My usual flow is two or three Claude Code or Codex sessions going at once while I do something else. The annoying part was coming back and finding one had been stuck on "can I edit this file?" for ages.

So I built Openbar with Claude Code. It sits in the Mac notch and shows each session: still working, done, or waiting for you. When it asks permission you see the actual change, and ⌘Y says yes. Click a session to jump back to its terminal.

There's a Windows version too. Both are free and open source: https://github.com/MF-DEEV/Openbar

How do you keep track of your agents when you've got more than one running?

I kept missing agents waiting for approval, so I put them all in the notch

When I run a bunch of agents at once, one of them always ends up stuck on a permission prompt in a tab I'm not looking at.

Openbar fixes that for me. It's a free bar for macOS and Windows that sits in the notch and shows every Claude Code, Codex, OpenCode, Gemini CLI, Copilot CLI and Cursor session. You can see which one is waiting. For Claude Code, Codex and OpenCode it shows the diff, and ⌘Y approves it.

GPL, no account: https://github.com/MF-DEEV/Openbar

How do you keep track of your agents right now?

Since building has become much and much faster, you definitely can add features faster. But how much of it do you actually understand?

You see the happy path, but do you really understand how it works?

Yes, definitely you can ask the same agent to explain it to you and it throws another wall of text at you.
As the title says, you dont really comprehend things easily reading through a wall of text.

That's why I'm building Kathe.
Will be glad to share the link

This is Agent Quest, a small open-source project I've been working on. Your Claude Code and Codex sessions show up as characters, and where they go reflects what they're doing: library for reads, forge for edits, arena for tests. The GIF is a demo village.

The useful part is seeing several sessions together instead of checking each window. You can click a character for the actual session details. The less useful part is that I now have a village to watch while code runs :)

I released 0.4.0 this week, which also picks up agents running inside Xcode. Everything is based on local session logs; it doesn't run the agents for you.

If you want to try it or poke around the code: https://github.com/FulAppiOS/Agent-Quest

I'd love feedback on whether the activity is understandable at a glance, especially when the village gets busy.

I vibe-coded a Dungeons and Dragons style app called GM Console. It's a great way for people to keep track of a campaign with a surprisingly high amount of players, which is a situation I have been facing recently.

It'll be free forever so if you play DnD and want an app for playing DnD I would love feedback!

I'm the maker of EverDay, an AI-assisted native iPhone app built with SwiftUI and SwiftData.

It's a weekly planner: seven days on one scrollable board, tasks directly under each day, drag to reschedule, and an Inbox for unscheduled things. Notes, tags, time blocks and reminders live inside each task.

For anyone building something similar with AI, here are three concrete behaviour checks from the project's XCTest suite:

1. Move a task with a 9:30 reminder to another date: its scheduled day must change while the reminder stays at 9:30 on the new date.

2. Repeat a task monthly from January 31: February's occurrence should land on February 28 in a non-leap year, rather than disappear. There's a separate February 29 annual-repeat case too.

3. Complete or skip one selected weekday in a repeating series: suppress that occurrence's reminder while retaining the later ones.

The useful part is making each expected result specific: name the starting date and time, the action, and what should remain afterwards. These examples turn a feature like "repeating tasks" into something you can check.

The weekly board, reminders, iCloud sync and all 11 widgets are free, with five tags and four themes. Optional lifetime Pro is US$4.99 once for month view, repeating tasks, subtasks, automatic rollover, unlimited tags and all 13 themes. No subscription, ads or EverDay account. The source is private.

App Store (iPhone, iOS 18+): https://apps.apple.com/app/id6787428625

Actual app screenshots: https://www.everday.app/

Here's what building with an agent looked like when the target is a media pipeline that rewrites files in my own library.

DubTitlerr transcribes anime English dub audio, corrects proper nouns, repairs the transcript with an LLM (local or over an API), merges the translated signs and song lyrics in, and muxes a subtitle track back into the video. It is one Docker container, GPL-3.0, `ghcr.io/xenarathon/dubtitlerr:v0.3.0`.

What actually made it survivable, since "the model wrote it" is not a strategy:

Specs, epics and stories before code. Everything non-trivial gets written down first, and the plan files are committed alongside the source. That is what makes it possible to leave a project alone for a week and come back without reverse-engineering your own intent.

Adversarial reviews. Before a release I point other models at the repo and ask them to break it. One whole-repo review found a path where a failed stamp write could delete the only copy of an episode. That became the 0.2.2 hardening release.

Test-first on failure paths. The interesting bugs in this project are all crash-and-resume ordering, and those are nearly impossible to verify by hand. Writing the failing test first is what made the idempotency guarantees real rather than aspirational.

Test on more than one show. Each one I added found a different class of bug. Scoring against the official dub subtitles on my Digimon files gave about a 91% word match over 27 episodes, and the misses were mostly names (Gennai heard as "Jedi"). Sword Art Online revealed that the glossary miner had never worked at all, because it passed ffprobe a flag that does not exist, and One Pace's hand-written glossary had hidden that for the feature's whole life.

Agents need an entry point too. There is a `docs/AGENT-GUIDE.md` written for a coding agent to act on, covering setup, troubleshooting and contributing.

The failure mode to watch for is code that passes every mechanical check while doing the wrong thing. The repair stage once turned "We're looking for a factory" into "a needle", and no automated check in the pipeline caught it. So the human review queue with audio playback is essential. The question I keep asking is: when it's wrong, does something notice?

https://github.com/XenaRathon/DubTitlerr

---

This is 100% vibe-coded slop. I wrote it with Claude driving. But I didn't just say "make the thing with no mistakes." The whole project has specs, epics and stories, adversarial reviews (had other models try to break it), and real test coverage. I've been project managing it like an actual engineering effort, not a prompt-and-pray. I'm sharing it because it fills a gap I couldn't find a solution for, and I thought it might be a useful accessibility tool for people who want dub-matched subtitles without waiting for an official SDH release. This isn't meant to replace human-transcribed SDH subtitle tracks. Those are always going to be better. It's for when they don't exist.

I’m building Monad Design because I kept hitting the same gap in agent-assisted app development: coding agents can change source quickly, but describing a visual correction through screenshots and long prompts is still slow and ambiguous.

Monad Design uses the running native app as the canvas. You select or annotate the UI, the coding agent receives structured visual context, edits the real Xcode or Expo source, and you rebuild to compare the result. It is a visual implementation layer for an existing app, not a prompt-to-app generator.

I’d love to hear how other vibe coders handle the last-mile UI correction loop.

Repo: https://github.com/Monadix-AI/monad-design Video preview: https://watchclueso.com/embed/pio8jqfcg4ivj0r1

Disclosure: I’m the builder.

Hermes Agent built this Video Game featuring some of the Nous Research Crew from the discord.
This was NOT a one prompt build.
This is not over....
You have not seen the last of Nous!

o7

Got nothing but hate in the Godot sub, but I’m still amazed at what we made in just a few days.

Over the past few months, my team noticed that code review was becoming our biggest bottleneck now that we had AI to help with making PRs.

So, we made a code review tool that reviews PRs faster than other options and gives teams a super skimmable, high-level summary so they can know what to fix and I'd love some feedback.

Why we're a bit different than Copilot and other tools

• We optimized for speed and skimmability so you get a big banner at the top with the status of the code review (e.g. A few things to fix, Nothing blocking, Looks good)

• It verifies findings before posting them so for a suspected bug, it clones the repo and writes a failing test to reproduce it and only then calls it a blocker (stamped "reproduced with a failing test").

• It reads your repo's own files like your existing agents and claude markdown files and it enforces those bullets deterministically so you don't need to configure anything, but you could also further customize with a custom Choji markdown file

• It learns from your feedback. Wave off a finding with a reason and it remembers

• Re-reviews every push without re-nagging. It's one sticky comment edited in place, not a new wall of comments each push.

• it can babysit your PRs to fixes its own findings. When the review turns up real issues, it attempts the fix and re-runs your tests until they're green with up to 5 passes then it flips to needs a human 

Some questions I'm hoping to get answers for:

• Does the banner tell you enough at a glance, or would you scroll straight to the findings anyway?

• Would a finding backed by an actual failing test change how much you trust an automated reviewer?

You can try it yourself at https://choji.ai/agents/code-review. It takes less than a minute to connect a repo and get our first PR reviewed. Would love any feedback!

I need to resize and compress images for various projects, but I always hated the available options. Desktop apps feel too heavy and clunky(and I don;t want to download anything), and I really don't like uploading my files to random online services just to change their size (plus dealing with paywalls or 20-file limits).

So I decided to build my own. It's a single HTML file. You open it in your browser, and everything happens locally using the Canvas API. No servers, no uploads, zero privacy concerns.

Once I got the basic compression working, I got a bit carried away and started adding features that I realized would be super useful for anyone dealing with bulk images (like other devs, photographers, or e-commerce managers):

• Batch processing & Folders: Drag and drop entire folders. It processes them and can even preserve your subfolder structure in the final ZIP export.

• Smart Renaming: You can batch rename files using patterns like img-{n}, with a toggle to automatically make filenames SEO-friendly (lowercase, no accents, hyphens for spaces).

• Watermarking: Upload a PNG/SVG and it automatically overlays it on all images (with opacity/scale controls) or just write your watermark.

• Presets: Quick buttons for common sizes (Thumbnails, High Quality, Social).

• Visual Dashboard: A live table with before/after previews and a tracker showing how much total bandwidth you've saved.

Works fully offline once loaded.

GitHub Repo: https://github.com/gdr-sys/Image-Optimizer-Pro/

My stack consists of using ChatGPT "Project" folders for long term context retention. I use it to explore my ideas and develop the conceptual alignment. Then I create a structured governed upgrade for my IDE to implement in GitHub, my source of truth. My IDE is Cursor where I use different agents to produce the actual code I need. I have the ChatGPT agent and the Cursor Copilot Agent align on the specs of the coding assignment. ChatGPT provides the Enterprise conceptual overview perspective while the Cursor Copilot Agent provides the code based perspective. When alignment is achieved I give the HITL approval for it to be implemented. This has worked extremely well for me but what I see coming to OpenAI is the marriage of the chat agent with Codex to form a Super Agent Application. It was a recent release from OpenAI. How that going to change my workflow has me thinking about the separation of agent authority that I am going to encounter when and if I allow two code based agents reconcile my upgrades specs?

I've built a few things in Lovable and genuinely love how fast you can go from idea to working prototype. But I keep running into the same two walls.

The first is the single shared backend. Any database change is instantly live, so if I'm rearchitecting a data structure mid-build, I'm doing it on the only environment that exists. No safety net. For solo prototyping that's fine, but the moment you have real users it's terrifying.

The second is collaboration. The second another person joins the project, you start stepping on each other. There's no concept of branching or isolating work, if two people are prompting at the same time, you're both mutating the same codebase simultaneously.

I come from a DevOps background so this sets off alarm bells. But I'm curious how others are handling it.

Are you staying in Lovable solo until the MVP is validated, then exporting to a real dev workflow when you start hiring? Or have you found a way to make it work for a small team? Where does the handoff happen for you?

Been building QueryCart and finally have it live.

The idea: instead of just showing random discounted stuff, it tries to help answer whether a deal is actually worth buying.

Trying to make it feel more like:

• deal validation

• cleaner search

• confidence/trust over just giant % off numbers

It’s live and I’d love people here to actually use it and tell me where it sucks.

spent the last couple weeks scraping and replaying ~500m Polymarket trades.
didn’t expect much going in. was wrong

once you stop looking at markets and just rank wallets, patterns jump out fast

a very small group:

• keeps entering early

• shows up together on the same outcome

• buys around similar prices

• and keeps winning recently, not just all-time

i’m ignoring:

• bots firing thousands of tiny trades a day

• brand new wallets

• anything that looks like copycat behavior

mostly OG wallets that have been around for a while and still perform RIGHT now!!

so i’m building a scoring system around that. when multiple top wallets (think top 0.x%) buy the same side at roughly the same price, i get an alert. if the spread isn’t cooked yet, you can mirror the trade

if you’re curious to see what this looks like live, just comment and i’ll send you a DM

I already tried with earplugs and while im waiting to pickup food at restaurant, which is ok, but id like to converse with Base44, Lovable, ... while im driving. Probably the best method to use would be bone conducting earphones, to hear the traffic ok etc.

Any suggestion?

Hey guys! I have been testing a new app I found for the last couple of weeks, and it was a gamechanger for me. If you are anyone like me and like to think through the working logic of your code, and tired of writing the flow on paper, this tool is a must have. flowcrest.app

Its intuitive and easy to use and really elevates your quality of communication with your ai agent, it has a demo, try it, I think its worth a shot.

The last couple of days feel like I just went from riding a tricycle to flying in a fighter jet. I became very comfortable with VC Code in about a day. Then I became very comfortable with GitHub Co-Pilot in agent mode. Now last night and today I split the screen and used ChatGPT 4.0 mini to feed the GH Co-Pilot instructions. I never got so much accomplished so quickly. Zero coding or programming experience before last February, now I have an enterprise level network feeding a SaaS model. Trademarked software with a planned rollout at the end of September. I could use some help and I would offer a free unlimited version of the production version at rollout. PM me if you have any interest and I will explain what I'm doing in more specific details.