I build AI products from zero to one, and I teach others how to do the same.Over 12+ years, I've taken AI/ML products from proof-of-concept to launch at Amazon, Nike, T-Mobile, and Disney. The work I love most is early-stage: validating ideas, getting executive greenlight, and shipping MVPs that prove value before scaling. At Nike, that approach drove a $100M annual reduction in product returns.I also teach what I build. I co-created and lead the AI Product Management Certificate at the University of Washington, where experienced PMs ship real GenAI products complete with evaluation plans and GTM strategies. Previously, I trained 4 cohorts at Product School.What I bring: the ability to translate messy AI capabilities into clear product value, align technical and business stakeholders, and move fast without breaking trust. I write about AI PM craft at aipmguru.substack.com.I'm currently exploring opportunities to lead 0→1 AI product work at companies solving meaningful problems. I'm especially energized by AI in healthcare and education, though I'm open to any domain where the product can genuinely improve lives. 

Shaili Guru

Hemant Bothra and I are running an in-person workshop where we're going to build something together - in the same room, like the old days.

3 hours. You'll leave with a working tool.

No coding experience needed (you read that right). We'll use Cursor (an AI coding assistant) and walk through everything step by step. If you've ever been curious about "vibe coding" but didn't know where to start, this is it.

Good fit if you're a PM who wants to actually build things, or just someone who learns better by doing than watching.

AI/ML Product Leader | Driving Innovation with AI | PM Mentor and Advisor

Open article: Build Your First AI Tool: ChatGPT Output Cleaner Workshop · Luma by luma.com

Build Your First AI Tool: ChatGPT Output Cleaner Workshop · Luma

Applied AI | Building with LLMs, RAG & LangChain | From Data Analytics to AI Architecture | Currently working on real-world GenAI use cases. Let’s connect if you're building AI products, LLM infra, or tools for devs.

I help businesses turn Al into real, measurable outcomes without unnecessary complexity. From solution strategies that scale to implementing practical Al solutions, my goal is to make Al actionable, human-centred and impactful.

With over 8+ years of experience in data analytics, AI product strategy, AI Automation, and customer success, I've helped companies across healthcare, SaaS, and enterprise optimise AI adoption, enhance decision-making, and deliver business results.

I specialise in:

➡️Al Product Strategy & Roadmaps: Designing and executing scalable Al solutions
➡️Customer Success Architecture: Enabling enterprise adoption and driving business impact
➡️Generative Al & Al Ops: Optimising Al systems for reliability and performance
➡️Data-Driven Insights: SQL, Python, cloud technologies (IBM, GCP, AWS) to build actionable solutions
➡️Bridging Tech & Business: Translating technical Al into measurable outcomes

Few Highlights:

➡️Led anomaly detection project with AI/ML integration at DecisionHUB
➡️Built automated SQL + Python pipelines for Al-driven insights
➡️Developed customer engagement dashboards that influenced product roadmaps
➡️Designed a Therapeutic Al voice agent

I also host the Lattency and Latte podcast, inviting guests to discuss Al, technology, and emerging trends, making complex topics practical and human-centred.

Renuka Mujumdar

We are hiring from India.

Bringing a few more people onto the team as we build and ship a product? If you happen to like messy problems, ownership, and shipping fast, just read on.

Open roles (India-based):

1️⃣ AI & ML Engineer

For people who enjoy both modelling and production. 

2️⃣ AI Project Lead

End-to-end owner for AI initiatives from problem framing to shipped outcome. 

3️⃣ Operations

Keep the engine running. You’ll own execution across projects, vendors, and internal coordination so builders can stay focused on building. Great fit if you’re organised, calm under chaos, and love turning ideas into repeatable processes.

4️⃣ Product Manager (AI)

You’ll define what we should build (and just as importantly, what we shouldn’t). Talking to users, writing crisp problem statements, prioritising, and working tightly with engineering to ship outcomes, not features.

If you are interested, drop a comment with the role + “interested”. I’ll compile the list and send assessments.

We’re still reviewing the last round of assessments for the previous hiring roles (Thanksgiving week slowed things down a bit), so those remain open for another week.

Assessments for these roles will be shared soon.

Helping businesses to leverage Al for real outcomes without making it complex | AI Product Strategist | Solution Strategist | Podcast @ Lattency and Latte | AI Automation

If you're a founder, you need these 4 AI automations to save 10+ hours per week.

Most founders waste time on repetitive tasks that AI can handle.

Email responses, customer outreach, content creation, scheduling, and reporting, all handled manually.

Here's what you can automate with the help of AI:

1. Customer Email Responses
→ AI drafts replies to common customer questions
→ I review and personalize before sending
→ Saves 2-3 hours per week

2. LinkedIn Outreach at Scale
→ AI generates personalized connection messages based on prospect profile
→ Automated sequences that feel personal
→ Consistent follow-ups without manual effort

3. Meeting Prep & Summaries
→ AI transcribes and summarizes meetings
→ Extracts action items automatically
→ No more manual note-taking

4. Competitor Monitoring
→ AI watches competitor activity
→ Alerts me to strategy shifts
→ I get insights instead of raw data

The pattern is simple: all you have to find repetitive tasks → Build an AI workflow → Get 90% of the result with 10% of the effort.

I have a complete guide on the prompts and the tools used, comment "tools", and I'll send you the guide.

#AI #founders #productivity

✨ India-based roles, assessments now live

✨A lot of folks reached out about the open roles last week, and replying one by one has become tricky. 

To make it easier for everyone, I’m sharing the assessment links here:

AI & ML Engineer → https://lnkd.in/g4ne98qX

AI Project Lead → https://lnkd.in/g_x59v8Q

Operations → https://lnkd.in/gKnWkwVM

Product Manager (AI) → https://lnkd.in/gWUrWFh7

If you are applying, just fill out the form for your role.

If you want me to notice your submission faster, leave a quick comment with the role you applied for.

You can also DM me if you prefer to connect directly.

I’ll pin the detailed post in the comments for quick reference.

Looking forward to going through the responses! Thanks for your interest!

You don’t need a 12-month roadmap to adopt AI. You just need the right starting point.

I had a good time speaking at the AI Innovators forum, presented by Founders Creative, yesterday on practical AI adoption.

I talked about the three pillars that keep teams grounded: innovation, complexity, and practicality.

I walked through a simple approach:

 → start small, bottom-up execution with the people who actually use the tools
 → cross-functional alignment so no one is building in a silo
 → keep the focus on business value because AI is a business decision
 → stay agile with solid MLOps so you can ship, learn, and adjust

Thank you, Vijay Ratthinam and Mala Ramakrishnan, for inviting me. Really appreciated the warmth and the energy in the room.

Thanks, Shobhna Upadhyaya, Sonali Parthasarathy, Nadia Patel, CFA Chalisa Prarasri, for the thoughtful conversation. 

Thanks to Meerah Rajavel for her inspiring journey, a reminder of what strong, grounded leadership looks like.

If you are exploring AI adoption, feel free to reach out.

A few days ago, I posted about hiring for ML Engineer, Software Engineer, Product Manager, and AI Engineer roles.

To say I'm very much overwhelmed and genuinely didn't expect this level of interest.

If I haven't responded to your message yet, sorry, I'm working through them, but it's taking longer than expected.

Therefore, instead of responding to everyone individually, I'm sharing the application process here.

Complete the assessment for the role(s) you're interested in. 

You can apply for multiple roles if they fit your background.

Assessment links will be open for the next 72 hours.

- ML Engineer Assessment: https://lnkd.in/gU4U8idH
- Software Engineer Assessment: https://lnkd.in/guazNZ9B
- Product Manager Assessment: https://lnkd.in/gDVBgt_H 
- AI Engineer: https://lnkd.in/gudKtZa2

P.S.: If you have questions or are unable to access the assessment, please drop a comment below or DM me.

If you’ve submitted, drop a “done” in the comments so I know you are in.

Best of luck with the assessment. 

Looking forward to seeing what you build.

You do not need 2 years to learn AI. You just need 12 focused weeks.

Most people waste months because they:

→ Start with advanced math, they'll never use 
→ Jump between random tutorials 
→ Never actually build anything 
→ Give up thinking "AI isn't for me"

Here's the actual path:

📍Weeks 1-2: AI Fundamentals Understand what AI actually is
📍Weeks 3-4: Essential Math Only the math you'll actually use.
📍Weeks 5-7: Python & Data Handling Write code. Clean data. The foundation for everything else.
📍Weeks 8-10: Build Real AI Projects. Use tools like ChatGPT API, Claude, and Hugging Face.
📍Weeks 11-12: Specialize & Get Interview-Ready Pick your lane (LLMs, computer vision, etc) and prep for AI roles.

The difference between spinning your wheels and making real progress is having a clear roadmap.

Comment "ROADMAP" and I will send you the complete 12-week plan with daily tasks, resources, and project ideas.

𝗬𝗲𝘀𝘁𝗲𝗿𝗱𝗮𝘆 𝗽𝘂𝘀𝗵𝗲𝗱 𝗺𝗲 𝗼𝘂𝘁 𝗼𝗳 𝗺𝘆 𝗰𝗼𝗺𝗳𝗼𝗿𝘁 𝘇𝗼𝗻𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗯𝗲𝘀𝘁 𝘄𝗮𝘆, 𝗜 𝗲𝗻𝗱𝗲𝗱 𝘂𝗽 𝗵𝗼𝘀𝘁𝗶𝗻𝗴 𝗺𝘆 𝗳𝗶𝗿𝘀𝘁 𝘄𝗼𝗿𝗸𝘀𝗵𝗼𝗽

Ankur and I had our very first workshop yesterday where we broke down how to build a simple RAG pipeline inside n8n.

Thank you to everyone who joined. The questions, the curiosity, it genuinely pushed us to share more and explain things in a way that actually clicks. 

It’s always motivating to see people try hands-on AI workflows for the first time.

We kept the session practical:
pulling text from Google Docs, chunking, embeddings, Supabase vector search, reranking, and routing everything to an LLM for a clean final answer.

What’s next:
🎥 Session recording is on the way.
🗓️ Upcoming workshop dates will be shared soon.

If you want the workflow, slides or the n8n file, drop a comment or DM me. 

Happy to share!

𝟓 𝐀𝐈 𝐛𝐥𝐨𝐠𝐬 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐫𝐞𝐚𝐝 𝐢𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐬𝐞𝐫𝐢𝐨𝐮𝐬 𝐚𝐛𝐨𝐮𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐩𝐚𝐜𝐞.

1. OpenAI Blog
In this blog, GPT, DALL-E, and frontier AI research are announced first. Not just product launches, but with actual research papers explaining how things work. If you want to understand what's coming next in AI, this is ground zero.

https://lnkd.in/g6Zxy3sH

2. Google DeepMind Blog
The blog is about the team behind AlphaFold, AlphaGo, and some of the most groundbreaking AI systems. They publish deep dives on reinforcement learning, scientific AI applications, and real-world problem solving and how AI is being used to solve actual hard problems.

https://lnkd.in/gBz-4YQR

3. University of California, Berkeley AI Research (BAIR)
The blog has academic research from one of the top AI programs in the world, covering robotics, computer vision, NLP, and theoretical ML. If you want to understand the "why" behind AI techniques, not just the "how," start here.

https://lnkd.in/gQGhReBD

4. Amazon Web Services (AWS) Machine Learning Blog
The blog has practical implementation guides for building and deploying AI systems at scale and covers everything from model training to production deployment on cloud infrastructure. Best for: developers and engineers who need to ship AI products, not just experiment.

https://lnkd.in/gqjf3FeU

5. Towards AI
It is a community-driven platform covering everything from beginner tutorials to advanced research breakdowns.Great mix of technical depth and accessibility. 

https://towardsai.net/p

Bonus mentions:

→ Massachusetts Institute of Technology News AI — Research + ethics perspectives
https://towardsai.net/p

→ Apple ML Research — How AI gets integrated into consumer products
https://lnkd.in/gzKHr6Ze

→ Machine Learning Mastery — Step-by-step tutorials for hands-on learning
https://lnkd.in/gzKHr6Ze

Here's what to focus on when you read these:

→ How researchers frame problems (not just solutions)
→ Trade-offs they make when building systems at scale
→ What didn't work (most blogs skip the failures, learn from them)
→ Tools and techniques mentioned repeatedly across blogs (those are the ones worth learning)

Reading these won't make you an expert overnight, but it'll give you context most people never get, and in AI, context > hype.

Save this post for future reference.

♻️Repost this post if you know someone who needs this.

#AI #blogs

5 Skills AI Architects Actually Need in 2026

By 2026, the challenge won’t be getting a model to run. It’ll keep it running reliably, securely, and cost-effectively inside messy, legacy enterprise environments.

1. Scalable AI Pipelines
➡️ Real AI engineering means pipelines that survive 10x traffic spikes without breaking.
➡️ Kubeflow or Airflow for orchestration
➡️ Kafka for backpressure between stages
➡️ Ray for distributed Python workloads

Pro tip: Plan for failure. One broken task shouldn’t take down the whole pipeline.

2. Hybrid Cloud + Edge Deployment
➡️ Modern AI infrastructure stretches across cloud and edge.
➡️ NVIDIA Triton for consistent model serving
➡️ TensorFlow Lite and ONNX Runtime for edge inference
➡️ KubeEdge to manage distributed deployments

Pro tip: Quantization should be a design decision, not an afterthought.

3. Data Governance That Prevents Headaches Later
➡️ Compliance is now a core part of enterprise AI.
➡️ Apache Atlas or DataHub for lineage
➡️ Right-to-deletion support
➡️ Full audit trails for every prediction and dataset

Pro tip: Split PII from features early. It makes compliance easier down the road.

4. Full Model Lifecycle Management
➡️ Production AI is about everything that happens after training.
➡️ MLflow for experiments
➡️ Evidently AI for drift checks
➡️ KServe for A/B testing
➡️ SHAP for explainability

Pro tip: Automate drift detection from day one. Models drift quietly until they break something.

5. Real Business System Integration
➡️ Good AI architecture fits into the systems companies already rely on.
➡️ FastAPI for real-time inference
➡️ Kafka for async workloads
➡️ Feature flags for safe rollouts
➡️ Confidence thresholds to avoid bad predictions

Pro tip: Build graceful fallbacks. The system should keep working even when the model doesn’t.

AI that survives production is still 80% infrastructure and 20% modeling. Master the infrastructure, and your models will actually deliver value.

If you are building AI for real-world environments, I’d love to hear what challenges you are running into.

(10) Renuka Mujumdar | LinkedIn

li_at

JSESSIONID

You can get $200 professional headshot in 90 seconds using AI.

Here's what most people do: 
→ Keep putting off updating their LinkedIn photo 
→ Book a $200+ studio session 
→ Schedule appointments, travel across town 
→ Hope the photographer captures the right vibe 
→ Wait weeks for edited photos

Now, Google's Gemini AI can take any decent photo of you and transform it into a LinkedIn-worthy professional headshot that looks like you hired a real photographer.

Most AI prompts create "obviously AI" headshots that look fake. The one I built maintains your authentic features while adding professional lighting, studio-quality background, and polished styling.

And all you need is a decent picture and the right prompt.

Comment "PHOTO" and I'll send you the exact prompt to use and step-by-step setup guide for Gemini.

𝐌𝐨𝐭𝐡𝐞𝐫𝐡𝐨𝐨𝐝 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐜𝐨𝐦𝐞 𝐰𝐢𝐭𝐡 𝐚𝐧 𝐢𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐦𝐚𝐧𝐮𝐚𝐥 𝐨𝐫 𝐚 𝐛𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐰𝐨𝐦𝐞𝐧.

I’ll be real: some days, it feels like I’m trading bedtime stories for work calls… and the guilt is real.

But I wouldn’t trade it for anything.

Motherhood has been the most high-stakes, high-reward project of my life. No corporate job ever taught me this level of patience (try convincing a toddler about broccoli during a client meeting).

No degree ever demanded this kind of creative problem-solving (turning a laundry basket into a pirate ship in under 5 minutes). 

No sprint ever felt like this much perseverance (showing up for them, even on days I feel like I’ve got nothing left).

And yes, there are trade-offs. 

Mom guilt, taking random trips, and missing the silly, everyday moments because I’m building something I hope makes them proud.

But I’m also building for them, to show them that women can write new playbooks, that love and ambition aren’t opposites, and that resilience is built one messy, beautiful day at a time.

Today, I’m grateful for my support system (my husband, my family), for the other builder moms swapping war stories in DMs, and on the hardest days for the reminder that none of us have this figured out, but we keep showing up anyway.

Motherhood isn’t the only way to learn these lessons. But for those who choose it (or live it), I see you, and I’m rooting for you. 💛

If you’re a working mom, what does “balance” really look like for you?

#motherhood #realtalk #resilience

Half the rejections I see happen because candidates misalign their skills to the wrong title.

People often confuse these three roles: ML Engineer, Software Engineer, and AI Engineer.

They're not the same role, and the difference matters if you're hiring (or applying).

Here's what each actually does:

👉 ML Engineer
Builds and optimizes machine learning models. Focuses on algorithms, data quality and model performance. Lives in Jupyter notebooks, spends 70% of time on data and experimentation.

👉 Software Engineer
Builds systems that scale. APIs, databases, infrastructure, deployment pipelines. Writes production code that millions use. ML models are just one tool in their toolkit.

👉 AI Engineer
The bridge between research and production. Takes ML models, integrates them into workflows, builds RAG systems, prompt engineering, and AI agents. Ship AI features, not just experiments.

The real difference between these roles is that it's about what problem you're solving and how deeply you go into.

If you want a complete breakdown on these roles, comment "GUIDE", and I'll send it to you.

PS: Make sure we are connected.

#machinelearning #AI #career

✨ 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐭𝐚𝐥𝐤𝐬 𝐚𝐛𝐨𝐮𝐭 “𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐛𝐞𝐭𝐭𝐞𝐫 𝐩𝐫𝐨𝐦𝐩𝐭𝐬,” 𝐛𝐮𝐭 𝐫𝐞𝐚𝐥 𝐩𝐫𝐨𝐦𝐩𝐭𝐬 𝐡𝐚𝐯𝐞 𝐚𝐧 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐦𝐨𝐬𝐭 𝐧𝐞𝐯𝐞𝐫 𝐬𝐞𝐞.

A good prompt isn’t just a clear sentence, it’s a set of instructions you quietly engineer behind the scenes.

Here’s my go-to checklist for prompts that actually deliver:

1. Set the role (Who’s answering?) Are you asking for advice from a career coach or an output from a Python script? Assigning a role instantly upgrades the relevance and depth of the answer.

2. Define the goal (What do you want?) The best prompts spell out what “useful” looks like. Do you want a summary, sample code, a strategic plan, or just raw ideas? Be precise about the win.

3. Add context (What’s the backstory?) Even top models can’t read your mind. Two sentences of context, why you’re asking, what’s happened already, and who’s involved, make the answer 10x smarter.

4. Set constraints (Boundaries, not handcuffs) Short? Formal? Bullet points only? Want to avoid clichés or “as an AI language model” disclaimers? State your non-negotiables up front.

5. Give feedback & iterate The real magic is in versions 2, 3, and 7. Tweak the prompt, rerun it, tighten up until it nails what you need. Don’t settle for the first swing.

One common misconception is that better prompts are always longer however, it is not always the case. The best are well-framed, not just wordy.

Prompting isn’t about scripting the perfect sentence, it’s about thinking like a designer and building clarity before chasing creativity.

What’s one prompt tweak that’s changed your results?

#AI #productivity #LLM

By 2026, AI skills won’t be about who knows the latest model.

They’ll be about who understands systems, data behavior, failure modes, and real-world tradeoffs.

As AI continues to advance rapidly, the skills and mindsets we need are also evolving.

For people building, leading, or learning in AI right now, What skills do you think will actually matter most in 2026?

Sharing perspectives helps all of us think more clearly. 👇

Are we overcomplicating AI, or are we just missing its true potential?

From building AI products, I have seen that success comes from clear objectives, clean data, and practical use cases not just complex models or hype.

I recently joined Faiza Ehsan on AllAboutAI.com to discuss how businesses can better understand AI and drive real-world impact.

If you are interested in practical AI, check out the episode and my blog here:

 🎧  Watch our full interview here: https://lnkd.in/gQjmcyxN
 📝  Read full  blog here: https://lnkd.in/g3sfckz7

AI Talks, SEO Walks |🎙️Host @AllAboutAI | Team Lead @Gaditek

Why do so many AI projects collapse before they even launch?
This week’s conversation with Renuka Mujumdar gets straight to the truth.

🎬 Catch the full conversation on YouTube - AllAboutAI.

The Bay Area has a way of turning casual tech meetups into mini masterclasses on AI and career development.

It was great meeting Sai at this event. I originally went just to meet him, but ended up finding the meetup interesting. Sai was also the first guest on my tech podcast, and he's the same in person, humble, thoughtful, and genuinely warm.

I also caught up with the other hosts, Devshree and Sriram. A lot of our conversations naturally drifted into work, AI skills, and the next steps people are exploring in tech careers.

I'm thinking of hosting a small tech meetup in the Bay Area soon. Interested? Drop a comment or DM, I'll keep it simple and worthwhile.

🇮🇳 India-based roles

Sharing this again since a lot of people are reaching out. 

We are currently reviewing applications across AI, ML, Product, and Operations, and yes, the assessments are still open.

If you are actively job hunting in India, this might be useful.

Open roles:
 • AI & ML Engineer
 • AI Project Lead
 • Product Manager (AI)
 • Operations

Assessments are live.

Comment with the role you are applying for and I will share the details in the comments.

AI data centers will consume 1,600 terawatt-hours by 2035. 
That is nearly 5% of the world's electricity.

And all I could think was: we are building intelligence that might outpace the infrastructure that powers it.

I have spent the last year building in AI. Hosting conversations about its potential on my podcast @latencyandlatte and watching founders race to build the next breakthrough model.

But no one's really talking about this part. Here's what the numbers show:

→ AI's energy demand will 4x in the next decade

→ If data centers were a country, they'd be the 4th largest electricity consumer globally

→ In the US alone, AI data centers are growing faster in energy use than EVs, hydrogen, and every other "green" tech combined

The true reality is that the technology we're building to optimize everything from healthcare to climate modeling could become the thing that strains the system most.

As one question keeps on running on my mind: What's the point of building smarter AI if we can't sustainably power it?

Because right now, we're in a race and it's not about who trains the biggest model or who gets to AGI first.

It's about who figures out how to build intelligence that doesn't burn the grid down in the process.

I believe the companies that will win in the next decade won't just be the ones with the best algorithms but next into energy-efficient architecture, renewable-powered infrastructure and self-optimizing AI. 

P.S. Do you think AI will hit an energy wall before it hits AGI? 

#ai #tech #sustainability 

Credits: Pascal BORNET

On November 22 at 1 PM PST, Ankur Bhargava and I are hosting a live workshop where you will build a RAG-powered conversational AI system without writing a single line of code or using low-code tools.

Here’s a quick rundown so we can jump straight into the hands-on work.

Please have ready before the workshop:
 → n8n account + credentials
 → Supabase project + API keys
 → Google Drive access (we will connect it live)

We will build a full RAG system using Supabase and n8n. You will learn chunking strategies, embeddings, retrieval, and how to convert your own documents into a working AI prototype.

What we will build:
 → Connect to Google Drive
 → Ingest and chunk documents
 → Create embeddings
 → Retrieve relevant info
 → Generate answers based on your files

📌 Join the workshop here: 

https://lnkd.in/gyN9QzuD

By the end, you will have a working prototype you can build on.

If anything here is unclear, just let me know. Happy to help.

I'm speaking at the AI Innovators Forum.

I’ll be speaking at the AI Innovators Forum on Balancing the AI Equation: Innovation, Complexity, and Practicality in AI Adoption.

The honest stuff around AI:
→ What works (and what doesn't)
→ How to think like a solution architect
→ Why execution beats perfection every time

If you're building in AI, pivoting into it, or just curious about what's real vs. what's noise, come through. 

See you there! 😀

📍 AWS Builder Loft, SF
🗓️ Mon, Dec 1, 3-6 PM PST

#AI #SanFrancisco #AIInnovators

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The Battle

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Engagements

Likes

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