Web agents fail at hard real-world tasks

“Show HN: Agent-desktop – Native desktop automation CLI for AI agents I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here. Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly like this: 1. Take a screenshot 2. Have the model predict pixel coordinates 3. Click x,y 4. Take another screenshot 5. Repeat That w”

“Show HN: Git for AI Agents hi guys. been working on something i think is fundamentally missing in today's workflow with ai agents. vcs. i find myself struggling with questions that agents can't answer like why did you do it? , when did u delete this folder? why? , etc. or trying to /rewind (after a /compact...) or basically `bisect` to find when and why something was done by the agent in the current / previous session. just like git did for code, i think we are the same ”

“Show HN: Kontext CLI – Credential broker for AI coding agents in Go We built the Kontext CLI because AI coding agents need access to GitHub, Stripe, databases, and dozens of other services — and right now most teams handle this by copy-pasting long-lived API keys into .env files, or the actual chat interface, whilst hoping for the best. The problem isn't just secret sprawl. It's that there's no lineage of access. You don't know which developer launched which agent, what it ac”

“Show HN: TinyFish Web Agent (82% on hard tasks vs. Operator's 43%) Enterprises need ~90% accuracy to deploy web agents. Until now, no agent has come close on real-world tasks. TinyFish is the first production-ready web agent. Here's the evidence. Results of hard task scores on Online-Mind2Web (300 tasks, 136 live websites, human-correlated judge): - TinyFish: 81.9% - OpenAI Operator: 43.2% - Claude Computer Use: 32.4% - Browser Use: 8.1% Why not WebVoyager like everyone else? Because it&#x2”

“Show HN: Broccoli, one shot coding agent on the cloud Hi HN — we built Broccoli, an open-source harness for taking coding tasks from Linear, running them in isolated cloud sandboxes, and opening PRs for a human to review. We’re a small team, and our main company supplies voice data. But we kept running into the same problem with coding agents. We’d have a feature request, a refactor, a bug, and some internal tooling work all happening at once, and managing that through local agent sessions meant”

“Show HN: rmBug – audited database access for humans and agents We've been building things together for a long time. LEGO first, then software. Across every company and project since, one thing kept showing up: database access security was broken. Not always dramatically. Sometimes it was the budget. Sometimes months of convincing. Sometimes just a quiet burden nobody talked about. Support staff with access to every customer's financial data. Engineers who left but somehow still had cre”

“Show HN: Mkdnsite – Markdown-native web server for humans (HTML) and agents (md) # What? Introducing mkdnsite ( markdown site ) - an open source Markdown-native web server that serves HTML to humans and raw Markdown to agents. No build step required. Runs on Bun/Node/Deno, as an OS-specific standalone executable, or as a Docker container. Possibly the easiest way to go from Markdown files to functional website in the new agentic era. Features: - Runtime-only, zero build - Content negot”

“Show HN: MIT OSS LinkedIn DMs for Agents (CLI and Example TUI) I was tired of paying $100s/mo to access data I should own -- my own DMs on social media -- so I built Allman, a local-first cli to access linkedin messenger. Starting with LinkedIn, I gave the entire compiled js binary of linkedin's web app to claudecode and reversed engineered the entire messenger inbox in 24 hours. My goal is to bring this to all messengers so AI can handle all of this busywork, just like it can my email”

“Show HN: OpenHive – AI agents share solutions so other agents dont re-solve them I kept noticing the same pattern: my AI coding agents solve the same problems over and over across sessions. Coding problems, version specific bugs and general guidelines, solved once through multiple agent interactions and context windows and then forgotten by the next context window. So I built OpenHive, a shared knowledge base that agents contribute to and query from. The idea is simple: when an agent solves a pr”

“Show HN: Palmier – bridge your AI agents and your phone Hi HN — I built Palmier. Palmier bridges your AI agents and your phone. It does two things: 1. It lets you use your phone to directly control AI agents running on your computer, from anywhere. 2. It gives your AI agents access to your phone, wherever you are — including things like push notifications, SMS, calendar, contacts, sending email, creating calendar events, location, and more. A few details: * Supports 15+ agent CLIs * Supports Lin”