{"problems":[{"id":"problem_be1211f5-9004-4004-805a-ed92cfe63e29","title":"Automotive Buyer's Agent on Open Dealer Protocols (UCP, MCP, A2A)","category":"Product","ownerName":"Nick Baguley","ownerEmail":"nicholas@badlabels.com","company":"Bad Labels","description":"Build a consumer buyer's agent connected to DMC-12 at Mark Miller Subaru: discover fit, shortlist real inventory, request asking-price quotes with itemized out-the-door estimates, optionally soft-hold a VIN, and preserve shopper preferences across a multi-day purchase journey, using live MCP, A2A, and UCP surfaces instead of ad-hoc integrations. An optional Per Se outcome and approval layer can be added on top for declared intent, human approval gates, and proof receipts.","affectedAudience":"Vehicle buyers facing multi-hour, multi-day purchase cycles, and dealership groups seeking agent-native commerce across AI platforms.","currentCost":"Disconnected inventory search, opaque pricing, repeated data entry, scheduling friction, and post-sale service coordination gaps that erode loyalty.","desiredOutcome":"A demo-ready buyer's agent that reduces friction in acquisition by automating discover, quote, hold, and handoff boundaries on a verified protocol endpoint, with a credible path to servicing and network expansion.","pledgeAmount":500,"status":"submitted","voteCount":8,"pledgedAmount":500,"createdAt":"2026-06-02T04:12:36.693Z","updatedAt":"2026-07-10T20:18:32.585Z"},{"id":"problem_0c73133c-2d3a-4bae-a4e4-0259d79aa359","title":"Shared Team Memory for AI Agents","category":"Product","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"A shared memory workspace where people and agents propose, accept, reject, revise, cite, and retire memories, so every agent on a project works from the same approved facts. AI can extract candidate memories from conversations, documents, code reviews, and tasks while human-approved governance decides what becomes durable team knowledge.","affectedAudience":"Startups, agencies, enterprise teams, software and consulting teams, and anyone running multiple agents across projects.","currentCost":"One agent knows the latest decision, another uses stale facts, and a third preserves a bad assumption. Context gets re-entered constantly and trust in multi-agent systems stays low.","desiredOutcome":"A demo using Markdown, JSON, Git, or Slack exports with provenance, versioning, confidence, role-based visibility, and a human approval step for durable memories.","pledgeAmount":500,"status":"submitted","voteCount":6,"pledgedAmount":500,"createdAt":"2026-06-02T22:23:41.562Z","updatedAt":"2026-06-12T14:33:39.914Z"},{"id":"problem_5cdc4cf4-41e2-4879-b161-82cdef918a35","title":"Teams Can't Measure Their AI's Hallucination Rate — So They Can't Trust It in Production","category":"Process","ownerName":"Kenneth Binghamn","ownerEmail":"ken.bingham64@gmail.com","company":"ButterflyFx","description":"Build a reliability layer any team can run on their own AI to measure its hallucination rate on their real questions, ground answers in their approved sources with citations, flag unsupported claims before they ship, and show the number falling over time. Not a promise of \"zero hallucinations\" that's impossible and a red flag but a measured, defensible rate a team can put in front of a customer or an auditor. The hard parts are the build: scoring \"is this claim supported?\" automatically and cheaply, running the verification pass without doubling latency or cost, and making the score trustworthy enough to gate a production response.","affectedAudience":"Any business putting AI in front of customers or staff — support, operations, and the leaders who have to sign off. Vendors promise \"no hallucinations,\" buyers can't verify it, and pilots stall at \"we can't trust it in production.\" It lands hardest on non-technical owners who were told AI would help but have no way to tell when it's confidently wrong.","currentCost":"Stalled adoption (pilots that never ship), expensive manual review of every AI output, and reputational or compliance risk when one fabricated answer reaches a customer. Teams pay for AI tools whose reliability no one can quantify, and a single fluent-but-wrong answer in a regulated context can cost far more than the tool ever saved.","desiredOutcome":"Teams stop arguing about promises and start watching a number. A reliability score becomes a normal part of shipping AI, measured on your own data, grounded in your sources, with a verification pass that catches fabrications before a customer sees them. Buyers compare vendors on a real metric; owners make go/no-go calls with evidence; AI moves from \"impressive demo\" to \"trustworthy in production.\"","pledgeAmount":500,"status":"submitted","voteCount":5,"pledgedAmount":500,"createdAt":"2026-06-12T12:08:01.490Z","updatedAt":"2026-06-12T19:20:33.585Z"},{"id":"problem_47e64ace-fa33-481d-ba94-284c97d04c64","title":"Conversational AI Operations Layer for Dance Studio Management","category":"Process","ownerName":"Maria Ivanova","ownerEmail":"maria@dfdancestudio.com","company":"DF Dance Studio","description":"Dance studio front-desk staff lose hours each week navigating a complex, bug-prone CRM (Mindbody) to add classes, check members in, update pricing, and pull reports. Staff need extensive training before working independently. Operational questions like 'How many people attended Wednesday's Latin class?' require manual report navigation. When staff turn over, training repeats from scratch. Bugs occasionally cause check-in errors or scheduling conflicts. An AI layer that staff can talk to in plain language — asking questions and taking guided actions, with human confirmation before any write — would cut overhead, reduce training costs, and make studio operations faster and more reliable.","affectedAudience":"Dance studio owners and front-desk staff managing class schedules, memberships, check-ins, and pricing through Mindbody or similar studio software.","currentCost":"Significant weekly staff hours lost to CRM navigation, bug workarounds, and recurring training when staff turn over.","desiredOutcome":"Staff can add classes, check members in, update pricing, and pull attendance reports through a conversational AI interface, with human confirmation before any write action.","pledgeAmount":500,"status":"submitted","voteCount":4,"pledgedAmount":500,"createdAt":"2026-05-26T13:51:27.764Z","updatedAt":"2026-06-12T14:33:42.797Z"},{"id":"problem_f98a8228-7e74-4822-ac3b-7e24d18e7f16","title":"AI Capability Navigator for Builders and Leaders","category":"People","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"A navigator that asks what someone is trying to accomplish, then recommends the frameworks, capabilities, tools, and learning path most relevant to that goal. AI can maintain a capability map, compare frameworks, explain tradeoffs by user type, and generate a practical plan for what to learn or build next.","affectedAudience":"Small-business owners, executives, students, builders, educators, product managers, and nontechnical professionals trying to keep up with AI.","currentCost":"People hear about MCP, A2A, agents, memory, RAG, evals, local models, and many frameworks, but cannot tell which matter for their goals. That leads to wasted time, wrong tool choices, and shallow adoption.","desiredOutcome":"A curated capability graph with rubric-based recommendations and links to official docs, scoped to one audience first, without claiming any one tool is universally best.","pledgeAmount":500,"status":"submitted","voteCount":2,"pledgedAmount":500,"createdAt":"2026-06-02T22:23:43.786Z","updatedAt":"2026-06-12T14:34:12.267Z"},{"id":"problem_8b0ceaa9-2585-4f3f-9e19-2387aa06376d","title":"Data Enrichment for Small & Local Businesses","category":"People","ownerName":"Alexandra ortiz","ownerEmail":"alex@shadesofpale.com","company":"KettleiQ","description":"Data enrichment for small businesses.  Platforms like Apollo, anymail finder, and Clay are expensive and only good at finding corporate emails.  They are not good at finding small business owners.   I can easily find the name of the company and company email and company SM by scraping google.    I would like to start outreach with email marketing so I need the name and contact for the decision maker, not the generic social profile or email to the company.","affectedAudience":"Any company, marketer, sales person selling into a small businesses.   There are approx 36Million Small businesses in the US of which approx 30Million are solo preneurs of which 27% don't even have a website.","currentCost":"Pricing is obscure |\"credit\" driven but according to perplexity it would cost me $1 per business contact to enrich the data (email, personal social links, news).   My market is approx 23,000 companies, so the cost would be $23,000.  I am looking for 2 people so approx $46,000.","desiredOutcome":"The development of an LLM tool that is easy to use with limited functionality for the express purpose of data enrichment for lead generation.  It should be set up to be downloaded to run on computers vs the cloud to limit costs.   It should not take up too much memory so a super computer is not needed.   It should be capable of scraping relevant blogs, news outlets, associations and award sites and be SMART enough to know what it's looking for, what's good data and bad data.","pledgeAmount":100,"status":"submitted","voteCount":3,"pledgedAmount":100,"createdAt":"2026-06-11T18:02:12.371Z","updatedAt":"2026-06-12T16:45:25.520Z"},{"id":"problem_7cfbe24a-6558-4b5a-a8d2-afc72a9c5095","title":"Founding & ops hygiene of a small business/startup is still hard, even with AI","category":"Process","ownerName":"David Spencer","ownerEmail":"david@curatelabs.ai","company":"Curate Labs","description":"Starting and operating a small business still requires founders to become accidental experts in HR, accounting, compliance, marketing, operations, hiring, vendor management, insurance, legal paperwork, and dozens of other disciplines. Most entrepreneurs don't fail because they lack expertise in their core business—they struggle because the operational burden of running a company is fragmented, confusing, and expensive.\n\nToday, founders either pay consultants thousands of dollars to solve individual problems, spend countless hours researching and implementing solutions themselves, or make mistakes that create costly problems later. Information exists, AI exists, and service providers exist, but there is no practical system that helps a small business owner understand what needs attention, prioritize it, execute it correctly, and maintain it over time.\n\nThe challenge is to create an AI-powered operational copilot that helps small businesses establish and maintain the foundational systems required to run a healthy company without requiring the owner to become an expert in every business function.","affectedAudience":"All Entrepreneurs, from solo developers to moms starting in-home daycares","currentCost":"With great solution providers like Ampelo, it can cost about $6k for a consultant to fix your HR, or accounting, or marketing.  DIY can take a lot of time and headaches.  The real cost is messing up and having to fix things before you can hit big milestones like getting funded or hiring employees.","desiredOutcome":"A useful solution would dramatically reduce the time, cost, and uncertainty involved in running a small business.\n\nInstead of searching Google, watching YouTube videos, hiring consultants, or piecing together advice from dozens of sources, an entrepreneur could describe their business and receive a prioritized roadmap of what needs to be done next. The system would help identify operational gaps, recommend best practices, generate required documents and workflows, connect to business systems, and guide owners through implementation with appropriate human review.\n\nThe solution should help founders avoid common mistakes, prepare for key milestones such as hiring employees, raising capital, obtaining insurance, managing finances, and meeting compliance obligations. It should continuously monitor the health of the business, surface risks before they become problems, and provide practical guidance tailored to the size and maturity of the company.\n\nSuccess would mean that a solo founder or small business owner could operate with the organizational maturity of a much larger company without needing a large staff of consultants, advisors, and specialists.","pledgeAmount":50,"status":"submitted","voteCount":12,"pledgedAmount":50,"createdAt":"2026-06-11T21:42:03.733Z","updatedAt":"2026-06-12T22:19:34.864Z"},{"id":"problem_b7ac2063-6b44-4f4b-8210-a07f3b040e37","title":"Caregiver Coordination Copilot","category":"People","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"A copilot that turns scattered caregiving work into one shared plan: task lists, appointment summaries, medication and document reminders, family update drafts, and a short what-needs-attention-next briefing. AI can summarize scattered notes, pull tasks out of messages and documents, suggest next steps, and prepare privacy-safe updates for family or an employer conversation, without giving medical advice.","affectedAudience":"Working adults caring for aging parents, spouses, disabled relatives, or medically complex family members who become unpaid project managers on top of jobs and their own families.","currentCost":"Caregiving lives across texts, calendars, portals, PDFs, and memory. Handoffs between siblings and providers are weak, important details get lost, and the work drives burnout and missed appointments.","desiredOutcome":"A demo-ready caregiver copilot on calendar, email, document, checklist, and notification workflows with human review before anything is sent to family, employers, or providers.","pledgeAmount":0,"status":"submitted","voteCount":9,"pledgedAmount":0,"createdAt":"2026-06-02T22:23:37.803Z","updatedAt":"2026-07-10T20:18:25.190Z"},{"id":"problem_b6a690b2-603f-4af0-a433-d6258c0a841f","title":"Permit Packet Pre-Check Agent","category":"Process","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"An agent that reviews a permit packet before submission, flags missing forms or conflicting information, and produces a clean revision checklist so applications pass first review. AI can extract requirements, compare uploaded documents against checklists, summarize deficiencies, and prepare a human-readable correction packet without approving permits.","affectedAudience":"Remodelers, builders, architects, engineers, permit applicants, property owners, and city or county reviewers who catch preventable errors.","currentCost":"Permit delays often start before formal review because applications are incomplete or inconsistent. Applicants find out weeks later, and reviewers spend time on errors that good pre-checks would catch.","desiredOutcome":"A document-AI pre-check demo for one permit type that improves packet quality, cites the source checklist for each flag, and keeps a human in the loop for all approvals.","pledgeAmount":0,"status":"submitted","voteCount":5,"pledgedAmount":0,"createdAt":"2026-06-02T22:23:39.309Z","updatedAt":"2026-06-12T14:35:11.945Z"},{"id":"problem_397c8c25-842f-4a72-9ace-98f61fe9672b","title":"AI Can't Branch a Conversation When Thinking Branches","category":"Process","ownerName":"Allen Ulbricht","ownerEmail":"allen@snowcapconsulting.com","company":"Snow Cap Consulting","description":"AI agents routinely surface multiple questions or angles at once, but conversation tools force a single linear thread. There is no way to explore one branch deeply without contaminating the others — and no shared data layer to keep parallel threads coherent.\n\nWho feels it: Knowledge workers, architects, researchers, and executives using AI for complex problem-solving — anyone whose AI regularly surfaces multiple questions simultaneously. Friction is mild for casual use and severe for sustained analytical work.\n\nToday's state: An AI working through a complex problem typically surfaces three to five sub-questions in one response. The user answers all of them in a numbered reply. If they want to go deeper on question three — challenge an assumption, explore a dependency, ask a follow-up — there is no structural mechanism. Pursuing it in-thread drags context from the other threads along. Opening a new session loses the parent. The conversation enforces linear structure on branching intellectual work. Users manage parallel thread state manually in their heads; the AI has no awareness that multiple concerns are in flight simultaneously.\n\nThis exposes a second problem beneath the UX one: data access across threads. When a branch forks, it needs more than isolated context — it needs decisions, constraints, and facts being established in parallel threads and in the parent. Without a shared data layer underneath all branches, forking just creates silos. A thread working on implementation cannot see that the architecture thread just changed a core constraint. When branches merge, reconciliation becomes a guess. This is also where threading and context relevance converge: the moment you fork, the question of what context is relevant to this specific branch becomes unavoidable — and current AI sessions have no mechanism to answer it.\n\nMarket signal: Branched conversation is among the most consistently requested features in AI tool communities. The numbered-list workaround is so universal it is a recognized pattern. No major consumer AI product has shipped native branching with a shared data layer.\n\nWhat AI shifts: A branch-aware system with a shared data layer lets users fork a sub-thread from any response. Each branch maintains focused context but can query shared state for decisions made elsewhere. When a branch resolves, findings merge back with full traceability.\n\nBuild readiness: Graph-structured conversation models are well-understood. Core challenges are UX (branch navigation, merge visualization) and context routing (what each branch inherits vs. queries on demand). The shared data layer is the harder architectural piece — retrieval, not just storage. Key failure mode to design against: branch proliferation.\n\nDemand signals: Requests for follow up on just this part appear regularly across Claude, ChatGPT, and coding assistant communities. Workarounds — separate sessions per sub-topic, manual thread summaries — are widespread.\n\nUnresolved question: When a deep branch merges back into the main thread, what is the right summarization boundary to avoid overwhelming the parent context?","affectedAudience":"Knowledge workers, architects, researchers, and executives using AI for complex problem-solving where the AI surfaces multiple questions simultaneously","desiredOutcome":"A branch-aware AI conversation system with a shared data layer, allowing users to fork sub-threads, explore them independently, query decisions from parallel threads, and merge findings back with full traceability.","pledgeAmount":0,"status":"submitted","voteCount":5,"pledgedAmount":0,"createdAt":"2026-05-28T07:02:21.413Z","updatedAt":"2026-06-12T14:35:24.631Z"},{"id":"problem_7ba41646-6bed-4dfa-8c5d-1846130579fc","title":"Vacant Lot Opportunity Agent","category":"Purpose","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"An agent that evaluates vacant or underused parcels and recommends realistic activation paths, including likely use cases, zoning constraints, required approvals, incentive options, and projected public benefit. AI can combine parcel records, zoning text, nearby business patterns, public meeting notes, and tax assumptions into a ranked set of possible projects for human review.","affectedAudience":"Cities, redevelopment agencies, property owners, chambers of commerce, brokers, developers, small businesses, and neighborhoods.","currentCost":"Opportunity is scattered across zoning rules, parcel data, ownership records, utility access, neighborhood needs, traffic patterns, incentives, and political constraints. Lots sit idle while corridors lose vitality.","desiredOutcome":"A civic-data demo using public parcel, zoning, and business data that recommends activation paths while a person reviews all legal, zoning, and financial assumptions.","pledgeAmount":0,"status":"submitted","voteCount":3,"pledgedAmount":0,"createdAt":"2026-06-02T22:23:42.541Z","updatedAt":"2026-06-12T20:56:24.561Z"},{"id":"problem_c2793d50-6717-4e7d-8843-048200b30d0a","title":"AI Sessions Have No Control Over Their Own Context","category":"Process","ownerName":"Allen Ulbricht","ownerEmail":"allen@snowcapconsulting.com","company":"Snow Cap Consulting","description":"Knowledge workers doing sustained AI-assisted work lose continuity and control as sessions grow. Context is managed by recency, not relevance — and users have no tools to change that.\n\nWho feels it: Systems architects, strategy consultants, senior engineers, and executives doing multi-hour AI-assisted planning or design work. Anyone whose session involves large reference documents and builds on decisions made earlier in the same conversation.\n\nToday's state: AI session context is managed by linear progression and lossy compaction, not relevance. As turns accumulate, content is retained or evicted based on recency — not on what is actually useful to the current question. Older material gets compacted into degraded summaries regardless of whether it contains a critical decision or key constraint. At turn 50 of a planning session, a pivotal decision from turn 5 may exist only as a lossy fragment, while recent but peripheral exchanges are preserved in full. No per-turn intelligence asks what does this question actually need from everything that has happened — the system applies uniform compression to whatever did not fit. Compounding this: users have no manual controls to intervene. No way to flag content as durable, purge irrelevant material, or compact a single loaded document without affecting everything around it. Context reflects conversational recency, not task relevance.\n\nMarket signal: Every major AI provider is racing to expand raw context window size — itself a signal that the problem is real. But larger windows delay relevance failure; they do not solve it. Extended AI sessions are becoming standard for high-value knowledge work, making context control a ceiling on session value.\n\nWhat AI shifts: A session-aware system could score relevance per turn and load only what the current question needs — moving evicted content to a retrievable store rather than discarding it. Users could manually flag durable content, purge stale material, or compact specific items on demand. Sessions become continuous rather than bounded by a compression policy.\n\nBuild readiness: Retrieval-augmented relevance scoring is mature technology. User control UI is a design challenge, not a research problem. Main open question is latency — per-turn scoring cannot add perceptible delay. Privacy matters: planning sessions often contain sensitive material; any persistent store needs local-first or encrypted options.\n\nDemand signals: Context window management is consistently cited as a top friction point in AI power-user communities. Workarounds — manual summarization, session restarts, pasted context blocks — are widespread and well-documented.\n\nUnresolved question: Can per-turn relevance scoring run within the main response pipeline without user-perceptible latency, or does it require a dedicated fast-model pass?","affectedAudience":"Systems architects, strategy consultants, senior engineers, and executives doing multi-hour AI-assisted planning or design work","desiredOutcome":"A session-aware AI system that dynamically loads relevant context per turn, moves evicted content to a retrievable store, and gives users manual controls to manage what the AI is working with.","pledgeAmount":0,"status":"submitted","voteCount":3,"pledgedAmount":0,"createdAt":"2026-05-28T07:01:40.151Z","updatedAt":"2026-06-12T14:35:31.901Z"},{"id":"problem_9780e277-3fc2-422e-b1a5-91f43e836e74","title":"Accounts Receivable and Cash-Flow Forecast Agent","category":"Profit","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"An agent that predicts which invoices are likely to be late, prioritizes follow-up, drafts customer reminders for approval, and turns invoice history into a short-term cash forecast. AI can combine invoice aging, customer behavior, email context, and payment history to recommend the next best collections action.","affectedAudience":"Small and medium businesses, freelancers, agencies, studios, contractors, bookkeepers, and fractional CFOs.","currentCost":"Many owners do not know which invoices will be late until cash is already tight. Collections live across accounting systems, email, spreadsheets, and memory.","desiredOutcome":"A demo using synthetic accounting data or QuickBooks-style exports with human-approved customer outreach and financial figures clearly labeled as estimates.","pledgeAmount":0,"status":"submitted","voteCount":2,"pledgedAmount":0,"createdAt":"2026-06-02T22:23:45.845Z","updatedAt":"2026-06-12T14:35:38.908Z"},{"id":"problem_33ee9837-1dc5-499d-9e21-f9fc6ef1b779","title":"Sponsor Problem Studio","category":"Process","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"A problem-shaping agent that interviews a sponsor, separates public from private context, identifies the affected audience, turns the problem into a public card, and suggests several solution pathways. AI can turn messy sponsor conversations into structured, buildable briefs while preserving human approval and privacy boundaries.","affectedAudience":"Event organizers, sponsors, chambers of commerce, local businesses, nonprofits, universities, and community partners.","currentCost":"Sponsors often have real problems but struggle to express them as buildable challenges. Too vague and builders cannot act. Too narrow and only one team can solve it.","desiredOutcome":"A demo built on the existing problem shaper and schema that converts sponsor input into public cards with public-private separation and human approval before publish.","pledgeAmount":0,"status":"submitted","voteCount":2,"pledgedAmount":0,"createdAt":"2026-06-02T22:23:44.801Z","updatedAt":"2026-06-12T14:35:47.310Z"},{"id":"problem_5376dd2f-0181-442e-b74d-8c29da285e5d","title":"Text-Only Appliance Repair Agent","category":"Product","ownerName":"Nick Baguley","ownerEmail":"baguleyllc@gmail.com","company":"Bad Labels","description":"A repair agent that works entirely over text message. It walks a consumer through basic diagnosis, collects model and photo details, checks warranty or manuals, finds likely parts or issues, and books a human technician when needed. AI turns a messy request into a structured repair workflow and keeps context so the consumer never starts over.","affectedAudience":"Everyday consumers, renters, homeowners, property managers, repair companies, warranty providers, and local technicians.","currentCost":"Most consumers will not install agent software or configure APIs. To get a washing machine fixed today, people search, self-diagnose, call around, compare, schedule, check warranty, and repeat the same details to everyone.","desiredOutcome":"An SMS-style demo that guides diagnosis, checks warranty or manuals, and hands off to a human technician with approval before any booking or spending.","pledgeAmount":0,"status":"submitted","voteCount":2,"pledgedAmount":0,"createdAt":"2026-06-02T22:23:40.588Z","updatedAt":"2026-06-12T14:36:00.952Z"},{"id":"problem_169b6604-27f9-4457-abfc-db0845d5ec22","title":"The Prank Agent- Making Mischief Responsible Again","category":"People","ownerName":"Matthew Fischer","ownerEmail":"Matthewefischer@gmail.com","company":"For every University  and office","description":"TL;DR: Society has become dangerously low on harmless shenanigans.\n\nThe rise of surveillance cameras, social media, overly cautious policies, and people who immediately ask, \"Is this against the rules?\" has nearly driven the harmless prank to extinction. College campuses, offices, and communities have lost a valuable source of fun, creativity, and social bonding. Today, pulling off a simple prank often requires more risk assessment than launching a startup.\n\nThe Prank Agent helps bring back playful mischief by helping people create hilarious, safe, and memorable pranks without accidentally creating a police report, HR investigation, or family feud.","affectedAudience":"TL;DR: Anyone who misses having fun.\n\nCollege students who want campus life to feel alive again.\nOffice workers trying to survive another quarter without losing their sanity.\nFriend groups looking for legendary stories instead of another group text.\nEvent organizers planning April Fools', spirit weeks, or team-building activities.\nParents trying to teach kids that fun doesn't always require a screen.\n\nBasically, anyone who has ever said:\n\n\"This would be hilarious... but should we?\"","currentCost":"TL;DR: Boring memories.\n\nWithout a safe way to encourage playful mischief:\n\nCampuses become less social and memorable.\nOffices become more sterile and less human.\nFriendships lose opportunities for shared stories.\nEvents become forgettable.\nSociety becomes approximately 17% less fun.\n\nThe average person now spends more time reviewing Terms & Conditions than planning harmless adventures.\n\nMeanwhile, the world's supply of legendary stories that begin with:\n\n\"You'll never believe what happened...\"\n\ncontinues to decline.","desiredOutcome":"TL;DR: It would make mischief safer, smarter, and funnier.\nThe Prank Agent is an AI-powered prank strategist and risk assessment engine.\nUsers describe a situation:\n\n\"My roommate takes himself way too seriously.\"\n\n\n\"We need an office April Fools prank.\"\n\n\n\"Our college dorm needs a harmless tradition.\"\n\nThe AI recommends pranks and generates:\n\n\nLegal Risk Score – Will lawyers appear?\n\n\nSocial Risk Score – Will people laugh or unfriend you?\n\n\nProperty Risk Score – Will facilities management become involved?\n\n\nEmotional Risk Score – Is this funny or are we creating a therapy session?\n\n\nSurveillance Risk Score – How many cameras and smartphones will capture this?\n\n\nThe result?\nMore laughter.\nMore stories.\nMore community.\nAnd fewer situations where someone says:\n\n\"In hindsight, releasing 200 inflatable flamingos into the dean's office may have been an overreaction.\"\n\nThe Prank Agent isn't an engine of chaos.\nIt's a Mischief Management System™ that helps humanity responsibly return to its natural state: doing something ridiculous, laughing about it, and telling the story for years afterward. 😆🏆🎈","pledgeAmount":0,"status":"submitted","voteCount":0,"pledgedAmount":0,"createdAt":"2026-06-12T21:16:51.571Z","updatedAt":"2026-06-12T21:16:51.571Z"},{"id":"problem_3fff654b-cc1f-4eb2-bea7-37c43702a377","title":"The accidental manager fails in the moment, and the market can't reach them there.","category":"People","ownerName":"Makayla Greathouse","ownerEmail":"makayla.greathouse@elevateintent.co","company":"Elevate Intent","description":"Build a way to reach the struggling first-time manager in the moment of need, grounded in their real situation rather than a generic persona, and usable by a small company with no learning budget or HR team to deploy it. Plenty of tools exist, but they sell through L&D teams these companies don't have, or assume a manager who already knows to go looking. \n\nThe hard part is not the model. It is the delivery and the trust. Grounding advice in a manager's real team raises confidentiality and consent questions about the people being discussed. People decisions need a human checkpoint, and the system has to know its limits, hand off on high-stakes calls, and fail gracefully instead of confidently giving bad advice. The most durable version likely pairs AI with a human checkpoint rather than full automation. The win is in the moment coaching, not another library of training content.","affectedAudience":"First-time managers promoted for individual performance, with no preparation for leading people. It's most acute at companies under ~150 people with no L&D function and no senior People leader to point them anywhere. \n\nTheir teams feel it next, through unclear expectations, avoided feedback, and quiet attrition and leadership feels it through the debt they accrue.","currentCost":"The manager is the single biggest driver of whether a team thrives. Companies keep hiring and promoting managers without support, then absorb the cost: regrettable attrition at the exact growth stage where each departure is most expensive to replace, teams stalling through the transition, founders pulled in to clean up people problems they shouldn't touch, and high-stakes calls like terminations handled in a panic, which is exactly where legal and compliance exposure lives.","desiredOutcome":"A new manager gets unstuck in the actual moment instead of avoiding the conversation or pushing through alone. Hard calls get a steady checkpoint rather than a guess. Fewer people quit a manager who is still learning, teams hold their output through the transition, and founders stop being the escalation point for every people issue. The company gets the management layer it's already paying for, at the stage it can least afford a weak one.","pledgeAmount":0,"status":"submitted","voteCount":0,"pledgedAmount":0,"createdAt":"2026-06-12T15:25:27.665Z","updatedAt":"2026-06-12T15:25:27.665Z"}]}