OpenAI’s Jobs Platform & Several New Products

OpenAI announced its upcoming Jobs Platform, a talent marketplace designed to match AI-savvy workers with employers through intelligent matchmaking and credentialing. In parallel, OpenAI’s broader vision hints at multiple AI-first tools (as teased in other updates), including:

• Collaboration tools for multiple ChatGPT users working together
• New AI coding assistants are replicating senior software engineers
• Agent-enabled documents and presentations
• AI-powered personal device and browsing integration
• Social sharing features, shopping recommendation agents, and custom models built on your data

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Why It Matters:

For professionals chasing growth and career relevance, OpenAI’s jobs initiative signals a shift. AI fluency will become a primary career axis, not just a nice-to-have skill.

• If you’re already building GenAI projects (like your personalized roadmap or maturity model), this move means your work may soon count not just internally but in talent marketplaces.
• For organizations, this is dual pressure, deploy AI and ensure your team understands it deeply enough.

In practical terms, treat your own AI learning as portfolio work, not just training. Build something you can show (agents that remember, workflows that deliver). Your next career move may not be “learn AI”, it may be “demonstrate AI”.

Gen AI ROI Calculator

At the start of building the GenAI Maturity Portal, I was convinced the most challenging work would revolve around crafting the models and intelligent agents. What I underestimated was the business side. How to demonstrate value, name the outcomes, and shift from project to investment mode. That’s why the new ROI tool in the GenAIMaturity.net Implementation Toolkit has helped me think through which Use Case to pursue and how to decide.

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While working with different customers, over time, one question kept resurfacing, not from developers, but from business leaders: “How do we know if this AI is paying off?”

How I Used It in My Project

As individual professionals and tech leads, you’re often asked to show proof: “What will this GenAI project deliver?” Until now, many of us have flown without a dashboard - pilot done, model shipped—but without a clear metric or business case.

In the portal I built, VIBE-coded with Claude Code.

Based on the selected use case, it provides all the details.

The early GenAI rush was fueled by experimentation; everyone raced to launch pilots, automate reports, or deploy chatbots. But 2025 is different. Leaders now expect financial and operational justification for every AI project.

Visit GenAIMaturity.net and generate your project's Personalized ROI

Top Stories of the Week: Qwen Deep Research Upgrades to Webpages & Podcasts

Alibaba’s Qwen team announced a significant upgrade to its Deep Research tool: users can now generate not only complete reports but also live webpages and podcasts from the same workflow. It’s powered by models like Qwen3-Coder, Qwen‑Image, and Qwen3‑TTS, so after defining the research scope, the system creates multi-modal deliverables ready to publish

Why It Matters: For professionals working on Gen AI projects (including those of us building competency through tooling and portfolios), this shift is a wake-up call. It shows that models aren’t just about chat or code, they are now platforms for content creation workflows: analysis, webpage, audio.

Favorite Tip Of The Week:

Working with agents means they need more than prompt recognition; they need skills. Anthropic’s “Equipping Agents for the Real World with Agent Skills” outlines how agents benefit from capabilities such as tool use, memory recall, state monitoring, and failure recovery.

After reading Anthropic’s new “Skills” feature, I tried it in my own projects. I was building workflow automations in Claude Code and found I kept rewriting prompts for the same tasks. Then I created a “skill” folder, scripts, instructions, and templates, and suddenly the agent just knew how to handle these tasks.

Potential of AI:

The Best two and a half hours spent listening to Andrej Karpathy's “We’re summoning ghosts, not building animals”

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Things to Know...

Grounding with Google Maps for Gemini API

While working on a few use cases, a requirement for a location-aware Gen AI assistant emerged; until now, achieving this has been difficult. The model would confidently claim a restaurant was open when it was closed, or describe a “quiet café” that turned out to be a lively nightclub. Now, the feature allows Gemini-powered models to pull live, geospatial data from Google Maps, covering over 250 million places worldwide, with details such as current business hours, user reviews, and walking time context.

Why it matters: For professionals building real-world Gen AI assistants across travel, retail, and location-based services, this isn’t just a nice add-on. It’s a necessity. Without grounding, agents make declarations; with it, they act like trusted local experts.

Where You Can Use It

• Real estate agents: Generate neighborhood summaries with nearby parks, schools, and transport.
• Retail planners: Create personalized in-store experience maps based on live customer context.
• Travel services: Offer dynamic recommendations (“Best cafe within 10 min walk now”) with verification.

Start With the Use Case, Not the LLM

When I first began guiding teams through Gen AI adoption, most conversations started with: “Which LLM should we use: Cohere, OpenAI, Anthropic, or Gemini?”

That’s a trap I’ve seen even experienced professionals fall into. The real question should be: “What business outcome are we solving for?”

An LLM-first mindset leads to tool sprawl, multiple APIs, overlapping features, and no measurable ROI.

A use-case-first approach, on the other hand, forces clarity:

• What process are we improving?
• What knowledge or data powers it?
• How will success be measured?

Only once those questions are clear does the LLM platform matter, and often, you’ll find the answer isn’t a single model, but a combination of tools that best fit the workflow.

A Simple Framework

1. Define the Outcome: Start with a business metric, time saved, revenue increased, risk reduced.
2. Identify the Friction Point: What’s slowing this process down? Data? Human effort? Latency?
3. Match Platform Capabilities: Choose the AI stack (e.g., LLM, vector DB, agentic tools) that targets that friction.
4. Prototype Fast, Measure Early: Build a thin slice of value before expanding.

The Opportunity...

Podcast:

• This week's Open Tech Talks episode 167 is "What Employers Really Look for in Candidates in the Age of AI with Bill Kasko". Bill Kasko is President and CEO of Frontline Source Group, a leading staffing and recruiting firm with a national presence.

Apple | Amazon Music

What Employers Really Look f... Sep 20 · OPEN Tech Talks: AI wort... 33:47   ​

Courses to attend:

• Build agentic AI systems that take action through iterative, multi-step workflows from Deep Learning. In this course taught by Andrew Ng, you’ll gain a fundamental understanding and practical knowledge to develop production-ready agentic applications, from design patterns to deployment and evaluation
• AI Skills training from Google: Several courses are available for you to start learning today.

Events:

• Gartner IT Symposium/Xpo 2025 conference, November 10-13, 2025, Barcelona, Spain
• World Summit AI, December 9-10, 2025, Doha, Qatar

Tech and Tools...

• NanoGPT: The simplest, fastest repository for training/finetuning medium-sized GPTs.
• Chat UI: A chat interface for LLMs. Chat UI only supports OpenAI-compatible APIs

That's it for this week - thanks for reading!

Reply with your thoughts or favorite section.

Found it useful? Share it with a friend or colleague to grow the AI circle.

Until next Saturday,

Kashif

The opinions expressed here are solely my conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.