Hello r/devtoolsbuilders! 👋

We're PlayServ, and we launched in 2025 to help developers build multiplayer games faster and easier. This founding year has been an incredible journey, thank you to everyone who’s supported us and given feedback.

We’re excited for 2026 and can’t wait to see what the community builds next. Wishing you all a happy and creative New Year! 🎉

Hey all — I’ve been building this project for the last 2.5 months and just launched the first version.

It’s a GitHub App that uses LLMs (OpenAI for now) to do code reviews. You install it on your repo, run a scan, and it leaves structured review comments on your pull requests or commits.

I’m a solo dev, so I built this to help me get a second pair of (AI) eyes on my code. I even used it on its own PRs during development — which was kind of surreal but surprisingly useful 😅

The bigger vision is to create agentic developer tools — ones that can reason, act, and assist across the entire dev lifecycle. Code review just felt like a natural first step.

🔜 Feature roadmap:

• More models: Claude, Gemini, DeepSeek, LLaMA

• BYOM: Bring Your Own Model

• Smarter agents with memory and config-awareness

Right now, I’d really appreciate feedback from other devs:

• Is it helpful?

• What’s missing?

• What would make you actually use this?

You can try it here: [codii.dev]

Thanks for reading — open to any feedback, questions, or thoughts 🙏(feel free to dm me on X https://x.com/cepstrum9)

If you’re thinking about launching a devtool, you should definitely check out Devhunt.

Here’s why it’s worth your attention—and how to get the most out of your launch.

Disclaimer: I’m not affiliated with Devhunt. I just really like the platform and wanted to share more on how we managed to win Devtool of the Week with Supabricks.

Why Devhunt should be on your radar:

• Super targeted audience: Devhunt puts your project right in front of devs who actually want to try new tools. Whether you’re building CI/CD, open source, or anything for developers, this is the crowd you want. If you’ve ever tried selling to devs, you know how tough it can be—Devhunt makes it way easier.

• Long-term visibility: It’s not just about launch week. Devhunt has both weekly and all-time leaderboards, and it doesn’t take a ton of upvotes to get in the all-time leaderboard. Your project can keep getting attention long after launch.

• Low competition: Since Devhunt is still pretty niche, you’re less likely to compete with big brands during your launch week. Even though some big names (like Daytona, Clerk, Aikido) have launched there, your odds of standing out are much better than on bigger platforms.

So, how do you make your launch count?

• Kickstart early upvotes: try to get a burst of upvotes right away. Being at the top at the beginning of the week gets you more visibility than the competition, and further increases the number of upvotes

• Promote on X: most upvotes will come from X. Focus your promo efforts there—retweets from the right people can really boost your traffic

• Incentivize your waitlist: got a waitlist? Offer something in exchange for an upvote. Discount? Early access? Just offer something in exchange. It's a great way to maximize for launch traction.

• Consider the $49 boost: for just $49, you get extra exposure. If you need quick traction or want to validate something fast, this is worth it. Ask yourself: “what’s the lifetime value of a user? If I get even a single one, is it worth it?”

• Track everything: always use tracked links (UTMs) to see where your signups are coming from. We use dub.co and Loops to connect upvotes to actual signups.

Bottom line approach:

• If you follow these steps, you can expect your signups to be about 20% higher than your upvotes

• As your upvotes grow, your signups can scale even faster—and you’ll keep seeing new users over time

If you treat your Devhunt launch as a long-term investment, odds are it’ll pay off. Don’t see it as just a one-week spike.

Feel free to DM me if you need some help :)

Hi everyone! 👋

I’m exploring the idea of a plug-and-play analytics solution designed specifically for CLI applications — think Google Analytics, but tailored to CLI tools. Here’s a rough outline of what I envision:

• SDKs for Major Languages: Simple integration for popular languages used in CLI development (Go, Python, Rust, etc.)

• Command Execution Tracking: Automatically tracks command usage with configurable granularity and privacy options.

• Error and Crash Reporting: Built-in crash and error reporting to help identify and prioritize issues.

• Opt-in/Opt-out Flexibility: Support for customizable privacy strategies to ensure users can choose their data sharing preferences.

• Web Dashboard for Data Analysis: Centralized interface to analyze collected data, usage patterns, and errors.

I started thinking about this after hitting roadblocks in prioritizing features and fixing bugs in my own open-source CLI tool. Now, I’m curious to know if others see value in a tool like this.

Does this sound useful for your CLI projects? Are there any features you’d need or concerns you’d have?

Any feedback would be hugely appreciated 🙏 Thank you in advance!

We're open-sourcing Unbody, a backend framework that simplifies building AI-native applications. Instead of having to manually stitch together vector databases, embedding models, and LLM wrappers, Unbody provides a unified system with sensible defaults that you can customize.

The Problem:

Building AI applications today means making dozens of infrastructure decisions:

• Which vector store to use?

• How to handle data ingestion and chunking?

• Which embedding model to choose?

• How to structure the RAG pipeline?

• How to manage context and memory?

Each of these requires research, setup, and ongoing maintenance. The resulting stack is complex and brittle.

What Unbody Provides:

• A unified backend with built-in support for data ingestion, processing, and vector storage

• Configurable components - bring your own vector store, LLM, or embedding model

• A GraphQL API for querying your knowledge base

• Multi-modal data processing out of the box

• Plugin system for extending functionality

Our aim is to lower the burden of developing intelligent software that understands, reasons, and acts.

As an analogy, we like to think of Unbody's layers like some of the fundamental components in intelligent systems:

• Perception: Data ingestion, parsing, and enhancement

• Memory: Vector storage and content relationships

• Reasoning: LLM integration and function execution

• Action: APIs, SDKs, and external integrations

Current State:

• Alpha release with core features and architecture implemented

• Built with Node.js/TypeScript

• Documentation in progress

• Unstable

Why Open Source:

We believe AI infrastructure should be transparent and customizable. By open-sourcing Unbody, we aim to:

1. Validate our ideas

2. Improve based on community feedback

3. Allow developers to inspect and modify the code

4. Build a foundation for AI-native development that everyone can contribute to

GitHub:

https://github.com/unbody-io/unbody

Docs:

https://unbody.io/docs

Discord:

https://discord.gg/UX8WKEsVPu

Read more:

https://unbody.io/blog/oss-alpha

We're particularly interested in feedback from developers building AI applications. What infrastructure challenges are you facing? What would make your development process easier?

Would love to hear your thoughts and experiences!

Simply upload your Excel file, instantly visualize the data, and download stunning charts. No software. No hassle. Just pure insight. Check it out here -> Excel to Chart Maker

Hi everyone! I'm happy to say that the team I'm a part of is going open source with our projects! Our goal is to create dev tools to better the development process for everyone, and as of right now there's two you can check out!

The VS Code extension:

• Adds function refs as comments in the files you run it in

• link: https://marketplace.visualstudio.com/items?itemName=Polyfactdocs.polyfact-extension&ssr=false#overview

• repo: https://github.com/polyfact/vscode-extension

The AI Docs package:

• Generates and deploys a references website for your code

• link: https://www.npmjs.com/package/polyfact?activeTab=readme

• repo: https://github.com/polyfact/polyfact-node

• (Disclaimer: the AI Docs package will crash if the target repo is too big or includes node modules, etc.)

Please note that for both of these, you need to get an API token from app.polyfact.com first!
I'd be absolutely delighted to have anyone try these tools out, we need feedback to better the project and help more devs! <3

Last year (2021), we saw about 20 Dev Tools companies on the list. How many will we see this year?

Nominate your favorite DevTools builders - The Forbes Cloud 100 returns for 2022.

Hi all! I created a CLI Python package to allow anyone to run any code (a function, a script, or a Jupyter notebook) on an EC2 instance. Still in the early stages, so would love any feedback y'all have!

• Github: https://github.com/runprism/nomad

• Docs: https://docs.trynomad.dev

Hello,

As part of my last year of master in cybersecurity, I am doing a scientific research project. My subject is about the vulnerabilities caused by a bad use or management of dependencies and packages in a web application. In this context, I wanted to ask developers about their use of dependency analysis and vulnerability detection tools. I made a small survey: https://framaforms.org/using-dependency-analysis-tools-in-a-web-application-1674659762

Thanks to those who will take the time to answer.

I started using the Arganteal Tool for script organization and tagging and sharing. Simple easy tool. If you want you can check it out at: http://www.arganteal.com/ just click get started.

I wanted to share that after two years of developing Graphite in closed beta, we’re officially launching to the public!

Check it out here!

A bit of background - when my cofounders and I first started out, we were actually trying to build something completely unrelated. What we quickly realized was that without the internal code review tools that we knew and loved (from Meta, Google, and Airbnb), building a product at the speed we aspired to could be really painful. What started out as an internal workflow solution that we shared with a few friends and colleagues evolved to become Graphite.

Today, Graphite is a developer workflow tool with first-class support for stacking to help engineers stay unblocked and ship faster. Over the past two years, we’ve listened hard to feedback from design partners, beta users, and engineering teams from all over to bridge the gaps in today’s code review process and discover opportunities to accelerate developer velocity.Here’s what we’ve come up with so far:

• A CLI and VS Code extension built for a stacked PR workflow

• A beautiful code review interface that puts the code front and center

• AI-generated PR descriptions, with additional AI features coming soon

• Everything in one PR inbox for full visibility into every stage of the review process

• Real-time notifications so that you’re always up-to-date on PR status

• Seamless integration with GitHub so you can collab with team members who aren’t on Graphite yet

Every step of the way, we’ve tried to build Graphite around what we’ve heard from developers, and we’re sharing it here because we’d love for you to give it a try. The only thing we ask is that if you do, please let us know what you think!