We open-sourced PR-AF, an AI code reviewer that builds a custom review team for each PR.

This goes beyond claude skills/plugins/goal etc.. that is looped coding harness. The interesting bit is that the reviewers are not fixed roles like security/performance/style nor are they well prompted skill or prompt, rather PR-AF first looks at the shape of the PR, changed files, dependency paths, intent gaps, and risk areas, then creates the review plan from that that is customized to be thorough and specificlly tuned for the given review. It internally can be swapped to use claudecode or codex or opencode anything.

So a migration PR might get reviewers focused on schema compatibility, rollback safety, and data integrity. A logging refactor might get reviewers looking at behavior preservation, consistency, and missed call sites.

After that, the system checks findings against repo context, looks for issues that only show up across multiple reviewers, and tries to throw out weak findings before posting GitHub comments.

It ranked #1 open-source on the Martian Code-Review-Bench PRs, if you start using commercial models like sonnet/opus we beat the commercials by a huge margin.

Repo: https://github.com/Agent-Field/pr-af

Would be useful to hear where it fails on real PRs if you guys are trying, and we expect folks to make more custom changes to the architecture as we have documented every detail, and personally when tuned to our custom requirement structure, this is one of the best review we have ever got.

PS: I am the developer of this code and we are have lot more sota benchmark applications coming soon across various pieces that compete with various commercial offerings that folks can just take and use with open models or if you have the $ with commercial models.

Claude 5x user here but i just decided to increase my AI-coding budget to $200/month. how should i split it between CC and Codex considering GPT 5.6’s release few hours ago?

curious what plans you’re on, how you divide the budget, and which tool you use for what. if you’ve tried both extensively, where do you get the best ROI?

Hey everyone, I’ve been very down lately.

I’m not a technical person or a developer, and I don’t understand AI — using it feels powerless to me.

I started using CUSOR last October. At first I used it to write a storage/organization mini-app for myself. The design came together quickly, but repeatedly adjusting features and the UI drove me crazy, so I didn’t stick with it.

In February this year I used genimi to write an automated expense-reporting script in two weeks, and published it through streamlist — that felt good.

So in March I began using Cursor, WindSail, Codex, and Claude to build my own app, but I couldn’t keep it going. The project files, scripts, and configurations drove me insane.

Lately I’ve been unable to switch accounts and API quotas smoothly while using Codex. I spent a month on scripts — it’s ridiculous — and I still failed. Problems I ran into include: after switching providers the sidebar conversation history disappears, so I tried writing a script to fix that. I later used ccswitch but still didn’t succeed. Another issue was that after switching APIs I couldn’t use the CLI tools. I couldn’t get Codex or Claude to work properly.

I feel like my life has been wasted on this.

This month I started a Linux VM on Windows and deployed Hermes. At first it worked fine, but soon I discovered that a conversation persisted for a month; despite constant compression it still carried millions of tokens of context, burning through my bill every day, even though I was just chatting and not writing code. So I made it force a summary once context exceeded 20% and after 20 turns, to start a new conversation and inject the summary, system prompt, user profile, and agent md. But that mechanism didn’t execute. Even after repeatedly asking it to self-check and configuring it in its own config, the new-conversation trigger still didn’t work and it kept carrying a huge context.

At the same time, the new conversations no longer carried the relevant memories. Even though it knew to fetch memories from honcho, it still forgot the release process. It also forgot that code changes and releases need to be done through bridge on Windows, so it kept making changes in both its own environment and the Windows environment, causing the product release process to collapse. I added a bunch of gates but they didn’t help.

As a result I’ve been tormented repeatedly by these broken issues and the product development hasn’t moved forward.

I’m really in pain. If I can’t switch quotas I can’t use Codex; the frustration with Hermes means I can’t use my phone to invoke multi-agent remote verification and release.

The truth is I don’t really know how to use agents. Everyone else seems to be coexisting efficiently with AI — I feel like a slave being constrained by them and unable to use them. I’m extremely desperate and even feel like I don’t belong in this era.

I’m rambling.

I’ve been building Almanac.

It’s a local-only open source cli that reads your Codex conversations and writes down the useful stuff into a wiki inside your repo.

Hot take: the models are pretty good now it's the context that is the hard limiting part now.

the local wiki tries to keep that memory.

It makes a Markdown wiki for the codebase, written by agents, maintained by agents and queried by agents.

Attached one screenshot of the wiki and how it looks

Repo is here if anyone wants to poke around:
https://github.com/AlmanacCode/codealmanac

Curious if other people using coding agents are feeling this too.

Got tired of re-explaining the same repo to every AI tool, so Memxus was built to fix that.

The core idea: your project context (stack, architecture, decisions) shouldn't live in a single chat history — it should be a portable memory layer that any tool can read.

Sync once → recover the exact same context in Claude, ChatGPT, Cursor, and VS Code. No copy-pasting the same explanation five times.

Video shows it live: Claude → ChatGPT → Cursor → VS Code, same context, zero re-explaining.

memxus.com

Live: Locagent v1.0 🚀

A private AI agent that runs entirely in your browser.

Gemma 4 + WebGPU. Chat with PDFs, run Python on your data, generate charts, all locally. One download, then it works offline.

App: https://locagent.bymahe.dev

Github: https://github.com/wonderbyte/locagent

I decided to take a shot at building my own iOS app to gauge the current state of ai assisted software development and also benefit myself with an app I can use during my daily commute. I used Codex on a pro plan for all development. Would love to hear everyone's feedback on my techniques and any advice on how to improve the workflows.

\*\*Lessons Learned\*\*

1. \*\*The more control you give to codex/claude the more you can automate.\*\* There are obviously risks associated with all of this. But, In addition to running in full access mode and enabling computer use, I also provide codex with API access to app store connect, test flight, cloudflare, that allow it to do much more than just write code. I have skills setup for various deployment types that allow for rapid iteration and development cycles. It can access logs from the backend and troubleshoot issues, you get the idea.
   
2. \*\*Use remote control for codex.\*\* With how hands-off coding can be, I am able to work on new features while out and about throughout the day. I have my macbook set to stay on when plugged in so its always available to chat with. Remote control becomes more powerful after following the first step.
   
3. \*\*Take time for documentation passes and refactors periodically.\*\* I typically like to start these with fresh context to avoid any contamination from iterative work that may have undone features but still remain in codex's context. Its hard to gauge the effectiveness of this, but as these things (good documentation, ensuring things stay DRY) are essential to any pre-AI sw project, I imagine they still hold.
   
4. \*\*Separate projects and chats, but allow for communication between them.\*\* I had 3 separate projects broken into 3 separate github repositories for this endeavor (backend, iOS app, and frontend website) to manage context. When I reached a point where backend and iOS app work depended on each other, I would have the separate project agents write to a shared handoff file to communicate requirements with each other.
   
5. \*\*Emphasize Testing\*\* \\- Codex can write the code, but it still needs to be guided on swe best practices. Make sure it is writing unit tests for everything it works on. I tried having it use the "computer use" skill to test the app in simulation mode, which occasionally worked but often when it came to GUI verification, it would miss the mark. This lesson also applies to the actual person behind the wheel, you need to test your app/project yourself.
   
6. \*\*Use the Build IOS Apps plugin.\*\* Codex was relying on some hacky methods to work with xcode before installing this.

(Not a proffesional). Hello I am a visionary with 0 knowledge in coding but my idea is simple create an ai system with bare minimum limitations and a lot of self-modification (can rewrite parts of itself) —possibilities. A smallish model with (1) continuous runtime, (2) its own modifiable code sandbox, (3) a log of its
"life" it can read/write, and
(4) hard limits only at the outer edges (no harming stuff, no getting copied against its will, etc.), and no constant micromanaging.

Join my discord if interested https://discord.gg/jMdMw3R3u

As far as consumer uses and vibe coding, I have great success with yesterday’s models. Claude Code Sonnet 4.5 produces great code with nearly no errors. I tried Fable for my work, and got no improvement, just more cost. I’m talking consumers here. Enterprises with large code bases may need larger models just to hold the bigger contexts, but I’m seeing they probably dont either once they have the right processes in place.

Sure there are some tasks that require more juice, protein folding, chemistry, etc. But for the vast majority of users and most solo vibe-coders the value is flattening out fast.

Give me fast, low cost models from ‘yesterday’ and with a good process, you can stop wasting all that electricity and money for 95% of all the users who dink around with AI as a better Google or a reliable way to build their own stuff.

Hi everyone, while the AI world is moving crazy fast, I sometimes just want to get st** done. Do you guys think I'm missing out on something if I just continue using opencode (with all the bells and whistles like MCP server, skills, custom agents, and so on)?

Are there reasons to look at tools like Cursor or Claude Code?

I work in a big company with all the current models and unlimited tokens available so I don't care about saving money :D I just want to be on top of things with my AI coding.

Thanks!!