Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
People struggle to find and connect with other furries in their local area through existing apps or communities.
X1mo agoToolDating & Community
Users seek a dating app that acts as an intermediary between Tinder's openness and Raya's exclusivity, avoiding common issues with Tinder.
X1mo agoToolDating & Community
Users managing multiple Outlook accounts face friction switching between them. A unified dashboard tool could consolidate inboxes, enable cross-account rules, and streamline notifications. [Google Search suggestion]
Google Trends1mo agoToolProductivityUS
Many fitness enthusiasts struggle to log and track daily macros (protein, carbs, fats) across fragmented apps. A lightweight tool could aggregate logging, show trends, and sync with popular fitness platforms. [Google Search suggestion]
Google Trends1mo agoToolSport & FitnessUS
Building and deploying custom AI models requires deep expertise, expensive compute resources, and complex infrastructure management. Many teams struggle with model optimization, inference costs, and operational overhead.
YC Graveyard2y agoToolB2B SaaS
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
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
Compare and book cheaper moving alternatives to U-Haul (peer-to-peer movers, local services, storage). Aggregates pricing and availability. [Google Search suggestion]
Google Trends1mo agoToolLogistics & DeliveryUS
Curated comparison and recommendation engine for smartwatch alternatives to Apple Watch for iPhone users, helping filter by price, features, and compatibility. [Google Search suggestion]
Google Trends1mo agoToolE-CommerceUS
Users struggle to create complex automation sequences for smart home devices. Most platforms require technical knowledge or extensive manual setup. A visual tool could simplify building multi-step routines. [Trending: "amazon alexa" with 500+ searches in BR]
Google Trends1mo agoToolProductivityBR
Homeowners lack real-time visibility into rate changes and struggle to time refinancing decisions. A tracker with alerts could notify users of favorable windows for action. [Trending: "mortgage rates" with 200+ searches in GB]
Google Trends1mo agoToolFinanceGB
Aggregated directory/comparison of free alternatives to Doodle Poll (When2Meet, Rallly, custom solutions) with feature matrix and ease-of-use ratings. [Google Search suggestion]
Google Trends1mo agoToolProductivityUS
Hacker News1mo agoToolOther
If not, why not?That way, people can choose whether they want to install various software betas based on how much SSD wear they are willing to accept.And since this data would be made public and available in one place, software developers would be more careful about excessive logging in their betas.
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
Hacker News1mo agoToolOther
hey HN, Jonathan and Guy here, creators of OneCLI (https://onecli.sh/). OneCLI is an open source vault for AI Agents.Traditional vaults are used to store your secrets and, on demand, provide them to you all in a secure way, trusting the person to keep them safe. We figured that in the agent's world, this is not the case, as you don't know what happens with the secret after it's delivered to the agent, or where it was saved. Or maybe someone even manipulated them to hand them over...From that understanding, we decided to build a network gateway that sits between your AI agents and the services they call. OneCLI matches the request by host/path, verifies the agent should have access, swaps the placeholder for the real credential, and forwards the request. the secrets set inside the OneCLI vault, encrypted on rest, or could fetch in realtime from your bitwarden / 1password wallets.Demo - https://www.youtube.com/watch?v=5e5pbPEzZfY.We started working on this by accident, even though our careers were in the security space. We were working on a devtool called ChartDB, an open-source DB tool. When OpenClaw took off back in January, we started using it to orchestrate agents on top of ChartDB. We quickly understood there is a big issue around auth. Agents need credentials to do real work, but to give them those secrets would not be the best idea. they keep them in their memory and also write them down to local files and their sessions as plain text. And we knew that agents can easily be fooled into giving up those API keys/secrets. So we needed some way to control the agent and stop prompt injections from tricking it into using its services for an attacker's benefit. Not providing the keys to the agent + adding alerts or human-in-the-loop for sensitive operations, in case someone manipulates the agent, and access logs are being audited.We use it ourselves daily. My OpenClaw runs my day across Attio, Gmail, and
Hacker News1mo agoToolAI
Hacker News1mo agoToolOther
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 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