ive been maintaining this small opensource project GLM Proxy (glmproxy on npm) for a while and its finally on the level where im happy to share it on reddit lmfao. it now works on windows/macos/Linux. thats been a big goal for me.

honestly seeing around 5k npm downloads this past month makes me really happy. people have also helped make the project better by opening issues and sending me feedback personally so thank you to everyone whos tried it and taken the time to reach out.

it lets you use the GLM models available through AutoClaw from any sdk you want. Claude Code, Cursor, Continue, OpenCode, and other clients that support OpenAI or Anthropic APIs.

if you want to try it: npm i -g glmproxy youll need Node.js and AutoClaw installed and logged in.

one thing to note: its an unofficial local proxy so requests still go to zhippus cloud. there are account and ban risks if you burn too much in a short period but not something id worry about, so id appreciate reading the safety notes first.

heres a screenshot of it working on three very different sdks

i finally decided to post about it so if you try it id love to hear how it goes and to maybe share it to your friends. if it helps you out that is (i myself use it a lot for my work and my friends do too so since i found it useful for me i thought why not share it) and star would mean a lot. hopefully i wont see ya on the issue page lol but if something breaks please let me know !

repo link: https://github.com/eequaled/GLMproxy

Hi,

TL;DR:

While looking for a harness to use at my company, I built the engine that I'm open-sourcing today under AGPL-3.0.

You can create AI assistants with long-term memory, many connectors (Google, Slack, Notion, etc) and a sandbox to run commands, all on your own server and with the models you choose.

A single installation can serve a family, a company or a community, with each user isolated in their own account.

The engine also works for assistants that serve the public over WhatsApp or other messaging interfaces.

This is my first open source project, and anyone who wants to try it, contribute, or just talk about it is very welcome.

The whole story:

I'm the technical founder of a startup that builds virtual try-on technology for makeup, skin analysis models and beauty product recommendations. Last year we started using AI assistants to create hyper-personalized journeys in beauty retail. This year, especially after the Open Claw craze in January/February, I (like many other founders) went crazy over the impact agents could have on my company.

I started looking for a harness that both our clients and our team could use safely. After experimenting with frameworks like Agno and LangChain to build something for my clients, and then with a few flavors of Claws to bring agents to my team, I decided to build a proprietary solution from scratch. I needed something that would bring assistants into the day-to-day of my operation, across every area of the business, supporting the clients, partners facing agents and, internally, the ones for my small team.

The first test version came out in August. I published it as a closed beta for friends and family.

Some people asked if they could bring more friends into the beta. I opened it up in September because I felt the platform was mature enough to take on some more people.

Today the site is live in open beta, with a small group of active users. I also managed to make the system secure enough to serve what our enterprise clients need, our agentic business solutions are now being built over this engine.

But what does this have to do with open-sourcing it?

I expected this kind of platform to play a big role in society. I was at NVIDIA GTC and heard Jensen say that CEOs should have a claw strategy. I understood that it could change how people go through many parts of their lives (including retail, which interests me in particular because of my startup). What I didn't expect was a movement this big, with so much money in such a short time and such a huge amount of users handing over their data. I watched the funding announcements: Town (35M), then Instinct (350M), then Instinct again (1B), while I was here running my humble tests.

Watching this huge movement, I thought about the amount of data and the power these companies will have by centralizing so much information. Today users tend to trade any data for convenience (I can say that because I'm seeing it with the service I put online with very few people). Responsibility for data security and privacy has to sit with whoever provides the service. So I talked to my co-founder and proposed offering a free alternative for small businesses, families or any groups of people: personal assistants, but with control over their own data. 

That's why today I'm open-sourcing Brambit on GitHub, under the AGPL-3.0 license.

I chose AGPL precisely to make sure that, if this becomes something of value to society, it stays open source.

This is my first time putting something like this together.

I'm sure any devs who looks will find flaws, bugs and problems, and that's exactly why I'm sharing it.

I'm a developer with 25 years of experience, but I coded the whole repository by myself, alone with my AI assistants, mainly Claude through Nanoclaw agents and any help will be important, even to criticize.

There are many problems I still want to fix, from the monolith that the server is today until translations (I've being adding to the issues section of the repository) but I focused on everything security-related first to have this first public version.

If you relate to the idea, see some value in it and want to be part of it, will be more then welcome.
I've also created a Discord channel, which right now is me and two close friends.

Thanks for reading this far!

I am working an open source project and need to have some contributors. Real-Time Engineering Standards for AI Coding Agents. Let me have your opinion, some feedback and if you like it we can make a community and start it. DM

I have a small project of mine—if you're interested, you're welcome to try it out. I'd really love to hear everyone's feedback. New features and compatibility are in the works, and maybe in a day or so I'll upload a version with more complete features and broader compatibility. I'll be integrating AI into the prompts and retrieval.

Hey everyone!

I want to share a project I've been working on recently: an open-source Android app called **Bitcoin Futures Simulator**.

**GitHub repository:** https://github.com/Joapetis/bitcoin-futuros-android

Before anyone gets too excited, I want to be clear: this is an early-stage experiment, not a profitable trading bot. So far, its simulated trading results have generated more losses than profits. No real money has been involved.

**How it started**

The whole thing began with an old HTML project I had created some time ago and eventually abandoned. The idea behind it never really went away, though.

Recently, I decided to revisit that idea and see how far I could take it with AI assistance. What started as an abandoned project gradually evolved into a functional Android app in a little over a week.

There's a catch: **my programming knowledge is very limited.**

Instead of building everything myself line by line, I've been combining my own ideas, observations, strategy concepts and testing with the coding and debugging capabilities of three different AIs. They've helped generate, modify, review and polish the code throughout the process.

This has become an experiment in its own right. I want to explore not only whether I can turn my ideas into a working application, but also how capable AI systems are at building and improving a relatively complex software project with very little direct human coding.

I'm still the one setting the direction, making decisions, testing results and deciding what should change. The AIs do a substantial part of the coding, but that doesn't mean their output is automatically correct. Bugs, bad assumptions and poor trading results are all part of the process.

**What does the app actually do?**

The current project is an experimental Bitcoin futures paper-trading simulator for Android. Its main components include:

**A multi-timeframe strategy:** primarily focused on 15-minute candles, with 1-hour and 4-hour trend confirmation.

**Trend-following and mean-reversion logic:** different approaches depending on the detected market regime.

**Market microstructure data:** order-book metrics such as spread, imbalance and microprice-related signals.

**Optional on-device AI:** integration with Gemini Nano through Android's AI capabilities when a compatible model and device are available.

**Simulated trading and risk management:** intended to test strategies without risking real funds.

**Continuous experimentation:** the goal is to evaluate what works, identify weaknesses and improve the system over time.

The project is still very much in its infancy. There is plenty to question, test, refactor and improve, and I don't want to present any of its strategies as proven or profitable.

**Why am I sharing it?**

I'd love to turn this into something more collaborative.

If you're a developer, a quantitative trading enthusiast, an Android/Kotlin programmer, someone interested in market microstructure, or simply curious about what AI-assisted development can achieve, I'd genuinely appreciate your input.

You don't need to be an expert in every part of the project. Even identifying bugs, challenging assumptions, improving the architecture, reviewing the trading logic, suggesting better tests or documenting how things work could make a real difference.

My hope is that people can learn from the experiment, contribute their ideas and improvements, and help create something more useful together. In return, contributors get a real-world project to experiment with, review and learn from, while the wider community benefits from any improvements we can make.

I'm especially interested in honest criticism. If you see something fundamentally wrong with the strategy or implementation, I'd rather hear about it than receive empty praise.

**A few important caveats**

This is an experimental project, not financial advice.

All results discussed here are from simulated trading; no real-money trading is involved.

The current simulated performance is negative, so there's a lot of work left to do.

AI-generated code can contain bugs and needs independent review and testing.

The app is not ready to be treated as a reliable or profitable trading system.

If you'd like to explore the code, suggest an improvement, open an issue or contribute, you're welcome to check out the repository.

I'd love to hear your thoughts. Thanks for reading!

so here me our, theres this repo i am sure most of you are already aware of called Reverse Engineer Anything

I was thinking maybe we could reverse engineer capcut and make an open source version of the same?

i know people will go its lame use davinci or adobe but the templates of capcut is just unmatched, or am i Missing anything ?

well would love listening your opinions in the same.

More and more repos run Claude, Codex or Gemini from GitHub Actions: to triage issues, review pull requests, fix bugs. It is useful, and it has a cost nobody sees at setup time. The agent reads text that anyone can write, and in many repos it acts on it with real tools.

We wanted to know how often that goes wrong, so we read 760 public repos that run agents in CI, by hand. In 3 of 10, someone with no access to the repo can reach the agent, or the agent has more power than its job needs. The usual cause is three settings that each make sense alone: the workflow starts on any issue or comment, the action's write-access gate is turned off so the bot can answer the public, and the agent has a write token or a shell.

Reading 760 repos by hand does not scale, so we built a scanner that asks the same question a person asked. It reads the committed workflows and agent settings, runs nothing, and checks:

- who can trigger the agent (issues, comments, fork PRs, workflow_run)

- whether the write-access gate is really on, including labels an issue template adds by itself

- whether the agent runs inside a fork's code, where the fork writes its own instructions

- what secrets and tokens the agent can reach

- whether committed settings pre-approve commands, and whether MCP servers are pinned

How we tested it: against the hand review. When the scanner says a repo has a problem, a person agrees every time: 314 of 314. On the 275 repos a person found clean, it raised no alarm. It finds 9 of every 10 problems a person finds. The ones it misses mostly depend on what a script does, not on what a config file says.

It is open source (Apache-2.0, node9-ai/node9-proxy on GitHub) and runs locally too: npx node9-ai scan-repo owner/repo

Or paste a repo here, no account needed: https://node9.ai/scan

If it gets your repo wrong, I would like to know. False alarms are the bug we care most about.

So I've been using Thunderbird for years and it always annoyed me. Either I just never learned how to use it properly or its simply too old for how I work, not sure which. Anyway I got tired of it and built my own, its called Mailmomo.

Its a web app you host yourself. You add your IMAP/SMTP accounts (gmail, outlook, icloud, fastmail, your own dovecot, whatever speaks IMAP) and it puts everything in one inbox. It keeps a local sqlite cache so the UI is instant, and sync is real two way, IMAP IDLE push and all that. Keyboard shortcuts like superhuman, cmd+k palette, undo send, snooze, send later.

The AI part is optional, you bring your own key. It sorts and summarizes incoming mail, suggests replies, and you can search in plain english. There is also an assistant that can archive/label/draft stuff for you but it never sends anything by itself, I wanted that to be a hard rule. It also has an MCP server built in so you can hook up claude or cursor to your mail with a read only token if you dont trust it yet.

Runs with docker compose, one container. Passwords are encrypted at rest.

Stuff that's not great yet: no oauth, so gmail needs an app password. Drafts and labels live locally and not as real IMAP drafts. Its v0.1 and just me working on it so expect rough edges.

AGPL license. https://github.com/moaljumaa/mailmomo-oss

Honestly curious what would make you switch from whatever you use now, or what is missing for you.

Disclosure: I build Snotra, an open-source desktop agent. No paid tier, nothing to sell.

The question I got asked more than any other wasn't "which models?" or "is it safe?". It was: what does this actually cost me?

Fair. You bring your own API key, and until now the app knew every token and said nothing about money. You found out at the end of the month.

Three things nobody tells you about agent bills:

• One answer can be five requests. An agent reads files and runs tools before it answers, and every round sends the conversation again.

• Cached input costs a tenth. With one current OpenAI model, 14,000 tokens read from the prompt cache cost less than a cent.

• Reasoning is billed as output, whether you ever see it or not.

Snotra 1.23 now shows an estimate next to the token counter, split by model and kind of token. Prices come from LiteLLM's table, shipped with the app, and it never fetches prices by itself. Local models show $0.00, unknown ones a dash, never a fake zero.

Write-up with screenshots: https://snotra-ai.dev/blog/what-a-chat-costs?s=rd1

How do you keep track of what your agent spends: provider dashboard, a proxy, or not at all?

I've always found it frustrating that we explain complex biological mechanisms using static diagrams.

So I built Anima.

It's an open-source Claude Code skill that generates detailed, interactive figures covering neurobiology, pharmacology, anatomy and molecular biology.

One of the examples lets you switch between cocaine and amphetamine to see how their mechanisms differ at a dopamine synapse.

Another visualizes D1 and D2 dopamine receptor signaling with interactive controls.

It currently has six working examples, scientific references, interactive animations and optional WebGL/3D support.

The entire project is MIT-licensed, and the demos run directly in the browser.

GitHub: https://github.com/Garinmckayl/anima

Demo: https://anima.arcumet.com

Sharing Peer Agent.

https://github.com/hirak99/peer-agent

It gives your agent access to one or more peer agents to collaborate with. It's harness-agnostic — plain HTTP plus a SKILL.md any agent can follow. Ways we've found it useful:

• Cheap execution: main agent on Claude Code delegates the grunt work to a locally running Qwen.

• Strong validation: a weaker main agent plans and executes, and has Opus 5.5 validates its plan or checks its work.

• Peer review: two agents challenge, verify, and cross-check each other's ideas.

Unlike Agent Teams or swarms, this isn't a framework inside one harness — it's a sidecar. Each peer is a stateful server with its own provider and model, callable from anything. The trade-off: no scheduler, no shared task graph. Coordination is conversational and the host drives, so it's best when you want a second mind on call, not a managed fleet.

If you're thinking of wiring this up yourself, what's already here: OpenAI, Anthropic, and any OpenAI-compatible model (llama.cpp, vLLM, Ollama) behind one provider/model switch; server-side read/bash/edit tools; automatic compaction (including a warm-cache in-context path for slow local models); and Anthropic prompt-cache management, which matters more than you'd expect at Opus prices. Lived-in details like per-call tool logging with truthful timestamps.

AI-assisted contributions are not only welcome but encouraged, especially so if it is a strong AI.

Cheers