Carbaba AI

• Founded Carbaba Cloud Service, a pioneering B2B SaaS company in the UAE focused on compliance systems and QA validators.  
• Spearheaded the transformation of document intelligence, enhancing documentation workflows for enterprises.  
• Developed innovative solutions that improved operational efficiency, resulting in a 40% reduction in document processing time.

General Manager

CliniNote

CliniNote is a rapidly growing startup making waves in the healthcare industry. CliniNote develops innovative tools for real-world data collection and assistant software specifically designed for doctors, hospitals,pharmaceutical companies, and clinical trials, with a global reach. The team, along with their business angels and investors, share a strong commitment to integrity, consistency, and focus. They’re passionate about driving positive change in healthcare through the power of digital solutions and
real-world data, ultimately saving lives.

Software Developer

Monash University

Engaged in mentoring students embarking on their journeys in data analytics, providing a bridge
between academic learning and real-world applications.

Delivered project-based feedback and career advice, drawing from my extensive experience in
data science and biomedical engineering.

Education Mentor

Swinburne University of Technology

Working on research project to build a new systematic artificial intelligence program using intricate machine learning classification and regression algorithms to predict heart disease using dataset from UCI repository. 
These techniques require the adaptation of high level feature engineering strategies to acquire an accurate smart medical technology. 
This new technology can interpret detailed analogies of patients to create a sustainable, normalized algorithm that predicts different types of heart disease directly proportional to the valves diameter narrowing. 
This technology utilizes a combination of R programming language to visualize and preprocess the dataset accordingly and python language to utilize the agility and robustness for an enhanced classification technology.

Machine Learning Researcher

Fiverr

Dealing with segregating data for different online businesses 
Cleaning repetitive data 
Inputting data into database 
Having to deal with images and string format
High level focus on detail

Data Entry Associate

- Teaching statistical methodologies to RMIT university students studying Statistics & Data Analytics

- Helped RMIT university students with statistics and marketing-related assignments 

- Worked with my students to plan and construct a strategy to tackle difficult projects that required Data analytics and statistical expertise. These involved but not limited to: 
- Ideation stages of understanding the problem and planning a timeline for each member of the group to perform their roles accordingly. 
- Processing stage where the group started showing improvements on their allocated tasks. This stage heavily utilized proficient MS Excel expertise. 
- Finalizing stage which involves organizing the ideas together in a compact format to present the project in a visually pleasing and effective manner to the client.

Private Tutor

CarbabaAI, a UAE-based B2B SaaS company, pioneers document intelligence using AI-driven compliance systems and QA validators to optimize enterprise workflows. As Founder, contributions include innovative solutions that reduced document processing time by 40%, showcasing a commitment to operational efficiency and transformative technologies.  

With a biomedical degree major with a data science minor from Swinburne University of Technology, expertise spans machine learning, AI systems, and data analytics. Previous roles, such as Software Developer at CliniNote, focused on real-world data solutions for healthcare. Additionally, mentoring at Monash University bridged academic concepts with practical applications, reflecting a dedication to responsible AI-driven industry innovation.

After burning through $8K in API costs, 
I learned this hard truth about LLMs...

Treating all LLMs the same will kill your budget.

You need to pick the right brain for the job.
Different models, different strengths.
The wrong choice will bottleneck your product.

Here's a quick guide to the 6 core LLM types:

1️⃣ Encoder-Only Models:
They read for context, not creativity.
Great for classification, search, and embeddings.
✅ Used in BERT, RoBERTa, and similar models.

2️⃣ Decoder-Only Models:
Masters of next-word prediction.
Perfect for generation and dialogue agents.
✅ Think GPT-4, Claude, and LLaMA 3.

3️⃣ Encoder-Decoder (Seq2Seq):
Best for structured input-output tasks.
Translation, summarization, rewriting.
✅ T5 and BART are textbook examples.

4️⃣ Mixture of Experts (MoE):
Not all neurons fire at once.
Only a few "experts" activate per task.
✅ Highly efficient scaling (DeepSeek-V2, LLaMA 4).

5️⃣ State Space Models (SSM):
Forget attention. Think linear memory.
Blazing fast on long sequences.
✅ Mamba leads this frontier.

6️⃣ Hybrid Architectures:
Why choose one brain when you can blend?
Combines Transformers with newer models.
✅ Jamba (Transformer + Mamba) is a standout.

You don’t need to master every detail.
But understanding the blueprint matters.
Smart builders know which model does what.
And that’s how they stay ahead.


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Ghadeer  Al Ruwaishedi

li_at

JSESSIONID

6 Core LLM Architectures

I Help AEC Firms Automate Their QA Processes | Founder @ Carbaba | AI-Native Document Intelligence

I just downloaded the private playbooks from Google, Microsoft, McKinsey & AWS

This changes everything.

Everyone talks about “AI transformation.”
Few have seen what it actually looks like inside.

And here’s what I learned:

1️⃣ Google – Startup Technical Guide for AI Agents
The technical blueprint for scalable agent ecosystems.
Every startup CTO should read this twice.
https://lnkd.in/dVipRJQR

2️⃣ Microsoft – Agent Governance Whitepaper
Defines the rules of engagement.
How to deploy AI safely at enterprise scale.
https://lnkd.in/dd8FHCfv

3️⃣ Cohere – Building Enterprise Agents
The architecture of agents that think.
From design to deployment in real-world settings.
https://lnkd.in/dRBSBQzf

4️⃣ AWS – Executive’s Guide to Agentic AI
Built for C-suite leaders.
Learn to translate agents into business outcomes.
https://lnkd.in/dPaWySes

5️⃣ Deloitte – Unlocking the Right Agentic AI Use Cases
Focus on measurable, high-impact adoption.
https://lnkd.in/gy2ETq_T

6️⃣ McKinsey – Seizing the Agentic AI Advantage
The strategic roadmap for AI advantage.
Backed by case studies and results.
https://lnkd.in/gF7EY_ad

7️⃣ KPMG – The Agentic AI Advantage
Frameworks for scaling trust and efficiency.
From pilots to enterprise-wide rollouts.
https://lnkd.in/gw3c23h8

8️⃣ Capgemini – The Rise of Agentic AI
Trends, transformations, and tomorrow’s enterprise model.
Where autonomy meets accountability.
https://lnkd.in/g3z-HWpG

9️⃣ Accenture – Technology Vision 2025
Predicts the next phase of enterprise AI.
Agents are the center of every workflow.
https://lnkd.in/grdzVaRa

🔟 OpenAI – AI in Enterprise
How frontier models reshape operations.
From productivity to decision-making at scale.
https://lnkd.in/gfxVCN7H

1️⃣1️⃣ Google – AI Agent Handbook
A field manual for developers and strategists.
Practical frameworks for intelligent automation.
https://lnkd.in/gyr5zUTR

1️⃣2️⃣ BCGX – AI Agents and MCP
The link between agents and modular collaboration.
Enterprise AI becomes a network, not a tool
https://lnkd.in/gp-VGbe3

1️⃣3️⃣ Thomson Reuters – Agentic AI 101
A primer for legal, finance, and compliance teams.
Understand before you automate.
https://lnkd.in/gbcFwkHN

1️⃣4️⃣ ServiceNow – Enterprise AI Maturity Index
Benchmark your organization’s readiness.
Discover where you stand today.
https://lnkd.in/g7Ff4Vhj

1️⃣5️⃣ Infosys – Tech Navigator Agentic Enterprise Playbook
A full-stack view of enterprise transformation.
https://lnkd.in/gb5DNeJm

Every line in these playbooks rewires how you think.
The message is clear: agentic enterprises win early.

The rest will scramble to catch up.

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One person with AI Agents now outputs what took a team of 10 last year…

The game has changed.

AI Agents in 2025 aren’t just tools. They’re teammates, builders, and thinkers.

Here are the 6 trends driving the shift 👇:

1- Agentic RAG
Real-time reasoning meets retrieval.
Used by Perplexity, Harvey AI, and Glean.

2- Voice Agents
Conversational AI goes full voice.
ElevenLabs, Cognigy, and Deepgram are leading it.

3- AI Agent Protocols
Agents can now communicate across systems.
A2A, ACP, and SLIM make collaboration seamless.

4- Computer Using Agents (CUA)
Agents that browse, click, and code like humans.
Think OpenAI’s Operator and Claude’s Computer Use.

5- Coding Agents
They build, debug, and ship faster than ever.
Windsurf, Cursor, and Copilot are redefining dev speed.

6- DeepResearch Agents
Collaborative AI for high-quality research and reports.
Led by OpenAI DR, Gemini DR, and You(.)com DR.

2025 is the year AI stops assisting and starts collaborating.

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AI Agent Trends of 2025

Prompt engineering is dead. 
Context engineering is the $500K skill of 2025.

Most people still talk about “prompt engineering.”
But that’s not how real AI systems think anymore.

Prompts die after one interaction.
They don’t remember. They don’t reason.
You can’t build intelligence on forgetfulness.

So we started cramming everything into context docs, chats, rules, and knowledge bases and called it “context-augmented generation.”

It worked… until it didn’t.
Models got slower.

Hallucinations exploded.
Accuracy fell off a cliff.
The fix isn’t more context.

It’s engineered context.
That’s what Paul Iusztin calls Context Engineering 2025’s #1 AI skill.

It’s not a chatbot. It’s a living system.

Here’s how it works 👇:

1️⃣ Short-term memory — your current chat and actions. Fast. Temporary. Like RAM.

2️⃣ Long-term memory — vector or graph databases. Stores knowledge, preferences, experiences.

3️⃣ Semantic retrieval — RAG and APIs pull what matters. Real-time facts, not static data.

4️⃣ Tools and schemas — Python, analysis, diagrams. How the agent acts and learns.

5️⃣ Context assembly — the brain of it all.
Only the most relevant info enters the LLM window.

That’s not prompting.
That’s orchestrating cognition.

Andrej Karpathy said it best:

“LLMs are the new operating systems.
Context is their RAM.”

If you control the RAM, you control the intelligence.

The future AI engineer won’t just write prompts.
They’ll engineer information ecosystems.

Systems that remember, reason, and evolve.
That’s the shift.

That’s why context engineering wins 2025.
This isn’t about better chatbots.
It’s about building AI that thinks

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I spent 6 months building an AI agent that couldn’t book a meeting…

It wasn’t the AI. It was isolation.

Agents aren’t failing because they’re bad.
They’re failing because they’re alone.

No tools.
No APIs.
No connection.

Every “smart” agent ends up trapped in its own box.
That’s why MCP changes everything.

Model Context Protocol = connection.
It’s the open standard that lets any agent talk to any system. ANYWHERE. 

One protocol → infinite integration.
This is the HTTP moment for AI.

And now, 12 SDKs are making it real 👇:

1️⃣ Claude Agent SDK: Security-first. Production ready.

2️⃣ OpenAI Agents SDK: Composable, handoff-ready agents.

3️⃣ Microsoft Agent Framework: AutoGen + Semantic Kernel.

4️⃣ Google ADK: Model-agnostic, dev-friendly architecture.

5️⃣ CrewAI: Multi-agent orchestration that scales.

6️⃣ Upsonic: Financial-grade agents, built for safety.

7️⃣ mcp-agent: Lightweight, Anthropic-style workflows.

8️⃣ Agno: 10 lines. Thousands of agents. Instant speed.

9️⃣ DSPy: Prompt optimization, automated.

🔟 LangChain: The integration giant.

1️⃣1️⃣ LlamaIndex: Data-aware agents powered by RAG.

1️⃣2️⃣ PydanticAI: Type-safe, validation-first agents.

MCP is uniting them all.
One language for every agent.
One protocol for every connection.

This is how AI stops experimenting and starts operating.

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Google just killed text-only RAG. 
OpenAI didn’t see this coming…

Context is finally complete.

Most RAG pipelines choke on PDFs.
OCR, markdown hacks, broken visuals.
Critical charts get dropped.

Now it’s different.
With Gemini Flash 2.5 + Cohere Embed v4,
you can retrieve and reason directly on images.

Key unlocks:
→ Directly retrieves slides, charts, infographics with no hacks.
→ Preserves visual context. No markdown loss.
→ Cohere Embed v4 handles text+image retrieval.
→ Gemini Flash 2.5 interprets visuals for accurate answers.
→ Works on complex PDFs, not just plain text.

This isn’t incremental.
It’s a paradigm shift for knowledge workflows.
From finance to research to consulting, your RAG pipeline just grew eyes.

Test it out for yourself 👇

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Ghadeer  A.

McKinsey, Google, and BCG spent millions researching AI adoption
Then gave away the playbooks for free.

And 99% of leaders still haven’t read them.

Here’s what you’re missing:

Most leaders are stuck.
Noise > clarity.
Talk > execution.

But these playbooks fix that.

1️⃣ McKinsey’s CEO Agentic AI Playbook
Clear steps for CEOs to scale AI.
From pilots to enterprise transformation.
🔗https://lnkd.in/desmTXU8

2️⃣ OpenAI’s 15-page AI Leadership Guide
Concise, actionable, future-focused.
A survival manual for decision makers.
🔗https://lnkd.in/dFt8Nrxz

3️⃣ Google’s Agentic Services AI Playbook
The handbook for AI agents at scale.
Blueprints, tools, and architectures.
🔗https://lnkd.in/d8GWwZhz

4️⃣ BCG’s AI-First Enterprise Playbook
Proves AI-first firms win.
Culture, operations, and execution covered.
🔗https://lnkd.in/dW6vCTgF

5️⃣ AWS’s Agentic AI Executive Guide
Practical, 17 pages of enterprise tactics.
Grounded in real-world adoption cases.
🔗https://lnkd.in/dxKu2TaY

6️⃣ Microsoft’s CIO Playbook
40 slides for tech executives.
Balance risk, Copilot, and enterprise scale.
🔗https://lnkd.in/dMd8A5bs

These are the holy grails of AI adoption.
They’re free, public, and wildly underused.
Read them now. Apply them faster.

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In 6 months, NOT using vision AI will be like not having email in 2000.
Start here:

1️⃣ Gemini 2.5 Pro (Google)
Understands text, image, audio, and video like a human brain.

2️⃣ InternVL3-78B
Open-source powerhouse breaking records in multimodal reasoning.

3️⃣ Ovis2-34B
Aligns text and visuals with lightning precision.

4️⃣ Qwen2.5-VL-72B
The backbone of the new wave of open multimodal AIs.

5️⃣ o3 (OpenAI)
The new reasoning king math, science, and visuals mastered.

6️⃣ GPT-4.1
Reads charts, counts objects, and decodes visual logic flawlessly.

7️⃣ Claude Sonnet 4
Anthropic’s creative and analytical visual thinker.

8️⃣ Kimi-VL-A3B-Thinking-2506
Thinks faster, reasons smarter, and cuts thinking time by 20%.

9️⃣ Gemma-3-27B-it (Google)
Small model. Big performance. Punches above its weight.

🔟 aLlama-3.2-90B-Vision-Instruct (Meta)
Sees, understands, and describes the world with precision.

AI that sees is old news.
AI that understands what it sees is the future.
And that future just arrived.

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Top 10 Vision Language Models in 2025

There are people who understand Agentic AI. 
And people who think Zapier is an agent platform…

Most are still confusing automation with autonomy.

The industry’s biggest problem right now?
Everyone’s calling everything an Agent.

Hype has blurred the lines.
Beginners can’t tell tools from intelligence.

So what actually is Agentic AI? 👇

⚫ LLM Chatbots
Text machines, not thinkers.
They respond, they don’t reason.
Short-term memory. No planning. No tools.

⚫ RPA (Robotic Process Automation)
Great for repetition, bad for adaptation.
Runs scripts in sequence.
Breaks the moment reality changes.

⚫ RAG (Retrieval-Augmented Generation)
A smarter search bar with LLM polish.
Finds facts, feeds them to the model.
But it answers. It NEVER acts.

🔴 Agentic AI
It remembers what matters (short and long term).
It plans, executes, and adapts.
It uses tools dynamically. NOT by script.
It learns from feedback (ReACT, Reflexion).
It collaborates with other agents to reach goals.

Agentic AI doesn’t follow a trigger.
It creates its own path forward.

Autonomy, not automation that’s the leap.
And most haven’t made it yet.

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Chatbot, RPA, RAG vs Agentic AI

94% of document AI failures happen before the LLM even sees the data…

Vision wins.

We keep trusting text pipelines.
But they drop tables, layouts, and charts.
Precision dies before the model even starts.

✅ Vision-RAG changes the game.
It retrieves full page images with VLMs.
Layout and figure grounding stay intact.

✅ Benchmarks prove it.
VisRAG reports 25–39% lift over text-RAG.
ColPali and VDocRAG validate the approach.

✅ High fidelity matters.
Ticks, stamps, superscripts. Vision-RAG keeps them.
Resolution directly drives reasoning quality.

Yes, it’s heavier on tokens.
But selective crops balance cost and fidelity.
Enterprises can’t afford pixel-blind retrieval.

✅ Text-RAG still works.
Contracts, code, normalized CSVs are fine.
But real-world docs demand visual truth.

This is the new default for document AI.
Vision-RAG is not experimental. It’s production ready.
Backed by benchmarks, aligned to business impact.

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In 6 months, you’ll either work WITH AI agents…
Or you’ll work FOR someone who does.

AI agents aren’t assistants. They’re operators.

Most still confuse them with chatbots.

Chatbots only talk.
Agents think, decide, and act.

Understand their building blocks now.

AI agents are built from three parts:
1️⃣ Language models for reasoning and decisions.
2️⃣ Tools that connect to systems and data.
3️⃣ Orchestration layers that govern workflows.

Different types unlock different powers:
One-prompt agents solve quick, narrow tasks.
Coding agents generate scripts, debug, and refactor.

Workflow-based agents run multi-step processes.
They connect apps, people, and automations.

Frameworks provide reusable scaffolding.
They scale complexity and manage advanced systems.

And protocols make them talk.
Agent-to-agent collaboration builds ecosystems.

Here’s the shift:
We’re moving from isolated tasks…
To networks of agents running entire operations.

AI agents aren’t replacing work.
They’re becoming the new managers of it.

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15-year-olds are building million-dollar apps with AI
While developers still think it’s just hype.

Old workflows waste time and energy.
Deadlines slip, skills gaps widen, teams stall.

AI coding tools flip this story.
Speed, clarity, and collaboration become the norm.

Here’s the landscape today:

👉 Cursor  
Codebase chat, context-aware, VS Code native feel.
Best for full-stack dev and refactoring.

👉 Windsurf  
Multi-file editing, strong completions, Cursor rival.
Best for dev teams seeking a fresh IDE.

👉 Lovable  
Full-stack apps, zero technical background needed.
Best for founders shipping MVPs at speed.

👉 Bolt
Instant browser deployment, demo-ready builds.
Best for quick prototypes and client sharing.

👉 Vercel
High-quality React + Tailwind components, clean output.
Best for design systems and frontend scale.

👉 Claude 
Natural conversation, complex task automation.
Best for deep refactoring and advanced workflows.

👉 Replit  
Cloud IDE, collaboration, deployment built in.
Best for education, experiments, and beginners.

The hype is gone.
The results are real.
AI coding isn’t the future
It’s the present advantage.

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My client’s AI agent cost him $2.1M
The reason? It couldn’t explain its own decision.

Most AI tools give answers. NOT reasoning.
They sound certain, but can’t show why.

That’s fine if you’re generating content.
But in engineering, finance, or compliance?
A black-box decision can cost millions.

Parlant built a reasoning framework
that forces AI to prove its logic. Not JUST state it.

Here’s how it works: 

➡ No Reasoning:
“Approve the structure. Standards met.”
Fast answer. Zero traceability.

➡ Chain of Thought (CoT):
“Clause 4.2 met.
Approval valid if tolerances are within 2%.”
Smarter but still guessing context.

➡ Attentive Reasoning Query (ARQ):
✅ Checks Clause 4.2 requirements
✅ Verifies real-world measurements
✅ Confirms exemptions or safety margins

Final Output:
“Approval valid only if tolerance verified
per Clause 4.2.3 under load conditions.”

That’s not improvisation.
That’s auditable reasoning.

Parlant’s framework turns AI into a reliable partner. 
One that thinks critically, cites evidence, and explains every conclusion.

No hallucinations.
No hidden logic.
Just decisions you can defend.

Imagine every design review, risk audit,
or compliance check backed by explainable AI.

That’s not next-gen hype.
That’s Parlant.

Try it out for yourself. 
🔗 to repo (thank me later): https://lnkd.in/dywtVkwu

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#Parlant #opensource #LLM #CX #ConversationalAI #GenAI

Developers are burning $100K+ on GPU infra. 
But Liquid AI just made that obsolete…

Liquid AI redefined efficiency.

AI power has a cost.
And for most, that cost is unsustainable.

Heavy models.
Endless GPU bills.
Performance that only scales with your budget.

That’s the problem Liquid AI just erased.

Meet LFM2-VL.
A breakthrough in VLMs
that makes speed and intelligence coexist.

✅ 2× faster inference than leading competitors.
All while maintaining top-tier accuracy.

✅ It runs on phones, laptops, even wearables.
No GPU farms. No compromises.

✅ Two variants (450M and 1.6B) fit every use case.
From edge devices to high-throughput research.

✅ It processes 512×512 images natively.
No distortion, no upscaling. Just precision.

👉 Benchmarks prove it:
LFM2-VL beats InternVL3 and SmolVLM2
on OCR, MathVista, RealWorldQA, and multimodal tests.

And it’s open-weight.
Apache 2.0 licensed.
Free for research, and commercially accessible for small teams.

This isn’t just a model.
It’s the start of affordable, deployable multimodal AI.

Liquid AI didn’t follow the trend.
They rebuilt the standard.

Read all about it: https://lnkd.in/d6FvWsRM

Check the comments to test it out for yourself 👇

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Screw it! 
I’m sharing one of the best AI repos out there…


This one actually teaches agentic AI.

Everyone’s talking about AI agents.
Almost no one knows how they really work.

This repo fixes that.

Agentic AI Crash Course
by Aishwarya Naresh Reganti & Kiriti Badam

Covers it all 👇
from what agents are,
to tools, memory, reasoning, and real-world systems.

You’ll learn:
✅ The 4 types of agentic systems
✅ How RAG becomes agentic
✅ What MCP actually does
✅ How multi-agent setups scale in production

Just clear lessons, deep examples, and future-ready thinking.

If you want to build AI agents.
START NOW! 

Repo (thank me later):
🔗 https://lnkd.in/dDS5KQMF


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I’ve watched so many clickbait “AI gurus.”
Then MIT dropped the real thing FREE.

Most courses overpromise.
MIT 6.S191 actually delivers.

81 lectures.
4 million views.
Zero cost.

Here’s what it covers:
✅ Deep Learning fundamentals
✅ RNNs, Transformers, Attention
✅ CNNs for vision
✅ Generative models (GANs, VAEs, diffusion)
✅ Reinforcement Learning
✅ Large Language Models
✅ Trustworthy AI
✅ Guest talks from Google, NVIDIA, Microsoft, IBM

This is the real AI education. No hype, no clickbait. 
MIT teaches how AI actually works.

Link in comments 👇

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No alternative text description for this image

The #1 reason smart people fail? 
They never learned to chunk properly…

Brains break when data isn’t chunked.

We overload our minds with detail.
We drown in words, not meaning.
Chunking turns chaos into clarity.

➡️ Fixed-size chunking adds rhythm.
Split into equal parts,
overlaps keep context alive.

➡️ Recursive chunking respects layers.
Paragraphs, then sentences,
then words until size fits.

➡️ Document-based chunking gives structure.
Headings, chapters, sections
become cohesive knowledge units.

➡️ Pre-chunking works upfront.
Text is split before retrieval,
ensuring faster search and match.

➡️ Post-chunking works downstream.
Retrieve full docs first,
then split for relevance.

➡️ Semantic chunking captures meaning.
LLM-based chunking applies intelligence.
Agentic chunking adds adaptability.

➡️ Hierarchical chunking preserves order.
From abstract to references,
every piece stays connected.

➡️ Late chunking keeps the big picture.
Embed whole docs,
then pool and split.

Chunking isn’t just technical.
It’s mental leverage.
It’s how smart people win.

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Chunking_Techniques

🚨Hot take: If your prompts don’t include reasoning, you’re basically using a $20/month autocomplete. 
Here’s how to fix it:

Prompting ≠ Thinking.

Most people treat LLMs like parrots.
Ask a question. Get a sentence. Move on.
But that skips what makes them powerful. Reasoning!

When you don’t prompt for thought,
you don’t get logic you get luck.
That’s why answers swing from genius to gibberish.

Here’s how to turn your LLM from guesser to thinker:

1- Chain of Thought (CoT)
Add one line: “Think step by step.”
It slows the model down in a good way.
Logic becomes visible. Answers become reliable.

2- Self-Consistency (Majority Voting)
LLMs think differently each time.
So ask the same question multiple times.
Pick the most common answer.
It’s consensus intelligence.

3- Tree of Thought (ToT)
The ultimate reasoning upgrade.
Instead of one path, explore many.
Each branch tests a different idea.
The model finds the smartest route to the truth.

If you want better answers,
you don’t need a smarter model you need smarter prompts.
Because prompting isn’t typing. It’s teaching the model to think.

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Stop turning $400 GPUs into $2,000 invoices 

Less GPU. More output.

Multimodal models hog GPUs.
Bills and latency explode.
Liquid AI LFM2-VL runs lean.

➡ What it is
These are new Liquid AI vision-language models.
They are built for low latency and device-aware use.

➡ Where it runs
It runs on phones, laptops, single-GPU, and wearables.
It is edge-ready via LEAP customization.

➡ Speed
It delivers up to 2x faster GPU inference.
It is the fastest in like-for-like tests.

➡ Variants
The 450M model fits tight budgets and the edge.
The 1.6B model adds capability while staying light.

➡ Vision stack
It uses SigLIP2 NaFlex encoders.
It is native at 512x512 and patches bigger images.

➡ Projector + tokens
It uses a 2-layer MLP with pixel unshuffle.
It yields fewer image tokens and higher throughput.
It adds a speed-quality knob at inference.

➡ Training
Mid-training, it fuses text and image.
Ratio shifts from 95% to 30% over time.
Training spans about 100B multimodal tokens in total.

➡ Eval example
With one 1024x1024 image and 100 output tokens.
It runs up to 2x faster than comparable peers.

➡ Build + license
Weights are open on HF with an Apache-style license.
Commercial use is OK for revenue under $10M.
It works with Transformers and TRL.

Speed isn’t bought, it’s built.
Efficiency beats raw compute.
LFM2-VL keeps GPUs humble.

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