One click to check if your API proxy secretly swapped in a cheaper model
Problem
More developers call models through third-party proxies, but fear being routed to cheaper or throttled models. Communities debate whether region, IP, or environment cause throttling, and some already run open-source fingerprint checks, yet ordinary users lack a simple, trustworthy, repeatable verification entrypoint.
Solution
Paste your proxy endpoint and key; the tool sends a standardized probe suite and compares latency, token distributions, refusal patterns, and knowledge boundaries against the official model to produce a routing/throttling risk score. Schedule re-tests, log changes, and compare multiple proxies side by side.
Find out how long your local LLM rig takes to pay for itself
Problem
People are told "just buy a Mac or a GPU and run models locally, it pays for itself," but nobody shows the actual math. Users don't know their daily token usage, the cloud model price they're comparing against, or how many months of depreciation it takes to break even, so they either overspend on hardware or miss real savings. Region-locked subscriptions, throttling, and shared accounts make cost judgment even murkier.
Solution
Enter your rig specs and price, target local model, daily token volume, and the cloud model you're comparing against; get payback days, monthly bill comparisons, and sensitivity analysis. Ships with presets for popular hardware and models, supports custom electricity rates and depreciation periods, and generates shareable reports.
Help novelists catch contradictions in characters, settings, and plot
Problem
The hardest part of writing a long novel is not prose but consistency across a 120k-word manuscript: eye colors, timelines, family trees, and world details often contradict earlier chapters. A French-market writing SaaS offers an AI assistant and character sheets but still suffers high churn, showing authors will pay for keeping a novel coherent, yet existing solutions are not good enough.
Solution
A consistency-focused checker: import a draft and automatically build profiles for characters, places, timelines, and key settings. Cross-check for contradictions, highlight suspicious passages, and suggest fixes. It is not a generic writing AI but a copy-editing copilot for long-form fiction.
Monitor AI answers for wrong prices, outdated products, and false brand claims
Problem
Brands can pay for AI visibility tools that show how often ChatGPT mentions them, how they compare with competitors, and what sources are cited. But the real commercial risk is when the AI answer is wrong: wrong price, discontinued product, outdated description. Existing tools answer whether you are mentioned, not whether the mention is correct.
Solution
Users enter brand, products, prices, and key facts. The product regularly asks major AI systems questions, captures answers, compares them against the source of truth, flags wrong prices, outdated descriptions, and competitor confusion, and generates an actionable correction list with screenshot evidence.
Auto-generate what to study next based on target company and role
Problem
The biggest pain in tech interview prep is not effort but direction. Someone grinds graph problems for a Saturday, then realizes the target company barely asks graphs and heavily weights system design, which they ignored. Most advice is generic: do more LeetCode or read Designing Data-Intensive Applications. None of it adapts to the specific company and role.
Solution
Users enter target companies, roles, and interview dates. The product generates a structured study roadmap based on public interview focus areas and question banks, dynamically adjusts daily tasks, and tracks progress. The core is direction and next-step guidance, not another LeetCode clone.
Let local small businesses pool orders for wholesale discounts
Problem
Cafes, restaurants, and retail stores often buy packaging, cleaning supplies, and inventory alone, paying high unit prices and shipping fees. Small businesses in New Zealand can pay around $50 to ship even the smallest package. They want to join orders with nearby peers but have no simple tool to start, match, and consolidate purchases.
Solution
A group-buying platform for local small businesses: post a purchasing need, get matched with nearby businesses that want similar items, combine orders, and place a grouped order to suppliers with shared costs and savings. The core is matching and cost sharing, not owning the supply chain.
Affordable competitor ad collection and organization for small Meta advertisers
Problem
Independent sellers and small teams want to study competitor ads, but they manually open each ad in Meta Ad Library, copy links, download assets, and repeat this dozens of times. Most ad-spy tools cost $50–$150/month and target agencies with deep pockets, leaving small sellers priced out.
Solution
A lightweight browser extension plus minimal web dashboard that saves ad images, videos, copy, and links from Meta Ad Library with one click. Organize by brand, category, and date, then search, tag, and export. It focuses only on competitor ad research, not complex campaign analytics.
Multiple customizable focus mode schedules for Android users, breaking the single-schedule limit
Problem
Android's built-in Focus Mode only allows one schedule. Many users need to switch between multiple focus periods in a day (e.g., morning work, lunch break, afternoon work), but the system limitation forces manual switching, which is inconvenient.
Solution
A lightweight Android app that lets users create multiple focus mode schedules. Each period can have different focus modes, allowed apps, and notification rules. The app automatically switches the system focus mode in the background according to the schedule, no root required.
Auto-answer missed calls for solo service businesses to recover lost customers
Problem
Many small service businesses (plumbers, HVAC, contractors) miss calls while heads-down with a customer. Most callers who can't reach them don't leave a voicemail—they just call the next business. This causes significant lost revenue, but owners often don't realize the cost.
Solution
A simple AI voice answering + SMS follow-up service. When a call goes unanswered, it automatically responds with natural voice, asks for the customer's need and contact info, then sends a confirmation SMS to the customer and pushes the info to the owner. The owner can call back later, preventing customer loss.
Local static security scan for AI agent skills to identify risks before use
Problem
The agent ecosystem is exploding with reusable skills, but no one knows if they are safe. Installing and using them directly may introduce data leaks, privilege escalation, and other risks. There is a lack of localized security audit tools.
Solution
A local CLI tool that performs static risk scanning on downloaded AI agent skill packages. It checks patterns like network requests, file access, command execution, and sensitive data reads, generates a risk report, and provides usage recommendations. Users can integrate it into their installation workflow.
Automatically records real time spent on each project for multi-project workers, no manual timers
Problem
People juggling multiple projects have a hard time accurately calculating how much time each project took at the end of a day, week, or month, relying only on memory. Existing trackers either require manual start/stop or depend on cloud analysis, which is expensive and raises privacy concerns.
Solution
A fully offline desktop automatic time tracker. It uses local AI to analyze active windows and input behavior, automatically categorizes time into different projects, and generates daily/weekly/monthly reports. Users can also define custom project tags and rules, and data never leaves the device.
Auto-organize vibe-coded legacy projects into AI-coding-agent-friendly structures
Problem
Many teams use tools like Lovable to quickly generate MVPs, but the codebase becomes hard to maintain. When AI coding agents are introduced, messy code organization and lack of documentation cause AI to fix one bug and introduce two more. Developers wonder if there's a way to make legacy codebases more readable for AI.
Solution
A CLI tool that scans a project and automatically generates an AI-coding-agent-friendly code map, module descriptions, and modification boundary suggestions. It doesn't rewrite business logic, but produces a context file consumable by AI, including key dependency graphs, implicit conventions, and danger zone markers, making tools like Claude Code and Codex more targeted when editing.
Crowdsourced and AI-assisted tracking of global infrastructure and industrial projects
Problem
A developer tried using AI agents to map major announced infrastructure and industrial projects worldwide, but data goes stale and inaccurate without community validation. Researchers and investors wanting to know what projects are coming online in a region or sector must rely on scattered news and expensive databases, with no transparent collaborative source.
Solution
A collaborative industrial project database. Browse global announced infrastructure and industrial projects by region, sector, and timeline. AI handles initial ingestion and periodic news scraping for updates, while community users add, correct, and vote. Each project shows status (planned, under construction, operational), key dates, funding sources, and source links, with a public API for further analysis.
Unified save states, cheats, and controller configs across web and desktop NES emulators
Problem
Several community-built NES and web emulator projects coexist, but saves, cheat codes, key mappings, and save states are incompatible across them. Players switching between phone, Mac, and browser face a fragmented experience with no neutral tool to manage this data.
Solution
A cross-emulator save hub that recognizes save and cheat formats from major NES emulators, then offers unified upload, download, format conversion, and cloud sync. It manages multiple save slots, notes, and version history per game, and can export back to the original emulator's format in one click. Optional controller mapping sync.
Automatically detect when the same prompt behaves differently over time or across models
Problem
Users complain that the same paid account and model can be brilliant one day and unhinged the next: following a recipe then dumping gutter oil into it, or abandoning instructions entirely. Developers suspect silent routing to older models but have no independent way to quantify whether the model got dumber today. They only have gut feelings and community rants.
Solution
A lightweight prompt regression testing tool. Users save critical prompts and expected output samples. The tool periodically or manually calls the target model with the user's API key, compares outputs for semantic drift, instruction-following loss, or format breaks, and shows a visual trend plus alerts. Multi-model side-by-side comparison helps confirm routing changes.
Run each MCP server once and share it across all your AI clients
Problem
As clients like Claude Desktop and Cursor spread, users configure the same MCP server in every client, spawning duplicate processes that waste memory, collide on ports, and make debugging painful. Today the community only has manual config edits, no unified manager, and users want a local MCP gateway that is set up once and available everywhere.
Solution
A macOS menu bar tool that registers and manages MCP servers centrally, starts a single shared process, and proxies requests from all connected AI clients. It shows health, call logs, and restart controls per server, and can inject the right config into Claude, Cursor, and others with one click.
One curl pushes alerts to all your devices, no app or SDK required
Problem
Indie developers and small ops teams want the lightest possible way to notify multiple devices when a self-hosted service, systemd unit, or custom script fails. Existing options either require installing an app and configuring an SDK, or buying into a heavyweight monitoring platform. Many users just want a simple webhook endpoint, not an entire ecosystem for one notification.
Solution
A minimal push service: users claim a username and get a webhook URL. Any script can POST a message and all signed-in devices receive a native-style notification. No app install, no complex auth, no SDK. Works both self-hosted and hosted, with copy-paste examples for systemd, shell scripts, and cron.
Dynamically pick different cancellation questions based on user lifecycle and activity
Problem
Most SaaS cancellation surveys ask every departing user the same questions. Someone who churns after two months and someone who leaves after two years may both say 'too expensive,' but the real reasons are completely different—and a single survey can't capture that.
Solution
An embeddable cancellation flow component that picks different retention questions and options based on signup tenure, activity, plan, and recent behavior, then aggregates answers by segment. Subscribers see 'new user churn reasons' versus 'long-time user churn reasons' side by side.
Help small teams reconcile remote hours without spyware-style monitoring
Problem
After hiring their first remote hourly workers, small shop owners found self-reported timesheets didn't match reality—but they didn't want screenshot or screen-recording tools that feel like surveillance and damage trust. There's no middle ground between accurate hours and respectful tracking.
Solution
A lightweight time tracker: workers start and stop work sessions with one click, and the system records active windows and focus duration, showing aggregated trends rather than screen contents. Owners see reconciliation gaps and can ask for confirmation; workers see their own data. Transparent, not invasive.
Auto-scrape competitor job pages every Monday and decode what their hiring says about their next move
Problem
Small business owners and analysts watch competitor pricing and content, but almost nobody watches hiring. Yet hiring is the earliest signal: three sales roles plus a partnerships lead means a distribution push; a compliance hire means entering a regulated market. By the time it's announced, you're months late.
Solution
Users add competitor job page URLs and keywords. The system scrapes weekly, diffs added and removed roles, groups them by function, and emails a short interpreted summary. The value isn't scraping—it's translating job changes into 'what they might be doing next.'