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Hey Radarians, this week is about AI getting far more capable.
Brain mapping, GPT-6 Astra, multi-agent workflows, medical AI risks, and a fresh app rescue side hustle.
Let’s dive in.
What’s Inside 📡
Radar News Brief
Steal This Workflow
AI Research
Tools Worth Watching
Prompt Radar
AI Side Hustle Idea
Learn AI with This Course
Radar News Brief
🧠 Google helps map an entire male fruit fly nervous system
Google Research and HHMI Janelia mapped 166,691 neurons and 125 million synaptic connections across the complete brain and central nervous system of a male fruit fly
AI stitched millions of electron-microscope images into 3D neuron shapes, while experts proofread the wiring so scientists can trace signals from senses to movement
Researchers can compare this male connectome with the female map at synaptic resolution, revealing sex-specific circuits tied to courtship, taste, vision and behavior
⚡ OpenAI launches GPT-6 Astra as Brockman calls it AGI
GPT-6 Astra scored 99.9% on ARC-AGI-3, 97.6% on FrontierMath Tier 4 and 100% on ExploitBench, while OpenAI calls it its top model for coding, science and work tasks
Astra can operate browsers and desktop apps, update CRMs, fill forms, build websites, analyze data and create polished documents, spreadsheets, presentations and reports
Astra is OpenAI’s first model to hit its Critical cyber threshold after finding two zero-days in tests; API pricing starts at $10 input and $50 output per million tokens
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More News on the Radar
Meta offers 95% discounts on Muse Spark if users let it use their prompts and outputs to help train its AI models
Nvidia says Lenovo and Acer will launch RTX Spark PCs in October 2026, bringing faster, more private AI processing to Windows
Microsoft announced Project Zenith, a Windows 11 setup for developers with local AI support and powerful hardware
⚡ Steal This Workflow
Build a Landing Page With Competing Agents
Stop asking one AI to research, write, design, and judge its own work. Split the landing page across specialist agents instead.
Give every agent the same goal
Start with one shared brief containing:
Product or service
Target customer
Main offer
Desired action
Existing research
Brand constraints
Then open separate agent threads for different jobs. Codex supports multiple agents working in parallel inside the same project.
Give each agent a specialty
Assign four roles:
Agent 1 — Researcher
Research the audience, pain points, objections, alternatives, and language customers actually use.
Agent 2 — Copywriter
Use that research to create the headline, offer, benefits, proof sections, objection handling, and CTA.
Agent 3 — Builder
Turn the brief and copy into a working responsive landing page.
Agent 4 — Conversion Critic
Review the page independently and look for weak messaging, confusing sections, missing proof, poor hierarchy, and unnecessary friction.
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A multi-agent project can keep research and building happening independently instead of forcing one model through every task sequentially.
Make the critic challenge the page
Give the fourth agent this instruction:
"Review this landing page as a skeptical first-time visitor. Identify the five biggest reasons I might leave without converting. Check message clarity, offer strength, credibility, objections, CTA placement, visual hierarchy, and unnecessary friction. Rank the problems by expected impact and give a specific fix for each. Do not praise the page unless it helps explain a recommendation."Run one final improvement pass
Send the critic’s findings back to the copy and build agents.
Let them revise the problems worth fixing, then review the finished page yourself before publishing.
🔎 AI RESEARCH: People Trusted Wrong AI Medical Advice Almost as Much as Doctors
What researchers studied:
MIT researchers asked 300 people to judge medical answers written either by doctors or by AI, including AI responses that doctors had rated as low accuracy.
What they found:
Participants struggled to tell AI-written answers from doctors’ answers. High-accuracy AI responses were often rated as more trustworthy, complete, and useful than doctors’ responses.
More concerning, low-accuracy AI advice was rated almost as positively as doctors’ advice. Some participants also said they would follow potentially harmful recommendations or seek unnecessary medical care because of it.
Why it matters:
AI can sound convincing even when its medical advice is wrong, making human medical oversight especially important in high-stakes situations.
Source: MIT Media Lab / NEJM AI, May 2025
🛠️ Tools Worth Watching
Adobe Podcast: AI audio tool for recording, editing, and transcription
DeepSeek Harness: Open-source coding agent environment similar to Claude Code
Lettertrace: Tracks brand visibility across ChatGPT, Claude, and Gemini
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📝 Prompt Radar: Review Code Before Shipping
What it does: Checks code for bugs, security problems, maintainability, and edge cases.
You are a strict senior code reviewer. Review the code I provide for correctness, edge cases, security risks, error handling, performance, maintainability, readability, unnecessary complexity, duplicated logic, API misuse, concurrency problems, and missing tests. Prioritize issues by severity and likelihood rather than commenting on every minor style preference. For each issue, explain the exact problem, when it could fail, its severity, and the smallest sensible correction. Identify assumptions that need verification. Then provide an improved version where changes are necessary. End with required tests, optional improvements, and whether you would approve the code for production in its current state. Code: [PASTE].Best for:
Code reviews
Pull requests
AI-generated code
💼 AI Side Hustle Idea: Vibe-Coded App Rescue Service
Most people don’t need “another AI app builder.” They need the app they already built to actually work reliably with real users.
The problem:
Apps made with Claude Code, Lovable, Base44, Replit, Bolt, or similar tools can look finished but still have broken authentication, API errors, deployment issues, weak security, messy code, or bugs that appear after launch.
Your AI service:
Audit the existing app, find what is breaking, fix the critical issues, improve security and structure, and make the product stable enough for real-world use.
Simple workflow:
Existing app → technical audit → fix bugs + integrations → secure auth/data → test edge cases → deploy stable version
Best clients:
Solo founders, indie hackers, agencies, creators, startups, and non-technical entrepreneurs building products with AI coding tools.
What you can charge for:
One-time rescue project + production-readiness audit + ongoing maintenance.
Learn AI with This Course
What you’ll learn:
Learn how to design effective prompts for foundation models, apply practical prompting techniques, improve AI responses, and use prompt engineering safely across different tasks and use cases.
Key topics:
Understanding prompt engineering principles, structure, and best practices
Using basic and advanced techniques to improve model responses
Recognizing prompt misuse and applying strategies to reduce risks
Until next week,
AI Radars