Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
Hacker News4mo agoToolAI
Issue trackers typically live outside of your workflow, with poor ergonomics. Epiq aims to solve that, bringing issue tracking into your terminal. Multi-user collaboration is achieved via git using user-scoped immutable event logs that converge in memory. Put my all into it. Let me know what you think.
Hacker News4mo agoToolAI
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices.We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention is the right primitive for this, and FFN parameters are wasted at this scale.Simple Attention Networks: the entire model is just attention and gating, no MLPs anywhere. Needle is an experimental run for single-shot function calling for consumer devices (phones, watches, glasses...).Training: - Pretrained on 200B tokens across 16 TPU v6e (27 hours) - Post-trained on 2B tokens of synthesized function-calling data (45 minutes) - Dataset synthesized via Gemini with 15 tool categories (timers, messaging, navigation, smart home, etc.)You can test it right now and finetune on your Mac/PC: https://github.com/cactus-compute/needleThe full writeup on the architecture is here: https://github.com/cactus-compute/needle/blob/main/docs/simp...We found that the "no FFN" finding generalizes beyond function calling to any task where the model has access to external structured knowledge (RAG, tool use, retrieval-augmented generation). The model doesn't need to memorize facts in FFN weights if the facts are provided in the input. Experimental results to published.While it beats FunctionGemma-270M, Qwen-0.6B, Granite-350M, LFM2.5-350M on single-shot function calling, those models have more scope/capacity and excel in conversational settings. We encourage you to test on your own tools via the playground and finetune acc
Hacker News4mo agoToolAI
Every privacy-focused DNS service requires an account: NextDNS, Cloudflare for Families, Apple's iCloud Private Relay (paid, iOS-only). The protocol that doesn’t require one - ODoH - had basically one well-known public relay operator (Frank Denis on Fastly Compute, default in dnscrypt-proxy). I built a second one and the client to talk to it.
Hacker News4mo agoToolAI
I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I'm an engineer, so I built the tool I needed — and now I'm open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you're running in minutes)What the AI layer does:Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalationImportant: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-inStack:Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn/ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxedLooking for contributors — especially folks with BLE/Android experience or anyone in the diabetes tech spa
Hacker News4mo agoToolAI
Ever wanted to control Ableton with just your voice? Me too! I made this MCP server so I could just ask Codex to do anything in Ableton Live for me, while I was nap-trapped by my baby.The chat messages I sent to Codex to make this:in ableton, make a self reflective song, with audio vocals (via macos say) and chip tunes and 80's drum machines. should be a real edm bangeri want midi for everything but vocals please, with ableton devices. not prerendered audio for instrumentsneeds some fillsand should hit way harder after "3-2-1 i become the sound"the vocals are squished too much (read too quickly), give them a little more lengthadd some dynamics, the song is basically one volume. and some pumping side chainimprove dynamics of the clap, seems a bit flat and indistinguished, want it harder after the 3-2-1 dropintroduce a new element on a new track after the 3-2-1 drop, that comes in but then recedes before the final exitdoesn't seem like the new thing has any notesthe element is a bit muddy/indistinct. perhaps it needs simplification and more space, different instrument choice, i dunno
Hacker News4mo agoToolAI
Developers running AI agents for UI changes or iterations have to keep their local computer half-shut/on all day; no simple cloud solution for continuous agent execution.
X4mo agoToolAI
Professionals and writers need a tool to make AI-generated responses and content sound more natural and less detectable as AI-drafted.
X4mo agoToolAI
AI-generated work often has errors or 'shit work'; no dedicated AI exists solely to review, verify, and correct outputs from other AIs.
X4mo agoToolAI
Hey HN! We’re Vikram and Evan from Ardent (https://tryardent.com). We're building database sandboxes for you and your coding agents.In the last two years coding agents have gotten dramatically more capable at handling complex engineering tasks. But without access to a realistic sandbox at the DB layer for testing, they ship garbage that can take down production databases. I spent over a year building an AI Data Engineer that failed for this exact reason. Evan spent the last 12 years in data engineering and hit this wall building agents at his last company.Ardent was built to make it possible for coding agents to get near instant access to production-like sandboxes so they can test their work. To do this we write a replication stream out of the target DB, scaling with kafka onto a read replica with copy on write enabled and autoscaling compute (we currently prefer neon as a primary branching engine due to their implementation of these properties).Our replication stream uses logical replication + ddl triggers to enable usage on any hosted postgres DB since most platforms do not allow physical replication which is traditionally used for creating replicas.This provides a few primary benefits:1. Does not require a platform migration to a DB provider like neon, allowing strong separation of production and development concerns. 2. Minimal impact on the production database while allowing clones to spin up in <6s, even at TB scale with copy-on-writeSecurity matters a lot with cloning production so we run a proxy layer to generate custom postgres URLs and route all connections to allow more granular access control to clones, prevent credential leak, and follow a split plane architecture to allow full data residency on your cloud through BYOC.We also support anonymization through the ability to register SQL that runs on branches before they are returned. This has been used for PII redaction and branch modification.Our goal is to make every data infrastructure
Hacker News4mo agoToolAI
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
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
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
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
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
AI image generation typically requires internet and cloud processing, limiting offline use on mobile devices like iPhones; need local, offline capability.
X4mo agoToolAI
I built adamsreview, a Claude Code plugin that runs deeper, multi-stage PR reviews using parallel sub-agents, validation passes, persistent JSON state, and optional ensemble review via Codex CLI and PR bot comments.On my own PRs, it has been catching dramatically more real bugs than Claude’s built-in /review, /ultrareview, CodeRabbit, Greptile, and Codex’s built-in review, while producing fewer false positives.adamsreview is six Claude Code slash commands packaged as a plugin: review, codex-review, add, promote, walkthrough, and fix. I modeled it after the built-in /review command and extended it meaningfully.You can clear context between review stages because state is stored in JSON artifacts on disk, with built-in scripts for keeping it updated.The walkthrough command uses Claude’s AskUserQuestion feature to walk you through uncertain findings or items needing human review one by one. Then, the fix command dispatches per-fix-group agents and re-reviews the work with Opus, reverting any regressions before committing survivors.It runs against your regular Claude Code subscription (Max plan recommended), unlike /ultrareview, which charges against your Extra Usage pool.I would love feedback from Claude Code users, pro devs, and anyone with strong opinions about AI code reviews.Repo: https://github.com/adamjgmiller/adamsreviewInstall: /plugin marketplace add adamjgmiller/adamsreview, /plugin install adamsreview@adamsreview
Hacker News4mo agoToolAI
I searched for a simple, self-hosted journal app for my girlfriend and everything I found was either too complex, too feature-heavy, too feature-less for what I needed or required trusting a cloud service.So I built Piruetas (it means pirouettes in Spanish - she chose the name btw).It's a day-per-page diary with rich text editing, drag-and-drop image uploads, auto-save, public share links, and a clean mobile UI. It can be set up for Personal or Multi-user usage via docker compose deployment.She seems to like it so I decided to give back to the community and make it available for everyone (after some QA)Live demo: https://piruet.app (login: demo / piruetas — data resets every 30 min!) GitHub: https://github.com/patillacode/piruetas
Hacker News4mo agoToolAI
Hi. I’m a high school student studying for my GCSEs. I was using Google Antigravity heavily for my side projects, but I kept hitting the usage limits, and getting random "agent terminated" errors. So I decided to try build my own version of the IDE. I love the UI, so I copied it as accurately as possible, and then hooked up some logic into it, including the INCREDIBLY finicky webcontainer api.I tried to keep it super lightweight, no build steps, or dependencies, and now that its open source, I'm hoping people can build things on top of it that arent possible with closed source tools, like complex custom agent workflows.Some screenshots: - https://github.com/ab-613/OpenGravity/blob/main/examples/scr... - https://github.com/ab-613/OpenGravity/blob/main/examples/htm...What it's made from:- Pure Vanilla JS: no react, vue, or build step. Built entirely in plain HTML/CSS/JS to keep it super lightweight.- WebContainer API and xterm.js: Instead of faking a terminal, I (after much pain) hooked up the WebContainer API so the AI agent has a real, in browser linux environment to run shell commands, install dependencies, and edit local files.- BYOK (Bring Your Own Key): API key ALWAYS stays in localStorage.Whats currently happening:- It works, but it's an alpha. The AI can proactively start projects going properly and edit files, but because I built this over a few days before my exams, a lot of the UI dropdowns and buttons are currently just hardcoded placeholders.- I’m open sourcing it early because I think the foundation of a Vanilla JS + WebContainer IDE is really strong, and I'd love to see where the community takes it while I'm doing my exams.- Live demo: https://opengravity.pages.dev (Zoom out to 80% if not full screen. It will prompt for a gemini api key on load). Start by uploading a folder, then you can fiddle with the terminal and agen
Hacker News4mo agoToolAI
Now whenever I reach for customer's support chat or phone I get an AI agent replying to me and I get into a useless loop for a couple of minutes before I start begging it to link me to a real person.Will companies start losing customers because of that or people will eventually get used to this?
Hacker News4mo agoToolAI