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AI Task Management

Task Overview
12 Active Tasks
7 AI Suggested
3 Due Today
47 Completed
AI Task Assistant
PRSM Task AI Ready to help optimize your workflow
I noticed you have research papers to review. Should I create a structured analysis template?
Your experiment results deadline is in 2 days. I can help prioritize related tasks.
Active Projects
Quantum ML Research
In Progress
67%
8 tasks Due: Dec 15
JS
ML
+2
Data Pipeline Optimization
Planning
23%
12 tasks Due: Jan 30
AB
CD
Create New Project
Task Categories
All Tasks
12
Research
4
Analysis
3
Coding
5
Current Tasks
Review quantum computing research paper draft
Quantum ML Research Due: Today High Priority AI Suggested
Upload experiment results to shared drive
Quantum ML Research Due: Tomorrow Medium Priority
Schedule team sync meeting for next sprint
Data Pipeline Optimization Due: Dec 20 Low Priority AI Suggested
Set up development environment for new pipeline
Data Pipeline Optimization Completed: Yesterday
Task Templates
Research Workflow

Complete research project template with literature review, hypothesis, and analysis steps

8 tasks
Data Analysis Pipeline

End-to-end data analysis workflow from collection to visualization

6 tasks
ML Experiment

Machine learning experiment setup, training, and evaluation workflow

10 tasks

Information Space Explorer

Research Subject
Quick Presets:
Knowledge Graph
Atomically Precise Manufacturing
Core Subject
Molecular Assembly
High Opportunity
Scanning Probe Techniques
Medium Complexity
Quantum Control Systems
High Complexity
AI-Driven Materials Discovery
Emerging Field
Surface Chemistry
Established Field
Computational Modeling
Medium Opportunity
Research Opportunities
Molecular Assembly Automation
Opportunity Score: 8.7/10

High potential for breakthrough in automated molecular manipulation using AI-guided robotic systems.

Feasibility:
75%
Impact:
95%
Quantum-Enhanced Precision Control
Opportunity Score: 6.4/10

Integration of quantum sensing with precision manufacturing could enable atomic-level accuracy.

Feasibility:
45%
Impact:
85%
AI-Designed Molecular Tools
Opportunity Score: 7.2/10

Machine learning approaches to design novel molecular machines for manufacturing applications.

Feasibility:
65%
Impact:
80%
Research Path Generator
Phase 1: Foundation (1-2 years)

Develop advanced scanning probe techniques with molecular precision positioning

Materials Science Instrumentation
Phase 2: Integration (2-4 years)

Combine quantum control systems with AI-guided molecular manipulation

Quantum Physics Machine Learning
Phase 3: Scale-up (4-8 years)

Develop automated molecular assembly systems for practical manufacturing

Automation Process Engineering
Knowledge Gaps & Challenges
Technical Challenges
Critical Thermal motion effects at molecular scale High Priority
Major Error correction in molecular assembly Medium Priority
Moderate Scalable parallelization methods Medium Priority
Experimental Limitations
Major In-situ characterization of assembly processes High Priority
Moderate High-throughput molecular positioning Low Priority
Computational Bottlenecks
Critical Real-time quantum mechanical calculations High Priority
Major Multi-scale modeling integration Medium Priority

My Files

Cloud Storage Connections
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Microsoft OneDrive integration

  • • Office 365 integration
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Enterprise file sharing and collaboration

  • • Advanced security controls
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Dropbox
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Simple file storage and sharing

  • • Easy file sharing
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File Browser
/ My Files
Project Alpha Local 2 days ago
analysis.ipynb OneDrive 1 hour ago
dataset.csv Local 3 days ago
Storage Overview
2.4 GB / 15 GB used
PRSM Local
1.2 GB
OneDrive
1.2 GB

Platform Integrations

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PRSM AI Marketplace

8,847 Total Assets
9 Asset Types
15,293 Active Creators
₦ 2.4M FTNS Traded
1,247 Today's Sales
Asset Categories
AI Models
2,847

Language models, fine-tuned models, multimodal AI

Datasets
1,523

Training data, benchmarks, research datasets

AI Agents
892

Autonomous agents, workflows, automation

MCP Tools
1,247

Model Context Protocol tools, integrations

Compute
456

GPU clusters, TPUs, computational infrastructure

Knowledge
734

Knowledge graphs, ontologies, semantic data

Evaluation
298

Benchmarking, safety testing, performance evaluation

Training
412

Fine-tuning, distillation, optimization services

Safety Tools
438

Alignment, bias detection, safety validation

Data Work
2,847

Annotation, labeling, transcription, human intelligence tasks

APIs & Integrations
1,156

Third-party APIs, webhooks, service integrations

Monitoring & Analytics
892

Performance monitoring, metrics, analytics dashboards

AI Model Discovery
Community
CodeWizard-34B

by @dev_genius

★★★★☆ (623 reviews)

Specialized code generation model trained on 100+ programming languages. Excellent for complex algorithms.

Code Generation Multi-Language 34B Params
₦ 45 / request
3.2K uses today
Premium
✓ Verified
Claude-3.5 Sonnet

by Anthropic

★★★★★ (3,456 reviews)

Advanced reasoning model with superior code understanding and mathematical capabilities.

Reasoning Code Expert Math
₦ 95 / 1K tokens
8.9K uses today
Free
Llama 2 Chat

by Meta

★★★★☆ (2,156 reviews)

Open-source conversational AI model. Great for general chat and basic reasoning tasks.

Open Source Conversational 70B Params
Free community supported
25.7K uses today
Enterprise
✓ Verified
Gemini Pro Advanced

by Google DeepMind

★★★★★ (1,892 reviews)

Multimodal AI with advanced reasoning, image analysis, and code generation capabilities.

Multimodal Vision Code Gen
₦ 78 / 1K tokens
6.1K uses today
Community
Fast
Mixtral 8x7B

by Mistral AI

★★★★☆ (987 reviews)

High-performance mixture of experts model with excellent multilingual capabilities.

Mixture of Experts Multilingual Open Source
₦ 32 / 1K tokens
11.3K uses today
Showing 4 of 2,847 models
Quick Actions
Trending Categories

PRSM Model Lab

Create custom AI models through automated distillation. Upload your data, select a base model, and let PRSM create optimized models tailored to your specific needs.

24 Models Created
87% Avg Compression
94% Accuracy Retained
Create New Model
1
Select Base Model

Choose a foundation model from the marketplace

GPT-4 Turbo

175B parameters

General Purpose

Accuracy: 96% Speed: Fast
Claude-3.5 Sonnet

Unknown params

Reasoning

Accuracy: 97% Speed: Medium
CodeLlama-70B

70B parameters

Code Generation

Accuracy: 89% Speed: Fast
2
Upload Training Data

Provide the data for your specialized model

Upload Files
Cloud Storage
Database
Drop files here or click to browse

Supports CSV, JSON, JSONL, TXT, and ZIP files

Uploaded Files
loan_applications.csv 2.4 MB • 10,000 records
3
Configure Distillation

Define your model requirements and objectives

Model Objective
Target Model Size
Minimum Accuracy
95%
Output Format
Deployment Target
4
Output & Sharing

Choose where to save and how to share your model

Save Location
Marketplace Sharing
Step 1 of 4
Estimated time: 15-30 minutes
Recent Models
Loan Risk Assessor v1.0

Financial risk assessment model

Created 2 days ago 95.3% accuracy 423 MB
Product Classifier

E-commerce product categorization

Created 1 week ago 91.8% accuracy 156 MB
Sentiment Analyzer Pro

Customer feedback sentiment analysis

Created 2 weeks ago 89.2% accuracy 298 MB

FTNS Token Economy

Wallet Overview
Total Balance
1,847 FTNS
≈ $3,694.00 USD
Available
1,347 FTNS
+127 this week
Staked
500 FTNS
12.5% APY
Multi-Tier Staking
Bronze Tier
Min: 100 FTNS
5.0% APY
• Basic marketplace access • Community voting rights
Silver Tier
Min: 500 FTNS
8.5% APY
• Premium model access • Revenue sharing eligible
Gold Tier
Min: 1,000 FTNS
12.5% APY
• Early model access • Enhanced rewards
Platinum Tier
Min: 5,000 FTNS
18.0% APY
• Governance voting • Creator royalties
Revenue Streams
IPFS Hosting
+24 FTNS
this week
Active
Model Creation
+156 FTNS
this month
Active
Quality Reviews
+8 FTNS
this week
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this month
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Fee: $0.00 Net: $0.00
Recent Transactions
Staking Reward
+2.4 FTNS
2 hours ago
Model Purchase
-85 FTNS
5 hours ago
Model Sale
+156 FTNS
1 day ago
Token Purchase
+500 FTNS
3 days ago
Governance Participation
847 Voting Power
500 Staked + 347 Available
Active Proposals
Marketplace Fee Reduction
2 days left
New Model Category Addition
5 days left

Advanced Staking Management

Multi-Tier Staking
Bronze Tier
Min: 100 FTNS
5.0% APY
• Basic marketplace access • Standard support
Silver Tier
Min: 500 FTNS
8.5% APY
• Priority marketplace access • Advanced analytics • Governance voting rights
Your Active Stakes
500 FTNS
Silver Tier
Staked 45 days ago
+18.75 FTNS earned
8.5% APY

Budget Allocation Management

45,000
Available FTNS
+5% from last month
35,000
Allocated FTNS
78% of budget
5,000
Monthly Burn
+12% from last month
9
Months Runway
Healthy runway

Current Allocation

100%

Allocated

Adjust Allocation

Research & Development 40% (20,000 FTNS)
Marketing & Growth 30% (15,000 FTNS)
Operations 20% (10,000 FTNS)
Reserve Fund 10% (5,000 FTNS)

Budget Tracking & Analysis

Recent Expenses

AI Model Training Compute
Research • June 25, 2024
1,250 FTNS
Social Media Campaign
Marketing • June 24, 2024
850 FTNS
Server Infrastructure
Operations • June 23, 2024
650 FTNS

Budget vs Actual

Research
Budget: 5,000 FTNS Actual: 3,750 FTNS
Marketing
Budget: 3,000 FTNS Actual: 2,550 FTNS
Operations
Budget: 2,000 FTNS Actual: 1,200 FTNS

Budget Forecasting & Planning

Scenario Planning

Conservative: 5% monthly growth, 18 months runway

Budget Alerts

!
Marketing Budget Warning
Marketing spend is approaching 85% of monthly allocation
Research Overspend
Research category has exceeded budget by 12% this month

Budget Optimization & Insights

Cost Saving Opportunities

Optimize Compute Resources
Switch to spot instances for non-critical workloads
-450 FTNS/month
Bulk Purchase Discount
Purchase 6-month service contracts for better rates
-280 FTNS/month

Cost Analysis

Cost per User Acquisition 125 FTNS
Average Monthly Burn Rate 5,200 FTNS
Efficiency Score 87%

AI-Powered Recommendations

HIGH
Reallocate Marketing Budget
Analysis shows 23% better ROI by moving 500 FTNS from social media to content marketing
MED
Research Spending Pattern
Consider quarterly batching of compute-intensive tasks for 15% cost reduction

Analytics Dashboard

Overview
1,247 API Requests Today +12.3%
₦ 145.30 FTNS Spent -4.8%
2.4s Avg Response Time -8.2%
99.7% Success Rate +0.1%
Usage Analytics
API Usage Over Time
Mon Tue Wed Thu Fri Sat Sun
Usage by Model Type
GPT-4 Turbo 42%
524 requests
Claude 3 Opus 28%
349 requests
DALL-E 3 18%
224 requests
Other Models 12%
150 requests
Cost Analysis
This Month
₦ 2,847.50 -15% vs last month
Daily Average
₦ 91.85 ~₦ 2.76M annual rate
Budget Remaining
₦ 1,152.50
Cost Optimization Insights
Switch to smaller models for simple tasks

You could save ~₦ 380/month by using GPT-3.5 for basic queries

Batch similar requests

Combining related API calls could reduce costs by ₦ 125/month

Performance Metrics
Response Times
Good
95th 75th Median Avg
Error Rates
Excellent
Rate Limit Errors 0.1%
Timeout Errors 0.2%
Server Errors 0.0%
Token Efficiency
Good
Avg Input Tokens 1,247
Avg Output Tokens 892
Efficiency Score 8.4/10
Reports & Export
Weekly Usage Report

Detailed breakdown of API usage, costs, and performance metrics

Raw Analytics Data

Export raw usage data for custom analysis and reporting

Cost Optimization Report

Personalized recommendations to reduce costs and improve efficiency

Scheduled Reports
Monthly Cost Summary Every 1st of month

PRSM Research Lab

Research Dashboard
12 Active Projects 3 in peer review
847 Papers Analyzed 152 this month
34 Hypotheses Generated 8 validated
28 Collaborators Across 7 institutions
AI Literature Review
AI Analysis Summary
247 papers analyzed across 15 journals
12 key themes identified with cross-references
5 research gaps requiring investigation
Key Papers & Insights
Error correction thresholds for surface codes with edge correlated noise
Impact Score: 9.2/10

Authors: Chen, et al. (2024) • Journal: Nature Quantum Information

AI Summary: Breakthrough in quantum error correction showing 2.1% threshold improvement using machine learning optimization for surface code decoding in realistic noise environments.

Quantum Computing Error Correction Machine Learning
Scalable quantum error correction with superconducting qubits
Impact Score: 8.7/10

Authors: Rodriguez-Smith, et al. (2024) • Journal: Physical Review Letters

AI Summary: Demonstrates 127-qubit error correction protocol with 99.9% fidelity, establishing new benchmarks for fault-tolerant quantum computation scalability.

Superconducting Qubits Scalability Fault Tolerance
Identified Research Gaps
High Priority Limited research on error correction under correlated noise in 100+ qubit systems
Medium Priority Insufficient benchmarking of ML-based decoders across different hardware platforms
AI Hypothesis Generator
Novelty: 8.9/10 Feasibility: 6.7/10
Breakthrough Hypothesis
Quantum-Classical Hybrid Error Correction Using Neuromorphic Processing

Hypothesis: Implementing neuromorphic computing architectures as classical co-processors for quantum error correction could reduce decoding latency by 100x while maintaining 99.9% fidelity through bio-inspired parallel processing of syndrome data.

AI Rationale: Current literature shows quantum error correction bottlenecks in classical processing. Neuromorphic chips excel at pattern recognition and low-latency parallel processing - ideal for syndrome decoding.
Suggested Experiments:
  • Benchmark Intel Loihi chips for surface code syndrome decoding
  • Compare latency vs. traditional GPU-based decoders
  • Test scalability with increasing qubit counts
Novelty: 7.4/10 Feasibility: 8.2/10
Significant Advancement
Adaptive Error Correction Thresholds Using Real-Time Noise Characterization

Hypothesis: Dynamically adjusting error correction thresholds based on real-time noise characterization could improve logical qubit performance by 40% compared to static threshold approaches.

AI Rationale: Recent papers show noise characteristics vary significantly over time. Machine learning could predict optimal thresholds dynamically.
Suggested Experiments:
  • Implement real-time noise monitoring on existing quantum hardware
  • Train ML models to predict optimal correction thresholds
  • Compare performance against static threshold baselines
Experiment Design Assistant
1 Objective
2 Methodology
3 Resources
4 Analysis
Research Objective
AI Suggestions:
Characterize noise correlations in multi-qubit quantum systems
Compare performance of ML vs. traditional error decoders
Experimental Methodology
Required Resources
Equipment
Personnel
Timeline
Analysis Plan
Generated Experiment Plan
Abstract

This experiment aims to characterize noise correlations in multi-qubit quantum systems to improve error correction strategies. Using a controlled laboratory approach with 50-qubit quantum processors, we will measure cross-talk effects and temporal correlations under various operating conditions.

Methodology
  • Systematic noise characterization using process tomography
  • Correlation analysis across different qubit geometries
  • Statistical validation using bootstrap resampling
Active Research Projects
Active
Quantum Error Correction Optimization
QEC-2024-001

Developing ML-enhanced error correction protocols for 100+ qubit systems with focus on correlated noise environments.

Progress
67%
Team: 5 researchers Due: March 2025
Planning
Neuromorphic Quantum Co-Processors
NQC-2024-002

Investigating neuromorphic computing architectures for real-time quantum error correction and syndrome decoding.

Progress
12%
Team: 3 researchers Proposal due: Feb 2025
Under Review
Adaptive Threshold Error Correction
ATE-2024-003

Real-time adaptation of error correction thresholds based on dynamic noise characterization and machine learning predictions.

Progress
89%
Team: 4 researchers Review: Jan 2025

PRSM Collaboration Hub

Real-time team collaboration and project management

Channels
general 3
research
publications 1
code-review
Direct Messages
SC
Dr. Sarah Chen 2
AR
Alex Rodriguez
MT
Dr. Maria Thompson
Active Projects
APM Development On Track
Quantum Simulation Behind
Ethics Review Board Review
general
8 members
SC
Dr. Sarah Chen Today at 2:30 PM
Just finished reviewing the latest APM simulation results. The precision improvements are impressive! 📊
👍 3 🚀 2
AR
Alex Rodriguez Today at 2:35 PM
Agreed! Should we schedule a team meeting to discuss the next phase? I've been working on the quantum control algorithms.
PRSM AI shared a file: apm_simulation_results_v2.pdf
Today at 2:40 PM
MT
Dr. Maria Thompson Today at 2:45 PM
Perfect timing! I'm available Thursday 10 AM EST. Also, I've created a new task in our Kanban board for the ethics review process.
Complete APM simulation validation
SC Dr. Sarah Chen Due: Dec 15 High
Validate simulation results and prepare report for peer review
Ethics review documentation
MT Dr. Maria Thompson Due: Dec 20 Medium
Prepare comprehensive ethics review for APM research protocols
Update quantum control algorithms
AR Alex Rodriguez Completed: Dec 10 Low
Optimize quantum control algorithms for better precision
APM Development Project
To Do
3
High
Implement precision control system
Design and implement high-precision control mechanisms for molecular manipulation
SC AR
Dec 18
Hardware Research
Medium
Literature review update
Update comprehensive literature review with latest APM research
MT
Dec 22
Documentation
Low
Team retrospective planning
Plan quarterly team retrospective and improvement initiatives
AR
Dec 30
Planning
In Progress
2
High
Simulation algorithm optimization
Optimize Monte Carlo simulation algorithms for better performance
65% Complete
SC
Dec 16
Software Optimization
Medium
Safety protocol development
Develop comprehensive safety protocols for lab equipment
40% Complete
MT
Dec 25
Safety Protocols
Review
1
Medium
Peer review preparation
Prepare research findings for peer review submission
SC MT
Dec 14
Review Publication
Done
2
Low
Quantum algorithm implementation
Successfully implemented quantum control algorithms
AR
Completed: Dec 10
Software Quantum
Medium
Initial research setup
Completed laboratory setup and equipment calibration
SC AR
Completed: Dec 5
Setup Hardware
Research Papers
15 files
Simulation Data
8 files
Code Repository
23 files
APM_Overview_v3.pdf
2.4 MB • Dec 12
Ethics_Review_Draft.docx
1.8 MB • Dec 11
Experiment_Data.xlsx
4.2 MB • Dec 10
molecular_structure.png
856 KB • Dec 9
quantum_control.py
12 KB • Dec 8
team_meeting_120823.mp4
156 MB • Dec 8
Team Members
3 online
SC
Dr. Sarah Chen
Lead Researcher
Working on simulation
AR
Alex Rodriguez
Quantum Engineer
Available
MT
Dr. Maria Thompson
Ethics Advisor
In meeting
JS
Dr. James Smith
Data Scientist
Last seen: 2h ago
Quick Actions
Project Status
Overall Progress
67%
Tasks Complete
12/18
Days Remaining
14
3 High Priority
2 In Review
1 Overdue

PRSM Governance Hub

Democratic platform evolution through community participation

23
Active Proposals
+3 this week
1,247
Total Votes Cast
+156 this week
89%
Participation Rate
+5% vs last month
2.4M
FTNS Treasury
Available for allocation
#PRO-2024-002 Economic Active Voting
Adjust FTNS Token Reward Distribution

Modify the current token reward distribution mechanism to increase incentives for model creators and reduce inflation pressure.

Marcus Chen
Marcus Chen Economics Advisor
Ends: Dec 25, 2024
58% For • 42% Against • 456 votes
#PRO-2024-003 Community Passed
Establish Community Code of Conduct

Create comprehensive community guidelines and enforcement mechanisms for maintaining healthy collaboration environment.

Anna Liu
Anna Liu Community Manager
Executed: Dec 1, 2024
Final Result: 87% Approval (782 votes)
Active Voting Sessions
Your Voting Power: 1,250 FTNS
Quadratic Weight: 35.4 votes
Emergency Safety Protocol Activation
⚡ Urgent • Ends in 6 hours
76% participation

Emergency vote to activate enhanced safety protocols following detection of potential adversarial inputs in the system.

0 votes 0 votes 35 votes
Platform Fee Structure Adjustment
5 days remaining
43% participation

Proposal to reduce platform fees for small model creators while maintaining revenue for platform development.

Option A 62% support

Reduce fees to 1.5% for creators earning <$1000/month

Option B 38% support

Implement tiered structure: 1% (0-$500), 2% ($500-$2000), 2.5% ($2000+)

2,437,429 FTNS
≈ $243,743 USD
+12,450 FTNS this month
Fund Allocation
Development 40%
Safety Research 25%
Community Rewards 20%
Marketing & Growth 15%
Funding Proposals
Enhanced AI Safety Research Initiative
Requesting: 150,000 FTNS
Approved

Funding for 6-month research project on advanced AI alignment and safety protocols.

Dec 1 Proposal Submitted
Dec 8 Community Review
Dec 12 Approved (89% vote)
Dec 15 Funds Disbursed
Community Education Platform
Requesting: 75,000 FTNS
Under Review

Development of educational resources and tutorials for new PRSM users and developers.

67% approval (234 votes)
Governance Participation Analytics
Voter Participation
Last 30 days
Week 1 Week 2 Week 3 Week 4 Week 5
+34% increase in participation
Proposal Success Rate
This quarter
68% Passed
Passed (17)
Rejected (8)
Top Contributors
By proposal activity
Dr. Sarah Chen
Dr. Sarah Chen 5 proposals • 89% success
Marcus Chen
Marcus Chen 3 proposals • 67% success
Anna Liu
Anna Liu 4 proposals • 75% success
Governance Health Metrics
92
Decentralization Score
Distribution of voting power
7.2
Anti-monopoly Index
Concentration prevention
89%
Active Participation
Community engagement

Participation Metrics

Voter Turnout 72.3%
Active Participants 1,847
Avg. Voting Power 49.2 FTNS

Proposal Performance

Pass Rate 68.4%
Avg. Discussion Time 5.3 days
Implementation Rate 91.2%

Treasury Metrics

Monthly Inflow 45,230 FTNS
Utilization Rate 23.7%
Reserve Ratio 76.3%

Governance Health

92
Decentralization
Token distribution is well balanced
78
Participation
Good voter engagement
85
Transparency
Open processes and communication
65
Efficiency
Room for process improvement

Anti-Monopoly Monitoring

0.23
Voting Power HHI
Top 5 holders 18.4%
Top 10 holders 31.7%
0.31
Proposal Creation
Unique proposers 127
Repeat proposers 23.6%

Governance Actions

Settings

Manage your account, UI preferences, API keys, and privacy settings.

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