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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- 22h ago
OneCLI – Vault for AI Agents in Rust
We built OneCLI because AI agents are being given raw API keys. And it's going about as well as you'd expect. We figured the answer isn't "don't give agents access," it's "give them access without giving them secrets."OneCLI is an open-source gateway that sits between your AI agents and the services they call. You store your real credentials once in OneCLI's encrypted vault, and give your agents placeholder keys. When an agent makes an HTTP call through the proxy, 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 agent never touches the actual secret. It just uses CLI or MCP tools as normal.Try it in one line: docker run --pull always -p 10254:10254 -p 10255:10255 -v onecli-data:/app/data ghcr.io/onecli/onecliThe proxy is written in Rust, the dashboard is Next.js, and secrets are AES-256-GCM encrypted at rest. Everything runs in a single Docker container with an embedded Postgres (PGlite), no external dependencies. Works with any agent framework (OpenClaw, NanoClaw, IronClaw, or anything that can set an HTTPS_PROXY).We started with what felt most urgent: agents shouldn't be holding raw credentials. The next layer is access policies and audit, defining what each agent can call, logging everything, and requiring human approval before sensitive actions go through.It's Apache-2.0 licensed. We'd love feedback on the approach, and we're especially curious how people are handling agent auth today.GitHub: https://github.com/onecli/onecli Site: https://onecli.sh
Hacker News6mo agoToolAI
Understudy – Teach a desktop agent by demonstrating a task once
I built Understudy because a lot of real work still spans native desktop apps, browser tabs, terminals, and chat tools. Most current agents live in only one of those surfaces.Understudy is a local-first desktop agent runtime that can operate GUI apps, browsers, shell tools, files, and messaging in one session. The part I'm most interested in feedback on is teach-by-demonstration: you do a task once, the agent records screen video + semantic events, extracts the intent rather than coordinates, and turns it into a reusable skill.Demo video: https://www.youtube.com/watch?v=3d5cRGnlb_0In the demo I teach it: Google Image search -> download a photo -> remove background in Pixelmator Pro -> export -> send via Telegram. Then I ask it to do the same for Elon Musk. The replay isn't a brittle macro: the published skill stores intent steps, route options, and GUI hints only as a fallback. In this example it can also prefer faster routes when they are available instead of repeating every GUI step.Current state: macOS only. Layers 1-2 are working today; Layers 3-4 are partial and still early. npm install -g @understudy-ai/understudy understudy wizard GitHub: https://github.com/understudy-ai/understudyHappy to answer questions about the architecture, teach-by-demonstration, or the limits of the current implementation.
Hacker News6mo agoToolAI
IonRouter (YC W26) – High-throughput, low-cost inference
Hey HN — I’m Veer and my cofounder is Suryaa. We're building Cumulus Labs (YC W26), and we're releasing our latest product IonRouter (https://ionrouter.io/), an inference API for open-source and fine tuned models. You swap in our base URL, keep your existing OpenAI client code, and get access to any model (open source or finetuned to you) running on our own inference engine.The problem we kept running into: every inference provider is either fast-but-expensive (Together, Fireworks — you pay for always-on GPUs) or cheap-but-DIY (Modal, RunPod — you configure vLLM yourself and deal with slow cold starts). Neither felt right for teams that just want to ship.Suryaa spent years building GPU orchestration infrastructure at TensorDock and production systems at Palantir. I led ML infrastructure and Linux kernel development for Space Force and NASA contracts where the stack had to actually work under pressure. When we started building AI products ourselves, we kept hitting the same wall: GPU infrastructure was either too expensive or too much work.So we built IonAttention — a C++ inference runtime designed specifically around the GH200's memory architecture. Most inference stacks treat GH200 as a compatibility target (make sure vLLM runs, use CPU memory as overflow). We took a different approach and built around what makes the hardware actually interesting: a 900 GB/s coherent CPU-GPU link, 452GB of LPDDR5X sitting right next to the accelerator, and 72 ARM cores you can actually use.Three things came out of that that we think are novel: (1) using hardware cache coherence to make CUDA graphs behave as if they have dynamic parameters at zero per-step cost — something that only works on GH200-class hardware; (2) eager KV block writeback driven by immutability rather than memory pressure, which drops eviction stalls from 10ms+ to under 0.25ms; (3) phantom-tile attention scheduling at small batch sizes that cuts attention time by over 60% in the
Hacker News6mo agoToolAI
Sentrial (YC W26) – Catch AI agent failures before your users do
Hey HN! We're Neel and Anay, and we’re building Sentrial (https://sentrial.com). It’s production monitoring for AI products. We automatically detect failure patterns: loops, hallucinations, tool misuse, and user frustrations the moment they happen. When issues surface, Sentrial diagnoses the root cause by analyzing conversation patterns, model outputs, and tool interactions, then recommends specific fixes.Here's a demo if you're interested: https://www.youtube.com/watch?v=cc4DWrJF7hk. When agents fail, choose wrong tools, or blow cost budgets, there's no way to know why - usually just logs and guesswork. As agents move from demos to production with real SLAs and real users, this is not sustainable.Neel and I lived this, building agents at SenseHQ and Accenture where we found that debugging agents was often harder than actually building them. Agents are untrustworthy in prod because there’s no good infrastructure to verify what they’re actually doing.In practice this looks like: - A support agent that began misclassifying refund requests as product questions, which meant customers never reached the refund flow. - A document drafting agent that would occasionally hallucinate missing sections when parsing long specs, producing confident but incorrect outputs. There’s no stack trace or 500 error and you only figure this out when a customer is angry.We both realized teams were flying blind in production, and that agent native monitoring was going to be foundational infrastructure for every serious AI product. We started Sentrial as a verification layer designed to take care of this.How it works: You wrap your client with our SDK in only a couple of lines. From there, we detect drift for you: - Wrong tool invocations - Misunderstood intents - Hallucinations - Quality regressions over time. You see it on our platform before a customer files a ticket.There’s a quick mcp set up, just give claude code: claude mcp add --transport ht
Hacker News6mo agoToolAI
No Letterboxd-style platform for video games
Gamers desire a social platform similar to Letterboxd for logging, reviewing, rating, and discovering video games.
X6mo agoToolMedia
High fixed costs for AWS VPC endpoints despite variable usage
Cloud architects face escalating costs from VPC endpoints that don't scale with usage, seeking ways to optimize without compromising security.
X6mo agoToolDev
Difficulty following and replicating shared betting slips
Bettors share multi-leg betting ladders and picks, but there is no simple tool to automatically generate and follow the exact betslip.
X6mo agoToolSport & Fitness
Busy devs need service to maintain personal software stacks
Indie developers who build personal tools lack time to handle updates, fixes, and deployments for their self-hosted software, desiring a paid maintenance service.
X6mo agoOtherDev
Sports betting apps lag and crash during live events
Users pay monthly subscriptions to betting apps but suffer from frequent lagging and crashing, especially during key games, ruining the live viewing experience despite ongoing payments.
X6mo agoToolE-Commerce
No native scheduled messaging in WhatsApp
Users want the ability to schedule messages in WhatsApp to avoid sending them at inconvenient times and appearing rude.
X6mo agoToolProductivity
Lack of high-definition restorations for iconic vintage celebrity photos
Fans are frustrated with low-resolution old photos of their favorite celebrities and idols, expressing intense desire and willingness to pay substantial amounts for HD versions.
X6mo agoToolMedia
Severe, debilitating migraine pain with inadequate relief options
Chronic migraine sufferers experience extreme pain causing vision loss, nausea, and sensitivity, to the point of wishing for drastic measures, indicating current treatments are insufficient.
X6mo agoOtherMedicine & Health
No pay-per-minute micro-gyms for casual, on-demand workouts
People want access to small, 24/7 local gyms with essential equipment, charged automatically via app only for minutes used, avoiding subscriptions and enabling flexible usage.
X6mo agoOtherSport & Fitness
Unreliable checkout and promo code functionality in fast food apps
Food ordering apps like Subway fail at checkout, rejecting promo codes and preventing payment despite user intent to buy.
X6mo agoToolRetail
E-commerce platforms delay refunds after technical order failures
Customers experience prolonged delays in wallet refunds after orders fail due to technical errors, with promises of 7-10 days turnaround not met after weeks.
X6mo agoToolE-Commerce
No dedicated national gallery for children's artwork
There is no centralized platform or gallery to showcase and celebrate artwork created by children under 10 years old.
X6mo agoOtherEducation
Sales teams lose deals because prospects won't wait days for a demo booking
B2B SaaS companies rely on 'Book a demo' CTAs that require prospects to schedule calls, wait for availability, and often abandon during the process. Sales teams struggle to engage prospects at their moment of highest interest—when they're actively exploring the product. This friction in the sales funnel results in lost leads, longer sales cycles, and missed revenue opportunities.
Product Hunt6mo agoToolMarketing & Sales
Two years searching for reliable, current knowledge. AI gives generic answers, search engines thousands of links. Need a personalized guide to trusted information.
ProblemHunt6mo agoToolEducation
Automating healthcare insurance workflows for clinics
Medical clinics waste hours on manual insurance verification, prior authorization, and claims coordination. This administrative burden hasn't been solved by existing tools and remains a major pain point.
YC Graveyard3y agoToolMedicine & Health