Ideas Lab
Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
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Digital therapeutics for chronic disease management without medication
Patients with chronic conditions need affordable, accessible behavioral interventions to manage symptoms and improve outcomes. Digital therapeutics can reduce reliance on expensive drugs with significant side effects.
YC Graveyard9y agoToolMedicine & Health
Signet – Autonomous wildfire tracking from satellite and weather data
I built Signet in Go to see if an autonomous system could handle the wildfire monitoring loop that people currently run by hand - checking satellite feeds, pulling up weather, looking at terrain and fuels, deciding whether a detection is actually a fire worth tracking.All the data already exists: NASA FIRMS thermal detections, GOES-19 imagery, NWS forecasts, LANDFIRE fuel models, USGS elevation, Census population data, OpenStreetMap. The problem is it arrives from different sources on different cadences in different formats.Most of the system is deterministic plumbing - ingestion, spatial indexing, deduplication. I use Gemini to orchestrate 23 tools across weather, terrain, imagery, and incident tracking for the part where clean rules break down: deciding which weak detections are worth investigating, what context to pull next, and how to synthesize noisy evidence into a structured assessment.It also records time-bounded predictions and scores them against later data, so the system is making falsifiable claims instead of narrating after the fact. The current prediction metrics are visible on the site even though the sample is still small.It's already opening incidents from raw satellite detections and matching some to official NIFC reporting. But false positives, detection latency, and incident matching can still be rough.I'd especially welcome criticism on: where should this be more deterministic instead of LLM-driven? And is this kind of autonomous monitoring actually useful, or just noisier than doing it by hand?
Hacker News6mo agoToolAI
Thermal Receipt Printers – Markdown and Web UI
Hacker News6mo agoToolDesign & Creative
Oxyde – Pydantic-native async ORM with a Rust core
Hi HN! I built Oxyde because I was tired of duplicating my models.If you use FastAPI, you know the drill. You define Pydantic models for your API, then define separate ORM models for your database, then write converters between them. SQLModel tries to fix this but it's still SQLAlchemy underneath. Tortoise gives you a nice Django-style API but its own model system. Django ORM is great but welded to the framework.I wanted something simple: your Pydantic model IS your database model. One class, full validation on input and output, native type hints, zero duplication. The query API is Django-style (.objects.filter(), .exclude(), Q/F expressions) because I think it's one of the best designs out there.Explicit over implicit. I tried to remove all the magic. Queries don't touch the database until you call a terminal method like .all(), .get(), or .first(). If you don't explicitly call .join() or .prefetch(), related data won't be loaded. No lazy loading, no surprise N+1 queries behind your back. You see exactly what hits the database by reading the code.Type safety was a big motivation. Python's weak spot is runtime surprises, so Oxyde tackles this on three levels: (1) when you run makemigrations, it also generates .pyi stub files with fully typed queries, so your IDE knows that filter(age__gte=...) takes an int, that create() accepts exactly the fields your model has, and that .all() returns list[User] not list[Any]; (2) Pydantic validates data going into the database; (3) Pydantic validates data coming back out via model_validate(). You get autocompletion, red squiggles on typos, and runtime guarantees, all from the same model definition.Why Rust? Not for speed as a goal. I don't do "language X is better" debates. Each one is good at what it was made for. Python is hard to beat for expressing business logic. But infrastructure stuff like SQL generation, connection pooling, and row serialization is where a systems languag
Hacker News6mo agoToolDev
Voygr (YC W26) – A better maps API for agents and AI apps
Hi HN, we’re Yarik and Vlad from VOYGR (https://voygr.tech/), working on better real-world place intelligence for app developers and agents. Here’s a demo: https://www.youtube.com/watch?v=cNIpcWIE0n4.Google Maps can tell you a restaurant is "4.2 stars, open till 10." Their API can't tell you the chef left last month, wait times doubled, and locals moved on. Maps APIs today just give you a fixed snapshot. We're building an infinite, queryable place profile that combines accurate place data with fresh web context like news, articles, and events.Vlad worked on the Google Maps APIs as well as in ridesharing and travel. Yarik led ML/Search infrastructure at Apple, Google, and Meta powering products used by hundreds of millions of users daily. We realized nobody was treating place data freshness as infrastructure, so we're building it.We started with one of the hardest parts - knowing whether a place is even real. Our Business Validation API (https://github.com/voygr-tech/dev-tools) tells you whether a business is actually operating, closed, rebranded, or invalid. We aggregate multiple data sources, detect conflicting signals, and return a structured verdict. Think of it as continuous integration, but for the physical world.The problem: ~40% of Google searches and up to 20% of LLM prompts involve local context. 25-30% of places churn every year. The world doesn't emit structured "I closed" events - you have to actively detect it. As agents start searching, booking, and shopping in the real world, this problem gets 10x bigger - and nobody's building the infrastructure for it. We recently benchmarked how well LLMs handle local place queries (https://news.ycombinator.com/item?id=47366423) - the results were bad: even the best gets 1 in 12 local queries wrongWe're processing tens of thousands of places per day for enterprise customers, including leading mapp
Hacker News6mo agoToolAI
No way to block late-night impulse purchases via Apple Pay
Users make regrettable impulse buys late at night using Apple Pay and need an app to automatically block payments during off-hours like 10pm to 7am.
X6mo agoToolFinance
Inability to disable voice messages in WhatsApp
Users are frustrated with receiving unwanted voice messages in WhatsApp and desire a feature to block them, forcing senders to type a message instead.
X6mo agoToolProductivity
Messaging apps forcing unwanted animated reactions and AI suggestions
Professional communications are undermined by intrusive animated emojis and AI writing suggestions that appear automatically in text messages, making users look unserious.
X6mo agoToolProductivity
Lack of hospital affordability ratings in Ghana
Patients in Ghana lack information on which hospitals offer effective treatment for various conditions at affordable prices, leading to overpayment.
X6mo agoToolMedicine & Health
Difficulty sourcing small quantities of specific fabrics for costumes
Cosplayers and costume makers struggle to find and purchase small amounts (e.g., half a yard) of niche fabrics like white short shag from specific suppliers.
X6mo agoOtherDesign & Creative
No discovery platform for Claude AI code examples
There is no StumbleUpon-like service to randomly discover and explore code skills and examples generated by Claude AI.
X6mo agoToolAI
No stock market for trading YouTube channels
Fans and investors lack a platform to buy, sell, or trade fractional ownership in YouTube channels like stocks, to bet on creators' growth.
X6mo agoToolMedia
No world quest tracker for Genshin Impact
Players lack a reliable way to track completed and pending world quests in Genshin Impact, causing frustration for completionists.
X6mo agoToolOther
Lack of internships for founders in traditional companies
Founders miss out on practical experience inside non-startup companies, creating significant blind spots in understanding corporate operations.
X6mo agoOtherHR & Career
AI-generated code introduces bugs and security issues that traditional reviews miss
Development teams using AI code generation tools struggle with hidden bugs, logic flaws, and security vulnerabilities that slip through standard code review processes. As AI-generated code becomes common, reviewers lack specialized tools to catch issues unique to AI outputs, leading to buggy code reaching production and delayed releases. Teams either accept increased defect rates or spend significantly more time on manual reviews.
Product Hunt6mo agoToolDev
Knowledge workers lose productivity context switching between tools and manually briefing AI on their workflows
Knowledge workers spend significant time context-switching between tools, re-explaining their work processes to AI assistants, and manually setting up automations. Each AI interaction requires re-briefing on preferences and past decisions, creating friction that prevents people from delegating routine cognitive work. This repeated context loss compounds across the day, reducing the value AI could provide.
Product Hunt6mo agoToolProductivity
Designers are losing their bearings in the era of AI — it's unclear which direction to develop in, which stack to master, and how to become a «highly valuable specialist».
ProblemHunt6mo agoToolEducation
Missing supermarket promotion comparison app like Flipp
In Brazil, there's no app that aggregates promotions from multiple supermarkets, allowing users to shop only deals without inflated prices on other items.
X6mo agoToolRetail
Advanced resource management and scaling challenges in high-concurrency Rust applications
Struggles with vertical/horizontal scaling in Rust for async tasks, WebSockets, API polling, DB sync; bottlenecks in networking, CPU affinity, memory per thread, without premature optimization.
X6mo agoArticleDev