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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Google Trends4mo agoToolProductivityUS
Protein Engineering Acceleration
Protein engineering is slow and expensive. Computational tools to predict and optimize protein sequences could accelerate research and reduce costs.
YC Graveyard6y agoToolBiotech
Open Banking/Bank Account Payments
Alternative payment method allowing users to pay directly from bank accounts instead of cards. Reduces friction for unbanked/underbanked users and cuts card fees.
YC Graveyard4y agoToolFintech
Cross-chain liquidity fragmentation
Users and developers face friction moving assets between blockchains and Layer 2s, losing value to multiple swaps, slippage, and manual routing complexity.
YC Graveyard8y agoToolCrypto/Web3
Community currency creation barrier
Communities, DAOs, and groups want to create their own digital currency or token system but face technical, legal, and operational friction.
YC Graveyard8y agoToolCrypto/Web3
Gigacatalyst – Extend your SaaS with an embedded AI builder
Hi HN, I’m Namanyay from Gigacatalyst (link: https://gigacatalyst.com/). Gigacatalyst allows sales, CS, and users to build one-off features, so your SaaS can support long-tail customer workflows and engineers aren’t pulled away from the roadmap.When you sell software to large businesses, you realize that each customer needs their own workflow and features. Traditionally, this either means long engineering roadmaps or the customers end up using workarounds.But what if everyone could build their critical missing features just by talking to an AI? That’s what we do at Gigacatalyst. We provide an AI customization layer for your customers, CS team, and sales team to build these missing critical workflows without needing any engineers at all. Think Lovable, but built on top of YOUR platform.We connect to your product's APIs, learn your data model and design system, and let non-technical users build governed apps via natural language - inside your product, under your brand.Here’s what it looks like in action: https://www.youtube.com/watch?v=_taSpSphH6EOne of our customers, a Series B company, saw their users (not engineers - managers, ops people, facility directors) build critical workflows like:- Parts stockout prevention: A maintenance manager typed "show me which parts will run out in the next 2 weeks based on usage over the last 90 days, accounting for vendor lead times." The app tracks consumption velocity, forecasts stockouts, and alerts before it's too late. He says it's prevented ~$500K in emergency downtime.- Invoice OCR from phone photos: Technicians kept losing paper invoices. The prompt: "upload a photo of the invoice, extract vendor name, date, amount, and line items, then match it to the purchase order and flag discrepancies." Now techs snap a photo on-site to automatically add to the system of record.- Restaurant emergency triage: A pizza chain's facilities manager was drowning in maintena
Hacker News4mo agoToolAI
Voker (YC S24) – Analytics for AI Agents
Hey HN, we're Alex and Tyler, co-founders of Voker.ai (https://voker.ai/), an agent analytics platform for AI product teams. Voker gives full visibility into what users are asking of your agents, and whether your agents are delivering, without having to dig through logs. Our main product is a lightweight SDK that is LLM stack agnostic and purpose-built for agent products. (https://app.voker.ai/docs)Agent Engineers and AI product teams don’t have the right level of visibility into agent performance in production, which results in bad user experiences, churn, and hundreds of hours wasted with spot checks to find and debug issues with agent configurations.Demo: https://www.tella.tv/video/vid_cmoukcsk1000i07jgb4j65u67/vie...We recently conducted a survey of YC Founders and 90%+ of respondents said that the only way they know if their Agents are failing users in production is by hearing complaints from customers. They push a prompt change hoping that it fixes the problem and doesn’t break something somewhere else, and the cycle repeats.We saw tons of observability and evals products popping up to try to address these problems, but we still felt like something was missing in the agent monitoring stack. Obs is good for individual trace debugging but is only accessible to engineers. Evals are good for testing known issues, but don't give insights into trends that teams don’t expect, so engineers are always playing catch up. Traditional product analytics tools do a good job tracking clicks and pageviews across your product surface but weren’t built ground up for agent products. Knowing what users want out of agents, and whether the agent delivered requires specific conversational intelligence / unstructured data processing techniques.We came up with the agent analytics primitives of Intents, Corrections, and Resolutions to describe something pretty much all conversational agents had in common: a user will
Hacker News4mo agoToolAI
Agentic interface for mainframes and COBOL
Hi HN, we’re Sai and Aayush, and we’re building Hypercubic (https://www.hypercubic.ai/), bringing AI tools to the mainframe and COBOL world. (We did a Launch HN last year: https://news.ycombinator.com/item?id=45877517.) Today we’re launching Hopper, an agentic development environment for mainframes.You can download it here: https://www.hypercubic.ai/hopper, and you can also request access and immediately get a mainframe user account to play with.There's also a video runthrough at https://www.youtube.com/watch?v=q81L5DcfBvE.Mainframes still run a surprising amount of critical infrastructure: banking, payments, insurance, airlines, government programs, logistics, and core operations at large institutions. Many of these systems are decades old, but they continue to process enormous transaction volumes because they are reliable, secure, and deeply embedded into business operations.A lot of that software is written in COBOL and runs on IBM z/OS. The development environment looks very different from modern cloud or Unix-style development. Instead of GitHub, shell commands, package managers, and CI pipelines, developers often work through TN3270 terminal sessions, ISPF panels, partitioned datasets, JCL, JES queues, spool output, return codes, VSAM files, CICS transactions, and shop-specific conventions.TN3270 is the terminal interface used to interact with many IBM mainframe systems. ISPF is the menu and panel system developers use inside that terminal to browse datasets, edit source, submit jobs, and inspect output. It is powerful and reliable, but it was designed for expert humans navigating screens, function keys, and fixed-width workflows, not AI agents.A simple COBOL change might require finding the right source member, checking copybooks, locating compile JCL, submitting a job, reading JES/SYSPRINT output, interpreting condition codes, patching fixed-width source, and resubmitting.Much of this
Hacker News4mo agoToolAI
Statewright – Visual state machines that make AI agents reliable
Agentic problem solving in its current state is very brittle. I fell in love with it, but it creates as many problems as it solves.I'm Ben Cochran, I spent 20+ years in the trenches with full-stack Engineering, DevOps, high performance computing & ML with stints at NVIDIA, AMD and various other organizations most recently as a Distinguished Engineer.For agents to work reliably you either need massive parameter counts or massive context windows to keep the solution spaces workable. Most people are brute forcing reliability with bigger models and longer prompts.What if I made the problem smaller instead of making the model bigger?I took a different approach by using smaller models: models in the 13-20B parameter range and set them to task solving real SWE-bench problems. I constrained the tool and solution spaces using formal state machines. Each state in the machine defines which tools the model can access, how many iterations it gets and what transitions are valid. A planning state gets read-only tools. An implementation state gets edit tools (scoped to prevent mega edits) and write friendly bash tools. The testing state gets bash but only for testing commands. The model cannot physically skip steps or use the wrong tool at the wrong time. It is enforced via protocol, not via prompts.The results were more promising than I would have expected. Across multiple model families irrespective of age (qwen-coder, gpt-oss, gemma4) and the improvements were consistent above the 13B parameter inflection point. Below that, models can navigate the state machine but can't retain enough context to produce accurate edits. More on the research bit: https://statewright.ai/researchSurprisingly this yielded improvements in frontier models as well. Haiku and Sonnet start to punch above their weight and Opus solves more reliably with fewer tokens and death spirals. Fine tuning did not yield these kinds of functional improvements for me. The takeaway it se
Hacker News4mo agoToolAI
Drivers find fuel and EV charging expense tracking tedious and inaccurate
Manual logging is slow; need quick entry, analytics on consumption/cost per km, and easy exports without accounts.
X4mo agoToolTransportation
Bot audits don't mimic real user experience
Website owners use bot-based tools for audits that fail to replicate real visitor behavior, leading to inaccurate insights on usability and performance issues.
X4mo agoToolDev
Hyperliquid traders miss critical insights like liquidation heatmaps and whale movements
Perpetual DEX lacks specialized terminal for heatmaps, tracking whales, and integrated prop trading.
X4mo agoToolFinance
SaaS founders' Reddit posts get auto-removed due to undetected subreddit rules
Promotional posts fail because of subreddit-specific culture and spam filters; need pre-post analysis for risk scores and tailored hooks.
X4mo agoToolMarketing & Sales
Frustration with searching multiple icon libraries
Developers and designers waste time searching across 10+ different icon libraries to find a single specific icon, leading to inefficiency in UI/UX design workflows.
X4mo agoToolDev
Risks from not understanding contract clauses
Individuals and small businesses sign contracts without fully grasping risky clauses, potentially costing revenue or control, as legal review is expensive or inaccessible.
X4mo agoToolLegal
Service businesses overwhelmed by customer calls checking job status
Lack of real-time tracking like Amazon packages leads to constant inquiries; need client portals and SMS updates.
X4mo agoToolBusiness
Unknown reasons AI systems don't recommend products
Founders struggle to understand and diagnose why AI systems like search engines or agents hesitate to recommend or cite their websites and products, missing out on AI-driven traffic.
X4mo agoToolAI
Lost access to Twitter bookmarks and likes
Users frequently lose or can't effectively search their saved Twitter bookmarks and likes due to platform limitations, making personal knowledge bases unusable.
X4mo agoToolProductivity