cepstrum9

neat minimalistic tool, giving it a go now, thanks for posting :)

There's a class of text that's too big for a context window but too small to justify a vector database. A codebase, a folder of notes, a Slack export. I built a small library - raglet - to solve this problem raglet for it. The idea is that you can create small knowledge bases - "raglets" out of local directories.

i.e

from raglet import RAGlet rag = RAGlet.from_files(["docs/", "notes.md"]) results = rag.search("what did we decide about the API design?", top_k=5) rag.save("my-notes")

Load it anywhere rag = RAGlet.load("my-notes")

Local embeddings (sentence-transformers, no API keys), saves to a plain directory you can git commit.

The benchmark numbers were more interesting than I expected: 1 MB (~262K tokens) | 3.5s build | 3.7ms search 10 MB (~2.6M tokens) | 35s build | 6.3ms search 100 MB (~26M tokens) | 6min build | 10.4ms search Honest limitations: .txt and .md only right now (PDF/DOCX is next), no file change detection, ~100 MB practical ceiling before build time gets unwieldy.

What would make this useful for your workflow? Would love to get your feedback.

hey all, while working on a SaaS im building, I had the need to decouple my prompts for code, so i could manage & organise them better, and I ended up building a small CLI tool to do that. the idea is that you can store prompts in yaml, version them and programmatically use them by ID. thats it, lmk what you think!

Hey, so, Codii is much simpler under the hood: it’s core is a smart prompt + LLM model that runs on pull requests. It doesn’t yet have deep static analysis, embeddings, repo-wide understanding or customisable rules — but it focuses on delivering helpful, lightweight code reviews with minimal setup.

GitHub Copilot Reviews is much more integrated into the GitHub platform and can access richer context like the full diff, surrounding files, and maybe repo history. It’s probably more sophisticated behind the scenes.

That said, Codii is:

• Easy to install (just a GitHub app)

• Fast to iterate on (I can tweak the prompts based on feedback)

• Customizable (it’s mine, so I can steer it toward indie dev use cases like onboarding help or documenting design choices)

In terms of requests and pricing, I believe GitHub Copilot free is going to offer 2k completions and 50 chats per month per user starting May 2025, and offers a Pro plan @19$ per user I think where you actually get access to much more than just reviews just to be clear, whereas Codii for now will be free until I gather feedback, but will be limited to 200 scans per month per user. Planning to offer a paid plan soon around the ~10$ mark

TL;DR: Codii is like a minimalistic, customizable code review assistant powered by GPT — not as powerful as Copilot Reviews yet, but very easy to set up and iterate on

Hey thanks! You re spot on, understanding the codebase context is key and maintaining history of the repo is perhaps the top problem to solve going forwards.

Also, there is context that’s not just code but documentation/guidelines etc, plenty of experimentation to be done there.

Customisable rules? YES - i ve got a lot of plans currently on this. I see two layers of customisation: - allow the user to customise when and how often reviews happen, and how they re triggered (i.e every commit/every x mins etc, avoid running prs on specific files I.e package.json changes etc) - pass in custom rules to the reviewer that work well for you(or team in the future). This will allow the tool to be more dynamic in nature as opposed to just LLM + your code.

Hope this answers your questions, thank you for the support!

First of all, thank you for taking the time to write this, I appreciate it!

- you re spot on the pricing - I have no clear plan yet on the tiers and the exact figure per month - there are 2 options for me here as I see it: a) implement token based pricing to cover pricing for workflows that use my service’s OPENAI key b) allow users to just use their own API key, and just charge the extra organisation layer that my app provides - this would probably be in ~3-5 p.m

- why consider codii - thanks for calling out the inconsistency bit, I ll update that to be phrased more correctly, the basic idea is - code reviews on every pull request as an extra QA check.

- your 3rd point is bang on - 10 is pretty low, and ideally I d be able to sustain a free tier with more scans per month(logistics a bit complex to get right). I ve actually updated that to 100 but have not reflected on the landing page, definitely something I need to do. Re target audience, you ve spotted my confusion there as well, this is indeed aimed for solo devs for now, in which case I should not be claiming team-based benefits. So I want to evolve this slowly starting from solo devs and working my way up to small teams. The action I’m taking from this is to focus on realistic target audience not the one I d like to have in 2 years time.

- re clunky landing page, again you’ve got a solid point there, clunkiness can feel repulsive, this is me basically sacrificing UI for functionality in the chase of feedback, the action I’m taking is to polish up th landing page so that it doesn’t scare people away .

Thank you for the encouragement and the solid feedback! This is exactly what Im after in the current stage!

I’ve spent the last 2.5 months building a github app that runs LLM-powered code reviews — and I just launched it.

The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood.

Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all without needing another human in the loop.

I believe agentic tools will play a huge role in the future dev experience — and code review is just the first node. Long-term, I want this to evolve into a multi-layer agentic assistant that supports everything from brainstorming and design to CI/CD.

Feature roadmap: - Model integrations beyond OpenAI: Claude, Gemini, DeepSeek, LLaMA - Bring Your Own Model (BYOM) - Smarter review agents: repo memory, config-awareness, custom rules

Right now I’m focused on getting feedback from devs and evolving based on what actually helps.

Try it here: [https://codii.dev]

I’d love feedback on:

- Are the reviews useful or noisy?

- What would make this fit into your workflow?

- Anything obviously missing, confusing, or broken?

Happy to answer any questions! (feel free to dm me on X https://x.com/cepstrum9)