Every idea that went through the panel.

“Building "Leakr - Find where your time leaks" finding Product Market Fit https://findtimeleak.com - I am building Leakr, which helps you find where your time leaks. How does it work: 1. ⁠Starts when you log in to your PC/Laptop 2. ⁠You add the task you'll start working on; the stopwatch starts 3. ⁠Based on the default list of distractions, when you visit any website listed in the distraction, Leakr pauses the stopwatch; & when you close that site, it continues the stopwatch 4. ⁠When you're done, you can add what you did and proof of work. Optional. What do users get? 1. ⁠A dashboard where they will know how many hours they spent working and can see where time leaked. 2. ⁠For which organisation/task/subject were they allocated how much time in a day, week and month? 3. ⁠Also, they know on which day of which month of which year. They did what? 4. ⁠When they know Time Leaked -> "Guilt Trip" -> "Behaviour Change". 5. ⁠User will develop -> increased focus, consistency and self-confidence.”

Clever pause mechanic, but without a sharp user and stronger hook than guilt, this disappears into free time-tracking noise.

“Music-Reactive Lighting Startup — Master Summary Updated April 2026 What We're Building An AI/ML-powered lighting automation system that synchronizes lights with live music in real-time. The goal is not simple beat-reactive automation, but lighting that approaches the quality of a professional lighting designer (LD) — understanding musical structure, timbre, emotion, and narrative arc — at a fraction of the cost. Product Architecture Hardware Proprietary non-DMX lights (simpler entry point; DMX adapter as optional add-on) Low-cost communication device bridging lights and server Physical enclosure/box (design TBD — potential in-house capability) Software LayerDescriptionAudio analysis engineReal-time ML processing: tempo, BPM, amplitude, frequency, onset, timbre, structure, articulation, harmonyLighting engineTranslates audio features into lighting commands via shadersSpatial mappingReads venue layout to optimize fixture placement and effectsVisualizerSoftware simulation of the lighting system; public-facing feature AND primary supervised learning environmentMobile appiOS/Android control interfaceWebsiteMarketing, onboarding, documentation ML Strategy Feature Extraction Roadmap PhaseFeaturesMVP (now)Tempo, beat, dynamics, frequency content6 monthsTimbre, articulation, structure, harmony12+ monthsEmotional valence, genre-specific semantics, narrative arc Training Data Methodology A daily pairwise comparison workflow using the visualizer as the data collection environment: 3 shader pairs per session (6 shaders), split-screen, same music playing to both 4 songs per session → ~12 comparisons, ~12 minutes/day 4–6 raters daily, cycling through a preset venue layout library Elo/tournament structure: high-performing shaders re-enter the pool; surfaces which shader metric combinations resonate best Controls: randomize shader left/right position, randomize from 15–20 track playlist across sub-genres, minimum 8–10 exposures before a shader is re-looped Track win rates per genre/song type, not just overall Key Unresolved Items Before Data Collection Begins Define rating metrics (what raters are explicitly scoring) Define shader parameter axes to vary systematically (color, speed, intensity, etc.) External rater recruitment plan (Montreal scene participants) Statistical pipeline: how Bradley-Terry scores feed back into shader pool selection Academic Grounding FieldRelevanceSensory EvaluationPanel design, fatigue, inter-rater reliabilityPreference ElicitationPairwise comparison methodologyPsychometricsRating instrument designBradley-Terry ModelStatistical backbone for tournament rankingPsychophysicsHuman perception of stimuli Top read: Lawless & Heymann — Sensory Evaluation of Food (highest ROI); Thurstone (1927) for theoretical foundation. Team PersonRoleCore SkillsCo-founder #1Business, product, UX/UI, marketing, strategyPhotography, tech consultingCo-founder #2Technical leadHardware, software, ML (depth TBD)Person #3Shader development & aestheticsVideo game artist, pure mathematics background; flex capacity toward physical box design or MLPerson #4Frontend developmentWebsite, eventual mobile apps (iOS/Android)Person (flex)Physical hardware installation & logisticsOn-site installs, transportationPerson (flex)Supervised learning & MLCurrently learning; working on training pipelineInterviewingSocial media specialist—InterviewingAccountant— Key Capability Gaps to Watch Deep audio ML expertise (MILA as potential hiring/advising source) Installation does not scale — temporary moat or bridge strategy needed Hardware engineering capacity (in-house vs. outsource) Business Model StreamDescriptionHardware salesOne-time cost, accessible pricingInstallation serviceNominal fee; specialist maps venue spatiallySaaS subscriptionPrimary recurring revenueRevenue-sharing (under consideration)1–2% of door/bar for DIY venues instead of flat fee Target Market TierWhoWhyPrimarySmall/medium venues, independent DJs, underground ravesCan't afford $500–2000+/event LDSecondaryProfessional LDs wanting AI co-pilotEfficiency, not replacementTertiaryLarge touring artistsCustomization without full-time LD Competitive Landscape CompetitorModelKey NotesSoundSwitch$10–15/month subscriptionIndustry leader, InMusic-owned, major DJ software integrationMaestroDMX~$500–600 one-timeClosest threat: standalone AI hardware, mobile app, no laptop neededAULIOSSubscriptionEuropean, DMX-focused, claims "first AI club lighting"Lightjams, DMXDesktop, A.I. LightshowVariousAutomation tools, mixed pricing Key gaps competitors don't fill: white-glove installation/spatial mapping, Montreal market presence, truly deep music understanding beyond beat-reactive automation. Go-To-Market Beachhead: Montreal underground electronic music scene (raves, warehouse parties, small clubs) Expansion path: Montreal → Toronto → Quebec City → Canada → US border cities Ecosystem: MILA, McGill CIRMMT, Solotech, Ampleman, SAT, MTELUS, Stereo, Igloofest, MUTEK, MEG Montreal”

“I'm a dad of two (5yo and a newborn). For years I've been making up bedtime stories for my older daughter — she picks the animal, I pick the lesson I want to sneak in that night. A fight with a friend, fear of the dark, whatever happened that day. It's become our thing. But some nights I'm just too exhausted to do it well, and I feel guilty about that. So I started building Lola Stories — an app that generates personalized bedtime stories. You pick the character, pick the moral, get a unique story with an illustration in seconds. You still read it to your kid. The app just does the creative heavy lifting on the hard nights. Still early stages. Before I go further I want to know if this actually solves a real problem for other parents — not just me. If you have kids between 3 and 7, I'd really appreciate 3 minutes of your time. There are also some example stories in the survey if you want to see what it looks like. 👉 https://tally.so/r/GxoLMo Thanks — every response genuinely helps.”

Real emotion, weak moat, prove exhausted parents pay for guilt-free ritual before building another infinite story machine.

“The Idea: The "Leak Detection" Engine An automated auditing tool that connects to a company's tech stack (Stripe, HubSpot/Salesforce, and Google Analytics) to find revenue leakage caused by data silos. The Problem As companies grow, their data gets messy. Common "leaks" include: Ghost Subscriptions: Users who have canceled in the CRM but are still getting service because the API call to the backend failed. Mismatched Pricing: Legacy customers being billed old rates that don't match the current terms of service. Attribution Gaps: High-value leads that closed but aren't traced back to the original marketing spend because of a broken tracking cookie or UTM. The Solution A "set and forget" dashboard that runs daily integrity checks across these platforms. It doesn't just show charts; it sends Actionable Alerts like: "Alert: 14 users in your 'Pro Plan' are being billed $49/mo, but your current Stripe configuration is $79/mo. Click here to sync." Why It’s a "Solid" Idea Immediate ROI: If you find $500/month in leaked revenue for a client, charging them $100/month for the software is an easy "yes." High Stickiness: Once a company relies on you to ensure their billing matches their CRM, you are deeply embedded in their financial workflow. Low Competition: Most tools focus on growth (top of funnel). Very few focus on integrity (middle of funnel). Monetization Strategy Tiered Pricing: Based on the volume of transactions or the number of integrations. The "Found Money" Commission: A one-time setup fee plus a percentage of the "leaked" revenue recovered in the first 30 days.”

Good audit service, weak SaaS, teams already see the leaks and only pay once to patch them.

“Dynamic Expiry Intelligence for Supermarkets 💡 Problem Supermarkets throw away huge volumes because they use static shelf-life rules and blunt markdown timing. Solution AI that predicts the optimal time to markdown perishables and reroute stock before expiry. Examples: chicken discounted 8 hours earlier yogurt moved to another branch strawberries repriced dynamically 1. Introduction – What Kigüi Does Kigüi is an artificial intelligence platform designed to optimize the daily operations of supermarkets and retailers by combining data analysis, automation and in-store execution. Using key inputs such as historical sales, stock levels, expiration dates and shelf images, Kigüi turns data into clear priorities, predictive alerts and concrete actions for in-store teams. 2. Why Kigüi Is Different Unlike traditional solutions that only report information, Kigüi acts as the retail copilot, prioritizing what truly matters in order to: prevent waste caused by product expiration avoid stockouts and shelf gaps ensure the correct execution of promotions free up operational time so teams can focus on strategic tasks 3. How Kigüi Works: Technology and Process A. Data Collection Kigüi automatically integrates multiple data sources: 📌 Real-time inventory and stock levels 📌 Historical sales data by SKU 📌 Expiration dates and rotation curves 📌 Shelf images and planograms This combination provides a complete and accurate view of each product and store. B. AI and Machine Learning Analysis The platform uses AI algorithms to: ✅ Detect demand patterns and expiration risk ✅ Estimate the probability of stockouts by SKU ✅ Identify gaps in shelf execution ✅ Prioritize actions with real business impact All of this is translated into predictive alerts and clear recommendations for in-store teams. C. In-Store Execution Kigüi doesn’t just inform — it guides daily action. The app and control panel provide: 🟢 Daily checklists and missions for store teams 🟢 Real-time alerts on operational priorities 🟢 Task completion verification 🟢 Price, label and expiration control 🟢 Photo evidence and activity tracking This turns daily work into a sequence of clear, measurable tasks. We convert expiring inventory into automated revenue recovery with zero added store labor. Instead of telling humans to markdown, we build systems where action happens automatically or where incentives clearly improve.Integrate with existing POS / loyalty apps / e-commerce channels. When expiry risk rises: prices update automatically offers pushed to loyalty users nearby bundles created automatically No staff app needed.”

“Dynamic Expiry Intelligence for Supermarkets 💡 Problem Supermarkets throw away huge volumes because they use static shelf-life rules and blunt markdown timing. Solution AI that predicts the optimal time to markdown perishables and reroute stock before expiry. Examples: chicken discounted 8 hours earlier yogurt moved to another branch strawberries repriced dynamically”

Real pain, but without instant ROI and a rip-and-replace wedge, supermarkets will admire it and keep their current tools.

“We help food businesses avoid creating surplus in the first place, then monetize unavoidable surplus intelligently. we target bakeries salad chains quick-service restaurants campus dining corporate cafeterias Predict tomorrow’s demand by SKU / meal category using: weekday weather holidays events local traffic historical sales and we have a Production Optimizer to recommend prep quantities and avoid food waste/spending excess money on food that will be thrown out Money saved, waste reduced, CO₂ reduced. we build: simulated daily demand data forecast next-day demand recommended bake quantities closing-time markdown engine dashboard with waste reduction %”

Real pain, weak wedge, forecasting is table stakes and bakery CAC likely kills this before the waste savings matter.

“Predictive Food Waste Prevention and Redistribution System using AI-driven demand forecasting and constrained optimization using things like day of week (Mon ≠ Sat) time of year / season weather (rain reduces restaurant traffic) holidays / events historical sales patterns reservations / bookings school/work schedules nearby events (concerts, conferences) scaled to supermarkets, restaurants, hotels,”

Prediction is table stakes, win on profitable redistribution or you're another dashboard nobody changes behavior with.

“AI spec writer - A developer pastes a rough idea or ticket → receives a complete, structured spec in under 30 seconds. Quality is consistent, editable, and exportable. Team saves at minimum 1 hour per feature spec”

Useful pain, commodity solution, without a vertical wedge or workflow hook, teams will just use the AI they already have.

“A company that runs free live online author talks for public libraries. Libraries pay a few thousand for a years worth of around 50 authors. Library users pay nothing. The reason libraries pay is because authors charge a fee to come in that can be thousands of dollars. most state library orgs don't have this program. I already know a bunch of authors because I am an author agent. Libraries don't get talks subsidized, and they definetly don't get access to 3 talks per month. They can also run watch parties at their library.”

Strong wedge, but library procurement will starve you before the bundle gets enough contracts to matter.

“A company that runs free live online author talks for public libraries. Libraries pay a few thousand for a years worth of around 50 authors. Library users pay nothing. The reason libraries pay is because authors charge a fee to come in that can be thousands of dollars. most state library orgs don't have this program.”

Useful convenience, but libraries already get author events subsidized, and your hardest problem is author supply, not selling Zoom.

“Hey everyone, I'm a frontend developer currently building a micro-SaaS aimed at simplifying short-form video distribution (YouTube Shorts, IG Reels, TikTok). The main reason I decided to build this is simple: the existing tools on the market are just way too expensive. Most of them are packed with bloated enterprise features and charge high monthly fees, which is a huge barrier for solo creators, indie hackers, or early-stage startups. My goal is to build a lightweight, affordable alternative with a super clean and minimalist UI that focuses entirely on getting the core job done—uploading once and distributing everywhere. Before I get too deep into development, I’d love to get some validation and feedback from this community: Market Demand: Do you also feel the pain of overpriced social media tools? Or do you just post manually to save money? Core Features: I recently got some great feedback about allowing custom captions/hashtags for each platform rather than just blindly copying and pasting the exact same text everywhere. Are there any other "must-have" features that would make this a no-brainer for you? Pricing: For a straightforward, no-fluff cross-posting tool, what pricing model or price point would you consider fair? Would love to hear your thoughts or any challenges you think I might face. Thanks!”

Cheap cross-posting is a race to free, and you have no wedge, customer, or proof anyone will pay.

“Hey folks, While working on a small SaaS, we noticed something interesting: We weren’t lacking leads. We were losing them in conversations. Most of our inbound came from WhatsApp, but: Replies were delayed No structured follow-ups Conversations scattered across team members Zero visibility on lead status So even with decent traffic, conversions were inconsistent. 💡 Insight It wasn’t a marketing problem. It was a system problem. WhatsApp is fast, but most businesses handle it manually. No pipeline, no tracking, no context. That gap = lost revenue. 🛠 What we built (Micro SaaS approach) Instead of building a full CRM, we focused on a narrow use case: 👉 WhatsApp lead management only MVP includes: Lead capture from WhatsApp Simple pipeline (New / Interested / Closed) Follow-up reminders Clean dashboard (no feature bloat) Goal: Increase response speed + bring structure to conversations 📈 Early thinking We’re validating a few things: Do small teams prefer this over heavy CRMs? Is WhatsApp-first workflow a strong enough niche? How much does faster response actually impact conversion? 🌐 Project link https://www.betaxlab.com/crm-with-whatsapp-business-growth Would love feedback from fellow builders: Is this niche too narrow or just right? What would you add/remove in a v1? Anyone else building in “chat → CRM” space?”

Real pain, no moat, you are building a side dashboard on top of a platform that will absorb you.

“Built an AI tool that checks what's ranking on Google before writing an article Been running content sites for a while and got frustrated that AI writers just generate generic content with no awareness of what's actually ranking for a keyword. So I built something that works differently. You enter a keyword, it pulls the top 10 Google results, analyses what topics they cover, grabs the People Also Ask questions and related searches, then generates an article structured to compete with what's already on page one. The whole thing runs on NextJS, Supabase, Stripe, and Anthropic's Haiku model for the writing. Costs me under £30/mo to run. Free tier gives you 3 articles a month if anyone wants to try it. Paid plans start at £19/mo for more volume. Keen to hear what people think or if you have questions about the build.”

Crowded market, weak wedge, and users may want research help, not another AI draft generator.

“A centralized SaaS and MCP server combo to give coding agents and LLMs true understanding of code, allowing them to search and navigate the code like a developer instead of grepping for strings. This can be added to ANY AI coding agent or assistant via plugins or MCP server. The SAME context is shared across the entire team to avoid disparities and hallucinations.”

Right layer, wrong moment, shared team context matters but agents already bundle this and MCP demand is still niche.

“A centralized SaaS and MCP server combo to give coding agents and LLMs true understanding of code, allowing them to search and navigate the code like a developer instead of grepping for strings.”

Right problem, missing buyer and wedge, plus you're betting on an unproven protocol before the market exists.

“Building the independent context layer for Al, the horizontal company state every serious Al stack will run on. turns fragmented knowledge, decisions, and processes into a governed, continuously evolving system of record that agents don't just query, but operate and collaborate on. Instead of reconstructing reality at runtime for every Al system, companies define their state once and every agent runs on it.”

Sharp wedge, premature market, no buyer, you're standardizing agent chaos before companies feel enough pain to pay.

“Un SaaS qui permet de facilement calculer le prix de vente optimal des impression 3D en prenant en compte plusieurs paramètres important. Il y a également un suivi des clients et des commandes.”

Good problem, vague buyer, commodity feature, they'll use a spreadsheet before paying for another 3D printing SaaS.

“I built a tool to stress-test business ideas before you spend time building them — would love feedback I’ve been working on a tool that helps evaluate business ideas by breaking them down into things like feasibility, costs, risks, and market clarity. Instead of just giving generic AI feedback, it gives a clear verdict (whether the idea is strong, needs refinement, or isn’t viable) along with specific next steps and a way to iterate and re-test the idea.”

Good problem, commodity product, broke buyer, free alternatives, no target customer.

“The problem: Finding your first customers is brutal. You know your product solves a real problem, but you don't know WHO those people are or HOW to reach them. Cold outreach works, but building a lead list manually takes forever: searching Google, visiting websites, hunting for contact emails, and figuring out if they're even a good fit. What I built: You list your product on betaFounder, and AI analyzes what your product does, who it's for, and what problem it solves. Then it goes out and searches the internet to find real companies and startups that actually have that problem. But it doesn't stop there, it crawls their websites, finds their contact emails (from contact pages, about pages, footers), and tells you exactly WHY your product would be useful to them. So instead of "here's 1,000 random emails from a database," you get something like: Acme SaaS acmesaas.comThey run a live web app with paying users but have no uptime monitoring Why they need you: "Your alerting tool would notify their small team instantly when their app goes down, before customers complain" Email: founders@acmesaas.com Every lead comes with context. Not just an email, but a reason to reach out. How it's different from Apollo/Hunter/Snov: Those tools give you filtered lists from a massive database. Great for volume, but zero context. You still have to figure out why each person should care about your product. This gives you fewer leads (about 250/week), but each one is matched to YOUR specific product with a personalized reason for outreach. Quality over quantity. Pricing I'm thinking: $99/month for 1,000 leads/month”

Good problem, weak wedge, and AI-matched leads break if founders still cannot turn relevance into replies.

“I was tired of manually comparing competitor pricing pages, so I created a tool to automate the task. It watches competitor pricing on a timed basis, archives past instances, and performs pattern detection through AI such as "pro plan is down by 20% after your launch." In addition, there’s the AI component which provides marketing and pricing insights based on the competitor's behavior. Current solutions are either only available for enterprises (Crayon, Klue $1k+/month) or simply alerts with no analysis whatsoever. What do I need to know? Are you tracking competitor pricing currently and how? Is there any existing solution that does this efficiently that I’m missing? Out of the three features, what would you pay for first?”

Promising wedge, but until someone pays for insights over alerts, you are automating a habit that may not exist.

“Forecastle - A smart deman forecasting platform for startups that uses 4 signals - seasonal trendings, news channels, Facebook's prophet + XGBoost ML model”

Solved market, no wedge, no buyer, until you pick a vertical and proprietary data, this is consultancy in product clothing.

“Maintenance.dev, rather than coding your own maintenance page and hooking it up to an API, you can just create a project, use our WYSIWYG editor to create a maintenance page, and drop a single script tag in, then you can toggle a maintenance page on and off in realtime via Maintenance.dev”

Sharp wedge, no buyer, you are monetizing a tiny hassle people already solve free.

“A tool that allows people to upload listings from other platforms like ebay for example, straight into shopify without having to do it manually”

Shopify already solves this, and you are automating a 15 minute task nobody urgently pays to avoid.

“Ai browser rpa automation, can scrape and automate any task moat is you can type automation in English and it will do it”

Good UX, no wedge, distribution beats prompts and you have neither a buyer nor a beachhead.