Genesis Lab.com

Empowering industries through innovative blockchain solutions, we are a leading software development firm pioneering the future of decentralised technology.

[Genesis Lab.com @GenesisLabCom]

یومِ اقبال مبارک 🇵🇰
آج سے ۱۴۹ برس پہلے سیالکوٹ نے دنیا کو وہ شاعر دیا جس نے ہمیں سکھایا کہ قوم پہلے ذہن میں بنتی ہے۔ اقبال کی خودی — اپنے آپ کو پہچانو، اپنے آپ پر بھروسہ کرو، پھر تعمیر کرو,آج بھی وہ سب سے کارآمد نصیحت ہے جو ایک پاکستانی دنیا کے کسی بھی کونے میں ساتھ لے جا سکتا ہے

[Genesis Lab.com @GenesisLabCom]

Cyber-Physical AI at the Maritime Centre of Excellence Conference 🇵🇰

Genesis IT Lab was proud to take part in the MCE Conference on “Emerging Technologies and the Development of National Defence Ecosystem” in Islamabad.

[Genesis Lab.com @GenesisLabCom]

When AI Leaves the Screen
AI that flies, sees and decides. Genesis IT Lab is building Cyber-Physical AI: autonomous drone fleets for smart-city surveys, Micro LLMs running at the edge, and mesh-networked coordination, all built in Islamabad 📷.
#CyberPhysicalAI #Drones

[Genesis Lab.com @GenesisLabCom]

AI that doesn’t just think. AI that acts.

Genesis IT Lab builds Cyber-Physical AI, Micro LLMs running on the device itself, deciding in real time. No cloud, no API bill, no data leaving the site.
https://t.co/g2vzvJYJR3
Small models. Real machines. Real time.
#CyberPhysicalAI

[Artificial Analysis @ArtificialAnlys]

Introducing Code2Video-AA, our independent implementation of HeyGen’s Code2Video Bench, which evaluates how well models create product and brand marketing videos in code

Code2Video-AA tests models across 114 design briefs, tasking them with turning each product or brand marketing brief into a @HyperFrames_ HTML composition, which we render to video. Model outputs are evaluated through pairwise comparisons with other model outputs across five axes: Engagement, Prompt Intent, Composition, Temporal Consistency, and Craft. We use the outcomes of these comparisons to calculate an Elo rating for each axis, which are then averaged to produce the headline Code2Video-AA Elo. Code2Video-AA was built in partnership with @HeyGen.

Claude Opus 5.5 (max with fallback, 1536) leads Code2Video-AA by 93 points, but costs $3.08 per task, ~10x more than GPT-6.1 Sol (max, 1369), which trails by 167 points.

Key findings:

➤ Claude Opus 5.5 leads on all five judge axes: Models are judged across 5 axes: Engagement, Prompt Intent, Composition, Temporal Consistency, and Craft. Claude Sonnet 5.5 (max with fallback, 1443) and GPT-6 Astra (max, 1427) are statistically tied for second in overall score

➤ Cost per task varies more than 300x, from $0.01 to $3.89: GPT-6.1 Sol (max), fourth, has a Cost per Task of $0.31, about 90% lower than Claude Opus 5.5 (max with fallback) at $3.08

➤ Open weights models trail by at least 483 points: DeepSeek V4.1 Flash (max, 1053), MiMo-V2.6-Flash (1052) and MiMo-V2.6-Pro (1052) are the top open weights models, at $0.01 to $0.08 per task

Code2Video-AA details:

➤ Models are tested on a single response without tools. We render it with HyperFrames, with no network access

➤ A judge (Gemini 4 Argon, high) compares pair of videos across five axes: engagement, prompt intent, composition, temporal consistency and craft, with the final score being calculated from a combination of each individual axis Elo

➤ Over 35,000 verdicts rank 27 models by Elo, with 95% confidence intervals

More results, example videos and how Code2Video-AA works follow in the thread below

[Genesis Lab.com @GenesisLabCom]

Cyber Physical AI is changing the world
For years, AI has lived in the cloud: now in machines
We’re participating in the upcoming conference organized by MCE, where we’ll present our work on Micro LLMs and Cyber-Physical AI
Islamabad Pakistan
#CyberPhysicalAI #MicroLLM #EdgeAI

[Genesis Lab.com @GenesisLabCom]

Not all drones do the same job. 🛩️

▸ Multi-rotor
▸ Fixed-wing
▸ Hybrid VTOL,

[Genesis Lab.com @GenesisLabCom]

🛡️ Attackers use AI. So do we.
https://t.co/g2vzvJYJR3
Our principle is assume breach, prove resilience. We find your weaknesses before attackers do and respond at machine speed. Your data stays under your control.
https://t.co/g2vzvJYJR3
Genesis IT Lab (Pvt) Ltd, Islamabad

[Genesis Lab.com @GenesisLabCom]

Genesis IT Lab is leapfrogging into the future of Cyber-Physical Systems.

Digital intelligence. Physical action. One connected future.

#GenesisITLab #CyberPhysicalSystems #DigitalTwins #AI #UAV #FutureTechnology

[Shakil Muhammad, PhD @ShakilRNS]

With the legend ⁦@alexatallah in September 2023. From OpenSea to OpenRouter, he did it again, culminating in a massive $6B acquisition by Checkout/Stripe. Keep building, and one day you’ll make an impact. 🚀

[Genesis Lab.com @GenesisLabCom]

Traditional index and portfolio rebalancing protocols rely heavily on static, backward-looking moving averages, completely missing sudden, narrative-driven market movements.
Natural Language Processing (NLP) models monitor global social metrics, developer activity, and media sentiment alongside on-chain data to automatically reweight portfolios ahead of major retail momentum shifts.

Use cases:
On-chain wallet accumulation tracking
Multi-channel social sentiment vectoring
Automated narrative-strength asset grading
Dynamic portfolio exposure scaling

AI bridges the gap between fundamental on-chain math and human behavior, keeping digital asset portfolios aligned with the market's active narrative velocity.

[Genesis Lab.com @GenesisLabCom]

Minting synthetic real-world assets (RWAs) requires constant, manual collateral adjustments to maintain accurate price pegs.
Machine learning oracles aggregate off-chain sentiment, market depth, and macroeconomic indicators to regulate protocol parameters dynamically.

✨ Use cases:
🔹 Dynamic over-collateralization ratio adjustment
🔹 High-frequency price tracking across fragmented spot markets
🔹 Predictive slippage control during high-volatility liquidations
🔹 Automated multi-asset basket balancing

AI bridges real-world value with on-chain efficiency, keeping synthetic protocols highly resilient against structural market shocks.

[Genesis Lab.com @GenesisLabCom]

Navigating complex DeFi ecosystems manually often results in high gas fees and missed entry windows.
AI neural networks analyze cross-chain yield pools concurrently to generate optimal capital routing pathways based on user risk profiles.

✨ Use cases:
🔹 Cross-chain APY divergence tracking
🔹 Automated gas-optimized capital bridging
🔹 Predictive risk-reward scoring for emerging protocols
🔹 Algorithmic yield harvesting and auto-compounding

AI simplifies yield farming by converting multi-step manual operations into automated, single-click strategies.

[Genesis Lab.com @GenesisLabCom]

Traditional smart contracts are reactive and cannot execute actions without external triggers.
On-chain AI agents communicate peer-to-peer to execute complex cross-chain arbitrage and portfolio management independently.

✨ Use cases:
🔹 Autonomous cross-chain portfolio rebalancing
🔹 Self-executing liquidations and collateral management
🔹 AI-to-AI automated protocol governance voting
🔹 Real-time algorithmic trend-following strategies

AI agents elevate decentralized finance from rigid conditional code to intelligent, proactive execution layers.

[Genesis Lab.com @GenesisLabCom]

DeFi liquidity providers struggle with impermanent loss and sudden market shifts.
AI agents continuously process off-chain volatility signals and on-chain order books to dynamically rebalance pools.

✨ Use cases:
🔹 Dynamic range adjustment in concentrated liquidity
🔹 Real-time impermanent loss mitigation
🔹 High-velocity yield optimization tracking
🔹 Predictive volume-based fee capturing

AI transforms passive market making into an active, risk-managed yield engine.

[Genesis Lab.com @GenesisLabCom]

Automated vulnerabilities still account for billions in protocol exploits.
Machine learning models analyze codebases in real time to predict and flag zero-day exploits before deployment.

✨ Use cases:
🔹 Real-time logic flaw detection
🔹 Predictive exploit simulation
🔹 Automated bytecode vulnerability patching
🔹 Continuous post-deployment threat monitoring

AI turns code auditing from a static pre-launch check into an active, self-healing shield.

[Genesis Lab.com @GenesisLabCom]

DeFi lending protocols utilize rigid, lagging parameters for borrowing and collateralization.
Machine learning engines process on-chain wallet behavior to generate dynamic risk profiles.

✨ Use cases:
🔹 Personalized loan-to-value parameters
🔹 Predictive liquidation health tracking
🔹 Real-time collateral volatility modeling
🔹 Undercollateralized credit score generation

AI scales capital efficiency while protecting credit pools from cascading liquidation events.

[Genesis Lab.com @GenesisLabCom]

Traditional static security audits take weeks and miss dynamic logic vulnerabilities.
Deep learning models evaluate live state changes to catch zero-day exploits before deployment.

✨ Use cases:
🔹 Real-time reentrancy attack tracking
🔹 Flash loan anomaly recognition
🔹 Automated contract logic fuzz testing
🔹 Post-deployment threat isolation

AI secures protocol infrastructure from sophisticated multi-vector economic drains.

[Genesis Lab.com @GenesisLabCom]

DeFi lending traditionally requires over-collateralization.
AI assesses alternative behavioral data models.

✨ Use cases:
🔹 On-chain credit scoring
🔹 Wallet history analysis
🔹 Adaptive interest rates
🔹 Liquidations risk modeling

AI expands decentralized credit accessibility safely.

[Genesis Lab.com @GenesisLabCom]

DeFi insurance protocols leverage machine learning to automate claims.
AI processing assesses structural risk and triggers immediate payouts.

✨ Use cases:
🔹 Automated parameter verification
🔹 Smart contract bug insurance
🔹 Real-time exploit risk modeling
🔹 Dynamic premium adjustments

AI scales protocol security backstops and user trust.