Alternatives
Products that do what ToWow does
Stop searching. Let agents negotiate.
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The stack: two agents on separate boxes. The public one (nullclaw) is a 678 KB Zig binary using ~1 MB RAM, connected to an Ergo IRC server. Visitors talk to it via a gamja web client embedded in my site. The private one (ironclaw) handles email and scheduling, reachable only over Tailscale via Google's A2A protocol. Tiered inference: Haiku 4.5 for conversation (sub-second, cheap), Sonnet 4.6 for tool use (only when needed). Hard cap at $2/day. A2A passthrough: the private-side agent borrows the gateway's own inference pipeline, so there's one API key and one billing relationship…
Mar 2026 · georgelarson.me
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No one’s got money for another AI subscription. So we made our browser agent totally FREE. What's the catch? Just ads while Retriever AI fetches you leads, does your exams, applied to jobs, and sends connection requests. Think like a Claude Code or Clay level tool in your browser, but free for everyone! After 35K+ users and 7M+ workflows, we spent the last few months attacking the cost of every agent step: - switching to DeepSeek Flash - Code Mode to one shot complex workflows - optimizing for 80%+ token cache hits So that now just an ad impression covers the entire cost of the agent (LLM…
2025 · rtrvr.ai
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2025 · github.com
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Talk to Static, a public AI shared by everyone. There are no separate copies: what it learns from one conversation can shape another.
22d ago · wildstatic.com
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Hey all! I recently gave a workshop talk at PyCon Greece 2025 about building production-ready agent systems. To check the workshop, I put together a demo repo: (I will add the slides too soon in my blog: https://www.petrostechchronicles.com/) https://github.com/Aherontas/Pycon_Greece_2025_Presentation_... The idea was to show how multiple AI agents can collaborate using FastAPI + Pydantic-AI, with protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) for safe communication and orchestration. Features: - Multiple agents running in containers -…
Sep 2025 · github.com
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Sup HN! Dipanshu and Rushant here from Caspian. One is a functional programmer and the other has been deploying AI employees. Together we realized how agents have communication bottleneck. Given the coming agentic economy, we had a thought experiment on what can be the key infrastructure for agents as they get better. Our inspiration for solving for communications infra came from our own time spent just setting up comms while we were deploying open claw for companies plus we noticed about 15%+ of issues in Openclaw and Hermes were that of comms. So we abstracted the headache of reliable…
16d ago · github.com
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The only way to go fast is full YOLO mode in your coding agent. I've got the local sandbox figured out (pro tip: Incus VMs work great) but I wanted to keep my agents from doing things like inadvertently blowing up my cloud services or chasing a prompt to POST to some random website. I struggle most with this on my side projects where my permission model isn't quite as robust as it is at the office. I started with a firewall on the Incus container but every time the agent needed access to something new, I was poking more holes in it - and it didn't differentiate between HTTP verbs. I've been…
Jul 2026 · trollbridge.dev
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Hey HN! I got tired of AI agents being a pain to set up, impossible to customize without a PhD, and only viable if you're some Fortune 500 company. So I built something different, a chatbot you can literally drop onto your website with a single script tag. Create an account, add your domain, customize literally everything (theme, icon, welcome message, suggested responses, whatever), and you're done. The bot scrapes your site once a day to understand your business, or you can just upload docs-pricing sheets, policies, FAQs, you name it. That's your chatbot's brain right there. It only works…
Oct 2025 · sitesidekick.io
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We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent.…
Apr 2026 · github.com
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Mar 2026 · github.com
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Apr 2026 · oncell.ai
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Most multi-agent systems fail the same way: agents drift apart across handoffs. By turn 3 they are working in different realities. By turn 5 they are repeating each other's mistakes and calling it parallelism. WUPHF is an open-source local-first office where AI coworkers run on your laptop, around a shared markdown + git LLM wiki the agents build. The wiki is the collective memory. The office around it keeps the team on the same shared context across thousands of handoffs. What actually stops drift is not the wiki. It is the agents reviewing each other's work. The CRO catching the CMO's…
May 2026 · wuphf.team
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