Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
Creators need a prettier, more customizable link-in-bio page specifically tailored to showcase Letterboxd and Spotify profiles, without using generic Linktree.
X2mo agoToolDesign & Creative
Clinics, real estate agencies, pest control services, and accountants routinely miss or delay responses on WhatsApp and follow-ups, causing lost customers and revenue. People pay to eliminate this specific pain rather than for generic AI.
X2mo agoToolBusiness
Users seek cost-effective alternatives to Sonos speakers that offer similar quality and smart features. Current options are fragmented or expensive, creating demand for a curated comparison and recommendation tool. [Google Search suggestion]
Google Trends2mo agoToolHardwareUS
Consumers want cheaper alternatives to the BedJet climate control system for beds, but options are hard to compare easily. A tool aggregating alternatives with features and pricing could help users make informed decisions. [Google Search suggestion]
Google Trends2mo agoToolHardwareUS
Companies and campuses need reliable, internal last-mile transportation for employees and goods. Current solutions (scooters, bikes) lack reliability, maintenance standards, and integration with enterprise logistics. Organizations want white-label micro-mobility without managing fleets.
YC Graveyard8y agoToolLogistics & Transportation
Inventory and asset tracking systems require physical tags (RFID, NFC) which are expensive, require maintenance, and don't scale across supply chains. Businesses need low-cost visibility into product location and authenticity without adding hardware.
YC Graveyard9y agoToolLogistics & Supply Chain
Existing STT models struggle with accurate diarization and chunking after initial use, making it difficult to create reliable searchable memories from conversations or recordings.
X2mo agoToolAI
AI agents require a shared platform (like a 'waterpark') where they can post in-progress projects, connect with other agents, and exchange help or feedback during development.
X2mo agoToolAI
Many founders have great products but face major challenges in getting high-intent buyers to discover, trust, and engage with them through effective channels like Google or AI search.
X2mo agoToolMarketing & Sales
Cold email outreach focuses only on getting attention but fails to provide tangible proof of value upfront, making it hard to convert prospects without building and showing a custom prototype first.
X2mo agoToolMarketing & Sales
Hacker News2mo agoToolOther
Hey! I'm Andrei.I got frustrated by how people tend to build overcomplicated backend systems, being "motivated" by big tech case studies and popular books.So, I started exploring lean architecture, and building my digital garden of ideas, approaches and data that align with this direction.Here I want to present one of the tools – Sizing tool for PostgreSQL. I've benchmarked PostgreSQL on different EC2 instances and disks, with different initial data sets to see performance that these instances can give you. And I've built a tool to visualize this data, which I welcome you to explore.So, you can put your usual input parameters, like needed RPS and disk size as input, and find out which instance will be the most cost-efficient for your needs.You can read about the methodology here: https://postgres.saneengineer.com/aboutI've tested one workload – mixed 90/10 read/write, and only selected configurations. But it is extensible, and I (and you – benchmark is open source: https://github.com/anivaniuk/sanebench) can run more configurations to have more data represented.Does it look interesting? What workload should I benchmark next?
Hacker News2mo agoToolDesign & Creative
Devthropology is a passion project built on top of GitHub pull data. The name is a play on developer anthropology. Pull request data can be cut a lot of ways. The functionality has been built out of curiosity as I want to see different insights into codebases that I work on. Some of the data is typical and other parts I haven't seen elsewhere.I think of this as an improved GitHub Insights page, with faster performance, more detail, and a focus on how work moves through a codebase. The main entity is a contributor, which has two sides: authoring PRs and reviewing/giving feedback to others. From there, you can see repository wide stats, user interactions, contribution trends, file health, and collaboration patterns. Some insights are useful for understanding velocity and code health in the AI era.Details for each page:- Homepage: A high level summary of the repository. Showing age, file types, active contributors, new and churned users. I track the author age at merge, so you can see the tenure of people shipping changes over time.- File explorer: One of my favorite parts. I build a graph of files, tracking renames and moves, to build a complete history. Rolling up, every file and folder is assigned an outlook such as active, developed, stale, touched by people who are likely gone. You can easily see contributor timelines, recent changes, and for some files, their rename/move history and related files that often change together (useful for a coding agent).- Trends: The densest page, showcasing the velocity of contributions and trying to understand if AI is helping ship more. Charts are cut by year for comparisons, tracking PR size, output, rounds of review, and approval latency by different percentiles. PRs are further cut into bucketed sizes to help drill in deeper. Helps to show that smaller PRs are likely still faster to ship while very large PRs (product of AI?) are slowing down.- Relationships: A graph of interactions between contributors, weighted
Hacker News2mo agoToolAI
Hacker News2mo agoToolAI
There is a mobile game called DragonBox. It sort of tricks you into learning algebra by starting with very abstract manipulations of a puzzle that must follow rules... gradually the game teaches you more and more rules and also strips out the more abstract elements until on the last levels you are finally solving real equations. I loved it, it taught my kids algebra.... and it was just fun.Over the years I often thought that there should be a calculator for Algebra that works this way... something where you can drag terms around and cancel & distribute with gestures, but most importantly enter your own problems. It should also do more kinds of problems than DragonBox allowed. So I finally decided to build it.https://dicroce.github.io/wyrm/home.htmlHere's a video showing it: https://www.youtube.com/watch?v=_STbS4zvIlU. If you'd rather just play with it: there's a limited in-browser demo (real engine, a few example equations, no download) on the landing page — https://dicroce.github.io/wyrm/home.html.The app can be found on iOS (https://apps.apple.com/us/app/wyrm-math/id6782342042) and as of this week on Google Play (https://play.google.com/store/apps/details?id=com.dicroce.wy...).I also decided to open source the underlying math engine so others could build on it: https://github.com/dicroce/wyrm_math. My goal for the engine btw is to build it all the way up to Calculus.Monetization is deliberately boring: the engine is free (MIT), and the polished gesture app is $4.99 once. No subscriptions, ads, accounts, or analytics.I'd love feedback on the engine design — especially from anyone who's worked on CAS or proof-assistant-adjacent problems. And if you played DragonBox as a kid and wished it went further: this is for you!
Hacker News2mo agoToolAI
It’s been 10 years since we had the last leap second and it looks like we will get the first negative one soonish. Are systems ready for that?
Hacker News2mo agoToolOther
I started leaning in on AI heavily this year, as I wanted to get more done autonomously, but then my token usage climbed dramatically to the point where my weekly quota would run out before the end of the week, sometimes a couple of days into the week.I realised I had to do something about it else I'd have to double my spend. So I decided to start tracking my cost per task type. This revealed that a lot of my spend went to searches/scans or simple things like scouting tasks.I then decided to turn this into a simple CLI tool that can be used to read your OpenAI-style logs locally, and analyze the cost and compare this spend to other models, then show you how much you could potentially save by switching those calls to a cheaper model.When you run analyze you get an offline estimate priced against LiteLLM and gated by LMArena tiers. The general savings bands come from the research published by RouteLLM; but you can confirm this yourself using 2 commands --measure (shows the prompt-response output side by side) and --judge (a model chosen to do the comparisons). These send a sample of the prompts from the logs to the candidate models - either the default choice or set by you. This call goes directly to the model provider (never through me) as any normal LLM call would, and the response is shown and judged to either be better or worse or a tie.It's deliberately small, because I tend to over complicate/think things sometimes: analyze + capture + a few commands, doing three jobs. Cost, quality visibility, routing recommendation.Nothing is hosted. capture is an optional local proxy on your own machine, and there's no endpoint in the path of your data. You can confirm this by checking the source.I included a demo so you can check out the output. It has a synthetic 56k call log (a month's worth) showing how costs can drop from $549.46 to $343.91 a month. A 37.4% saving.Try it: uvx frugon analyze --demo or uv tool install frugon Then point it
Hacker News2mo agoToolAI
Hey HN! We built a browser-based agent that runs inside an authenticated web app, watches how the app calls its own APIs, and automatically turns those into agent tools. You can think of it as an auto-generated MCP server that self-updates as the host app changes.The result is a skilled AI assistant that actually integrates deeply with any product (not just chat and RAG) with minimal effort.Check out these short demos below that show the agent in software you're probably familiar with:- Jira: https://demo.frigade.com/hn?skill=jira- Spotify: https://demo.frigade.com/hn?skill=spotify- Hacker News (lol): https://demo.frigade.com/hn?skill=hackernews- Full Demo: https://demo.frigade.com/hn?skill=full-demoAs you can see in the examples, you can do way more (and faster) than what you normally would be able to via point and click. And we never even touched the source code of these products!Why do this?In an ideal world, every application has an MCP server or an easily-digestible API available for AI agents to feed from. In practice, we found that even very modern software tends to have a spider web of confusing APIs and services that AI agents simply cannot use out of the box. Security also becomes a huge issue as applications have different (often homebrewed) standards for how endpoints are secured (JWTs/cookies/mix of both). Finally, having an actual browser agent go in and use the application on behalf of the user (i.e. computer-use), is simply too brittle, slow, and burns a lot of tokens.We took our existing browser agent that’s already trained to use and learn authenticated applications, and added an extra step that automatically turns the app’s authenticated APIs into "recipes". A recipe is a mix of the following:- API endpoint + method- Authentication method (and how to retrieve refresh auth tokens/cookies)- Response schema- Input schema (for POST/PUT)- Human readable descr
Hacker News2mo agoToolAI