fafa.👩🏻‍💻

Data Maven with a Dash of Espresso ☕️ | Turning Numbers into Narratives | Senior Customer Insights Director | Tweets fueled by caffeine and curiosity

[fafa.👩🏻‍💻 @ds_fafa_]

Google AI Essentials 🤖

👋 Introduction to AI
⚙️ Maximize Productivity With AI Tools
🎨 Discover the Art of Prompt Engineering
🛡️ Use AI Responsibly
🚀 Stay Ahead of the AI Curve
https://t.co/KkXyhN2Ktp

[fafa.👩🏻‍💻 @ds_fafa_]

Sign Up for Free: Boost your Tech knowledge in 5 minutes ⏲️

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Read by +1,250,000 software engineers and tech workers 🖱️💼

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[fafa.👩🏻‍💻 @ds_fafa_]

Think you need a PhD to work with large language models?

-> Python and ML basics are all you need to start
-> Self-paced labs fit around your schedule
-> Hands-on projects build practical intuition

Join 437,000+ learners today:

https://t.co/XMRsiQONcv

[fafa.👩🏻‍💻 @ds_fafa_]

Worried your coding skills are not levelled for the AI era?

-> Build apps using LLMs and agentic workflows
-> Learn prompt engineering and AI integrations
-> Earn an employer-recognised certificate from IBM

Future-proof your dev career:

https://t.co/KKHvNg5H13

[Samuel Wong @samuel_wong_]

Why Your AI Demo Will Die in Production

95% of enterprise AI pilots fail to launch. Why?

https://t.co/0Z0Y0ihHLm

[Samuel Wong @samuel_wong_]

AI Is Making Software Cheap. Now Companies Must Get Leaner

https://t.co/wtGwijc8A1

[LangChain @LangChain]

With LangSmith Fleet, you can start creating agents using every day language.

Enroll in our LangChain Academy Quickstart course to learn how to build no-code agents for real work. https://t.co/k2AhiIz7fB

[OpenCode @opencode]

Nemotron 3 Ultra is now free on OpenCode

text · 1M context · fully open source

NVIDIA's latest open source model

[fafa.👩🏻‍💻 @ds_fafa_]

Swamped with daily tasks and struggling to organise your workload?

-> Master generative AI in under 10 hours
-> Speed up workflows and customise prompts
-> Learn practical skills from Google experts

Reclaim your schedule today:

https://t.co/KkXyhN3iiX

[fafa.👩🏻‍💻 @ds_fafa_]

Hitting a salary ceiling or struggling to land top ML eng roles?

> Gain job-ready skills in Gen AI, RAG, and fine-tuning
> Earn an employer-recognised certificate from IBM
> Tap into a field with a $169k median entry salary

Future-proof your career now:

https://t.co/YvByCTDjSc

[fafa.👩🏻‍💻 @ds_fafa_]

Swamped with daily tasks and struggling to organise your workload?

-> Master generative AI in under 10 hours
-> Speed up workflows and customise prompts
-> Learn practical skills from Google experts

Reclaim your schedule today:

https://t.co/KkXyhN3iiX

[Unsloth AI @UnslothAI]

2-bit Gemma 4 12B GGUF, only 4.66 GB on disk, managed to cite 15 sites from a single prompt.

Try this locally on >6GB RAM via Unsloth Studio.

GitHub: https://t.co/aZWYAtakBP

[CyrilXBT @cyrilXBT]

https://t.co/rr7P7Qk0ux

[Nate Herk @nateherk]

The hardest part of building a good skill or Claude Code OS is getting your knowledge out of your head and into the system.

So use this skill.

Give this a 2 min read.

[Samuel Wong @samuel_wong_]

The Next Frontier of Visual AI Is Code

https://t.co/CtQCn50Akn

[fafa.👩🏻‍💻 @ds_fafa_]

The AI Promise: A Pyramid View of What Lingers

If AI demos are the top note, the value that actually lasts comes from the deeper layers underneath: platform architecture, governance, and the people who make the system real

https://t.co/ZX4xpucM09

[fafa.👩🏻‍💻 @ds_fafa_]

Stop building Foxconn factories for your agents

https://t.co/14oQ7P8v4x

[Samuel Wong @samuel_wong_]

What I Learned Coaching 200+ People into AI/ML Roles

The truth about who makes it, and who doesn’t

https://t.co/41GQbx4emq

[Palantir @PalantirTech]

“Pointing an LLM at hundreds of disconnected, ungoverned databases gets you a system that hallucinates, is insecure, and unauditable. For something as consequential as our nation’s agricultural data, that is not just useless — it’s dangerous. The Ontology has been the key to delivering AI-enabled technology to every farmer in the country.”

At AIPCon 10, the USDA demonstrates how the Ontology now underpins national food supply security.

[Andrew Ng @AndrewYNg]

New course on serving LLMs efficiently -- how do you serve models to many concurrent users at low latency and reasonable cost? This short course is built with @RedHat and taught by @cedricclyburn.

Efficient LLM serving requires efficient memory management. A 70B-parameter model takes ~140 GB just to load the weights. On top of that, every active request needs its own chunk of GPU memory, the KV cache, to store the token context it has built up so far. In this course, you'll learn to reduce a model's memory footprint with quantization and serve it using vLLM, which handles many concurrent requests efficiently through smart memory management.

Skills you'll gain:
- Quantize a model and measure the accuracy tradeoff
- Serve a model with vLLM and watch it handle concurrent requests efficiently
- Benchmark your deployment and make informed tradeoffs between speed, cost, and accuracy

Join and learn to serve LLMs efficiently:
https://t.co/x04xMbFlkO

[Artem Zhutov @ArtemXTech]

https://t.co/7TD7XtYH6F