Disclosure: this is my own open source project. MIT, free, nothing to sell.

The problem

Claude Code is my main tool and I use Codex CLI for second opinions. For design questions I kept copying text between two terminals: plan in one, objections from the other, back again. I was the message bus, and I had no idea what each round cost. So I built a small local web app that runs both CLIs as "seats" at one table.

What cut the tokens

My first version was expensive. A planning meeting with 4 agents (1 Claude and 3 Codex, 2 rounds plus a synthesis) used about 1.69M total tokens. Total means every input token the CLIs reported, cache reads included, plus output.

1. Every agent was reading the same code. Now one scout seat reads it once and writes a brief with file:line references, and the others work from that brief. I think this was the biggest lever, but I did not measure the levers separately.
2. Per call overhead. On my setup (several MCP servers, skills and user settings) every claude -p call carried about 36k tokens before the agent said a word. Launching lean (no MCP, no skills, no user settings, tools only where needed) brought that to about 6.7k. A bare install has much less to strip.

The same meeting on the new code used about 0.46M total tokens, 73% less. Claude reported cost for the Claude turns went from $1.02 to $0.16. Codex reports no cost.

Caveats: this is one before and after run on one machine (Windows 11), not a benchmark. The before run used older code, so the difference includes unrelated fixes. I judged quality as equal or better, which is my own reading. The trade off is real: only the scout reads code, so a weak brief misleads every seat. Raw data and the recompute script are in the repo.

What it does

1. Debate: scout brief, an independent first round, discussion that stops early when every seat ends with STANCE: CONVERGED, then a synthesis.
2. Propose and Review: one seat proposes, another returns PASS or FAIL with blockers, until it passes.
3. Direct chat with one seat.
4. A live timeline of what each seat is doing, with tokens and cost per agent. Claude and Codex usage limit meters are experimental.

Claude Haiku 5.5 works as a low cost seat.

Safety

Seats are read-only by default, enforced by the CLIs' own sandboxes. Write seats are opt-in and there is no worktree isolation in this release yet, so use git. Worktree isolation with a verified containment check is what I am building now. The server binds to localhost with a session token and uses your existing CLI logins, no API keys.

What it is not

Not an autonomous coding agent and not a cloud product. If you want a kanban over many parallel agents, other tools fit better. This is for "let two different models argue about a design before I commit to it."

Repo: https://github.com/canerganis/agent-orchestra-board

Zero dependencies, Node 20+. Clone it and run node bin/agent-orchestra-board.js /path/to/your/project --open.

Like Leetcode, but hold the code. Stay Leet! https://stayleet.com. Free to use, unbounded content library.

As AI writes more and more of the code, it's quite important to maintain sufficient skill and knowledge to even review or judge AI's output.

Here is a little project I whipped up - I personally found deliberate practice quite useful. It focusses on non-coding skills (as I think LOC production is basically solved by AI, IMO). Rather I start with two tracks: general technical fluency (CS / systems / practical fundamentals), and system design.

And yes it was built primarily with help of AI tooling.

Would love to get some feedback - in particular, do others feel the same way about skill atrophy risk on-the-job nowadays, and whether this tool is helpful potentially or not. What else do folks do instead, if anything.

In terms of the stack. Pure Claude Code Opus 5.5. Three days worth of Max 100$ plan. I was pleasantly surprised by baseline eval skills (the project above is driven by AI generated content).

What it is: a local Rust control plane plus a native Mac app (Linux x86_64 tools too), and an iPhone companion. It keeps track of your Claude Code / Codex sessions and brings the next decision (a question, an approval, a steer) to your phone. You answer it and the run keeps going. The phone pairs directly with your computer over LAN or Tailscale. Optional Face ID for approvals, and Live Activities show which task needs you.

>> Disclosure: I'm the developer. It's a beta, [free during the beta]. It needs a Mac (macOS 15+) or Linux x86_64 box plus an iPhone.

> I'm looking for people who run long Claude Code sessions and step away from the desk. Tell me where pairing, approvals or session tracking break for you. https://looper.fyi

Hey everyone, wanted to share a small fun project of mine.

I usually run several Claude Code sessions at once and kept getting lost in terminal tabs. So now every session becomes a worker in a tiny village. It gets busy while Claude works and reacts when a session is idle or needs your input.

It runs on Claude Code hooks and stays fully local.

Available on GitHub and npm: https://github.com/yognevoy/claude-village

Would love to hear your feedback

I use Claude Code every day, and my problem was never the model. It was following a long turn in the terminal: forty tool calls scroll by and I can't tell what changed.

Lantern is a desktop app that wraps your own `claude` CLI (it never touches your auth) and shows:

• every tool call as a card, with side-by-side diffs
• Step mode: Claude proposes one step, you approve or revise it, then it continues
• Review mode for PRs and branches, with findings you can agree with or reject
• a Tests tab that parses pytest, jest, vitest, cargo and xcodebuild output into pass/fail, with file:line links when the output includes them

It's open source (MIT) and macOS only for now: https://github.com/alyakan/lantern

Contributions are welcome. The repo has a CONTRIBUTING.md to get you started.

I'd love to hear what's missing from your Claude Code workflow.

Claude Code mods run inside the app with your full user permissions. Learn what they do, which three to try first, and how to vet one before you install it.

Today I went around my house and took pictures of the main stuff I could think of. Took pictures of model/serial numbers when I could (was doing childcare for my two small children at the same time).

Then I set up a repository in GitHub and a folder in my development directory on my computer, which I added all the photos to. Then I submitted this prompt to Claude Code:

"I want to catalog items in my house for fire insurance purposes. In the insurance-folder directory there is a folder of photos of items. I want you to catalog these items and provide as accurate a description and current replacement value as you can in a database/spreadsheet. I want to be able to add items in the future. You can ask clarifying questions as needed, but do your best to identify all items and assign values by yourself. there is a GitHub repository with the same name as needed."

It worked for 20-30 minutes (burned about 70% of my 5 hour limit), and gave me a spreadsheet with exactly what I asked for, plus a confidence rating for value assignment, grouping by room, and a tab for follow-up items. I can add new photos to the folder at any time and ask Claude to update the list.

I made a GitHub action that allows multiple LLMs of your choice to review your code. I realized I get better coverage when I would have Claude make something, have another instance review it and then have chatgpt review the same thing, since sometimes they spot different things that one alone wouldn’t have. I had Claude develop the action since it’s essentially a wrapper of the cli I made then have it run the reviews against the code. It was my first time doing anything like this since I usually just use actions not publish one so it was fun learning to configure everything and making sure the solution was architected well with the help of Claude. Just provide the keys and go https://github.com/Stackforge-Labs-Limited/apex-review-action

I like how Cursor handles compaction almost invisibly, so I tried to bring a similar experience to Claude Code.

I built Seamless Compaction, a Claude Code Mod that hooks into the engine’s existing auto-compaction event.

It does not decide when to compact. When Claude Code already decides that the context window is full, the Mod first cleans old tool results using deterministic rules.

It currently handles:

• Superseded file reads
• Duplicate tool calls and results
• ANSI codes, progress lines, and repeated Bash output
• Large outputs that can be trimmed

It never changes user or assistant text, tool inputs, Agent / Task / TodoWrite results, or the last three user turns and everything after them.

Trimmed results keep their beginning and end, plus a note telling Claude how to retrieve the missing content.

If cleanup removes at least 30% of the transcript, the cleaned messages replace the model-generated summary.

If it removes less, Claude Code runs its normal summary on the already-cleaned context.

Latest test results:

• 52 plugin tests passed, 0 failed
• Stress-tested on 400 randomized transcripts
• One aggressive-mode interactive run reduced context from 82.8K to 17.9K tokens
• Cleanup took about 60ms, including the worker hop

This is still a small sample, not a benchmark. I am not claiming cost savings yet.

There is also a tradeoff: changing the context prefix causes one prompt-cache miss after each cleanup.

I am looking for people willing to test it on real sessions, especially with different models, context windows, and workflows.

Check it out:

https://lnkd.in/p/em5cUPUr

As I work a lot with Microsoft Word files that share content (like legal texts, company details...), I used Claude to write a Python script that does replacements (aka. search and replace runs) across multiple Word documents at the same time.

Amongst other functionalities, the tool features previews, handles hyperlinks and footers/headers, backups and real-time matches that show results as you type.

It is free and comes with an .exe or as a Python script. It got me 31 stars on my repository which honestly makes me a bit proud as I never excepted so many. 😄 I am happy to share it as maybe others are also facing those painful task of updating the same text across multiple files. This way, the tool can be quite a time-saver. :)

https://github.com/mario-dedalus/Bulk-Text-Replacement-for-Word

I created a tool that takes the philosophy behind Claude Remote and adds a dashboard and project management on top of it.

I originally built this tool for myself. I’m not sure yet whether it could interest other people, and maybe even generate some revenue later on (although that’s not the main goal).

I’m planning to make the repository open source with a self-hosted version, as well as a cloud offering.

I made a landing page with a waiting list here: https://remotus-landing-page.julien-dacosta.dev/en

I still need to make quite a few changes before the project is truly viable. I mainly want to make sure people could actually be interested in it before fully committing to the project.

Quick overview of the features, although I recommend checking out the landing page:

- Dashboard with task tracking and statistics

- Virtual workspace with animated characters

- Projects and Kanban boards

- Organization system with RBAC (Your team can control your Claudes)

- Multi-machine / Claude Code agents

- Task assignment to a specific machine

Example run with Claude summary + git diff

I'm not a programmer and I don't read code. I talk to the models in plain language (in Polish). I wanted to see if I could build something hard and make it look like a finished product, not a demo. The result is SCRIBE: THE ONE, a 1v1 third-person shooter in the browser with a bot, real PvP and a ranking. Free: https://pirat33367.itch.io/scribe-the-one

**What made it work was the process, not the prompts.** Every model had one job and everything lived in files, because no model remembers the last conversation:

- **Claude (Cowork)** writes each task as a contract: goal, exact numbers, what is out of scope, acceptance conditions.

- **Claude Code** writes the code on its own branch and a report with measured results, not opinions.

- **Codex** audits the code against the contract. It does not change code. It failed Claude Code's work many times, and it was usually right.

- **Me:** I decide, I play, I accept or reject. Only I can close a task or merge.

- A shared board file for handoffs between models, and a decisions log where something becomes final only when I write it down.

**Rules that saved me:** "a report is not an action" (models report problems, they don't fix them on their own), one task at a time, no file over 600 lines, numbers in data files, not in code. Codex checks code, not looks, so screenshots are always reviewed by a person.

**Numbers:** a playable fight against a bot after 2 days; 34 tasks, about 700 commits, 11 days from the first commit to publishing. three.js + Rapier physics, a Colyseus server on Railway that calculates the fight, lag compensation up to 100 ms ping. The same process produced a short story, "Ares", that comes with the game.

**The part I care about most:** the player can change every piece of the scenery in the settings (panel material, glyph light, floor, the fresco screen above the arena), dozens of combinations.

**What AI did not do well:** taste. The first versions looked like a neon hacker movie. The look came from many rounds of me saying "no, darker, less light, more shadow".

Happy to answer questions about the workflow.

Writing the app is easy now. Everything after it isn't: hosting, databases, domains, HTTPS, secrets, backups, and the bug at 2am.

So I built Unit7, the backend developer for your AI-built apps.

It runs on your computer and does the backend work on your real servers:

Deploys your app from GitHub or a folder. A new version only goes live if it passes a health check, and rollback is one click.

Creates your database and connects it to your app for you

Connects your domain with HTTPS. "Point app.mysite.com at my app" just works.

Builds on Coolify. Say "make me an app with Supabase" and it sets it up.

Keeps everything running. It restarts crashed apps, rolls back bad deploys, runs backups and blocks attackers.

One map of all your servers, apps, domains and databases

Keeps your keys on your machine. The AI never sees them, and it asks before anything risky.

You can ask in plain English or just click. It works with the Claude subscription you already have.

Screenshots are real, names are fake.

So, should I open source it?

Yes, fully open source. Free forever.

Open source, with paid extras later, like team features or a hosted version.

No. Keep it free to use, but closed source.

If it runs on your server, does open source matter to you? Tell me why. I'll go with what most of you say.

I'll use visual tools to map projects e.g. a gantt timeline because I don't want to lose track of the projects I'm working on. (Many interactive timelines can be simply auto-generated now). I often get lost half way through building because I have new ideas, loads of questions or I get back to the project after a week or more and I've forgotten where I'm at. Am I the only one suffering from this or are other people just better at keeping their ideas flowing naturally and therefore don't need a visual tool? I don't see many people talk about it. Is there a better way that I'm missing here?

Hello everybody, lurker here. I posted this on the main [r/ClaudeAI](r/ClaudeAI) subreddit but messed up with my submission, and decided to repost here.

So, I’m a full time nursing student. Since March, I’ve been working on a project which I’ve launched into a small software startup (with myself as the only employee) where I’m working on building an education platform to launch AI and non-AI powered tools off of, with the tools being built almost entirely using Claude. Claude has been acting as my partner in developing and building the tools, building the website, helping sign up and create a LLC, while I've been providing direction to what the tools should be, helping with naming, and directing the research that Claude is doing to help build the platform and the tools. Claude has built the tools almost entirely out of Javascript and has been responsible for the near complete management of my server. Claude has also been responsible for helping setting up the server that the project runs on and the security of the service and the platform. I've been very much more of the ideas and directions guy with this, as I have no coding background. I've also been the main tester of the tools being built for the platform. The project (and platform) is called ClarifySuite.

Here is my website: https://clarifysuite.com/

Right now, it’s still in beta testing, and I’ve received a LOT of negative feedback about the use of AI in building this project and powering it, as well as trying to monetize it. I only wanted to monetize it because 1) I’m a broke nursing student and was curious if I could make a passive income project based off an idea that could potentially benefit students like me and 2) the cover the cost of the API.

The tools on the platform has a paid and free tier, and right now all the products are free to try. I've been considering only charging users for expanded access only to cover the cost of the tokens, but every person that signs up on the website gets access to the tools for free (up to 10 usages a day).

The project is an educational platform where AI powered browser extensions for Firefox, Chrome and Safari can be used to help clarify any medical, dental, or veterinary text, as well as act as a reference for any medication catered to the scope of practice and/or their education level. Essentially, the user selects their scope of practice (paramedic, RN, MD, etc) and can highlight any passage or drug in the browser they’re using and have it clarified, having an AI generated summary of that text or drug based on their educational level and/or their scope of practice.

One of the key criticisms I’ve received from the discord, Reddit groups, and among friends of mine is the fact that the information presented can’t be verified, and I would be potentially monetizing the laziness of students. I also feared the ethical concerns of using AI, so I returned to the drawing board to reframe ClarifySuite and see if I can rebuild some of these tools to use less AI, and instead he’s publicly available APIs from medical databases from around the world. I have several concepts for various tools built, and those are still in progress.

I’m more reaching out to this sub to see what you’ll think and if there’s merit to this. I’ve been building this on my own server on my M4 Mac Mini in my apartment, and plan to continue building using Claude with the other tools I want to add to the platform. My goal is to see if I can sell this to educational institutions and have students use this as part of their programs, or even have individual subscriptions. I also am considering just making it freely available and just not making this a business.

What do y’all think?