Alternatives
Products that do what Boilerworks – don't pay tokens to re-solve auth and CI on every project does
Every time you spin up a new project with an AI agent, you're burning tokens on the same problems. Auth. Connection pooling. CI pipeline. Docker setup. Database migrations. Backend wired to frontend. Problems that were solved on the last project, and the one before that. Boilerworks is a CLI that skips all of it. 26 production-ready templates across 10 stacks. Your agent starts on the actual problem, not the scaffolding. pip install boilerworks boilerworks init What you get out of the box: multi-stage CI, Docker Compose, Terraform, structured logging, test infrastructure, auth scaffolding.…
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Howdy all. I'm Zack :wave:. I've been thinking about the problem of misguided AI pull requests and figured I'd throw a possible solution out there for feedback. Basically, CleverCrow lets supporters give tokens to a GitHub repo (or set of issues in that repo) for the maintainers to use to build/fix stuff. The fun implementation challenges have been around implementing the pooling dynamics and keeping the maintainers in charge while the backers are motivated to support their work.
Jun 2026 · clevercrow.io
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We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
Oct 2025 · github.com
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Deep Work Plan▲114Models matter. Context matters more. Give your agent a plan.
Jun 2026 · deepworkplan.com
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Skip the setup and run OpenClaw & Hermes, fully managed
18d ago · cloudways.com
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I am excited to share pippy, which allows users to create configurable pipelines using Github Actions. If you have used Azure pipelines, in summary, this would be Azure Pipelines meets Github Actions. Cloud version: https://app.pippy.dev/login (closed beta) I am also open sourcing command line version: https://github.com/nixmade/pippy. Key features: - Automatic Rollback - Datadog Monitoring - Pause/Resume The product is built using open source orchestrator. Orchestrator: https://github.com/nixmade/orchestrator Orchestrator allows…
2024 · pippy.dev
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Hi HN , I got tired of writing the same boilerplate over and over — DB setup, auth, routes, security — every time I built a backend. So I built Pipo360 — an AI-powered tool that generates production-ready backends in under 60 seconds, from just a plain-text description. How it works: Type what you need “Create a task management API with user auth and MongoDB” Hit Generate Get real, exportable code Auth (JWT) Database schema CRUD routes Deployable to Vercel, AWS, etc. No templates. No lock-in. Just code that works. Why it’s different: Built with Gemini AI + human supervision (to ensure real…
2025 · pipo360.xyz
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Hello Hacker News! We're Yangqing, Xiang and JJ from lepton.ai. We are building a platform to run any AI models as easy as writing local code, and to get your favorite models in minutes. It's like container for AI, but without the hassle of actually building a docker image. We built and contributed to some of the world's most popular AI software - PyTorch 1.0, ONNX, Caffe, etcd, Kubernetes, etc. We also managed hundreds of thousands of computers in our previous jobs. And we found that the AI software stack is usually unnecessarily complex - and we want to change that. Imagine if you are a…
2023 · lepton.ai
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I'm working on a CLI that helps you create the boilerplate of your project in multiple languages and frameworks. In case of front-end frameworks/libraries, with cli you'll be able to create the project, set up git on your local, add the UI-framework, testing framework, and linting, and in the future releases you'll be able to even add pre-designed components. The CLI currently supports React, Angular, Vue, Svelte and Node.js will be released today. Also soon, you'll be able to deploy your applications to AWS with a single command. Appreciate your feedbacks and all the contributions are…
2022
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2024 · pipedream.com
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I've been running Claude agents for various automation tasks — monitoring crypto news, syncing Todoist, running health checks — and I kept hitting the same problem: there's no clean way to deploy an agent that just runs on a schedule without a human babysitting it. Every agent framework I looked at was built around chat interfaces or one-shot workflows. I wanted something closer to cron for AI agents — define a task, give it a schedule, let it run forever. So I built Ductwork. You define tasks as simple JSON files — a prompt, a schedule, optional memory and skills — and ductwork handles…
Mar 2026 · github.com
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I got sick of: - Re-writing the same Terraform for the same common AWS infrastructure - Re-writing the same Rails code to build the same webapp - Re-configuring the same monitoring systems & CI/CD pipelines - Spending a week in dependency & configuration hell wiring it all together When I was launching my first two startups, I wished I had a strong starting point with all of the best practices & technology configured out-of-the-box. Every tool I found wanted to lock me in to some PaaS provider (that might not even exist in a year) who wanted to take a premium on my infrastructure spend…
2024
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Hi HN, We are excited to share patchwork - an open-source CLI for dev chore automation that you can use with your LLM of choice. Dev Teams can orchestrate custom workflows (called ‘patchflows’) using a combination of reusable steps and prompt templates to fix vulnerabilities, upgrade breaking dependencies, generate documentation, and more. We built scanning tools in the past, and saw how overwhelmed developers get with their DevSecOps pipelines. LLMs have the potential to help - but there is a need for an 'outer-loop' solution that can be customized to accommodate the processes, priorities,…
2024 · github.com
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It uses your local Claude and GitHub CLI and git worktrees, so there's nothing to configure beyond what you already have. Our AI coding workflow was a hodgepodge of scripts and ad-hoc processes, so we built this to centralize and streamline - and most importantly adapt as things change. Some cool features so far (lots more planned): 1. One click github issue to agent working on it. 2. Ratcheting mode, where it watches CI for code review comments and build failures, and automatically fixes them. Would love genuine feedback, bug reports, PRs, suggestions, etc.
Feb 2026 · github.com
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Each AI has strengths - Claude reasons well, Gemini handles long context, Codex integrates with GitHub. But switching between them means losing context. Built HiveTechs: one workspace where Claude Code, Gemini CLI, Codex, DROID, and 7 others run in integrated terminals with shared memory. Also added consensus validation - 3 AIs analyze independently, 4th synthesizes. Real IDE with Monaco editor, Git, PTY terminals. Not a wrapper. Looking for feedback: hivetechs.io
Dec 2025 · hivetechs.io
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Hi HN, we’re Jessie and Eric. We’ve been baking away at Cakework (https://www.cakework.com/), which is a way to build async backends without needing to manage cloud infrastructure. Cakework is for operations that take time or more compute, like file processing, report generation, or machine learning. Devs write backends as Python functions and deploy them with our CLI. They use our client SDKs to make requests, get status, and get processing results. Each request runs with its own CPU and memory parameters in its own microVM, with no timeouts. Devs can query for failures and…
2023 · cakework.com
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AgentState to solve a problem I kept running into: managing state for multi-agent AI systems is surprisingly hard. When you have multiple AI agents that need to coordinate, persist their state, and query each other's status, you typically end up with a mess of Redis/Postgres setups, custom queuing, and manual synchronization code. The whole thing is ~3MB, written in Rust for performance and safety, runs in Docker, and handles 1000+ ops/sec. I've been running it in production for AI workflows and it's been rock solid.
2025 · github.com
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Describe your complex tasks in plain English and watch them executed with proper reasoning. Think 10x power of No-Code AI Workflow tools. Example use cases: - Identify what customers are saying about a product - Create a Dockerfile (single GPU) for any GitHub repo - Monitor competitor activity on socials
2025 · workor.live
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