7gE3lv8
2️⃣ I Turned ChatGPT Into My $100K Business Strategist With One Document: https://youtu.be/D9DpUDntQRc?si=OLxxt858lIZzAEUU
3️⃣ The 3 Systems That Separate Successful Businesses From Failures: https://youtu.be/giqA6apieWc?si=9w2rApqGYU8vq1j-

CHAPTERS:
00:00 - Introduction
00:40 - Meet Hayden
05:01 - What is The Master Prompt Method?
09:10 - Demonstration
30:48 - How to start

-----------------------
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The Master Prompt Method: Unlock AI’s Full Potential (Part 1)

Signal's Meredith Whittaker warns that agentic AI — autonomous assistants with broad app access — creates new attack surfaces, privacy hazards, and cloud custody risks.

At the United Nations’ AI for Good Summit, Signal President Meredith Whittaker delivered a powerful warning about the security risks of agentic AI – advanced AI systems that can autonomously perform tasks by directly accessing your devices, apps, and data.

Whittaker explains how agentic AI could undermine privacy and security by requiring root-level access to your browser, messages, contacts, and more – creating a massive attack vector for hackers. While Signal is designed at the application layer to avoid such risks, AI agents bypass these protections.

Key concerns she outlines include:
🔍 How AI agents gain dangerous access to sensitive systems
🔐 Why this violates cybersecurity best practices
⚠️ The risk to governments, journalists, human rights workers, and everyday users
📱 Examples like Spotify, restaurant bookings, and messaging apps
💣 The threat of data honeypots and geopolitical vulnerabilities
🛡 Strategies to secure AI, including small language models and access controls

Whittaker urges policymakers, tech leaders, and users to rethink AI deployment before it’s too late. Agentic AI isn’t just a convenience—it could be the biggest cybersecurity threat of the decade.

If you care about privacy, cybersecurity, AI ethics, and data protection, you need to watch this.

#AI #CyberSecurity #AgenticAI #SignalApp #MeredithWhittaker #Privacy #AIForGood #TechSecurity #ArtificialIntelligence #DataProtection #OnlinePrivacy #UNSummit

AI Security Warning from Signal app's Meredith Whittaker – The Hidden Dangers of Agentic AI

Yoav Shoham on orchestration, agents, and building production-ready AI — practical patterns to reduce hallucinations and what this means for AI in recruiting.

In this episode of AI Basics, Jason sits down with Yoav Shoham — Stanford professor emeritus and co-founder of AI21 Labs, creators of Jurassic-2, Wordtune, and the new orchestration system Maestro.

They unpack:
• Why enterprise AI struggles with reliability
• What orchestration really means (and why LLMs alone aren't enough)
• The pitfalls of “agent-washing”
• Small vs large models, agent-to-agent protocols, and where real opportunities lie

This one is for founders building with AI — if you're navigating hallucinations, chasing automation, or exploring multi-agent workflows, this episode is a must.

Timestamps:
(0:00) Yoav Shoham joins Jason to discuss AI Basics.
(1:05) What AI21 Labs is building — from Jurassic-2 to Maestro
(5:49) The overuse and confusion of the seductive term “agent”
(8:22) Small models vs large models: what's best for enterprise?
(10:53) The verticalization of AI: legal, accounting, and beyond
(12:17) The challenge of agent-to-agent communication and shared semantics
(17:08) What’s overhyped vs underhyped in AI — Yoav’s advice for founders

Explore Further:

Google Cloud’s Report:  'Future of AI: Perspectives for Startups'
👉 Read the full report (https://goo.gle/futureofai)

More AI Basics Episodes
👉 Visit: https://thisweekinstartups.com/basics

Links from episode:
• Google Cloud: https://cloud.google.com/
• AI21 Labs: https://www.ai21.com
• Maestro: https://www.ai21.com/maestro
• Jamba: https://www.ai21.com/jamba

Learn About Google’s A2A (Agent-to-Agent) Protocol - the next evolution in agent interoperability and coordination:
• Official A2A Announcement: https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
• Hands-on A2A Tutorial: https://medium.com/google-cloud/getting-started-with-google-a2a-a-hands-on-tutorial-for-the-agent2agent-protocol-3d3b5e055127

Follow Yoav:
X: https://x.com/yshoham  
LinkedIn: https://www.linkedin.com/in/yoavshoham/ 

Follow Jason:
X: https://twitter.com/Jason
LinkedIn: https://www.linkedin.com/in/jasoncalacanis

Follow TWiST:
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YouTube: https://www.youtube.com/thisweekin
Instagram: https://www.instagram.com/thisweekinstartups/
TikTok: https://www.tiktok.com/@thisweekinstartups
Substack: https://twistartups.substack.com/

Orchestrating Smarter AI Systems with Yoav Shoham | AI Basics with Google Cloud

Learn how AI interviewers and analysts can boost hire quality and cut turnover—case study: call-center turnover fell from 53.2% to 34.6%—plus ethics, governance, and practical steps for TA leaders.

ERE Recruiting Innovation Summit is coming to Las Vegas, NV, April 29-30, 2025. Get your tickets now! https://www.ererecruitingconference.com

In this episode of "The Source!" 

Curious about the role of AI agents in modern hiring? Concerned about looming regulations or the future of talent acquisition? Join Jim Stroud as he dives into these hot topics with Diana Tsai, CEO of Upwage.com. In this fast-paced conversation, they’ll explore how AI-driven agents could revolutionize sourcing, screening, and the entire candidate experience—while also discussing how government oversight might shape these advancements. Don’t miss this enlightening exchange packed with insights to keep your recruitment strategies on the cutting edge.

Connect with Diana Tsai on LinkedIn
https://www.linkedin.com/in/dianatsai/

Reinventing Recruitment: The Rise of AI Agents

Learn six repeatable AI workflows—repo mapping, plan-first dev, vibe coding, AI debugging, AI-assisted reviews, and context engineering—and how to use them to improve recruiting systems.

My site: https://natebjones.com
My substack: https://natesnewsletter.substack.com/
The story: https://open.substack.com/pub/natesnewsletter/p/the-complete-52-page-guide-to-ai?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Takeaways
 1. Codebase Mapping & Onboarding: Aim the model at a repo, prompt for summaries, graphs, or PR analysis, and refine manually to slash ramp-up time for both engineers and non-coders.
 2. Plan-First Development: Treat AI as an architect—force it to outline edge cases, pseudo-code, and road-maps before it writes a single line, then code against the approved plan.
 3. Natural-Language “Vibe Coding”: Clear, intent-rich prompts let tools like Lovable or Cursor turn plain English into deployable apps; ambiguity is the only bottleneck.
 4. AI-Augmented Debugging & Testing: Paste explicit error traces, ask for root-cause analysis, loop fixes in a sandbox, and guard against regressions.
 5. AI-Assisted Reviews & Refactors: Use the model as a pre-pull reviewer for scoped diffs, but require human sign-off to avoid runaway edits.
 6. Context Engineering for Consistency: Maintain repo-level rules files (e.g., .cursor-rules, claude.md) so prompts stay on brand, hallucinations drop, and multi-agent workflows stay coherent.
 7. Durable Patterns Trump Tools: Tools will churn (ChatGPT-5, Gemini, etc.), but these six workflows form a stable playbook you can slot any new model into.

Quotes
“We keep chasing shiny tools, but the only thing that survives the hype cycle is a repeatable workflow.”
“Knowing how to code is now as easy—and as valuable—as knowing how to cook.”
“AI just made it possible for you to be your own technical founder; the gatekeepers are gone.”

Summary
I break today’s AI-powered coding chaos into six enduring workflows—mapping codebases, planning before coding, natural-language “vibe coding,” AI-driven debugging, AI-assisted reviews, and rigorous context engineering. By spotlighting real tactics from leaders using tools like Devin, Cursor, Claude, Lovable, and Windsurf, I show how these patterns remain stable even as models and products churn. Mastering them lets anyone—from indie hackers to product execs—build faster, onboard quicker, and avoid brittle prompt hacks. If you’ve never shipped code, start small, follow the patterns, and discover that becoming your own builder is finally within reach.

Keywords
AI development, workflow patterns, codebase mapping, onboarding, plan-first development, vibe coding, natural language coding, AI debugging, code reviews, context engineering, Devin, Cursor, Claude, Lovable, Windsurf, model context protocol

The 6 Proven AI Workflows That Survive Every AI Hype Cycle

Explore how the IPO divide in Silicon Valley—private AI firms, big cash bonuses, and longer liquidity timelines—is changing recruiting, compensation, and enterprise hiring.

Michael Lev-Ram, CNBC Contributor, joins CNBC's 'Money Movers' to discuss expectations for OpenAI's strategy in staying private.

Silicon Valley's IPO divide: Here's what you need to know

Explore how Amazon's NovaAct is pioneering useful general intelligence to augment human capabilities in recruiting. Discover the future of AI agents and their role in transforming hiring processes.

We’re all hearing that AI agents will enable AGI, but they can’t yet reliably perform even basic computer tasks. It turns out that getting AI to click, type, and scroll is more challenging than getting it to generate code. How can we build general-purpose agents that can do anything we can do on a computer?

This is our goal at the Amazon AGI SF Lab. In this talk, I’ll propose a new approach to agents that we call Useful General Intelligence. After describing how we’re solving the biggest challenges in computer use while enabling developers to access our tech in it’s earliest developmental stages, I’ll show real workflows that developers have built with Nova Act, our agentic model and SDK.

About Danielle
Danielle is a cognitive scientist at the new Amazon AGI SF Lab. She received her PhD from Northwestern, where she studied the evolution and development of language. Previously, she was at Google and Adept.

Recorded at the AI Engineer World's Fair in San Francisco. Stay up to date on our upcoming events and content by joining our newsletter here: https://www.ai.engineer/newsletter

Useful General Intelligence — Danielle Perszyk, Amazon AGI

Discover 10 essential lessons from deploying RAG agents in production, focusing on AI in recruiting and enterprise AI challenges. Learn how to overcome context and system design barriers to maximize AI ROI.

The latest generation of LLMs is demonstrating impressive test time reasoning capabilities.  However, to be truly valuable in an enterprise setting requires those agentic capabilities to be applied to the right enterprise data. In all the excitement around AI agents, many of us have somehow forgotten the timeless adage “garbage in; garbage out” – language models can only do their job if they are contextualized properly. In this talk, Douwe Kiela will share lessons learned from deploying enterprise RAG systems at scale and how to design a system robust enough for the Fortune 500.

Recorded live at the Leadership Track Session Day from the AI Engineer Summit 2025 in New York. Learn more at https://ai.engineer and purchase tickets to our next event, the AI Engineer World's Fair, in SF June 3 - 5 here: https://ti.to/software-3/ai-engineer-worlds-fair-2025

About Douwe

Douwe Kiela is the CEO and Co-Founder of Contextual AI. He is also an Adjunct Professor in Symbolic Systems at Stanford University. Previously, he was the Head of Research at Hugging Face and a Research Lead at Meta’s Fundamental AI Research (FAIR) team, where he pioneered Retrieval-Augmented Generation (RAG) among other key AI breakthroughs. His work in multimodality, alignment, and evaluation has set new standards in the field of AI and has made systems safer, more reliable, and more accurate.

RAG Agents in Prod: 10 Lessons We Learned — Douwe Kiela, creator of RAG

Explore the realities of AI agents in recruiting and beyond. Learn from AI expert Sayash Kapoor about why many AI agents underperform and how to build more dependable, effective systems for practical applications.

Is 2025 the year of AI agents? Will reasoning models allow agents to solve challenging open problems? From software engineering to web task automation, it has been claimed that agents will solve challenging open problems. Unfortunately, current agents suffer from many shortcomings that reduce their utility in real-world tasks — look no further than Rabbit R1 and the Humane Pin. In this talk, we will explore how current agents fall far short of their claimed performance in the real world and understand best practices for improving agent evaluation. Learn how to avoid known pitfalls and build AI agents that actually matter.

Recorded live at the Agent Engineering Session Day from the AI Engineer Summit 2025 in New York. Learn more at https://ai.engineer and purchase tickets to our next event, the AI Engineer World's Fair, in SF June 3 - 5 here: https://ti.to/software-3/ai-engineer-worlds-fair-2025

Sayash Kapoor is a Senior Fellow at Mozilla, a Laurance S. Rockefeller Graduate Prize Fellow in the University Center for Human Values, and a computer science Ph.D. candidate at Princeton University's Center for Information Technology Policy. He is a coauthor of AI Snake Oil, a book that provides a critical analysis of artificial intelligence, separating the hype from the true advances. He has written for outlets like WIRED and The Wall Street Journal, and his work has been featured in The New York Times, The Atlantic, Washington Post, Bloomberg, and many others. Kapoor has been recognized with various awards, including TIME’s inaugural list of the 100 most influential people in AI.

Building and evaluating AI Agents — Sayash Kapoor, AI Snake Oil

Explore expert insights from Barry Zhang on creating effective AI agents. Learn when to use agents versus workflows, and how to build scalable, reliable AI systems for recruiting and complex tasks.

Recorded live at the Agent Engineering Session Day from the AI Engineer Summit 2025 in New York. Learn more at https://ai.engineer and purchase tickets to our next event, the AI Engineer World's Fair, in SF June 3 - 5 here: https://ti.to/software-3/ai-engineer-worlds-fair-2025

About Barry:

Barry is a member of technical staff on Anthropic's Applied AI team, focusing on developing agentic systems with enterprises and startups. Previously, he was a tech lead on the Monetization genAI team at Meta, where he claimed the inaugural 'AI Engineer' title. He holds degrees in Computer Science and Industrial Engineering from Northwestern

How We Build Effective Agents: Barry Zhang, Anthropic

Discover how AI is reshaping HR technology with new architecture, natural language interfaces, and decentralized processing. Learn about the future of recruiting and enterprise systems powered by AI.

In this episode I discuss our findings on AI systems architectures, and how they are going to completely change our HR Technology stack. As you’ll hear, I’m not predicting a quick disruption of $50+ billion in corporate software, but over the next decade we will see a quantum shift. (Quantum meaning “from A to B” not a gradual transformation.)



Listen in and you’ll see how relevant this is to your own HR Tech and systems decisions, your AI adoption and transformation strategy, and your vendor analysis as you look for tools and platforms. And for those of you driving AI transformation in HR, you’ll hear about how to think of AI as a “creator platform” not just a new set of apps and agents.



I also discuss the upcoming Venus release of Galileo and how it supports this architecture and extends our AI leadership into management development and leadership support.



Takeaways


     
     
  •  AI is fundamentally changing how we interact with technology.
     
     
  •  The cloud has enabled rapid application deployment and integration.
     
     
  •  AI eliminates the need for traditional user interfaces.
     
     
  •  New AI architectures allow for decentralized processing.
     
     
  •  Professionals are now empowered to build their own applications.
     
     
  •  AI tools will become ubiquitous in the workplace, driving an internal creator economy.
     
     
  •  Learning management will evolve with AI integration.
     
     
  •  The next generation of AI companies will emerge from current disruptions.
     
     


Additional Information



The L&D Revolution: Docebo Goes AI-Native, Sana Expands, What Do You Do? (https://joshbersin.com/2025/06/the-ld-revolution-docebo-goes-ai-native-sana-expands-what-do-you-do/) 



The Workday Economy – As AI Disrupts, A New Strategy Emerges (https://joshbersin.com/2025/06/the-workday-economy-a-bold-new-strategy-emerges/) 



Yes, HR Organizations Will (Partially) Be Replaced by AI, And That’s Good (https://joshbersin.com/2025/05/yes-hr-organizations-will-partially-be-replaced-by-ai-and-thats-good/)

AI Agents

AI Agents

How Rapid Mini-Apps and AI Quirks Are Changing Workflows — Including AI in Recruiting

AI Agents

The Master Prompt Method: How I Use AI in Recruiting and Every Corner of My Business

News in AI

AI Security Warning from Signal's Meredith Whittaker — The Hidden Dangers of Agentic AI

AI Agents

Orchestrating Smarter AI Systems (and What It Means for AI in Recruiting)

AI Agents

AI in recruiting: Reinventing Recruitment with AI Agents

AI Agents

6 Proven AI Workflows That Survive Every AI Hype Cycle

AI Agents

AI in recruiting: What Silicon Valley’s IPO Divide Means for Talent, Teams, and Hiring

AI Agents

AI in Recruiting: Building Useful General Intelligence with Amazon's NovaAct

AI Agents

AI in Recruiting and Enterprise AI: 10 Lessons Learned from Deploying RAG Agents in Production

AI Agents

AI in Recruiting: Building and Evaluating AI Agents for Real-World Success

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How We Build Effective Agents: Insights on AI in Recruiting and Beyond

AI Agents

How AI in Recruiting and Architecture Are Changing Everything in HR Technology