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Why Spec-Driven Development is the Future of AI Coding
Why Spec-Driven Development is the Future of AI Coding
Written By Priyanka Vergadia
We're living through an incredible moment where AI can write decent code and build entire applications in minutes. But while everyone's obsessing over better prompts and flashier demos, most developers are missing the real game-changer: specifications.
The harsh reality? AI coding often feels like a beautiful, chaotic dance where you end up debugging code you didn't write, playing endless rounds of "prompt tennis" with your AI assistant. After 47 prompts, you're thinking, "I could have built this myself in half the time."
There's a better way. GitHub's new open-source tool SpecKit is bringing spec-driven development to the forefront of AI coding—and it's exactly what we need. Here is a video I did recently on this topic showing a demo.
The Problem with "Vibe Coding"
When you throw a vague prompt like "build me a React to-do app with drag and drop, local storage, and dark mode" at your AI assistant, you're essentially asking it to be a mind reader. You give it a high-level idea and expect it to somehow know:
All the implementation details
Edge cases you haven't thought of
Your preferred architecture patterns
Your exact requirements and constraints
It's like walking into a coffee shop and saying "I want something caffeinated that tastes good," then expecting the barista to nail your exact preference for a medium roast oat milk cortado with a dash of cinnamon.
AI is incredible at following instructions, but terrible at filling in the massive gaps we leave.
Enter Spec-Driven Development
Spec-driven development flips the script entirely. Instead of specifications being just scaffolding you build and discard, they become executable blueprints that directly generate working implementations. The spec isn't documentation anymore—it's the blueprint that builds your software.
What is Spec-kit?
SpecKit structures this approach into four clear phases that act as guardrails for both you and your AI:
Phase 1: Specify
Describe not just what you want to build, but why you're building it and who it's for. This creates a detailed specification document that becomes your single source of truth.
Focus on:
User journeys and experiences
Who will use this app?
What problems does it solve?
How will users interact with it?
What does success look like?
Phase 2: Plan
Get technical. Define your tech stack, architectural decisions, and data models. Set the rules of the game before the AI starts playing.
Include:
Technology standards and constraints
Integration requirements
Performance targets
Compliance needs
Multiple plan variations to compare approaches
Phase 3: Break Down Tasks
Instead of one giant "build me an app" prompt, you get a prioritized list of small, testable, specific tasks. Each task is focused enough that the AI can nail it in one shot with minimal debugging.
Transform vague requests like "build authentication" into concrete tasks like "create a user registration endpoint that validates email format."
Phase 4: Implement
The AI works through each task individually. You review, test, and approve each piece before moving on. Instead of reviewing thousands of lines of code dumps, you review focused changes that solve specific problems.
The AI knows:
What to build (from the specification)
How to build it (from the plan)
Exactly what to work on (from the tasks)
Why This Actually Works
This approach succeeds where pure prompting fails because it addresses a fundamental truth about language models: they're exceptional at pattern completion but terrible at mind reading.
When you provide clear specifications, technical plans, and focused tasks, you're giving the AI actual clarity instead of forcing it to guess. Instead of mind-reading your intent, it has concrete direction.
Use Cases
1. Greenfield Projects
Starting something completely new? Spend time creating a spec and plan upfront to ensure the AI builds what you actually intend, not just some generic solution based on common patterns.
2. Feature Work in Existing Projects
This is where spec-driven development becomes incredibly powerful. Adding features to complex existing codebases is hard, but creating a spec forces clarity on how new features should interact with everything already there.
3. Legacy Modernization
When rebuilding legacy systems where original intent is lost to time, you can capture essential business logic in a modern spec, design fresh architecture in the plan, and let AI rebuild without carrying forward technical debt.
The Bottom Line
If you're tired of playing prompt roulette with your AI coding assistant, spec-driven development offers a structured path forward. SpecKit is completely open-source and ready to use right now with GitHub Copilot, Claude Code, Gemini, and Cursor.
The future of AI coding isn't just about better prompts—it's about better specifications. When we give AI the clarity it needs, it can finally deliver on its promise of truly collaborative development.
Stop asking AI to read your mind. Start giving it the blueprints it needs to build exactly what you envision.
Vertical AI is the New SaaS
Vertical AI is the New SaaS
Sep 3
Written By Akhil Mohan
How industry-specific AI is reshaping enterprise software just like SaaS transformed computing two decades ago
Twenty years ago, I witnessed the SaaS revolution firsthand. Companies were struggling with expensive on-premise software deployments, complex maintenance cycles, and rigid licensing models. Then came Salesforce, Workday, and ServiceNow—not just offering better software, but fundamentally reimagining how businesses consume technology. They didn't just digitize existing processes; they created entirely new paradigms around subscription models, cloud-native architectures, and continuous delivery.
Today, we're standing at a similar inflection point with Artificial Intelligence. But this time, the transformation isn't about moving from desktop to cloud—it's about moving from horizontal, general-purpose AI to Vertical AI: purpose-built intelligent systems designed for specific industries and workflows.
The Horizontal AI Foundation: Necessary but Not Sufficient
The current AI landscape is dominated by horizontal platforms—ChatGPT, Claude, Gemini, and their enterprise variants. These models are remarkable achievements, capable of reasoning across domains, generating content, and solving problems with unprecedented sophistication. They've democratized access to AI capabilities and proven the transformative potential of large language models.
However, much like the early days of computing when we had powerful but generic mainframes, horizontal AI faces inherent limitations when applied to specialized enterprise use cases:
Data Limitations: General models are trained on publicly available internet data, missing the proprietary datasets that drive real business value—medical records, legal precedents, financial transactions, manufacturing telemetry.
Compliance Gaps: Industries like healthcare (HIPAA), finance (SOX, GDPR), and legal services operate under strict regulatory frameworks that generic AI models weren't designed to navigate.
Workflow Friction: Horizontal AI requires users to adapt their processes to the tool, rather than embedding intelligence seamlessly into existing professional workflows.
Limited Context: Without deep domain knowledge, even the most sophisticated general models struggle with industry-specific nuances, terminology, and decision-making frameworks.
Vertical AI is the New SaaS
What is Vertical AI Revolution
Vertical AI represents the natural evolution beyond horizontal platforms—AI systems built from the ground up for specific industries, trained on domain-specific datasets, and designed to integrate natively into professional workflows. The market data strongly supports the rising importance of vertical AI solutions. Vertical AI is on the rise, with this year's vertical winners surpassing the other category winners to capture over $1B in combined funding in 2025 YTD AI 100: The most promising artificial intelligence startups of 2025 - CB Insights Research, according to CB Insights' AI 100 report. The healthcare sector exemplifies this growth trajectory, with the global AI in healthcare market size estimated at USD 26.57 billion in 2024 and projected to reach USD 187.69 billion by 2030, growing at a CAGR of 38.62% AI In Healthcare Market Size, Share | Industry Report, 2030.
Key Differentiators of Vertical AI:
1. Specialized Training Data While horizontal AI models train on broad internet corpora, Vertical AI systems ingest industry-specific datasets: clinical trial data for healthcare AI, case law databases for legal AI, financial market data for fintech AI. This specialized training creates models that understand industry context, terminology, and decision-making patterns.
2. Regulatory Compliance by Design Rather than retrofitting compliance, Vertical AI systems are architected with regulatory requirements as first-class constraints. Healthcare AI models are HIPAA-compliant from inception; financial AI systems are built with SOX controls and audit trails embedded.
3. Workflow-Native Integration Instead of requiring users to switch contexts, Vertical AI embeds directly into the tools professionals already use—EMR systems for doctors, case management platforms for lawyers, trading platforms for financial professionals.
4. Domain-Specific Performance By focusing on narrow use cases within specific industries, Vertical AI systems can achieve superhuman performance in their domains while maintaining explainability and auditability.
How AI is Reshaping Consulting
The consulting industry has long been the intellectual backbone of business transformation — guiding organizations through complexity using human expertise, frameworks, and playbooks. But today, something unprecedented is underway: the rise of Artificial Intelligence as both a disruptor and an amplifier of consulting.
The headlines are provocative: “Is consulting becoming irrelevant in the age of AI?”
The reality is the opposite. AI isn’t making consulting obsolete — it’s fundamentally reshaping how consultants create value, deliver outcomes, and engage with clients.
The bottom line: Consulting is shifting from a purely advisory service to a technology-enabled, implementation-focused discipline — one that delivers measurable impact faster than ever before.
5 AI Driven Cloud Defense: A Step-by-Step Guide
As organizations increasingly depend on the cloud, the playbook for cyber defense is undergoing a foundational shift. The sheer scale, complexity, and velocity of today’s threats mean traditional approaches can’t keep up. Artificial intelligence is a true game-changer in cloud security, unlocking smarter, faster, and more agile protections. Here’s how AI is remaking the future of cloud defense, step by step.
The 5 AI-Driven Moves Elevating Cloud Security – From smart detection to rapid resilience, these steps define tomorrow’s digital defenses.
1. Intelligent Threat Detection
AI instantly spots suspicious activity to keep threats out.
Modern cloud security starts with vigilance. AI powerfully analyzes activity across your cloud in real time, using advanced pattern recognition to catch anomalies and emerging threats other tools might miss. This means the earliest possible warning—so breaches can be stopped before they begin.
2. Rapid AI Driven Response
AI launches defenses the moment danger is detected.
When milliseconds matter, speed is everything. AI doesn’t wait for manual intervention: as soon as a threat is flagged, it can instantly activate automated defenses, from locking accounts to isolating risky environments. This rocket-fast action transforms threat response from minutes to moments.
3. Automated, Real-Time Mitigation
Incidents are contained automatically, slashing response time.
Response isn’t just fast—it’s hands-free. AI-enabled automation triggers real-time containment and mitigation playbooks, stopping malware, reverting compromised changes, and restoring safety without waiting for human hands-on. This relentless automation keeps damage minimal and recovery swift.
4. Dynamic Global Defense
AI adapts defenses everywhere—no matter where threats emerge.
Threats don’t respect borders, and neither does advanced cloud security. AI learns from attacks worldwide, continuously updating defenses for your entire cloud ecosystem. Whether risks appear in one region or another, your security posture adapts globally and instantly.
5. Laser-Focused Resilience
AI fortifies your cloud for faster recovery after any attack.
Resilience is everything when the stakes are high. Even if attackers get through, AI helps you bounce back quicker—guiding recovery efforts, restoring key systems, and focusing resources to where they’re needed most. This smart resilience limits impact and builds trust in your cloud journey.
In Summary
AI is changing the rules of cloud security. By combining sharp intelligence, automated speed, global adaptivity, and purposeful resilience, these five moves ensure your defenses are always one step ahead. That’s the power—and the promise—of the AI-driven cloud.
No Cloud, No AI Agents: How cloud powers AI
No Cloud, No AI Agents: How cloud powers AI. The rise of Agentic AI represents a fundamental shift in how applications operate. No longer are intelligent systems siloed, static, or limited to following pre-baked rules. Today’s most advanced agents can make decisions, learn, adapt, plan, and act autonomously—all thanks to the immense power of the cloud. But how does this new class of Agentic AI actually harness the cloud? Let’s explore, using a “mission control” diagram to unpack the anatomy of autonomous, cloud-native intelligence.
The Mission Control Center: Agentic AI at the Core
At the heart of our diagram—and this new technological era—is the Agentic AI Brain. Think of it as mission control for autonomous cloud applications. It’s where goals are set, data is interpreted, and complex decisions are made in real time.
No Cloud, No AI Agents: Cloud Powers Agentic AI
Agentic AI Brain:
Mission control for autonomous cloud applications, orchestrating every action, memory recall, plan, and collaboration.
Everything else in the agent’s ecosystem is an extension of, or a resource for, its intelligence.
1. Cloud Functions: Empowering Real-World Action
First up on our “mission control” board are Cloud Functions. These are the agent’s toolset—a collection of ready-to-use, infinitely scalable programs that can be triggered on demand to do real work:
Action and Automation: Cloud functions translate the agent’s decisions into action—sending emails, analyzing images, or updating records.
Operating Environment: Instead of being tethered to a single device or server, these tools can execute anywhere in the cloud, scaling up or down as needed.
In essence: When an agent decides, “It’s time to act,” cloud functions are the hands that make it happen.
2. APIs & Data Lakes: Memory, Knowledge, Context
Intelligent autonomy demands more than brute force—it needs a memory and a way to learn. In our diagram, that’s what APIs & Data Lakes represent:
Knowledge Base: APIs let the agent pull in fresh information—weather reports, user profiles, real-time market data, and more.
Long-Term Memory: Data lakes serve as vast repositories where the agent’s experiences, logs, and learned models can be stored and recalled.
Integration: The ability to connect and combine disparate data sources is what gives agentic systems true context-awareness.
In practice: This is how an agent “remembers” your preferences, adapts to new info, and generates the right answer—every time.
3. Scheduling Tools: Planning and Time Management
Autonomy isn’t just about acting now, but about knowing when (and in what order) to act. Scheduling Tools are the agent’s calendar, planner, and logistician:
Optimizing Tasks: They help agents schedule jobs, set reminders, balance workloads, and avoid conflicts—across users, systems, and services.
Coordination: Tasks can be rescheduled, chained, or repeated, allowing agents to handle complex workflows over time.
For the agent: This means turning a to-do list into a robust, ever-adaptive action plan.
4. Micro-agents: Collaboration and Specialization
The last piece is all about teamwork: Micro-agents. Instead of trying to be a jack of all trades, Agentic AI can delegate:
Specialized Sub-agents: Each micro-agent can focus on a particular function—one handles data cleaning, another books appointments, another negotiates with APIs.
Collaboration: The central agent coordinates, while micro-agents execute, report back, and even work together, dynamically forming teams as challenges arise.
The result: A cloud-native hive of intelligence, where expertise and responsibility are distributed for efficiency and resilience.
Why the Cloud is the Agent’s Perfect Home?
The entire architecture radiates outward from the agentic core, empowered at every step by the cloud:
Unlimited scalability: New tools, knowledge, and agents can be added seamlessly.
Always-on connectivity: Agents can tap global resources and operate 24/7.
Modularity: Each function—tools, memory, planning, collaboration—is a plug-and-play cloud service, making development and scaling simple.
Conclusion
As Agentic AI continues its rise, its strength will come not just from smarter algorithms, but from deeper integration with the cloud. The future belongs to self-directed applications—secure, scalable, and endlessly adaptive—operating from a "mission control” at the heart of the cloud.
The image you see above isn’t just a map; it’s a blueprint for the next generation of software.
Perplexity Comet AI Browser: The AI Agent That Will Change How You Work Online
Perplexity Comet AI Browser: The AI Agent That Will Change How You Work Online
Aug 3
Written By Priyanka Vergadia
Are you ready for a browser that does more than just display web pages? I just got early access to Perplexity's new Comet AI browser and I'm absolutely blown away by what it can do. Unlike Chrome, Comet acts as a true AI agent that can read your emails, schedule meetings, book restaurants, and manage complex workflows—all while you focus on what matters most.
What makes Comet different?
In my new video (watch below!), I show Comet creating LinkedIn posts automatically, turning a chaotic to-do list into smartly scheduled calendar events, and managing an entire video production workflow—all within one intuitive interface.
What is GitHub Spark: The Full Demo Inside
Vibe coding on steroids with GitHub Spark. 🚀 GitHub Spark Just Changed Everything About App Development GitHub has just launched Spark, an AI-powered coding platform that turns natural language descriptions into fully functional web applications. No coding required, no setup headaches, and one-click deployment to production. This isn't just another AI coding assistant – it's a complete paradigm shift in how we build software. ⚡ What You'll Learn: What GitHub Spark is and how it works Live demonstration of building multiple apps with just natural language Why this matters for developers, designers, and entrepreneurs Honest breakdown of pricing and limitations The future of AI-powered development 🔥 Key Highlights: Vibe Coding Full-stack applications generated from plain English Integrated with Claude Sonnet 3.5, GPT-4o, and other leading AI models One-click deployment with enterprise-grade hosting Complete GitHub ecosystem integration Real-time live previews and instant iteration 💰 Pricing & Access: Currently available in public preview for GitHub Copilot Pro+ subscribers ($39/month) Includes 375 Spark messages, unlimited manual editing, hosting, and AI inference 🛠️ Perfect For: ✅ Rapid prototyping and MVP development ✅ Internal tools and personal projects ✅ Learning full-stack development concepts ✅ Non-technical founders validating ideas ✅ Experienced developers eliminating boilerplate work
Complete Beginners Guide to Hugging Face
hey everyone and welcome back to my
channel where we talk about cloud tech
and AI And today we're diving into a
platform that you must know about if
you're doing anything to do with AI It
is Hugging Face Now Hugging Face has
been called the GitHub of machine
learning and for all the great reasons
It is literally becoming the community
where AI models and creations are shared
across everybody So by the end of this
video you will understand what it is
exactly and why it matters to you even
if you are not somebody who codes every
day So stick around All right So this is
Demo
the hugging face homepage And the first
thing that you'll notice in here is
their tagline which is the AI community
building the future That really sums up
what they're really about right it's a
collaborative platform where people are
sharing AI tools models data sets and
even AI apps Now if you scroll down
you're able to see features models that
are trending and recently uploaded
content Um and this gives you the taste
of what's popular in the AI community
And before we dive deeper I would
recommend that you sign up and create an
account because you would need one Uh
most of the content you'll be able to
just see um without uh having an account
but if you want to use the models and
save your favorites and things like that
you will need an account Now um going
into the models tab this is where all
the models are found You can see that
they've got millions of models in here
and you can filter them by different
tasks in the categories like natural
language processing and classification
and audio and tabular
And you could also filter them by
libraries and data sets and languages
and
licenses Now here let's check out u
Microsoft's popular um 54 reasoning
model And I wanted to see how far I can
go with this So each model has got its
own page with documentation and I
clicked on deploy and it literally just
took me right away into the machine
learning studio in Azure AI and I was
asked to create a workspace and as soon
as I gave it all the details with the
name and everything um it was able to
create that workspace for me and
deployed that model the 54 model um from
hugging face into Azure AI machine
learning studio
That was absolutely amazing I just it
just took me a few clicks to do this And
you can see it's creating that now And
once it is created I can go to the
workspace And in this workspace if I
click on endpoints if I click on
endpoints I'm able to go into Azure
OpenAI service And that's where my 54
endpoint is And if I want to use this
endpoint I can click on continue And
that takes me into Azure AI Foundry
where I'll be able to um experiment with
this with this deployed model It tells
me my target URL the key that it created
for me I'm able to see how to use this
with my API key and um some samples of
how to use this model Um I am also able
to go play with it in the playground and
test it out So I gave it a prompt Um and
um I was really trying to go with like
the dog traveling uh to the mountains
where he meets a robot and um that robot
is helping a bird um survive in the cold
um and they all become friends for life
So um I was just playing around with
with um a prompt but the idea here is
that you're able to go from looking at a
model in hugging phase to actually able
to deploy that model in Azure AI found
uh foundry and uh Azure AI machine
learning studio Um and then I clicked on
deploy that um endpoint as a web
application
And right now that is what you're seeing
um with the Azure AI web application
being created um as a part of this
deployment It's able to deploy a web
application right from that um that
model and endpoint that we just created
Once the model this takes seconds maybe
a minute or so to to get created with
the deployment assets and stuff things
like that And once the app is deployed
I'm able to see that app in Azure AI
foundry in my web apps section There it
is the 54 experiment I click on that app
and there we have it An entire chat
application built from hugging face
choosing a model 54 reasoning goes into
Azure AI machine learning studio foundry
and builds it out for me as a web
application Going back into our hugging
Walkthrough
face interface we're able to uh let's
look at the the the data sets tab Now
this tab is where all your data sets are
There are thousands of these in there
and um you can preview the samples in
there You can also filter the data sets
of through languages tasks libraries all
of that Um and then if you click on one
you're able to actually see the samples
of that of the data set um and start
using them Um and now the next thing is
one of my favorites which is the spaces
section of hugging face Now this is
where things get really really exciting
especially for non-coders So spaces is
this interactive AI application that
anyone can use right within the hugging
face browser experience And think of
them as like readytouse AI tools I
clicked on one here which is called
describe anything um in uh by created by
Nvidia And when you go into that model
again right in the browser I'm not doing
anything else I can upload an image And
uh once I do that um I can type my
description and I can get my description
for for the regions of my images This is
my dog sitting on a chair in a park And
um I selected different parts of that
image And um this model is able to this
demo is able to tell me what are in
these different parts Um I selected the
tree first and then I selected my dog
himself and uh it was able to do a
really good job at telling me what is in
this image Um and if I wanted I can take
this space and deploy it for myself
whether locally or um or in cloud Um and
but before we do that let's look at
another example So I go back into my
spaces I can really um you know
categorize by image generation 3D
modeling all the different options up
top Um and I went into stable diffusion
which is another one of the very common
and very popular libraries in gener of
uh image generation models And um I
tested this one out right here in spaces
with a prompt serene lake at sunset with
mountains in the background and a golden
retriever watching the sunset I let it
generate the image And there we have it
Um I don't know if I like the first one
The second one's okay
Um but it it did what I wanted it to do
Um and let's say I'm happy with with
what it's I love the third and the
fourth images Um they really do what I
asked it to do The the good part the
best part the part that I want to show
you is I can run this space Let's say I
like it I can run it locally I can run
it um I can clone the repo um and um and
start working with it right from here
just like how we deployed the um the
five for model in Azure AI and uh with
that um let's look at the docs the docs
section is uh your knowledge center this
is where you are going to get deep
deeper technical information the docs
are organized by different categories
like the client libraries deployment
interface core ML libraries like the
transformers which is one of the very
famous libraries diffusers tokenizers um
and a lot more like radio Um and then
the next thing the last thing I want to
talk to you about is the community
section This is where people ask
questions and share ideas and learn The
blog part of the of the community is
amazing you'll see a lot of people
contributing to the blogs and you'll see
um what's happening right now um and and
what's hot right now Then the learn
section is one of my favorites The LLM
course and the agent course are some of
the best courses out there on AI and
machine learning right now The LLM
course goes from transformers all the
way up to fine-tuning And then the agent
course covers everything from intro to
agents to to a lot more So that my
friends was hugging face and we've
toured every major section of the
platform Whether you are just curious
about AI want to use existing models or
are developing something with AI or want
to contribute hugging face is definitely
a platform to check out Now go explore
And if you liked this video and found it
helpful please hit that like and
subscribe button to get more tech and AI
content And drop a comment if you have
questions and which AI platform I should
cover next And thank you for watching
See you next timehey everyone and welcome back to my
channel where we talk about cloud tech
and AI And today we're diving into a
platform that you must know about if
you're doing anything to do with AI It
is Hugging Face Now Hugging Face has
been called the GitHub of machine
learning and for all the great reasons
It is literally becoming the community
where AI models and creations are shared
across everybody So by the end of this
video you will understand what it is
exactly and why it matters to you even
if you are not somebody who codes every
day So stick around All right So this is
Demo
the hugging face homepage And the first
thing that you'll notice in here is
their tagline which is the AI community
building the future That really sums up
what they're really about right it's a
collaborative platform where people are
sharing AI tools models data sets and
even AI apps Now if you scroll down
you're able to see features models that
are trending and recently uploaded
content Um and this gives you the taste
of what's popular in the AI community
And before we dive deeper I would
recommend that you sign up and create an
account because you would need one Uh
most of the content you'll be able to
just see um without uh having an account
but if you want to use the models and
save your favorites and things like that
you will need an account Now um going
into the models tab this is where all
the models are found You can see that
they've got millions of models in here
and you can filter them by different
tasks in the categories like natural
language processing and classification
and audio and tabular
And you could also filter them by
libraries and data sets and languages
and
licenses Now here let's check out u
Microsoft's popular um 54 reasoning
model And I wanted to see how far I can
go with this So each model has got its
own page with documentation and I
clicked on deploy and it literally just
took me right away into the machine
learning studio in Azure AI and I was
asked to create a workspace and as soon
as I gave it all the details with the
name and everything um it was able to
create that workspace for me and
deployed that model the 54 model um from
hugging face into Azure AI machine
learning studio
That was absolutely amazing I just it
just took me a few clicks to do this And
you can see it's creating that now And
once it is created I can go to the
workspace And in this workspace if I
click on endpoints if I click on
endpoints I'm able to go into Azure
OpenAI service And that's where my 54
endpoint is And if I want to use this
endpoint I can click on continue And
that takes me into Azure AI Foundry
where I'll be able to um experiment with
this with this deployed model It tells
me my target URL the key that it created
for me I'm able to see how to use this
with my API key and um some samples of
how to use this model Um I am also able
to go play with it in the playground and
test it out So I gave it a prompt Um and
um I was really trying to go with like
the dog traveling uh to the mountains
where he meets a robot and um that robot
is helping a bird um survive in the cold
um and they all become friends for life
So um I was just playing around with
with um a prompt but the idea here is
that you're able to go from looking at a
model in hugging phase to actually able
to deploy that model in Azure AI found
uh foundry and uh Azure AI machine
learning studio Um and then I clicked on
deploy that um endpoint as a web
application
And right now that is what you're seeing
um with the Azure AI web application
being created um as a part of this
deployment It's able to deploy a web
application right from that um that
model and endpoint that we just created
Once the model this takes seconds maybe
a minute or so to to get created with
the deployment assets and stuff things
like that And once the app is deployed
I'm able to see that app in Azure AI
foundry in my web apps section There it
is the 54 experiment I click on that app
and there we have it An entire chat
application built from hugging face
choosing a model 54 reasoning goes into
Azure AI machine learning studio foundry
and builds it out for me as a web
application Going back into our hugging
Walkthrough
face interface we're able to uh let's
look at the the the data sets tab Now
this tab is where all your data sets are
There are thousands of these in there
and um you can preview the samples in
there You can also filter the data sets
of through languages tasks libraries all
of that Um and then if you click on one
you're able to actually see the samples
of that of the data set um and start
using them Um and now the next thing is
one of my favorites which is the spaces
section of hugging face Now this is
where things get really really exciting
especially for non-coders So spaces is
this interactive AI application that
anyone can use right within the hugging
face browser experience And think of
them as like readytouse AI tools I
clicked on one here which is called
describe anything um in uh by created by
Nvidia And when you go into that model
again right in the browser I'm not doing
anything else I can upload an image And
uh once I do that um I can type my
description and I can get my description
for for the regions of my images This is
my dog sitting on a chair in a park And
um I selected different parts of that
image And um this model is able to this
demo is able to tell me what are in
these different parts Um I selected the
tree first and then I selected my dog
himself and uh it was able to do a
really good job at telling me what is in
this image Um and if I wanted I can take
this space and deploy it for myself
whether locally or um or in cloud Um and
but before we do that let's look at
another example So I go back into my
spaces I can really um you know
categorize by image generation 3D
modeling all the different options up
top Um and I went into stable diffusion
which is another one of the very common
and very popular libraries in gener of
uh image generation models And um I
tested this one out right here in spaces
with a prompt serene lake at sunset with
mountains in the background and a golden
retriever watching the sunset I let it
generate the image And there we have it
Um I don't know if I like the first one
The second one's okay
Um but it it did what I wanted it to do
Um and let's say I'm happy with with
what it's I love the third and the
fourth images Um they really do what I
asked it to do The the good part the
best part the part that I want to show
you is I can run this space Let's say I
like it I can run it locally I can run
it um I can clone the repo um and um and
start working with it right from here
just like how we deployed the um the
five for model in Azure AI and uh with
that um let's look at the docs the docs
section is uh your knowledge center this
is where you are going to get deep
deeper technical information the docs
are organized by different categories
like the client libraries deployment
interface core ML libraries like the
transformers which is one of the very
famous libraries diffusers tokenizers um
and a lot more like radio Um and then
the next thing the last thing I want to
talk to you about is the community
section This is where people ask
questions and share ideas and learn The
blog part of the of the community is
amazing you'll see a lot of people
contributing to the blogs and you'll see
um what's happening right now um and and
what's hot right now Then the learn
section is one of my favorites The LLM
course and the agent course are some of
the best courses out there on AI and
machine learning right now The LLM
course goes from transformers all the
way up to fine-tuning And then the agent
course covers everything from intro to
agents to to a lot more So that my
friends was hugging face and we've
toured every major section of the
platform Whether you are just curious
about AI want to use existing models or
are developing something with AI or want
to contribute hugging face is definitely
a platform to check out Now go explore
And if you liked this video and found it
helpful please hit that like and
subscribe button to get more tech and AI
content And drop a comment if you have
questions and which AI platform I should
cover next And thank you for watching
What is Synthetic Data and how to use it effectively in your AI Projects
Researchers predict we'll exhaust all fresh text data on the internet in less than 30 years. This looming "data cliff" is why synthetic data is becoming the secret sauce of AI development—our escape hatch from running out of training material.
If you're working with AI systems or curious about how modern language models are trained, understanding synthetic data isn't just helpful—it's becoming essential. Let's dive into what it is, how it works, and why it might be the key to AI's future.
C-Suite’s Guide to Building Lasting ROI with AI Investments
How to build lasting ROI on AI investments: Every meeting I am in, the customer executives are asking: “What’s the ROI on my AI project?” The honest answer I have to share is: you won’t see it in a few days—or even a few months. That’s because AI, unlike a traditional technology rollout, is not a one-off project. It’s a habit. And like any habit, it takes time, commitment, and cultural change to form—before the real value emerges.
Traditional project approaches frame AI as a one-time initiative with defined start and end points, typically measured in weeks or months. In contrast, the habit approach recognizes AI as an ongoing process of integration into daily workflows that spans months to years. Research on habit formation indicates that individuals require an average of 66 days to form basic habits, with a range of 18-254 days depending on complexity. For organizations, this timeline extends considerably longer—typically 120 days for organizational changes and up to 365 days for full AI integration.
AI Created It, But Who Owns It?
Navigating the complexities of AI copyright can feel like stepping into a legal minefield. As generative AI tools become essential for creators, artists, and businesses, the question of ownership is more critical than ever. Who holds the intellectual property rights to AI-generated content? Is it you, the AI developer, or does it belong to the public domain? Our latest post demystifies the current state of AI copyright, exploring key court cases, the debate around "authorship," and the crucial concept of "fair use." Get the clarity you need to create and innovate with confidence.
OWASP Top 10 for LLMs and GenAI Cheatsheet
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What is Model Context Protocol (MCP)?
To understand Model Context Protocol (MCP), let's start with a familiar concept: APIs in web applications.
Before APIs became standardized, web developers faced a significant challenge. Each time they needed to connect their application to an external service—whether a payment processor, social media platform, or weather service—they had to write custom code for that specific integration. This created a fragmented ecosystem where:
Developers spent excessive time building and maintaining custom connectors
Each connection had its own implementation details and quirks
Adding new services required significant development effort
Maintaining compatibility as services evolved was labor-intensive
APIs (Application Programming Interfaces) solved this problem by establishing standardized ways for web applications to communicate with external services. With standardized APIs:
Developers could follow consistent patterns to integrate services
Documentation became more standardized and accessible
Updates to services were easier to accommodate
New integrations became significantly faster to implement
MCP addresses the exact same problem, but for AI applications.
Just as APIs standardized how web applications connect to backend services, MCP standardizes how AI applications connect to external tools and data sources. Without MCP, AI developers face the same fragmentation problem that web developers faced before standardized APIs—they must create custom connections for each external system their AI needs to access.
What is MCP?
Model Context Protocol (MCP) is an open protocol developed by Anthropic that enables seamless integration between AI applications/agents and various tools and data sources. Think of it as a universal translator that allows AI systems to communicate with different external tools without needing custom code for each connection.
Latest Large Context Model (LCM) Benchmark Explained: L-CiteEval
Latest Large Context Model (LCM) Benchmark Explained: L-CiteEval
Dec 27
Written By Priyanka Vergadia
As language models continue to evolve, one of the most significant challenges has been handling long-form content effectively. In this article, we'll explore how modern Large Context Models (LCMs) are pushing the boundaries of context windows and what this means for developers working with AI applications.
The Evolution of Context Windows
The landscape of context windows in language models has evolved dramatically:
GPT-3.5 (2022): 4K tokens
Claude 2 (2023): 100K tokens
GPT-4 (2024): 128K tokens
Claude 3 (2024): 200K tokens
Gemini Ultra (2024): 1M tokens
Anthropic Claude (experimental): 1M tokens
This exponential growth in context window sizes represents a fundamental shift in how we can interact with AI systems. For perspective, 1M tokens is roughly equivalent to 750,000 words or about 3,000 pages of text.
We’ll explore and understand LCM with the help of a recent research paper (L-CITEEVAL: DO LONG-CONTEXT MODELS TRULY LEVERAGE CONTEXT FOR RESPONDING?), which highlights the importance of large context with benchmark.
ONE life change I wish I'd made sooner in my tech career
There's a lot I accomplished professionally this year – from an amazing job change to completing 4 more terms of my MBA, learning countless new things, and exploring more of the world. While I share my professional journey on LinkedIn, this blog post is different. It's raw, even a bit vulnerable. I'm sharing this story hoping it might inspire even one person. This year taught me a profound truth: health truly is wealth. Here's my journey. Read on..
RAG Cheatsheet
Ever wondered why sometimes you get misleading answers from generic LLMs? It's like trying to get directions from a confused stranger, right? This can happen for many reasons, some of them are that the LLM is trained on data that is out of date, it cannot do the math or the calculations or it is just hallucinated. That is where RAG comes in.
What is Agentic RAG? Simplest explanation
Traditional RAG systems, while foundational, often operate like a basic librarian - they fetch relevant documents and generate responses based on them. Agentic RAG, on the other hand, operates more like a research team with specialized experts. Let's dive deep into when and why you'd choose one over the other.