Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
AI cannot reliably handle repetitive tasks requiring great detail and precision, leading to poor results and forcing manual work instead.
X1mo agoToolAI
Hi everyone, 1 I've run into the same problem over and over again. When an item wears out, I can't buy the exact same one anymore. It's been discontinued. Or it has been changed while keeping only the same name.2 I assume this isn't only my problem. I've heard many stories about people not being able to buy the same shoes, T-shirt, perfume, or jacket again.3 I understand why manufacturers do this. I have no questions for them. What matters more to me here is us — customers, or simply people. And I started wondering: what could be done to support a customer's choice for years?4 This is where I want to bring concepts from software and hardware engineering into the clothing industry — LTS and APIs. What if we freeze the standard and support it? We already have things like SATA or CPU sockets. Why not build a similar concept for clothing? Then the API at the point of contact with the customer is divided into a physically immutable part (color, material, design) and a flexible part (logistics, ingredients). Let's start with the simplest base — a T-shirt. Why not give it LTS support? The idea is that the exact same T-shirt could be sold for 1, 5, or 20 years.5 Where does my logic break down? What am I overlooking? Are there any similar projects? And would you become a customer of such a company?Thanks.
Hacker News1mo agoToolAI
I was recently laid off after 18 years, and gave myself 100 days to build soething useful in public. CheapFoodMap is a crowdsourced map of meal under $10, excluding franchises, local good eats only.It's inspried by 거지맵 (Begger's Map) a Korean crowdsourced map students use to find cheap eats.Ocverage is heaviest in Texas, since I live in Dallas, but have 1200 meals across 15 US cities. Seed data came from Google Review, 4.2 star or higher with at least 500 reviews, and verified price under $10 per menu item.Things I would love feedback on : whether the price-freshness model makes sense, and what would make you trust the price on a site like this. How to encourage people to update prices, since inflation is making food price very frequent.https://cheapfoodmap.comAny and all suggestion will be super helpful. Thank you!
Hacker News1mo agoToolAI
Hi HN, Rohit here from Tokenless (https://usetokenless.com/), which I’m building alongside co-founders Andrew and Kev. We’re building an API gateway which routes agent traffic dynamically turn-by-turn between different models to save on AI spend.The cost of AI tokens is top-of-mind for many. Companies like Uber and Salesforce have been complaining about blowing their yearly AI spend faster than expected.Frontier models are amazing for dev work, but are so expensive. Open-source models are cheap and rapidly improving, closing the gap with frontier models, but aren’t quite there yet.Tokenless gets you the best of both worlds–routing harder turns to smarter models only when needed, which keeps costs low.Before Tokenless, I was doing a PhD at Princeton. While using coding/other agents, I constantly agonized over model choice, to make sure my AI spend was going as far as possible on my academic Cursor account.At the same time, I was doing LLM research, and a small technique I developed while in recovery from NeurIPS submission season seemed to hit SOTA pretty fast. I was surprised that such simple ideas could do routing well.We’ve been able to develop a version of the router that matches the performance of Claude Fable 5 at half the cost. The blog post on our website explores the technical details on how we did this (https://usetokenless.com/blog/building-tokenless/).Highlights: - Our approach queries multiple models at once and uses their progress to make decisions (this technique is novel AFAIK, let us know if you know anyone else doing this). - Switching models doesn’t destroy the cache if the routing algorithm is aware of when the cache is hot/cold.To come: - Adding Kimi K3, all other GPT efforts and more to the routerGo ahead and sign up on usetokenless.com and try using Tokenless with your agent, you’ll get $20 of free credit. Here’s a demo on how to use it: https://youtu.be/sjZWriclclsTokenless provi
Hacker News1mo agoToolAI
Hacker News1mo agoToolAI
HN is great for the links people share, but a big part of the value I get comes from reading the discussion around them. I realized I was always opening the article in one tab and the comments in another, constantly switching back and forth.I figured there was probably a simpler way, so I threw together this userscript to merge the two.1. Clicking a link from Hacker News opens the article with a side panel containing the discussion. It doesn't require your credentials, is resizable, and is easy to tweak if you want to customize it.2. If you land on an article that has previously been shared on HN, the script finds the existing discussion and adds a button in the top-right to open the panel.Feedback welcome.
Hacker News1mo agoToolAI
Hacker News1mo agoToolAI
Hey HN! I wanted to share this OSS project I've been working on.It's called Yap and its a small menu-bar app for macOS that does voice to text for any input. You'll set a hotkey, press it, talk, press it again, and the text gets pasted into whatever field you were in. Everything runs locally and never leaves your computer. Fully OSS and MIT licensed.With macOS 26, Apple recently added two new APIs, SpeechAnalyzer and SpeechTranscriber, that do streaming on-device speech to text using models the OS ships and manages. So the app ships no model of its own and loads nothing before the first word. A recent benchmark put Apple's model slightly ahead of Whisper Small on accuracy and about 3x faster (see: https://news.ycombinator.com/item?id=48894752). On Mac, there's really no need anymore to download models or pay for expensive APIs.A lot of existing dictation tools do one of a few things I wanted to avoid with this OSS project. They either:- cost money (for something that's literally built into the OS)- bundle memory-intensive models (e.g. Whisper or Parakeet)- webapps wrapped in Electron- Intel macs straight up don't work- closed source- use third-party APIs that will have access to all your transcriptsIt's around 3,000 lines of native Swift in a 4 MB app and idles near 60 MB of memory. Audio comes off AVAudioEngine into SpeechAnalyzer with volatile results turned on for the live preview, history is stored in SwiftData. There's no network code in it at all.Repo and a demo available here: https://github.com/FrigadeHQ/yapHappy to answer questions and would love to hear any feature requests!
Hacker News1mo agoToolAI
Hi HN! This is George, Lucas, and Jona from Rise Reforming (https://www.rise-reforming.com/). We’re developing a process to convert gas produced at landfills, farms, and wastewater plants (“biogas”) into higher value chemicals. Our technology is modular, designed to be deployed and operated on-site. Think of us as a chemical project developer; we sit between biogas producers (suppliers) and chemical end users (customers). We pay biogas producers for their gas and we make money from selling our chemicals. We're starting with dimethyl ether (DME) as our beachhead chemical because of its high-margin use case in the cosmetics industry and ultimately targeting methanol – a versatile and widely used industrial chemical.Being in a two sided market allows us to target two large problems.(1) On the chemical side: The multi-trillion dollar U.S. chemical and fuel industries are vulnerable to geopolitical conflicts and climate-driven natural disasters. The Iran war has caused global methanol prices to skyrocket – even in the U.S., a net exporter of methanol. (https://www.spglobal.com/energy/en/news-research/latest-news... the US). In 2021, Winter Storm Uri wiped out 60% of U.S. organic chemicals production for at least a month (https://www.dallasfed.org/research/swe/2021/swe2102/swe2102c...). The problem? Centralized production and fossil-fuel dependence. The solution isn't unknown; decentralized, fossil-free production could insulate supply chains from these shocks. But distributed green chemical production has yet to become cost-competitive with the status quo. Unlocking it requires the right feedstock paired with the right process and strategy.Also, the chemical industry’s reliance on fossil fuels makes it responsible for 5-6% of global greenhouse gas emissions. About 40% of the industry’s well-to-gate emissions come from just the extraction, processing, and transportation of these foss
Hacker News1mo agoToolAI
TLDR, Let's Seal gives the finger to Adobe and every doc signing tool (docusign, google, etc) who pay to play with the Adobe Approved Trust List and then charge you for something that should be free.Currently even the person checking if a document/contract is sealed or code is authentic has to also be inside the same Adobe walled garden too. Verification, the part that should be free is the part everyone charges for. Thats the shape Let's Encrypt fixed for TLS, and I wanted the same thing for documents and files.The core idea therefore needed to go a bit beyond e signatures and i created an open standard (SEAL), plus free tools that implement it.When you seal a file, three independent things happen.1. it gets a signature from a certificate authority, chaining to a public root. 2. its record is appended to an RFC 6962 transparency log. and 3. its SHA256 is timestamped on a public blockchain (Bitcoin) via OpenTimestamps. Those three give you integrity, transparency and a timestamped proof. And importantly, none of those depend on Let's Seal and none are gated.You can verify with the tools you already have, no Let's Seal account and no Let's Seal software. A sealed PDF carries a standard PAdES signature, so any PDF reader validates it. A sealed build artefact carries a cosign compatible signature and a SLSA provenance attestation. The Bitcoin timestamp verifies with stock ots.3 ways to use it.1. The free web app. We kindly have backing from Backblaze to cover storage costs for the foreseeable. So you can upload or issue any number of documents, get a public proof page at /d/<hash> and verify it at https://verify.letsseal.org for free. Multiple accounts, multiple seats, enterprise functions. Free.2. Self host the whole thing. Apache-2.0, one Next.js app plus a signing service that holds the CA key on localhost. Storage is any S3-compatible bucket or local disk. If you'd rather run your own root of trust, yo
Hacker News1mo agoToolAI
The UK MET office recently redesigned their site, adding a lot of additional whitespace, scrolling, and animations. This significantly reduced its usability for me, and left me wanting an ‘at a glance’ weather site.I made https://brolly.sh, a minimalist, plain text weather forecasting site. You can use it to view weather from around the world, with: 7 day forecast; Previous day log (so you can confirm it definitely was cooler / hotter / wetter / drier yesterday!); Hourly rain, wind, temperature, conditions; Hourly UV, air quality and pollen, including pollen type specific forecasts within the EU / UK; Location search and last 5 locations; Location specific units.I mostly made the site for myself, if anyone else also benefits from it that’s an added advantage.You can check out the weather in York, UK at https://brolly.sh/forecast/RWFP2qW8, or search for a location at https://brolly.shThe site is deliberately styled as a single long scrollable column, to work on mobile phones. You can view it on desktop too, there's just a lot of horizontal padding. I naturally took a lot of inspiration from plaintextsports.com. Despite not being a sports fan, I love its aesthetic. But, you'll hopefully see that this site isn't a rip off, and has its own deliberate look and feel.Visualisations are really important to showing information at a glance. I spent a lot of time designing the different visualisations and making them work with only characters. My favourite is the hourly heat map used for pollen count.It's also frustrating to have an interactive site, where you can't share a page with a friend and have them see what you're seeing. To solve this, all page state (i.e. location, selected day, expanded / collapsed sections), is stored in the URL. You can share or bookmark the specific view, and know that you'll always be able to come back to it.The site uses PocketBase. It’s written
Hacker News1mo agoToolAI
Hi HN, we built world-model-optimizer, an open source tool to continually improve models specialized to agents.Agent traces you already capture are opportunities to get signal on how to make your model cheaper, faster, better.We do this by continuously a) distilling relevant chain of thought from larger open source models into smaller ones, b) model routing to frontier + OS models, and c) token compaction to remove noise and save on tokens.`wmo build` allows you to build a simulation to optimize against with your agent traces with your OpenRouter key`wmo optimize` trains a router, compaction, and distills chain of thought from a larger model into your specialized model`wmo serve` gives you an endpoint for your modelWhen you call your model, behind the scenes a router decides which tasks should go to the frontier versus your model. Tinker continually trains as new traces arrive.We're also working on a hosted solution that does continual training + serving for you https://experientiallabs.ai
Hacker News1mo agoToolAI
After hitting high MAU milestones like 1M, AI apps experience significant drop-offs as retaining active users proves harder than acquiring them initially, requiring different strategies for sustained engagement.
X2mo agoToolAI
I like writing Markdown, but do not like writing it inside a plain textarea.I wanted something I could use anywhere by dropping in a single web component:```<writemark-editor name="body"></writemark-editor>```That became Writemark.It renders Markdown while you write, but Markdown remains the value you read, store, and submit. It also has source, split, and preview modes, along with slash commands, tables, task lists, code blocks, native form support, and an API for adding your own controls. There are no runtime dependencies and no required framework or built in toolbar.It is fully vibecoded. The process was very iterative. I knocked something out, tried using it, found bugs, fixed them, and repeated that cycle until I had something I liked writing in and that performed reasonably well.It did not begin as an experiment about AI generated software -- just did not want to use textareas anymore. I like Markdown, and I wanted one component that I could use anywhere without bringing along an entire editor framework.It is still very young. The parser is handwritten, the component is essentially one large JavaScript file, and I am certain there are edge cases waiting to be found. If you find one, I am happy to fix it.I have tried to give it a decent safety net. It currently has 951 Playwright checks across Chromium, Firefox, and WebKit, along with hostile input cases, sanitizer fuzzing, and differential tests against CommonMark.I built this because I wanted it for myself, but I think it turned into something kind of cool. I hope some of you enjoy it. I would love hearing what you think, especially if you try it and manage to break something.- Github: https://github.com/Brostoffed/writemark- NPM: https://www.npmjs.com/package/writemark-editor
Hacker News1mo agoToolAI
I got tired of Googling basic course info, so I made a free directory of every US courseGoogle filters for golf course results are terrible so I built a better way to browse courses using OSM as the backbone.... https://golfcoursebrowser.com/It's a work in progress and mostly US for now, but I want to expand to the rest of North America, the EU, and the rest of the world ASAP.It's free, no ads, no login, no bs. If you spot anything wrong (bad info, a missing course, wrong scorecard), you can flag it right on the course page. I actually read those and fix them.Still a lot of missing info, but I'm filling out more and more each day. The goal is the most complete, and current golf course directory in the world, verified and maintained by actual golfers. I think is attainable in the next few months. The base layer is OpenStreetMap, cross-checked against course websites and enriched with scorecards, USGA rating/slope, and public/private status. User corrections from golfers who know their home course have beaten every commercial data source we check against.
Hacker News1mo agoToolAI
Hey hn, this has been something I've been working on for the last few months and is finally robust enough to really show off.I've been pretty tired with the design outputs of LLMs for a while, and I've always thought diffusion offered much more creative / on brand design outputs, even before they were able to render text.I had enough conviction for this to leave my role over at Figma to build Diffui. The goal is to allow for you to design your full web app as quickly as possible, in a figma-like interface, and then hand that off to an agent to build. The page shows some interactive examples. Happy to be an open book here and chat about the approach, the process, etc!
Hacker News1mo agoToolAI
Trifle is an open-source time-series analytics library that aggregates nested counters instead of storing raw events. All in the database you already have. After rebuilding it twice over 10 years, it now tracks ~1B events a day at my day job.It started in 2015 as my own Rails APM. I plugged into ActiveSupport::Notifications, got a few small users, and one bigger one whose scraping app broke everything. That sparked the core idea: aggregate counters into pre-defined time buckets, so a single write increments multiple buckets at once. The APM eventually faded away without much traction.Later in 2021 I needed analytics at my day job. Instead of going for something out there I revised the idea of Trifle as a more generic analytics library, borrowing some data warehouse ideas. First used Redis, then Postgres, eventually MongoDB. Hence why Trifle::Stats comes with multiple drivers that keep the DSL unified while storage layer changes with your needs. In our case (huge write volume, some reads) PG read faster but slowed on large writes.The nested values are the whole trick here. Single: Trifle::Stats.track( key: 'requests::aws::s3_uploads', values: { count: 1, status: { request.response_code => 1 }, size: payload.bytes, duration: { sum: request.duration, count: 1 } } ) builds up counts for requests, success rate, result status codes, duration for multiple time buckets at once. Single bucket from 2am then looks like: { count: 14, status: { 200: 12, 500: 2 }, size: 5628341, duration: { sum: 43, count: 14 } } If request.duration is in seconds, then sum stored under duration would be in seconds as well.Success rate is never stored, but it is calculated by dividing 200s over total number of requests. Same with average duration: sum over count. You ask for a metrics key, granularity and timeframe and you get back aggregated values at each point. Ready for charts or to answer "Average response time over last 30 days&qu
Hacker News1mo agoToolAI
Hi HN, we are Marcos and Harrison, cofounders of Palmier (https://palmier.io). We are building Palmier Pro, an open source macOS video editor, with built-in AI generation and a local MCP server that connects to your agent. Here are a few demos:- Making some AI transitions: https://www.youtube.com/watch?v=hbM_-eR1GX4- Multicam editing with Codex: https://www.youtube.com/watch?v=SjS2q2LT1q8- Cutting long form clips into shorts: https://www.youtube.com/watch?v=PR66eN2ouuQWe built Palmier Pro as an internal tool when we were making AI launch videos for other startups. The main problem it solved in the beginning was the back-and-forth between AI generation platform and video editor. The iteration loop was awkward: AI videos → download → import to editor → edit → realize we need to change the AI video → repeat. So we built a minimal video editor where we could let Claude generate AI videos inside the editor.As we gave more and more tools to the agent, we wanted to push to see what else agents can do in the video editing space. So today, your Claude/Codex can:- Manage projects inside Palmier Pro- Import media from a public URL or filesystem to the project, and organize them in folders- Search media (by embedding footages using SigLIP2 running locally)- Edit the timeline (tracks/clips/keyframes operations)- Generate images, videos, sound effects, captions, music- Export videosThere are two ways for LLMs to interact with the editor: by connecting to the local MCP server, or using the in-app chat. Both use the same tools and APIs exposed by the video editor.We have seen people using MCP server to connect to their own workflow to automate massive-scale video editing (e.g. given this same podcast style, replicate it with other footages that I have). We have also seen people using the in-app chat where it lives closer to the editor UI, with lower latency for faster iteration.We don't believe that AI is go
Hacker News1mo agoToolAI
I’ve been building Echo (https://echo.tracerml.ai/), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task.It started with a simple experiment. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined.That hypothetical system performed substantially better than any individual model in the pool. Of course, it is not something you can actually deploy because it relies on knowing which decisions were good after seeing the result. Echo is my attempt to recover some of that advantage without having that information in advance.For each request, Echo decides how much computation to allocate, which models should participate, and how their work should be combined. Some prompts may only need a relatively small amount of inference, while others benefit from multiple models working on different parts of the problem.One thing that surprised me while building it was how complementary the models are. A model that is clearly weaker overall can still be extremely useful on particular problems or as part of a combination.On my first evaluation mix, Echo consistently performed better than the best individual model in its pool. It also reached roughly the same aggregate result as Fable, which I used as one of the stronger comparison systems, at around one third of the inference cost.There are still some cases where Echo makes the wrong allocation or combination decision. I’m currently spending a lot of time understanding those failures, as well as testing whether the same approach holds up on coding and agentic tasks where measuring the quality of each decision becomes much harder.I built a chat interface (echo.tracerml.ai) and an OpenAI-compatible API (https://echo.tracerml.ai&#x
Hacker News1mo agoToolAI
Hi Hacker News, I'm Louis. I built Screenpipe (https://screenpipe.com), an app that records your screen and audio locally (only!), and gives AI agents a searchable memory of what you've seen, said, and heard. This makes it easier to automate your repetitive tasks, turn them into SOPs (Standard Operating Procedure) and so on.I made a HN-style demo video at https://www.tella.tv/video/build-your-ai-second-brain-with-s... and there’s a marketing video at https://www.youtube.com/watch?v=c1jV6E9pyug.I’ve been obsessed with this for a long time. I’ve been maintaining a “second brain” since 2020, in which I would store journals, handwritten notes, music I listen to, projects I'm working on, conversations I have with people, personal CRM etc. I experimented a lot of RAG in the early days with ParlAI, hundreds of fine-tuned GPT2 models, and GPT3 (https://forum.obsidian.md/t/fine-tuning-openai-api-gpt3-on-y...). Later I built Ava, the first Obsidian AI plugin, which grew to a few thousands of users quickly. It then became Embedbase, an API to make it easier to build AI apps powered by RAG.What I learned from all this is how important it is for the models to have context about what you’re doing on your computer, in order to get them to do what you want.In the early days there was fine tuning but it was too much pain, then there was tool calling so that AI can access software you use but still kinda not autonomous enough. needing micro management. Then MCP came, but it felt too static, and non technical users struggled to build and use MCP. Then we got skills. Most recently we’ve seen Karpathy’s LLM-maintained wiki, Garry's GBrain, etc., where an agent incrementally maintains a persistent collection of Markdown pages. New sources update entity pages, strengthen or contradict existing claims, and improve a synthesis that compounds over time. I like this pattern, but it still begins with someone s
Hacker News1mo agoToolAI