Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
Inputting everything into AI tools for emails, presentations, memos, and meeting notes adds an extra step to every task, making the overall process take twice as long instead of more efficient.
X1y agoToolProductivity
Dog owners concerned about pets swallowing food too quickly without chewing, posing health risks, seek effective training methods or tools as current approaches are insufficient.
X6mo agoToolOther
Players frustrated by inability to fully turn off pop-ups from expansions (e.g. Parenthood), festival notifications, Social Bunny, ThriftTea repeats, and school/career chance cards, disrupting immersive gameplay despite existing partial deactivation options.
X6mo agoToolOther
Codex, Amp, Cursor, and others are starting to standardize around AGENTS.md (https://agents.md/) — a unified Markdown file that coding agents can use to understand a codebase. By contrast, CLAUDE.md feels too specific to Claude Code. It doesn’t work as well when collaborating with other developers who aren’t using Claude Code.
GitHub1y agoToolDev
Every MCP server injects its full tool schemas into context on every turn — 30 tools costs ~3,600 tokens/turn whether the model uses them or not. Over 25 turns with 120 tools, that's 362,000 tokens just for schemas.mcp2cli turns any MCP server or OpenAPI spec into a CLI at runtime. The LLM discovers tools on demand: mcp2cli --mcp https://mcp.example.com/sse --list # ~16 tokens/tool mcp2cli --mcp https://mcp.example.com/sse create-task --help # ~120 tokens, once mcp2cli --mcp https://mcp.example.com/sse create-task --title "Fix bug" No codegen, no rebuild when the server changes. Works with any LLM — it's just a CLI the model shells out to. Also handles OpenAPI specs (JSON/YAML, local or remote) with the same interface.Token savings are real, measured with cl100k_base: 96% for 30 tools over 15 turns, 99% for 120 tools over 25 turns.It also ships as an installable skill for AI coding agents (Claude Code, Cursor, Codex): `npx skills add knowsuchagency/mcp2cli --skill mcp2cli`Inspired by Kagan Yilmaz's CLI vs MCP analysis and CLIHub.https://github.com/knowsuchagency/mcp2cli
Hacker News6mo agoToolAI
Agent Kanban has 4 main features:GitOps & team friendly kanban board integration inside VS Code Structured plan / todo / implement via @kanban commands Leverages your existing agent harness rather than trying to bundle a built in one .md task format provides a permanent (editable) source of truth including considerations, decisions and actions, that is resistant to context rot
Hacker News6mo agoToolAI
Hello Hacker News! We're Filip, Stavros, and Vivek from Terminal Use (https://www.terminaluse.com/). We built Terminal Use to make it easier to deploy agents that work in a sandboxed environment and need filesystems to do work. This includes coding agents, research agents, document processing agents, and internal tools that read and write files.Here's a demo: https://www.youtube.com/watch?v=ttMl96l9xPA.Our biggest pain point with hosting agents was that you'd need to stitch together multiple pieces: packaging your agent, running it in a sandbox, streaming messages back to users, persisting state across turns, and managing getting files to and from the agent workspace.We wanted something like Cog from Replicate, but for agents: a simple way to package agent code from a repo and serve it behind a clean API/SDK. We wanted to provide a protocol to communicate with your agent, but not constraint the agent logic or harness itself.On Terminal Use, you package your agent from a repo with a config.yaml and Dockerfile, then deploy it with our CLI. You define the logic of three endpoints (on_create, on_event, and on_cancel) which track the lifecycle of a task (conversation). The config.yaml contains details about resources, build context, etc.Out of the box, we support Claude Agent SDK and Codex SDK agents. By support, we mean that we have an adapter that converts from the SDK message types to ours. If you'd like to use your own custom harness, you can convert and send messages with our types (Vercel AI SDK v6 compatible). For the frontend, we have a Vercel AI SDK provider that lets you use your agent with Vercel's AI SDK, and have a messages module so that you don't have to manage streaming and persistence yourself.The part we think is most different is storage.We treat filesystems as first-class primitives, separate from the lifecycle of a task. That means you can persist a workspace across turns, share it betw
Hacker News6mo agoToolAI
Sup HN,So I got tired of bouncing between Flightradar, MarineTraffic, and Twitter every time something kicked off globally, so I wrote a dashboard to aggregate it all locally. It’s called Shadowbroker.I’ll admit I leaned way too hard into the "movie hacker" aesthetic for the UI, but the actual pipeline underneath is real. It pulls commercial/military ADS-B, the AIS WebSocket stream (about 25,000+ ships), N2YO satellite telemetry, and GDELT conflict data into a single MapLibre instance.Getting this to run without melting my browser was the hardest part. I'm running this on a laptop with an i5 and an RTX 3050, and initially, dumping 30k+ moving GeoJSON features onto the map just crashed everything. I ended up having to write pretty aggressive viewport culling, debounce the state updates, and compress the FastAPI payloads by like 90% just to make it usable.My favorite part is the signal layer—it actually calculates live GPS jamming zones by aggregating the real-time navigation degradation (NAC-P) of commercial flights overhead.It’s Next.js and Python. I threw a quick-start script in the releases if you just want to spin it up, but the repo is open if you want to dig into the backend.Let me know if my MapLibre implementation is terrible, I'm always looking for ways to optimize the rendering.
Hacker News6mo agoToolAI
Average users check their phone 186 times a day, interrupting every 5 minutes and hindering deep reasoning; no simple tool exists to track pickup frequency and build awareness.
X6mo agoToolProductivity
Despite 'one-click' promises for distributed staking like EF DVT-lite, still ends up manually babysitting nodes, as previous solutions required constant manual intervention.
X6mo agoToolVC & Startups
Drivers in areas like Nairobi and highways face 6k fines per violation without prior alerts for approaching traffic cameras.
X6mo agoToolTransportation
LLM models like Claude Sonnet experience sudden drops (e.g., 9%+) in performance, impacting developers who lack real-time tracking tools.
X6mo agoToolAI
Collectors struggle to maintain a shareable list of owned items (e.g., photocards) to avoid purchasing duplicates when building collections.
X6mo agoToolRetail
Users want to see what friends or community members are currently watching on YouTube, similar to AirPods sharing for music.
X6mo agoToolMedia
Still manually reviewing and annotating code even with Anthropic and other AI code review tools available, feeling like a loser for not having full automation.
X6mo agoToolDev
Pool cleaning company owner spends 3 hours every morning manually routing technicians based on locations and sending appointment reminders, a repetitive task done for 9 years causing massive time waste.
X7mo agoToolLogistics & Delivery
After an API returned success but failed to apply a DNS change correctly, leading to 18 hours of downtime, now manually verifying every deployment before downtime periods like Shabbos because monitoring doesn't catch the issue.
X6mo agoToolDev
PRs often depend on other PRs that need to be merged first, requiring manual waiting and rebasing of branches each time.
X7mo agoToolDev
Organizations with Anthropic contracts want to use Claude-powered applications but face friction finding reputable solutions, assessing quality, and managing separate vendor relationships. Currently, they must search independently, negotiate individual contracts, and lose visibility into their total Claude spend across tools. This creates delays in accessing AI solutions and increases procurement overhead.
Product Hunt6mo agoToolProductivity
Developers, researchers, and knowledge workers face limitations when using AI for sophisticated problem-solving that requires nuanced reasoning, multi-step analysis, or handling complex scenarios. Current AI models often provide oversimplified answers or break down when tasks require deeper cognitive processing. This forces users to either accept inadequate solutions or spend significant time manually breaking down complex problems into simpler components.
Product Hunt6mo agoToolAI