reasonit.app

#Reason It Application Meta

#Autonomous System Reasoning (ASR) | Satisfying Multiple Mission Goals

#ASR: Satisfying Simultaneous Mission Goals

#ASR: Satisfying Unplanned Mission Goals

#ASR: Ensuring Dedired Mission Outcome

#ASR: Exploring Utility Of Common Knowledge

#ASR: Exploring Function Of Common Knowledge

#ASR: Exploring Common Knowledge Of Playbooks

#ASR: Analyzing Value Of Machine Decisions

#ASR: Analyzing Accuracy Of Machine Decisions

#ASR: Migitating Undesirable Action

#ASR: Maintaining Autonomy

#ASR: Balancing Harcore Safeguards

#ASR: Balancing Contextual Safeguards

#ASR: Law Of Autonomous Reasoning

#ASR: Standars Of Autonomous Levels

#ASR: Hierarchical Mission Architecture

#ASR: Science Driven Exploration

#ASR: Target Characterization System

#ASR: Autonomous Robotic Planetary Exploration

#ASR: Tier Scalable Mission Architecture

#ASR: Flagging Targets Of Interest

#ASR: Diffusion-of-Thought (DoT)

#ASR: Chain-of-Thought Reasoning

#ASR: Solving math problems through self-correction abilities

#ASR: Generative Pre-trained Transformer (GPT)

#ASX: Momentum trading | Taking advantage of, and making profits from, upward trends in stock | Strategy makes calculated bet stock market recent winners will remain winners

#ASX: Relative Strength Index (RSI) | Momentum oscillator | RSI measures speed and change of price movements | identifies overbought or oversold conditions in market | Helps to determine potential reversal points or trend strengt | RSI helps identify potential entry and exit points | RSI is calculated using formula to compare average gains and losses over specified period | RSI > 70: stock overbought | RSI < 30: stock oversold | RSI > 80: extremely overbought stock | RSI < 20 extremely oversold stock | RSI typucal timeframe: 14-days

#ASX: Good trade | In major trend direction | Not have wild gyrations | Nearby support area to provide reasonable stop

#ASX: 52-week highs | If price has broken out above its 52-week range, there must be some factor having generated enough momentum to further continue price movement in the same direction

#ASX: Earnings Per Share (EPS)

#ASX: Price to Earnings (P/E) ratio

#ASX: Micro stocks

#ASX: Price-to-Sales (P/S) ratio

#ASX: Assessing growth potential of high-growth sectors (like tech stocks)

#ASX: P/S: indicates how much investor equity is required to generate $1 of revenue

#ASX: P/S: good when the value is below 1

#ASX: Small caps with the best P/S ratios on the ASX value: < 0.08

#ASX: Direct Lithium Extraction (DLE)

#Tampere University | Pneumatic touchpad | Soft touchpad sensing force, area and location of contact without electricity | Device utilises pneumatic channels | Can be used in environments such as MRI machines | Soft robots | Rehabilitation aids | Touchpad does not need electricity | It uses pneumatic channels embedded in the device for detection | Made entirely of soft silicone | 32 channels that adapt to touch | Precise enough to recognise handwritten letters | Recognizes multiple simultaneous touches | Ideal for use in devices such as MRI machines | If cancer tumours are found during MRI scan, pneumatic robot can take biopsy while patient is being scanned | Pneumatic device can be used in strong radiation or conditions where even small spark of electricity would cause serious hazard

#California Wildfire sensing | Fast moving flames | Smoke filled canyons | Santa Ana winds | Fire map | Firefighters | Evacuation zone | Power shutoff | Death tol | Firefighting personnel | Damage | Economic loss | Evacuation orders | Evacuation warnings | Brush fire | Recycled water irrigation reservoir | Animals relocated | Evacuation alert | Schools closed | Fires fueled by hurricane-force winds | Schools to be inspected and cleaned outside and in, and their filters must be changed | Feeding centers | Hilly areas | Evacuation bag: solar-powered charger, mask, extra clothing | Drone interfering with wildfire response hit plane | Structure: home, multifamily residence, outbuilding, vehicle | Beachfront properties destroyed | Looting | Curfew | Red Cross

#A-list celebrity home protector | Burglaries targeting high-end items | Burglary report on Lime Orchard Road | Burglar had smashed glass door of residence | Ransacked home and fled | Couple were not home at the time | Unknown whether any items were taken | Lime Orchard Road is within Hidden Valley gated community of Los Angeles in Beverly Hills | Penelope Cruz, Cameron Diaz, Jennifer Lawrence, Adele and Katy Perry have purchased homes there, in addition to Kidman and Urban | Kidman and Urban bought their home for $4.7 million in 2008 | 4,100-square-foot, five-bedroom home built in 1965 and sits on 1¼-acre lot | Property large windows have views of the canyons | Theirs is one of several celebrity properties burglarized in Los Angeles and across country recently | Connected to South American organized-theft rings

#Professional athlete home protector | South American crime rings | Targeting wealthy Southern California neighborhoods for sophisticated home burglaries | Behind burglaries at homes of professional athletes and celebrities | Theft groups conduct extensive research before plotting burglaries | Monitoring target whereabouts and weekly routines via social media | Tracking travel and schedules | Conducting physical surveillance at homes | Attacks staged while targets and their families are away | Robbers aware of where valuables are stored in homes prior to staging break-ins | Burglaries conducted in short amount of time | Bypass alarm systems | Use Wi-Fi jammers to block Wi-Fi connections | Disable devices | Cover security cameras | Obfuscate identities

#Agentic AI | Artificial intelligence systems with a degree of autonomy, enabling them to make decisions, take actions, and learn from experiences to achieve specific goals, often with minimal human intervention | Agentic AI systems are designed to operate independently, unlike traditional AI models that rely on predefined instructions or prompts | Reinforcement learning (RL) | Deep neural network (DNN) | Multi-agent system (MAS) | Goal-setting algorithm | Adaptive learning algorithm | Agentic agents focus on autonomy and real-time decision-making in complex scenarios | Ability to determine intent and outcome of processes | Planning and adapting to changes | Ability to self-refine and update instructions without outside intervention | Full autonomy requires creativity and ability to anticipate changing needs before they occur proactively | Agentic AI benefits Industry 4.0 facilities monitoring machinery in real time, predicting failures, scheduling maintenance, reducing downtime, and optimizing asset availability, enabling continuous process optimization, minimizing waste, and enhancing operational efficiency

#AI models deployed in embedded systems at edge | Brushless DC motors | Hall effect sensors | Optical encoders | Sensorless motor control | Field-oriented control | Artificial intelligence at edge | Three fundamental modalities: vision, sound, and motion | Using AI models to infer information about device environment | Linear algorithms | Software and hardware combination | Deploying multiple AI models in embedded devices requires edge processors designed to run AI | Embedded systems using AI can be considered open | Sensor fusion utilizes combined data from multiple sensors | AI-based vision systems are more adaptable to natural variations inherent in object inspection | Objects can be identified and inspected more quickly with greater flexibility | Strong multimodal AI, a single model will process multiple types of data | Control algorithms will use inputs generated by AI, inferred from multiple sources of data | AI inferencing in data flow | AI-enabled image sensors are perfect for gesture detection | Event detection based on sound is an active area of development | On device learning in real time

#Robot autonomy system combining the benefits of Visual SLAM positioning with advanced AI local perception and navigation tech | Visual Al technology | AI-based autonomy solutions | Visual SLAM | Dynamic obstacle avoidance | Constructing accurate 3D maps of the environment using sensors built into robots | Algorithms precisely localize robot by matching what it observes at any given time with 3D map | Using AI driven perception system robot learns what is around it and predicts people actions to react accordingly | Intelligent path planning makes robot move around static and dynamic obstacles to avoid unnecessary stops | Collaborating with each others robots share important information like their position and changes in mapped environment | Running indoors, outdoors, over ramps and on multiple levels without auxiliary systems | Repeatability of 4mm guarantees precise docking | Updates the map and shares it with the entire fleet | Edge AI: All intelligence is on the vehicle, eliminating any issue related to the loss of connectivity | VDA 5050 standardized interface for AGV communication | Alphasense Autonomy Evaluation Kit | Autonomous mobile robot (AMR) | Hybrid fleets: manual and autonomous systems work collaboratively | Equipping both autonomous and manually operated vehicles with advanced Visual SLAM and AI-powered perception | Workers and AMRs share the same map of the warehouse, with live position data of each of the vehicles | Turning every movement in warehouse into shared spatial awareness that serves operators, machines, and managers alike | Equiping AGVs and other types of wheeled vehicles with multi-camera, industrial-grade Visual SLAM, providing accurate 3D positioning | Combining Visual SLAM with AI-driven 3D perception and navigation | Extending visibility to manually operated vehicles, such as forklifts, tuggers, and other types of industrial trucks | Unifying spatial awareness across fleets | Unlocking operational visibility | Ensuring every movement generates usable data | Providing foundation for smarter, data-driven decision-making | Merging manual and autonomous workflows into a single connected ecosystem | Real-time vehicle tracking | Traffic heatmaps | Spaghetti diagrams | Predictive flow analytics | Redesigning layouts | Optimizing pick paths | Streamlining material handling | Accurate vehicle tracking | Safe-speed enforcement | Pedestrian proximity alerts | Lowerung insurance claims | Ensuring regulatory compliance | Making equipment smarter, scalable, interoperable, and differentiable | Predictive maintenance | Fleet optimization | Visual AI Ecosystem connecting machines, people, processes, and data | Autonomous robotic floor cleaning | Industry 5.0 by adding people-centric approach | Visual AI to providing real-time, people-centric decision-making capabilities as part of autonomous navigation solutions | Collaborative Navigation transforming Autonomous Mobile Robots (AMRs) into mobile cobots | Visual AI confering robots the ability to understand the context of the environment, distinguishing between unobstructed and obstructed paths, categorizing the types of obstacles they encounter, and adapting their behavior dynamically in real-time | Automatically generating complete and very accurate 3D digital twin of an elevator shaft | Autonomous eTrolleys tackling last-mile problem |Autonomous product delivery at airports

#Robots shaping the future of autonomous operations | Integrating robot insights they gather into workflows | Autonomous mobile robots reshaping how people and technology work together | Redefining the loop itself | Human expertise and robotic intelligence complementing one another | Autonomous inspection robot data can be accessed remotely, trended over time, and used to prevent failures before they happen | Interoperable systems: autonomous robots naturally embedded into digital platforms, analytics tools, and plant operations workflows | Building autonomous, intelligent operations where robots, people, and data systems form unified workflow

#Structural model at Bousquet | Results for the final three holes at the Paquin target confirmed a broad gold-bearing structural zone | Olympio to use the new assays to review and refine the Bousquet geological model and plan follow-up drilling | Planning underway for high-impact drilling guided by updated structural model at Bousquet | Bousquet gold project is located along Quebec prolific Cadillac Break

#Industrial Humanoids | Robotic coworker | Industrial automation shifting from classic, specialized robots to more general purpose robots | Robots that are more adaptable, quick to learn, and retaskable | Robots working together and supporting people | Robotic teammates

#Enterprise humanoid robot Atlas | Material handling applications

#Think tokens in AI | Inside think tokens is AI Chain of Thought (CoT), which represents its internal reasoning process before it outputs a final answer | Reasoning contains: | Problem analysis: breaking down complex prompts into smaller, manageable parts | Fact retrieval: searching internal knowledge or planning search queries | Step-by-step logic: solving math, coding, or logic problems sequentially | Self-correction: catching mistakes, evaluating alternative approaches, and refining strategy | Safety checks: reviewing request against safety guidelines | Higher accuracy: giving AI time to think drastically improves its performance on complex tasks | Transparency: allows users to see exactly how AI arrived at a specific conclusion | Debugging: developers can look inside thoughts to find where a logic chain broke down | In AI interface like DeepSeek-R1 or OpenAI reasoning model, text between these tokens is hidden behind a collapsible Thinking Process dropdown so it does not clutter final response

#Chain of Though token | Chain of Thought token (CoT) | Any individual unit of data (word, syllable, or character) generated by Large Language Model (LLM) while it formulates its intermediate reasoning steps | Acts as model internal scratchpad | Allows midel to map out complex logic, solve multi-step problems, and self-correct before presenting a final conclusion | Autoregressive context: LLMs generate text one token at a time | Each CoT token produced serves as immediate context for the next token, building a step-by-step logic chain | CoT tokens function similarly to variables in a computer program, temporarily storing values and intermediate states required to solve broader task | Modern reasoning models allocate a specific internal thinking budget of tokens to handle complex problems, a higher number of thinking tokens usually correlates to better accuracy on difficult tasks | Visible CoT tokens are generated directly in visible text output, usually prompted by phrases like lets think step by step | Standard models use standard CoT prompting via Prompt Engineering Guide | Hidden (Internal) CoT Tokens are processed behind scenes in a native thinking phase before any text is shown to user | Advanced reasoning models separate compute stage from final response | If model must generate hundreds or thousands of intermediate tokens, time-to-response increases significantly | API providers charge for CoT tokens at standard output token rate, meaning thinking increases overall cost of query | Overthinking: models can waste tokens over-analyzing simple questions that they could have easily answered directly | Alternative frameworks like Chain of Draft (CoD) or compression tools like TokenSkip are used to dramatically minimize token footprint while keeping reasoning sharp

#NVIDIA.$500 billion initiative | Establishes independent compute financing platforms to turn AI hardware into a brand-new financial asset class | Announced via Memorandums of Understanding (MOUs) in August 2026 | NVIDIA has partnered with six of Wall Street premier asset managers | Apollo Global Management | BlackRock | Blackstone | Brookfield Asset Management | Goldman Sachs | KKR | Core objective is to treat graphics processing units (GPUs) and AI factories as income-generating infrastructure, similar to commercial real estate, toll roads, or aircraft leases | Third-Party Capital Mobilization | Wall Street firms will source, vet, and individually underwrite loan proposals for hyperscalers, frontier labs (like OpenAI and Anthropic), and enterprises | GPUs as Loan Collateral: borrowers secure massive loans using NVIDIA hardware itself as collateral, functioning on premise that compute has clear intrinsic resale and rental value | NVIDIA Financial Backstop: NVIDIA provides residual support, promising to backstop up to 25% (or $125 billion) of individual deals to stabilize hardware value if a borrower defaults | Secondary Liquidity Ecosystem: If a client defaults, NVIDIA and its partners plan to quickly re-rent or relocate affected chips to other waitlisted data centers, protecting enders from total capital loss | Wall Street financial engineering introduces massive benefits for NVIDIA corporate ecosystem and financial metrics | Handing credit analysis and capital pool over to independent institutional giants validates genuine market demand | Unlocks kong-duration revenue share: beyond selling silicon upfront, NVIDIA could capture up to a 35% revenue share above breakeven from these platforms, potentially adding a 10%+ upside to FY2029 earnings per share | Secures CUDA ecosystem: by subsidizing and simplifying financing hurdle for startups and enterprises, NVIDIA locks customers deeper into its proprietary CUDA software stack, keeping competitors out | BlackRoc CEO Larry Fink likened this initiative to 1970s creation of mortgage-backed securities, calling it the next era of financial engineering | If underlying economic demand for AI tokens and services keeps pace, this structure ensures NVIDIA remains undisputed gatekeeper of global infrastructure | Bringing independent long-term institutional capital to infrastructure market demand is genuine | 35% revenue share above breakeven could provide more than 10% upside Nvidia fiscal 2029 earning