Pa

Patchwork – Open-source framework to automate development gruntwork

Hacker News

Patchwork – Open-source framework to automate development gruntwork

Hi HN! We’re Asankhaya and Rohan and we are building Patchwork. Patchwork tackles development gruntwork—like reviews, docs, linting, and security fixes—through customizable, code-first 'patchflows' using LLMs and modular code management steps, all in Python. Here's a quick overview video: https://youtu.be/MLyn6B3bFMU From our time building DevSecOps tools, we experienced first-hand the frustrations our users faced as they built complex delivery pipelines. Almost a third of developer time is spent on code management tasks[1], yet backlogs remain. Patchwork lets you combine well-defined prompts with effective workflow orchestration to automate as much as 80% of these gruntwork tasks using LLMs[2]. For instance, the AutoFix patchflow can resolve 82% of issues flagged by semgrep using gpt-4 (or 68% with llama-3.1-8B) without fine-tuning or providing specialized context [3]. Success rates are higher for text-based patchflows like PR Review and Generate Docstring, but lower for more complex tasks like Dependency Upgrades. We are not a coding assistant or a black-box GitHub bot. Our automation workflows run outside your IDE via the CLI or CI scripts without your active involvement. We are also not an ‘AI agent’ framework. In our experience, LLM agents struggle with planning and rarely identify the right execution path. Instead, Patchwork requires explicitly defined workflows that provide greater success and full control. Patchwork is open-source so you can build your own patchflows, integrate your preferred LLM endpoints, and fully self-host, ensuring privacy and compliance for large teams. As devs, we prefer to build our own ‘AI-enabled automation’ given how easy it is to consume LLM APIs. If you do, try patchwork via a simple 'pip install patchwork-cli' or find us on Github[4]. Sources: [1] https://blog.tidelift.com/developers-spend-30-of-their-time-... [2] https://www.patched.codes/blog/patched-rtc-evaluating-llms-f... [3] https://www.patched.codes/blog/how-good-are-llms [4] https://github.com/patched-codes/patchwork [Sample PRs] https://github.com/patched-demo/sample-injection/pulls

Share card

Actual performance

116points
24comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, llama, ide · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, host, users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Ev
Evvo – an open source framework for distributed evolutionary algorithms75%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Evvo – an open source framework for distributed evolutionary algorithms

Hacker News3
Me
MetricFlow – open-source metric framework81%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

MetricFlow – open-source metric framework

Hacker News98
Di
Dissect – An open source DFIR framework73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Dissect – An open source DFIR framework

Hacker News8
RedBlue
RedBlue48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open-Source Hypervideo Framework

Indie Hackers1apis
ZenML
ZenML50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

extensible, open-source MLOps framework

Indie Hackers4$1/moai
Gi
Gitcoin – Bounties for Open Source Development70%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Gitcoin – Bounties for Open Source Development

Hacker News1
Kubestack
Kubestack22%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open-source Terraform Gitops Framework for Teams

Indie Hackers3open-source
Op
Open-Source GitOps Framework for K8s Based on Terraform and Kustomize65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open-Source GitOps Framework for K8s Based on Terraform and Kustomize

Hacker News10
op
open source framework for building nanoservices81%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

open source framework for building nanoservices

Hacker News4
Dr
DrMock, open-source C++ testing/mocking framework63%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

DrMock, open-source C++ testing/mocking framework

Hacker News3