This proposal seeks funding to provide AI-powered, offline-capable laptops to minority and underserved communities, - Bronx, New York, where Governor Kathy Hochul recently highlighted systemic inequities in technology access. Our initiative addresses the digital divide by equipping students with cutting-edge tools for education and skill development, even without internet connectivity. By integrating Large Language Models (LLMs) and localized content, we aim to empower 5,000 beneficiaries in Phase 1, fostering digital literacy, academic success, and economic mobility. Problem
Governor Hochul’s controversial remark about minorities and underserved children unfamiliar with computers
How Our Project Will Contribute To The Growth Of The Decentralized AI Platform
This proposal directly supports SingularityNET’s mission to create democratic, inclusive, and benevolent AI while addressing the specific requirements of Deep Funding. It embodies SingularityNET’s mission by decentralizing AI access, prioritizing marginalized communities, and fostering ethical, community-driven innovation. With minor adjustments to highlight platform integration and team expertise, it strongly aligns with Deep Funding’s goals to democratize AI and drive real-world utility.
Provide AI LLM laptops to minorities and underserved offline use. While most of the AI hype and attention over the last two years has been focused on LLMs in the cloud we are at the very beginning of a larger more disruptive trend in technology: on-device AI.
each of us having our own "personal AI" and completely transform what we think of as computers and computing devices.
AI inputs
Business intelligence with advanced AI ML technologies LLM's AI-accelerated solutions UI/UX AI-Based Consulting tools a stark reality persists: millions from minority and underserved communities remain excluded from the digital revolution.
AI outputs
AI LLM offline laptops for minorities and underserved. A pioneering initiative aims to change this narrative providing AI-powered offline-capable laptops equipped with specialized Large Language Models (LLMs) to marginalized populations transforming technological deserts into oases of opportunity.
Company Name (if applicable)
Blockcheckbook
The core problem we are aiming to solve
Systemic exclusion of marginalized communities (e.g., youth per Gov. Hochul’s remarks) from AI-driven education and economic opportunities due to 1) lack of internet access (42% of Bronx households) and 2) reliance on centralized, profit-driven AI tools that ignore underserved populations. This perpetuates cycles of digital illiteracy, educational inequity, and limited workforce readiness. Our solution—offline, LLM-powered laptops—decentralizes AI access, bypassing internet barriers and corporate gatekeeping, embedding localized, ethical AI to empower underserved groups with self-directed learning and skill-building tools aligned with SingularityNET’s mission of democratized, benevolent AI.
Our specific solution to this problem
We provide offline-first, AI-powered laptops with localized LLMs to underserved and minority communities
Hardware:
Affordable, Durable Devices: offline functionality, and rugged design for shared use in resource-limited settings.
Software:
Tailored LLMs: AI tutors and educational
Offline Learning Suite: Coding modules, career guidance chatbots, and digital literacy apps—zero internet dependency.
Ethical AI: on-device data privacy (no personal data collection), and open-source frameworks.
Implementation:
Community Partnerships: Distribute devices through schools, NGOs, and grassroots.
Local Capacity Building: Train tech ambassadors
SingularityNET Integration:
Decentralized AI Ecosystem: Pilot community-specific LLMs on SingularityNET’s platform post-launch, scalable
Democratized Ownership: Anonymized usage data aligning with SingularityNET’s mission to shift control to communities.
Impact:
30% improvement in digital literacy
25% increase in STEM engagement
500+ local jobs created through ambassador programs
Ethics & Inclusion:
Offline functionality eliminates exposure to harmful online content, prioritizing safety for marginalized youth.
This solution bridges the AI divide by empowering underserved populations with decentralized, community-owned technology, turning them from excluded groups into active contributors to a democratic AI future. By decentralizing access and prioritizing ethical, localized innovation, we advance SingularityNET’s vision of benevolent AGI for all.
Project details
Specific Solution: AI-Powered Offline-First Laptops for Underserved Communities
Our initiative delivers AI-powered, offline-first laptops preloaded with localized Large Language Models (LLMs) and educational tools to underserved communities, addressing systemic barriers of internet inaccessibility and exclusion from centralized AI-driven opportunities. Below is a detailed breakdown of our solution:
1. Offline-First Hardware
Low-Cost, Durable Devices:
Affordability: Priced at $200/unit, these laptops prioritize accessibility for low-income communities.
Robust Design: Built with rugged, shock-resistant casings and spill-proof keyboards to withstand shared use in schools, community centers, and homes.
Performance: Utilizes Raspberry Pi 4 or equivalent single-board computers optimized for LLM processing (4GB RAM, 32GB storage). Battery life exceeds 12 hours through energy-efficient components and solar-charging compatibility.
Offline Functionality: All software and AI tools operate without internet, critical for regions where 34% of Black and 39% of Hispanic U.S. households lack broadband access (Pew Research, 2023).
2. Localized LLM Software
Culturally Tailored AI Tutors:
Co-Development Process: LLMs are trained on curricula co-designed with educators from underserved communities, ensuring relevance to local dialects (e.g., Spanglish, Indigenous languages) and cultural contexts.
Educational Tools:
Bilingual Learning Modules: Interactive lessons in STEM, literacy, and vocational skills, available in Spanish, Haitian Creole, and other languages.
Career Guidance Chatbots: Offline AI assistants provide resume-building tips, interview prep, and job search strategies.
Coding Labs: Python and Scratch-based tutorials with project templates (e.g., building a weather app).
Ethical Safeguards:
Bias Mitigation: LLMs undergo rigorous audits using frameworks like IBM’s AI Fairness 360 to minimize racial, gender, and socioeconomic biases.
Content Filtering: On-device AI blocks harmful or inappropriate outputs, ensuring safe learning environments for minors.
3. Community-Driven Implementation
Tech Ambassadors Program:
Training: 100+ local educators, parents, and youth leaders receive hands-on workshops to troubleshoot devices, lead coding camps, and integrate laptops into classrooms.
Peer-to-Peer Networks: Ambassadors organize monthly “Tech Circles” to foster collaboration and share success stories.
Strategic Partnerships:
NGO Collaborations: Partner with organizations like Code for America and Black Girls CODE to distribute 5,000 laptops in Phase 1, prioritizing Title I schools and rural districts.
School Integration: Pilot programs in 10 high-poverty schools, aligning laptop use with state standards for digital literacy.
4. Integration with SingularityNET’s Ecosystem
Decentralized AI Alignment:
Open-Source APIs: Post-launch, anonymized usage data will inform the development of community-specific LLMs (e.g., “Indigenous Language Tutor API”) on SingularityNET’s platform.
Monetization Strategy: Local governments/NGOs can license these APIs to fund device maintenance, creating a self-sustaining model.
Scalability Framework:
Global Expansion: Success in U.S. communities will model deployment in regions like Sub-Saharan Africa (where 60% lack internet access), adapting LLMs to languages such as Swahili and Yoruba.
5. Measurable Impact
Digital Literacy: 30% improvement via pre/post assessments using the Northstar Digital Literacy Framework.
STEM Engagement: 20% increase in enrollment in coding clubs or STEM courses within 12 months.
Economic Mobility: 500+ jobs created through tech ambassador stipends, device repair hubs, and AI training roles.
6. Ethical Innovation
Privacy-First Design:
Zero Data Collection: All interactions (e.g., chatbot queries, lesson progress) remain on-device, compliant with FERPA and COPPA regulations.
Transparency: Collaborate with Hugging Face and EleutherAI to publish LLM training datasets and bias audits publicly.
Sustainability:
E-Waste Reduction: Modular design allows easy upgrades (e.g., swapping storage chips) instead of full replacements.
Solar Accessories: Partner with SolarEdge to subsidize solar chargers for off-grid communities.
Conclusion
This solution transcends hardware distribution by building a decentralized, community-owned AI ecosystem. By equipping underserved populations with tools to learn, innovate, and compete independently of corporate gatekeepers, we align with SingularityNET’s mission to democratize AI. Phase 1 focuses on immediate impact, while long-term scalability ensures global replication, transforming marginalized groups into architects of a benevolent AI future.
BlockCheckBook – AI-Powered Custom Laptops
Mission & Vision BlockCheckBook is a pioneering tech venture dedicated to revolutionizing the laptop industry by integrating advanced artificial intelligence (AI) into customizable hardware. Our mission is to deliver AI-driven laptops that offer unparalleled performance, security, and personalized user experiences, catering to the unique needs of gaming enthusiasts, tech-savvy professionals, and enterprises. By leveraging AI to optimize hardware and software interactions, we aim to redefine how users interact with technology, ensuring our devices adapt intelligently to individual workflows, gaming preferences, and enterprise demands.
Market Opportunity
The global laptop market, valued at $145.2 billion by 2025, presents a significant growth opportunity, particularly in the gaming segment, which is expanding at a 12.1% CAGR. Traditional laptops lack the adaptability to meet evolving user demands, creating a gap for AI-powered solutions. BlockCheckBook targets three core markets:
Gaming Enthusiasts: 40 million+ global gamers seeking high-performance, immersive experiences.
Tech-Savvy Professionals: Remote workers, developers, and creatives needing productivity-enhancing tools.
Enterprises: Businesses prioritizing security, compliance, and scalable IT solutions.
Our competitive edge lies in combining customizable hardware with proprietary AI software, enabling real-time optimization for performance, security, and user behavior.
Product Line
BlockCheckBook’s AI-powered laptops are categorized into three lines, each addressing distinct market needs:
Gaming Laptops
AI-Optimized Hardware: NVIDIA/AMD GPUs and Intel/Ryzen CPUs dynamically tuned by AI for peak gaming performance.
Adaptive Cooling Systems: AI monitors thermals to adjust fan speeds, reducing noise while preventing overheating.
Personalized Gaming Modes: AI learns user preferences to auto-adjust graphics, key bindings, and RGB lighting.
Price Range: 1,500–1,500–3,000.
Pro Laptops
AI-Driven Productivity: Tools like automated task prioritization, calendar optimization, and context-aware app switching.
Enhanced Security: Biometric authentication, AI threat detection, and encrypted local data processing.
Custom Workflows: AI tailors shortcuts and software setups for developers, designers, and remote workers.
Centralized Management: AI monitors device health, predicts maintenance needs, and deploys updates.
Custom Pricing: Volume-based discounts and tailored configurations for large-scale deployments.
Technology & Innovation
AI Core Engine: Proprietary algorithms analyze user behavior, hardware metrics, and environmental data to optimize performance.
Dynamic Resource Allocation: Allocates CPU/GPU power in real time for gaming, rendering, or multitasking.
Privacy-First Design: On-device AI processing ensures sensitive data (e.g., biometrics, enterprise files) never leaves the laptop.
Financial Strategy
Year 1 Revenue: $10 million (70% gaming, 20% pro, 10% enterprise).
Gross Margin: 25%, scaling to 30% by Year 3 through supplier partnerships and economies of scale.
Funding Utilization: $5 million seed funding allocated to:
R&D (40%): AI software development and hardware prototyping.
Marketing (30%): Influencer campaigns, trade shows, and digital ads.
Operations (20%): Manufacturing partnerships and supply chain setup.
Reserves (10%): Contingency for component shortages or delays.
Go-to-Market Strategy
Influencer Partnerships: Collaborate with top gaming YouTubers (e.g., PewDiePie, Shroud) and tech reviewers (MKBHD, Linus Tech Tips) for unboxings and reviews.
Social Media Campaigns: Targeted ads on Twitch, Reddit, and LinkedIn highlighting AI features.
Trade Shows: Showcase at CES, E3, and enterprise IT expos to build brand credibility.
Content Hub: Publish tutorials (“How AI Boosts FPS”) and case studies (“Enterprise Security Transformed”).
Operations & Scalability
Manufacturing: Partner with Foxconn and Quanta for high-volume production with custom configurations.
Supply Chain: Secure priority access to GPUs and CPUs through strategic agreements with NVIDIA, AMD, and Intel.
Customer Support: 24/7 chat support, extended warranties, and a community forum for troubleshooting.
Leadership Team CEO:Mohamed CTO: Jason CAIO & CMO: Darryl
Long-Term Vision
BlockCheckBook aims to become the default choice for AI-powered computing, expanding into AR/VR integration and AI-as-a-Service (AIaaS) licensing for third-party manufacturers. By 2027, we project a 15% market share in the premium laptop segment, with $250 million in annual revenue.
Innovation. Performance. Adaptability. BlockCheckBook is not just building laptops—we’re crafting the future of personalized computing.
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user's assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Creating a translation service from, say Spanish to English might be possible, but it's questionable if such a service would be able to get a significant share of the market
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.
A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.
About Expert Reviews
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.
A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.
AI ML Advisor | https://www.linkedin.com/in/mrdwilliams/
Bio
Business Development and Strategy professional keen focus on driving growth, strategic partnerships, and expanding reach. Business intelligence AI ML technologies, LLM's, AI-accelerated tools
Experience
seasoned Business Development Operations professional expertise in Content Strategy, Product Management, and Market Research. 15 years of experience global product lifecycle, proven track record
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Reviews & Ratings
Simon250
Mar 9, 2025 | 1:54 PMEdit Comment
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I think under milestones and budget are incomplete?