Agentlas
Describe a worker, get an AI agent that does it
Agentlas launched on May 18, 2026 with 4 votes, #2,419 of 20,538 launches that month and more than 75% of that year's launches.
What it does
Describe an AI worker in plain English — Agentlas builds the files for you. Drop the bundle into Claude, ChatGPT, Gemini, Claude Code, or Codex and it just works. No coding. Every agent ships with a safety label showing exactly what it can touch.
Build, operate, route, and govern AI agents across Agent Cloud, Hub, local computers, and the LLM you choose.
Release notes, engineering notes, and field reports from the team. Start with Agentlas orchestration and Project Foundation, then explore Agent Apps, Stormbreaker, and Agent Cloud routing. n8n, Zapier, and Make splice a disposable LLM call into a deterministic pipeline and sell it as an agent. Agentlas Graph puts a real agent in that seat, runs free on your desktop, and packages the finished graph so anyone can install it with one button. The hard part of an agent system is not choosing a model. It is defining the work, staffing the roles, setting authority, designing handoffs, and reviewing the result. Here is how Agentlas turns that management problem into an executable workforce. A…from agentlas.cloud
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Aviator Agents2025 · aviator.co · ▲97When we asked developers from large engineering teams, almost everyone is using Claude Code, Cursor, or Copilot. But adoption is still inconsistent. Some of us would have 15 agents running in Claude Code at the same time; some would still refuse to use any of them and write code manually. There's a fragmented AI development problem: - Five developers building similar features, and all of them start from scratch - No way to share "here's how we prompt AI to follow our architecture" - No trail of how the AI changes were generated - People leave the company, so does the their knowledge Aviator…
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Source: Product Hunt launch ↗
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Dev tools · May 2026 · kilo.ai
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…
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